**π‘ Problem Formulation:** When working with time data in Python, it often becomes necessary to adjust the precision of timedelta objects. Specifically, users of the pandas library may need to perform a ceiling operation on a TimedeltaIndex object with microseconds frequency. This means rounding up time differences to the nearest microsecond. For example, given a TimedeltaIndex of ‘2 days 00:00:00.001500’, the desired output after a ceiling operation would be ‘2 days 00:00:01’.

## Method 1: Using `TimedeltaIndex.ceil()`

Function

This method involves the direct usage of the `TimedeltaIndex.ceil`

function to round up the time to the nearest microsecond. The `ceil()`

function is specifically designed to handle such rounding operations with ease and precision.

Here’s an example:

import pandas as pd # Create a TimedeltaIndex object t_delta_index = pd.to_timedelta(['2 days 00:00:00.001500', '1 days 23:59:59.000250']) # One-liner using lambda and ceil t_delta_ceil_lambda = t_delta_index.map(lambda x: x.ceil('us')) print(t_delta_ceil_lambda)

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

This snippet demonstrates the use of a lambda function to apply the `ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

import pandas as pd # Create a TimedeltaIndex object t_delta_index = pd.to_timedelta(['2 days 00:00:00.001500', '1 days 23:59:59.000250']) # Perform the ceil operation using the Micro() offset t_delta_ceil_offset = t_delta_index + pd.offsets.Micro() - pd.Timedelta('1us') print(t_delta_ceil_offset)

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

This code snippet creates a `TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

A quick and elegant solution when handling individual timedelta values can be to use a lambda function along with the `ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

import pandas as pd # Create a TimedeltaIndex object t_delta_index = pd.to_timedelta(['2 days 00:00:00.001500', '1 days 23:59:59.000250']) # One-liner using lambda and ceil t_delta_ceil_lambda = t_delta_index.map(lambda x: x.ceil('us')) print(t_delta_ceil_lambda)

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

This snippet demonstrates the use of a lambda function to apply the `ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

import pandas as pd import numpy as np # Create a TimedeltaIndex object t_delta_index = pd.to_timedelta(['2 days 00:00:00.001500', '1 days 23:59:59.000250']) # Custom ceil function def custom_ceil(timedelta_series): microseconds = timedelta_series.dt.total_seconds() * 1e6 return pd.to_timedelta(np.ceil(microseconds), unit='us') # Perform the ceil operation using the custom function t_delta_ceil_custom = custom_ceil(t_delta_index) print(t_delta_ceil_custom)

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

This code defines a custom function named `custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

Pandas offers date offsets which can be used for a variety of date operations. The `pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

import pandas as pd # Create a TimedeltaIndex object t_delta_index = pd.to_timedelta(['2 days 00:00:00.001500', '1 days 23:59:59.000250']) # Perform the ceil operation using the Micro() offset t_delta_ceil_offset = t_delta_index + pd.offsets.Micro() - pd.Timedelta('1us') print(t_delta_ceil_offset)

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

This code snippet creates a `TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

A quick and elegant solution when handling individual timedelta values can be to use a lambda function along with the `ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

import pandas as pd # Create a TimedeltaIndex object t_delta_index = pd.to_timedelta(['2 days 00:00:00.001500', '1 days 23:59:59.000250']) # One-liner using lambda and ceil t_delta_ceil_lambda = t_delta_index.map(lambda x: x.ceil('us')) print(t_delta_ceil_lambda)

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

This snippet demonstrates the use of a lambda function to apply the `ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

import pandas as pd # Create a Series of timedelta values s = pd.Series(pd.to_timedelta(['2 days 00:00:00.001500', '1 days 23:59:59.000250'])) # Perform the ceil operation s_ceil = s.dt.ceil('us') print(s_ceil)

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

In this example, a Series containing timedelta values is created. The `.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

