5 Best Ways to Floor a timedelta to a Specific Resolution with Python Pandas

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💡 Problem Formulation: When working with Python’s Pandas library, you might encounter the need to round down or floor a Timedelta object to a specified resolution, such as seconds, minutes, or hours. For example, given the input ‘2 days 05:34:09.765432’, the desired output after flooring to the nearest whole hour would be ‘2 days 05:00:00’.

Method 1: Using Timedelta.floor()

This method uses the Pandas Timedelta.floor() function, which allows you to floor a Timedelta to a specified frequency. When flooring, all smaller units are truncated.

Here’s an example:

import pandas as pd

# Create a Timedelta
td = pd.Timedelta('2 days 5 hours 34 minutes 9 seconds')

# Floor the Timedelta to 'H' (hour resolution)
floored_td = td.floor('H')
print(floored_td)

Output:

2 days 05:00:00

This code snippet demonstrates how to create a Pandas Timedelta object and floor it to hour granularity. The floor('H') method call transforms the original Timedelta to drop any minute, second, or microsecond components.

Method 2: Using Timedelta// with Frequency String

The // division operator along with the frequency string can also be used to floor Pandas Timedelta amounts. The resulting Timedelta is truncated to the nearest whole number of the specified unit.

Here’s an example:

import pandas as pd

# Create a Timedelta
td = pd.Timedelta('1 day 3 hours 45 minutes 27 seconds')

# Define hour as frequency string
hour = pd.Timedelta('1H')

# Floor to the nearest whole hour
floored_td = td // hour * hour
print(floored_td)

Output:

1 day 03:00:00

In this snippet, we use // to divide the original Timedelta by an hour-long Timedelta, effectively flooring the original time span to full hours. Multiplying by the hour-long Timedelta reconstructs the floored Timedelta.

Method 3: Using numpy.floor() with Timedelta.total_seconds()

Numpy’s floor() function coupled with the Timedelta.total_seconds() method can floor Timedelta to the nearest whole second before converting back to a Timedelta object.

Here’s an example:

import pandas as pd
import numpy as np

# Create a Timedelta
td = pd.Timedelta('15 hours 29 minutes 9.765432 seconds')

# Convert to seconds and floor
floored_td = pd.Timedelta(np.floor(td.total_seconds()), unit='s')
print(floored_td)

Output:

15 hours 29 minutes

This code creates a Timedelta and uses the total_seconds() method to convert it to a total representation in seconds. Numpy’s floor() function then floors the value to the nearest integer. A new Timedelta is constructed using the floored value in seconds.

Method 4: Using Timedelta.components and Manual Reconstruction

By accessing Timedelta.components, you can manually floor a Timedelta to a desired component by reconstructing the Timedelta using only the desired parts of the original.

Here’s an example:

import pandas as pd

# Create a Timedelta
td = pd.Timedelta('2 days 4 hours 12 minutes 30 seconds')

# Floor the Timedelta by extracting components
td_components = td.components
floored_td = pd.Timedelta(days=td_components.days, hours=td_components.hours)
print(floored_td)

Output:

2 days 04:00:00

This method extracts the days and hours from the Timedelta.components and creates a new Timedelta object using only these values, effectively discarding any minutes and seconds, thus flooring the original Timedelta.

Bonus One-Liner Method 5: Using Pandas offsets

Pandas offsets module can be used to round down Timedelta objects in conjunction with datetime operations.

Here’s an example:

import pandas as pd

# Create a Timestamp and Timedelta
timestamp = pd.Timestamp('2023-01-01 03:21:45')
td = pd.Timedelta('45 minutes')

# Floor the Timestamp + Timedelta to nearest hour
floored_td = (timestamp + td).floor('H') - timestamp
print(floored_td)

Output:

01:00:00

In this succinct snippet, we add a Timedelta to a Timestamp and use the .floor('H') method to floor to the nearest hour, then subtract the original Timestamp to obtain a floored Timedelta.

Summary/Discussion

  • Method 1: Timedelta.floor(). Easy to use and precise for standard frequency strings. However, it is limited to frequencies supported by Pandas.
  • Method 2: // with Frequency String. Offers precise control and good for custom frequencies, but is a bit more verbose and less direct than using built-in methods.
  • Method 3: numpy.floor() with Timedelta.total_seconds(). It’s good for second-level precision and for those familiar with numpy, but less straightforward for other resolutions.
  • Method 4: Manual Reconstruction. This method provides maximum control over which components to floor. However, it is more manual and error-prone for complex Timedelta manipulations.
  • Bonus Method 5: Pandas offsets. It works well if you’re also dealing with Timestamps and need to floor a range, but may not be as intuitive for Timedelta-only operations.