**💡 Problem Formulation:** When working with time series data in Python’s Pandas library, you may encounter situations where you need to truncate or ‘floor’ time deltas to a consistent minute frequency. For example, you might have a TimedeltaIndex with varying times and want to align them to minute intervals, discarding excess seconds and milliseconds. The goal is to transform an input like ‘0 days 00:03:45.678’ into a floored output such as ‘0 days 00:03:00’.

## Method 1: Using `floor()`

Method

The `floor()`

method in Pandas is specifically designed to round down time intervals to a specified frequency. This method is highly flexible and can handle different time units, ensuring consistency across your time series data.

Here’s an example:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

This code snippet uses the days and minutes components from the original TimedeltaIndex and reconstructs a new TimedeltaIndex containing only the floored minute values.

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

import pandas as pd # Create a TimedeltaIndex td_index = pd.to_timedelta(['0 days 00:03:45.678', '0 days 00:17:59.123']) # Floor using one-liner with Timedelta properties floored_td_index = pd.to_timedelta(td_index.days, unit='D') + pd.to_timedelta(td_index.components.minutes, unit='T') # Display the result print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

This code snippet uses the days and minutes components from the original TimedeltaIndex and reconstructs a new TimedeltaIndex containing only the floored minute values.

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

This snippet converts the TimedeltaIndex to integer nanoseconds, performs integer division by the number of nanoseconds in a minute, and then converts it back to a TimedeltaIndex, effectively flooring the values.

## Bonus One-Liner Method 5: Using Timedelta Properties

Pandas Timedelta objects have properties that allow direct access to their components. A quick one-liner can combine these properties to floor each component to minutes.

Here’s an example:

import pandas as pd # Create a TimedeltaIndex td_index = pd.to_timedelta(['0 days 00:03:45.678', '0 days 00:17:59.123']) # Floor using one-liner with Timedelta properties floored_td_index = pd.to_timedelta(td_index.days, unit='D') + pd.to_timedelta(td_index.components.minutes, unit='T') # Display the result print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

This code snippet uses the days and minutes components from the original TimedeltaIndex and reconstructs a new TimedeltaIndex containing only the floored minute values.

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

import pandas as pd # Create a TimedeltaIndex td_index = pd.to_timedelta(['0 days 00:03:45.678', '0 days 00:17:59.123']) # Perform a vectorized floor operation minutes_in_ns = 60 * 1e9 floored_td_index = (td_index.astype('int64') // minutes_in_ns).astype('timedelta64[ns]') * minutes_in_ns # Display the result print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

This snippet converts the TimedeltaIndex to integer nanoseconds, performs integer division by the number of nanoseconds in a minute, and then converts it back to a TimedeltaIndex, effectively flooring the values.

## Bonus One-Liner Method 5: Using Timedelta Properties

Pandas Timedelta objects have properties that allow direct access to their components. A quick one-liner can combine these properties to floor each component to minutes.

Here’s an example:

import pandas as pd # Create a TimedeltaIndex td_index = pd.to_timedelta(['0 days 00:03:45.678', '0 days 00:17:59.123']) # Floor using one-liner with Timedelta properties floored_td_index = pd.to_timedelta(td_index.days, unit='D') + pd.to_timedelta(td_index.components.minutes, unit='T') # Display the result print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

The custom floor function calculates the total seconds of the time delta, floors it to minutes, and returns the floored value. This is applied to each element of TimedeltaIndex.

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

Vectorized operations in Pandas are executed at a lower level, which can lead to significant performance improvements. Using `//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

import pandas as pd # Create a TimedeltaIndex td_index = pd.to_timedelta(['0 days 00:03:45.678', '0 days 00:17:59.123']) # Perform a vectorized floor operation minutes_in_ns = 60 * 1e9 floored_td_index = (td_index.astype('int64') // minutes_in_ns).astype('timedelta64[ns]') * minutes_in_ns # Display the result print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

This snippet converts the TimedeltaIndex to integer nanoseconds, performs integer division by the number of nanoseconds in a minute, and then converts it back to a TimedeltaIndex, effectively flooring the values.

## Bonus One-Liner Method 5: Using Timedelta Properties

Pandas Timedelta objects have properties that allow direct access to their components. A quick one-liner can combine these properties to floor each component to minutes.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

import pandas as pd # Create a TimedeltaIndex td_index = pd.to_timedelta(['0 days 00:03:45.678', '0 days 00:17:59.123']) # Define a custom floor function def custom_floor(td): return pd.Timedelta(minutes=int(td.total_seconds() // 60)) # Apply the custom floor function floored_td_index = td_index.apply(custom_floor) # Display the result print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

The custom floor function calculates the total seconds of the time delta, floors it to minutes, and returns the floored value. This is applied to each element of TimedeltaIndex.

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

Vectorized operations in Pandas are executed at a lower level, which can lead to significant performance improvements. Using `//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

import pandas as pd # Create a TimedeltaIndex td_index = pd.to_timedelta(['0 days 00:03:45.678', '0 days 00:17:59.123']) # Perform a vectorized floor operation minutes_in_ns = 60 * 1e9 floored_td_index = (td_index.astype('int64') // minutes_in_ns).astype('timedelta64[ns]') * minutes_in_ns # Display the result print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

In this code, the TimedeltaIndex is first rounded to the nearest minute. Then, any value that was not already at the minute floor is subtracted by one minute. This ensures all values are rounded down.

