**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.