Скачать или смотреть Resolving Month Shift and Business Days in pandas Dataframes

Discover how to dynamically change months in a `pandas` dataframe while ensuring accurate calculation of the last business day for financial data analysis.
---
This video is based on the question https://stackoverflow.com/q/63710485/ asked by the user '0buz' ( https://stackoverflow.com/u/12149587/ ) and on the answer https://stackoverflow.com/a/63710685/ provided by the user 'Andy L.' ( https://stackoverflow.com/u/10189214/ ) 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: Pandas change month while calculating the corresponding last business day

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
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.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Resolving Month Shift and Business Days in pandas Dataframes

In data manipulation using Python's pandas library, it is common to work with datetime objects, especially when analyzing financial data. One common scenario arises when you need to dynamically increase the month in a date column while also ensuring that the last business day remains accurate. This post will walk you through how to tackle this problem effectively.

Understanding the Problem

Suppose you have a pandas dataframe with a column containing dates that consists of the 1st day of the month and the last business day of that month. The dataset contains dates only up to the 1st of the previous month. Your goal is to increment the month by one and adjust the last business day accordingly.

Sample Data

Here’s an example of what your input data might look like:

MyDateMyValue31/Mar/2020001/Apr/2020a30/Apr/2020b01/May/2020c29/May/2020d01/Jun/2020e30/Jun/2020f01/Jul/2020g31/Jul/2020h01/Aug/2020iThe corresponding output should correctly reflect the last business day of the updated month.

Desired Output

MyDateMyValue30/Apr/2020001/May/2020a29/May/2020b01/Jun/2020c30/Jun/2020d01/Jul/2020e31/Jul/2020f01/Aug/2020g31/Aug/2020h01/Sep/2020iBreaking Down the Solution

To arrive at the desired output, we can break the problem down into two key parts:

Increment the Month: Use the relativedelta function to change the month to month + 1.

Calculate Last Business Day: Use the pd.offsets.BMonthEnd() to find the last business day of the updated month.

Implementing the Code

Below is a step-by-step implementation of the solution:

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

Explanation of the Code

Importing Libraries: We import both pandas and relativedelta to work with date manipulation.

Data Preparation: A sample dataframe is created and the MyDate column is converted to datetime format.

Changing the Month: Each date in MyDate is incremented by one month using relativedelta.

Identifying Business Month Ends: We create a boolean mask to detect whether the date is a month-end or not.

Adjusting Dates:

For month-end dates, we add the next business month-end.

For other dates, we add one month beginning.

Conclusion

Using the above code, you can dynamically adjust the month while calculating the corresponding last business days accurately in a pandas dataframe. This method not only streamlines your data manipulation but also ensures that your financial data remains reliable and easy to analyze.

If you have any questions or would like to explore further manipulations using pandas, please feel free to join the conversation in the comments below.