## Method 3: Using `apply()`

with a Custom Function

The `apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

import pandas as pd # Create a TimedeltaIndex td_index = pd.to_timedelta(['0 days 00:03:45.678', '0 days 00:17:59.123']) # Define a custom floor function def custom_floor(td): return pd.Timedelta(minutes=int(td.total_seconds() // 60)) # Apply the custom floor function floored_td_index = td_index.apply(custom_floor) # Display the result print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

The custom floor function calculates the total seconds of the time delta, floors it to minutes, and returns the floored value. This is applied to each element of TimedeltaIndex.

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

Vectorized operations in Pandas are executed at a lower level, which can lead to significant performance improvements. Using `//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

import pandas as pd # Create a TimedeltaIndex td_index = pd.to_timedelta(['0 days 00:03:45.678', '0 days 00:17:59.123']) # Round and then subtract 1 minute if not already floored rounded_td_index = td_index.round('T') floored_td_index = rounded_td_index - pd.to_timedelta((rounded_td_index != td_index).astype(int), unit='T') # Display the result print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

In this code, the TimedeltaIndex is first rounded to the nearest minute. Then, any value that was not already at the minute floor is subtracted by one minute. This ensures all values are rounded down.

## Method 3: Using `apply()`

with a Custom Function

The `apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

import pandas as pd # Create a TimedeltaIndex td_index = pd.to_timedelta(['0 days 00:03:45.678', '0 days 00:17:59.123']) # Define a custom floor function def custom_floor(td): return pd.Timedelta(minutes=int(td.total_seconds() // 60)) # Apply the custom floor function floored_td_index = td_index.apply(custom_floor) # Display the result print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

This snippet creates a TimedeltaIndex object and uses the `floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

While the `round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

import pandas as pd # Create a TimedeltaIndex td_index = pd.to_timedelta(['0 days 00:03:45.678', '0 days 00:17:59.123']) # Round and then subtract 1 minute if not already floored rounded_td_index = td_index.round('T') floored_td_index = rounded_td_index - pd.to_timedelta((rounded_td_index != td_index).astype(int), unit='T') # Display the result print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

In this code, the TimedeltaIndex is first rounded to the nearest minute. Then, any value that was not already at the minute floor is subtracted by one minute. This ensures all values are rounded down.

## Method 3: Using `apply()`

with a Custom Function

The `apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

import pandas as pd # Create a TimedeltaIndex td_index = pd.to_timedelta(['0 days 00:03:45.678', '0 days 00:17:59.123']) # Floor the TimedeltaIndex to minute frequency floored_td_index = td_index.floor('T') # Display the result print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

This snippet creates a TimedeltaIndex object and uses the `floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

While the `round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

import pandas as pd # Create a TimedeltaIndex td_index = pd.to_timedelta(['0 days 00:03:45.678', '0 days 00:17:59.123']) # Round and then subtract 1 minute if not already floored rounded_td_index = td_index.round('T') floored_td_index = rounded_td_index - pd.to_timedelta((rounded_td_index != td_index).astype(int), unit='T') # Display the result print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

import pandas as pd # Create a TimedeltaIndex td_index = pd.to_timedelta(['0 days 00:03:45.678', '0 days 00:17:59.123']) # Floor the TimedeltaIndex to minute frequency floored_td_index = td_index.floor('T') # Display the result print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

This snippet creates a TimedeltaIndex object and uses the `floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

While the `round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

import pandas as pd # Create a TimedeltaIndex td_index = pd.to_timedelta(['0 days 00:03:45.678', '0 days 00:17:59.123']) # Floor the TimedeltaIndex to minute frequency floored_td_index = td_index.floor('T') # Display the result print(floored_td_index)

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

`floor()`

method with the ‘T’ argument, which represents minute frequency, to floor each time delta to the nearest minute below. The output confirms the transformation to minute precision.

## Method 2: Using `round()`

Method with Custom Floor Logic

`round()`

function is generally used for rounding to the nearest specified frequency, it can be combined with custom logic to ensure the result is always rounded down, effectively flooring the values.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those new to the concept or without a solid understanding of time representations in nanoseconds.**Bonus One-Liner Method 5:**Using Timedelta Properties. It’s a quick hack and easy to comprehend. However, it may not cater to all edge cases with different time units and requires understanding of the timedelta components.

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 3: Using `apply()`

with a Custom Function

`apply()`

function allows you to run a custom-defined function against all values in a series or dataframe. This method is highly customizable, but it typically incurs higher computational costs than vectorized operations.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Method 4: Vectorized Floor Operation with `//`

and `to_timedelta()`

`//`

for integer division in combination with `to_timedelta()`

can floor the entire TimedeltaIndex without applying a function to each element.

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Bonus One-Liner Method 5: Using Timedelta Properties

Here’s an example:

Output:

TimedeltaIndex(['0 days 00:03:00', '0 days 00:17:00'], dtype='timedelta64[ns]', freq=None)

## Summary/Discussion

**Method 1:**Using`floor()`

Method. This is the most straightforward and recommended approach. It’s clear, concise, and efficient, but it requires understanding of the frequency aliases used by Pandas.**Method 2:**Using`round()`

Method with Custom Floor Logic. Good for more complex requirements but may be slightly less efficient due to the additional arithmetic operation.**Method 3:**Using`apply()`

with a Custom Function. Highly customizable, but it’s less efficient for large datasets because it is not a vectorized operation.**Method 4:**Vectorized Floor Operation. Offers excellent performance on large datasets due to vectorization, but can be a bit obscure for those