When finer control is needed or more complex operations are involved, one can use NumPy’s `np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

import pandas as pd import numpy as np # Create a TimedeltaIndex object t_delta_index = pd.to_timedelta(['2 days 00:00:00.001500', '1 days 23:59:59.000250']) # Custom ceil function def custom_ceil(timedelta_series): microseconds = timedelta_series.dt.total_seconds() * 1e6 return pd.to_timedelta(np.ceil(microseconds), unit='us') # Perform the ceil operation using the custom function t_delta_ceil_custom = custom_ceil(t_delta_index) print(t_delta_ceil_custom)

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

This code defines a custom function named `custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

Pandas offers date offsets which can be used for a variety of date operations. The `pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

import pandas as pd # Create a TimedeltaIndex object t_delta_index = pd.to_timedelta(['2 days 00:00:00.001500', '1 days 23:59:59.000250']) # Perform the ceil operation using the Micro() offset t_delta_ceil_offset = t_delta_index + pd.offsets.Micro() - pd.Timedelta('1us') print(t_delta_ceil_offset)

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

This code snippet creates a `TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

A quick and elegant solution when handling individual timedelta values can be to use a lambda function along with the `ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

This snippet shows the creation of a `TimedeltaIndex`

object which represents a time difference in days, hours, minutes, and microseconds. The `ceil()`

function is then called with the argument ‘us’ to specify that rounding should be to the nearest microsecond, effectively rounding up the time values.

## Method 2: Using `pandas.Series.dt.ceil()`

Another approach is to create a Series object and then apply the `dt.ceil()`

function. This is particularly useful if the time deltas are a part of a Series and you want to perform the operation within the context of a DataFrame. The `dt`

accessor provides functions to work with datetime-like properties in a Series.

Here’s an example:

import pandas as pd # Create a Series of timedelta values s = pd.Series(pd.to_timedelta(['2 days 00:00:00.001500', '1 days 23:59:59.000250'])) # Perform the ceil operation s_ceil = s.dt.ceil('us') print(s_ceil)

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

In this example, a Series containing timedelta values is created. The `.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

When finer control is needed or more complex operations are involved, one can use NumPy’s `np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

import pandas as pd import numpy as np # Create a TimedeltaIndex object t_delta_index = pd.to_timedelta(['2 days 00:00:00.001500', '1 days 23:59:59.000250']) # Custom ceil function def custom_ceil(timedelta_series): microseconds = timedelta_series.dt.total_seconds() * 1e6 return pd.to_timedelta(np.ceil(microseconds), unit='us') # Perform the ceil operation using the custom function t_delta_ceil_custom = custom_ceil(t_delta_index) print(t_delta_ceil_custom)

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

This code defines a custom function named `custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

Pandas offers date offsets which can be used for a variety of date operations. The `pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

import pandas as pd # Create a TimedeltaIndex object t_delta_index = pd.to_timedelta(['2 days 00:00:00.001500', '1 days 23:59:59.000250']) # Perform the ceil operation t_delta_ceil = t_delta_index.ceil('us') print(t_delta_ceil)

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

This snippet shows the creation of a `TimedeltaIndex`

object which represents a time difference in days, hours, minutes, and microseconds. The `ceil()`

function is then called with the argument ‘us’ to specify that rounding should be to the nearest microsecond, effectively rounding up the time values.

## Method 2: Using `pandas.Series.dt.ceil()`

Another approach is to create a Series object and then apply the `dt.ceil()`

function. This is particularly useful if the time deltas are a part of a Series and you want to perform the operation within the context of a DataFrame. The `dt`

accessor provides functions to work with datetime-like properties in a Series.

Here’s an example:

import pandas as pd # Create a Series of timedelta values s = pd.Series(pd.to_timedelta(['2 days 00:00:00.001500', '1 days 23:59:59.000250'])) # Perform the ceil operation s_ceil = s.dt.ceil('us') print(s_ceil)

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

In this example, a Series containing timedelta values is created. The `.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

When finer control is needed or more complex operations are involved, one can use NumPy’s `np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

import pandas as pd # Create a TimedeltaIndex object t_delta_index = pd.to_timedelta(['2 days 00:00:00.001500', '1 days 23:59:59.000250']) # Perform the ceil operation t_delta_ceil = t_delta_index.ceil('us') print(t_delta_ceil)

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

This snippet shows the creation of a `TimedeltaIndex`

object which represents a time difference in days, hours, minutes, and microseconds. The `ceil()`

function is then called with the argument ‘us’ to specify that rounding should be to the nearest microsecond, effectively rounding up the time values.

