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Скачать или смотреть Filtering Rows in Python Pandas by Working Days: Excluding Weekends and Holidays

  • vlogize
  • 2025-04-06
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Filtering Rows in Python Pandas by Working Days: Excluding Weekends and Holidays
Python Pandas filter rows by days of difference in two columns with weekend and holidayspythonpandasdate differenceweekend
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Описание к видео Filtering Rows in Python Pandas by Working Days: Excluding Weekends and Holidays

Learn how to effectively filter rows in a Pandas DataFrame based on the working days difference between two date columns, accounting for weekends and `holidays`.
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This video is based on the question https://stackoverflow.com/q/77270062/ asked by the user 'Gustavo' ( https://stackoverflow.com/u/3784224/ ) and on the answer https://stackoverflow.com/a/77270184/ provided by the user 'Shubham Sharma' ( https://stackoverflow.com/u/12833166/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Python Pandas filter rows by days of difference in two columns with weekend and holidays

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The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

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Filtering Rows in Python Pandas by Working Days: Excluding Weekends and Holidays

In the world of data analysis, managing dates can be tricky. One common challenge is determining the number of working days between two date columns in a Pandas DataFrame. For professionals handling project timelines, task allocations, or any date-driven analysis, this is a crucial skill.

In this post, we will explore how to filter rows of a Pandas DataFrame based on the number of working days between two dates, while excluding weekends and specified holidays.

The Problem at Hand

Imagine you have a DataFrame containing different cases, each characterized by two date columns, DATE1 and DATE2. The objective is to keep only those rows where the difference in working days between DATE1 and DATE2 is more than two days, while making sure to account for weekends and holidays.

For instance, given the example below:

NAMEDATE1DATE2CASE110/12/202310/13/2023CASE210/12/202310/16/2023CASE310/12/202310/18/2023CASEX10/12/202310/19/2023We want to filter out CASE3 and CASEX, where the working days difference exceeds two days due to weekends and the holiday on 10/17/2023.

Step-by-Step Solution

1. Define a List of Holidays

Begin by defining a list of holidays that will affect your calculations:

[[See Video to Reveal this Text or Code Snippet]]

2. Parse Date Columns to Datetime

Next, ensure that the date columns are parsed to the appropriate datetime format:

[[See Video to Reveal this Text or Code Snippet]]

After this step, your DataFrame will beautifully transform into:

NAMEDATE1DATE2CASE12023-10-122023-10-13CASE22023-10-122023-10-16CASE32023-10-122023-10-18CASEX2023-10-122023-10-193. Count Working Days

Next, we calculate the number of working days using np.busday_count:

[[See Video to Reveal this Text or Code Snippet]]

This will return an array containing the working days count, such as [1, 2, 3, 4] corresponding to each case.

4. Filter Rows Based on Working Days

Now, it's time to filter the DataFrame based on our criterion:

[[See Video to Reveal this Text or Code Snippet]]

After filtering, you'll have:

Valid Rows (Two days or fewer)

NAMEDATE1DATE2CASE12023-10-122023-10-13CASE22023-10-122023-10-16Invalid Rows (More than two days)

NAMEDATE1DATE2CASE32023-10-122023-10-18CASEX2023-10-122023-10-19Conclusion

Handling date calculations in Pandas can be simplified by utilizing a few essential methods. In this guide, we successfully filtered out rows based on business day differences while accounting for weekends and holidays. Now you can manage your DataFrame efficiently based on time-sensitive criteria!

Feel free to adapt this approach to suit your specific data analysis needs. Happy coding!

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