Скачать или смотреть How to Filter Pandas DataFrame Rows by Matching Phase Numbers in Two Columns

Learn how to filter rows in a Pandas DataFrame based on matching `phase` numbers in two columns using simple string operations.
---
This video is based on the question https://stackoverflow.com/q/72934634/ asked by the user 'nerd' ( https://stackoverflow.com/u/18313588/ ) and on the answer https://stackoverflow.com/a/72934787/ provided by the user 'ko3' ( https://stackoverflow.com/u/13921399/ ) 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: get pandas rows with same specific word in two columns

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.
---
Filtering Pandas DataFrame Rows by Matching Phase Numbers

Pandas is a powerful library for data manipulation and analysis in Python, and often we find ourselves needing to filter data based on certain conditions. One common scenario involves comparing specific values in multiple columns. In this guide, we will tackle a specific problem: how to filter rows in a Pandas DataFrame where two columns contain the same phase number.

The Problem

Imagine you have a Pandas DataFrame structured as follows:

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

In this DataFrame, each data1 and data2 entry includes a phase number denoted by phaseX (where X represents the phase number). You want to extract only those rows where the phase number in data1 matches with that of data2. If you have phase1 in the data1 column, you should have phase1 in the data2 column as well.

Desired Output

Your goal is to filter the DataFrame to obtain the following result:

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

The Solution

To achieve this filtering, you don't need to delve into complex regular expressions. Instead, we can use straightforward string manipulation provided by Pandas.

Step-by-Step Explanation

Here’s how you can filter the DataFrame:

Split the Columns: First, we will split the strings in both data1 and data2 columns by the underscore _ character. This will allow us to isolate the phase part of each string.

Compare the Phase Numbers: After splitting, we will compare the second element of the split results, which contains the phase number.

Generate a Mask: This comparison will produce a boolean mask that we can use to filter the DataFrame.

Implementation

Here’s the code that you can use:

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

Breakdown of the Code

str.split("_", expand=True): This function splits each string in the columns at each underscore _ and expands the results into separate columns.

[1]: This selects the second element from the resulting DataFrame (i.e., the phase number).

==: This operator compares the two sets of phase numbers, returning True where they match and False otherwise.

Finally, df[...] uses the boolean mask to filter the DataFrame.

Conclusion

Using the techniques outlined above, you can easily filter rows in a Pandas DataFrame based on matching phase numbers in two columns without the need for complicated regular expressions. Just remember to leverage string manipulation tools provided by Pandas for efficient data processing!

Now you can apply this method to your own datasets whenever you need to filter based on specific conditions in multiple columns.