## Method 2: Using `pandas.Series.dt.ceil()`

Another approach is to create a Series object and then apply the `dt.ceil()`

function. This is particularly useful if the time deltas are a part of a Series and you want to perform the operation within the context of a DataFrame. The `dt`

accessor provides functions to work with datetime-like properties in a Series.

Here’s an example:

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

`.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

`np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

import pandas as pd # Create a TimedeltaIndex object t_delta_index = pd.to_timedelta(['2 days 00:00:00.001500', '1 days 23:59:59.000250']) # Perform the ceil operation t_delta_ceil = t_delta_index.ceil('us') print(t_delta_ceil)

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

object which represents a time difference in days, hours, minutes, and microseconds. The `ceil()`

function is then called with the argument ‘us’ to specify that rounding should be to the nearest microsecond, effectively rounding up the time values.

## Method 2: Using `pandas.Series.dt.ceil()`

`dt.ceil()`

function. This is particularly useful if the time deltas are a part of a Series and you want to perform the operation within the context of a DataFrame. The `dt`

accessor provides functions to work with datetime-like properties in a Series.

Here’s an example:

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

`.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

`np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

`.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

`np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

object which represents a time difference in days, hours, minutes, and microseconds. The `ceil()`

function is then called with the argument ‘us’ to specify that rounding should be to the nearest microsecond, effectively rounding up the time values.

## Method 2: Using `pandas.Series.dt.ceil()`

`dt.ceil()`

function. This is particularly useful if the time deltas are a part of a Series and you want to perform the operation within the context of a DataFrame. The `dt`

accessor provides functions to work with datetime-like properties in a Series.

Here’s an example:

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

`.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

`np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

object which represents a time difference in days, hours, minutes, and microseconds. The `ceil()`

function is then called with the argument ‘us’ to specify that rounding should be to the nearest microsecond, effectively rounding up the time values.

## Method 2: Using `pandas.Series.dt.ceil()`

`dt.ceil()`

function. This is particularly useful if the time deltas are a part of a Series and you want to perform the operation within the context of a DataFrame. The `dt`

accessor provides functions to work with datetime-like properties in a Series.

Here’s an example:

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

`.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

`np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

object which represents a time difference in days, hours, minutes, and microseconds. The `ceil()`

function is then called with the argument ‘us’ to specify that rounding should be to the nearest microsecond, effectively rounding up the time values.

## Method 2: Using `pandas.Series.dt.ceil()`

`dt.ceil()`

function. This is particularly useful if the time deltas are a part of a Series and you want to perform the operation within the context of a DataFrame. The `dt`

accessor provides functions to work with datetime-like properties in a Series.

Here’s an example:

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

`.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

`np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

object which represents a time difference in days, hours, minutes, and microseconds. The `ceil()`

function is then called with the argument ‘us’ to specify that rounding should be to the nearest microsecond, effectively rounding up the time values.

## Method 2: Using `pandas.Series.dt.ceil()`

`dt.ceil()`

function. This is particularly useful if the time deltas are a part of a Series and you want to perform the operation within the context of a DataFrame. The `dt`

accessor provides functions to work with datetime-like properties in a Series.

Here’s an example:

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

`.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

`np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

object which represents a time difference in days, hours, minutes, and microseconds. The `ceil()`

function is then called with the argument ‘us’ to specify that rounding should be to the nearest microsecond, effectively rounding up the time values.

## Method 2: Using `pandas.Series.dt.ceil()`

`dt.ceil()`

function. This is particularly useful if the time deltas are a part of a Series and you want to perform the operation within the context of a DataFrame. The `dt`

accessor provides functions to work with datetime-like properties in a Series.

