5 Best Ways to Round the TimeDeltaIndex with Microsecond Frequency in Pandas

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πŸ’‘ Problem Formulation: When working with time series data in pandas, sometimes data indexed by time deltas need to be rounded to the nearest microsecond for uniformity or aggregation purposes. Below, we detail five methods of achieving this, starting with a TimeDeltaIndex like TimedeltaIndex(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'], dtype='timedelta64[ns]') with a desired output resembling TimedeltaIndex(['00:00:00.000000', '00:00:00.000000', '00:00:00.001000'], dtype='timedelta64[ns]').

Method 1: Using TimedeltaIndex.round()

This method involves using the round() function provided by the pandas TimedeltaIndex class to round to the nearest specified frequency. It’s directly supported and easy to use, making it an ideal starting point for most rounding operations involving pandas timedeltas.

Here’s an example:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.
import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.round('us')
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This code snippet demonstrates how to round a TimedeltaIndex to the nearest microsecond using round(). After converting a list of string representations to a TimedeltaIndex, the round() function is called with ‘us’ as the frequency to indicate the microsecond level.

Method 2: Using numpy.around()

The numpy library offers a generic rounding method around() which allows rounding of arrays to a given number of decimals. This can be applied to the total number of microseconds in our TimedeltaIndex.

Here’s an example:

import pandas as pd
import numpy as np

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = pd.to_timedelta(np.around(td_index.microseconds, decimals=0))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

After initiating a TimedeltaIndex, this code uses numpy.around() to round the microseconds to the nearest whole number. The result is passed to pd.to_timedelta() to recreate the TimedeltaIndex.

Method 3: Flooring with TimedeltaIndex.floor()

Similar to rounding, sometimes a consistent floor operation is required. Pandas offers the floor() method to always round down to the nearest specified frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
floored_td_index = td_index.floor('us')
print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:00'],
               dtype='timedelta64[ns]', freq=None)

The floor() method works like round() but ensures that the result is the largest previous duration mark in microseconds, effectively rounding down.

Method 4: Ceiling with TimedeltaIndex.ceil()

In contrast to flooring, sometimes an operation that always rounds up is needed. The ceil() method provided by pandas rounds a timedelta up to the nearest given frequency.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
ceiling_td_index = td_index.ceil('us')
print(ceiling_td_index)

Output:

TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:01', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

Using pandas ceil() function rounds each timedelta in the index up to the nearest microsecond frequency.

Bonus One-Liner Method 5: Lambda Function with round()

A flexible and concise way to round timedeltas is to use a lambda function alongside the round() method. This can be handy for customized rounding behavior in more complex scenarios.

Here’s an example:

import pandas as pd

td_index = pd.to_timedelta(['00:00:00.000123', '00:00:00.000456', '00:00:00.000789'])
rounded_td_index = td_index.map(lambda t: t.round('us'))
print(rounded_td_index)

Output:

TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:00', '0 days 00:00:01'],
               dtype='timedelta64[ns]', freq=None)

This one-liner utilizes a lambda function to apply round() directly to each element of the TimedeltaIndex. Here, each timedelta is individually rounded to the microseconds frequency.

Summary/Discussion

  • Method 1: Using TimedeltaIndex.round(). Straightforward and concise. Preferred for standard rounding.
  • Method 2: Using numpy.around(). Offers flexibility and useful for handling complex rounding operations across multiple array elements.
  • Method 3: Flooring with TimedeltaIndex.floor(). Reliable for always rounding down. May not be suitable if upward rounding is sometimes required.
  • Method 4: Ceiling with TimedeltaIndex.ceil(). Good for always rounding up. The opposite of floor – may not be suitable for always rounding down scenarios.
  • Method 5: Lambda Function with round(). Offers maximum customization. Requires a bit more code than the built-in functions.