Here’s an example:

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

`.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

`np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

object which represents a time difference in days, hours, minutes, and microseconds. The `ceil()`

function is then called with the argument ‘us’ to specify that rounding should be to the nearest microsecond, effectively rounding up the time values.

## Method 2: Using `pandas.Series.dt.ceil()`

`dt.ceil()`

function. This is particularly useful if the time deltas are a part of a Series and you want to perform the operation within the context of a DataFrame. The `dt`

accessor provides functions to work with datetime-like properties in a Series.

Here’s an example:

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

`.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

`np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

object which represents a time difference in days, hours, minutes, and microseconds. The `ceil()`

function is then called with the argument ‘us’ to specify that rounding should be to the nearest microsecond, effectively rounding up the time values.

## Method 2: Using `pandas.Series.dt.ceil()`

`dt.ceil()`

function. This is particularly useful if the time deltas are a part of a Series and you want to perform the operation within the context of a DataFrame. The `dt`

accessor provides functions to work with datetime-like properties in a Series.

Here’s an example:

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

`.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

`np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

`.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

`np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

object which represents a time difference in days, hours, minutes, and microseconds. The `ceil()`

function is then called with the argument ‘us’ to specify that rounding should be to the nearest microsecond, effectively rounding up the time values.

## Method 2: Using `pandas.Series.dt.ceil()`

`dt.ceil()`

function. This is particularly useful if the time deltas are a part of a Series and you want to perform the operation within the context of a DataFrame. The `dt`

accessor provides functions to work with datetime-like properties in a Series.

Here’s an example:

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

`.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

`np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

object which represents a time difference in days, hours, minutes, and microseconds. The `ceil()`

function is then called with the argument ‘us’ to specify that rounding should be to the nearest microsecond, effectively rounding up the time values.

## Method 2: Using `pandas.Series.dt.ceil()`

`dt.ceil()`

function. This is particularly useful if the time deltas are a part of a Series and you want to perform the operation within the context of a DataFrame. The `dt`

accessor provides functions to work with datetime-like properties in a Series.

Here’s an example:

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

`.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

`np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

`.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

`np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

object which represents a time difference in days, hours, minutes, and microseconds. The `ceil()`

function is then called with the argument ‘us’ to specify that rounding should be to the nearest microsecond, effectively rounding up the time values.

## Method 2: Using `pandas.Series.dt.ceil()`

`dt.ceil()`

function. This is particularly useful if the time deltas are a part of a Series and you want to perform the operation within the context of a DataFrame. The `dt`

accessor provides functions to work with datetime-like properties in a Series.

Here’s an example:

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

`.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

`np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

object which represents a time difference in days, hours, minutes, and microseconds. The `ceil()`

function is then called with the argument ‘us’ to specify that rounding should be to the nearest microsecond, effectively rounding up the time values.

## Method 2: Using `pandas.Series.dt.ceil()`

`dt.ceil()`

function. This is particularly useful if the time deltas are a part of a Series and you want to perform the operation within the context of a DataFrame. The `dt`

accessor provides functions to work with datetime-like properties in a Series.

Here’s an example:

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

`.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

`np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

`.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

`np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

object which represents a time difference in days, hours, minutes, and microseconds. The `ceil()`

function is then called with the argument ‘us’ to specify that rounding should be to the nearest microsecond, effectively rounding up the time values.

## Method 2: Using `pandas.Series.dt.ceil()`

`dt.ceil()`

function. This is particularly useful if the time deltas are a part of a Series and you want to perform the operation within the context of a DataFrame. The `dt`

accessor provides functions to work with datetime-like properties in a Series.

Here’s an example:

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

`.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

`np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

object which represents a time difference in days, hours, minutes, and microseconds. The `ceil()`

function is then called with the argument ‘us’ to specify that rounding should be to the nearest microsecond, effectively rounding up the time values.

## Method 2: Using `pandas.Series.dt.ceil()`

`dt.ceil()`

function. This is particularly useful if the time deltas are a part of a Series and you want to perform the operation within the context of a DataFrame. The `dt`

accessor provides functions to work with datetime-like properties in a Series.

Here’s an example:

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

`.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

`np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

`.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

`np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

object which represents a time difference in days, hours, minutes, and microseconds. The `ceil()`

function is then called with the argument ‘us’ to specify that rounding should be to the nearest microsecond, effectively rounding up the time values.

## Method 2: Using `pandas.Series.dt.ceil()`

`dt.ceil()`

function. This is particularly useful if the time deltas are a part of a Series and you want to perform the operation within the context of a DataFrame. The `dt`

accessor provides functions to work with datetime-like properties in a Series.

Here’s an example:

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

`.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

`np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

object which represents a time difference in days, hours, minutes, and microseconds. The `ceil()`

function is then called with the argument ‘us’ to specify that rounding should be to the nearest microsecond, effectively rounding up the time values.

## Method 2: Using `pandas.Series.dt.ceil()`

`dt.ceil()`

function. This is particularly useful if the time deltas are a part of a Series and you want to perform the operation within the context of a DataFrame. The `dt`

accessor provides functions to work with datetime-like properties in a Series.

Here’s an example:

Output:

0 2 days 00:00:01 1 2 days dtype: timedelta64[ns]

`.dt.ceil()`

function is then utilized with the frequency set to microseconds. The ceil operation is applied, rounding the times up to the nearest microsecond, and the resulting Series is printed.

## Method 3: Using Custom Function with `np.ceil()`

`np.ceil()`

in conjunction with a custom function. This allows for performing the ceil operation at a more granular level. Note that this method may involve additional steps to handle the datetime units correctly.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`custom_ceil`

that converts a TimedeltaIndex object into total microseconds, applies the NumPy `ceil()`

function, and then converts the result back to a TimedeltaIndex. This method gives the same result as the built-in `ceil()`

function but provides an example of how more complex custom rounding can be implemented.

## Method 4: Using Round-Up with `pd.offsets.Micro()`

`pd.offsets.Micro()`

offset specifically targets microseconds, providing a way to round up Timedelta values to microsecond precision.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`TimedeltaIndex`

and then adds a one-microsecond offset, subsequently subtracting one microsecond to effectively round up to the next microsecond. While this method works, it’s a less direct approach compared to using `ceil()`

.

## Bonus One-Liner Method 5: Using `ceil()`

with a Lambda Function

`ceil()`

operation. This one-liner approach can be particularly handy for inline operations or when working within list comprehensions.

Here’s an example:

Output:

TimedeltaIndex(['2 days 00:00:01', '2 days'], dtype='timedelta64[ns]', freq=None)

`ceil()`

operation to each element of the `TimedeltaIndex`

without the need for a separate function definition. This approach keeps the code concise and readable.

## Summary/Discussion

**Method 1:**`TimedeltaIndex.ceil()`

. Direct and straightforward. May not offer flexibility for complex scenarios.**Method 2:**`pandas.Series.dt.ceil()`

. Convenient within pandas DataFrame context. Another direct approach but may add overhead if conversion from other types is needed.**Method 3:**Custom Function with`np.ceil()`

. Flexible and customizable. More complex and requires a better understanding of time units conversion.**Method 4:**Round-Up with`pd.offsets.Micro()`

. Utilizes pandas offsets. Less conventional and potentially confusing.**Method 5:**Lambda Function. Quick and elegant for simple operations. Not suited for very complex operations and readability may suffer in more elaborate uses.

Emily Rosemary Collins is a tech enthusiast with a strong background in computer science, always staying up-to-date with the latest trends and innovations. Apart from her love for technology, Emily enjoys exploring the great outdoors, participating in local community events, and dedicating her free time to painting and photography. Her interests and passion for personal growth make her an engaging conversationalist and a reliable source of knowledge in the ever-evolving world of technology.