Скачать или смотреть How to Use Pandas groupby and Conditional Filtering to Organize Your Data

Learn how to effectively filter data in `Pandas` by leveraging `groupby` and conditional checks to enhance your data processing.
---
This video is based on the question https://stackoverflow.com/q/62591835/ asked by the user 'Ank' ( https://stackoverflow.com/u/5181947/ ) and on the answer https://stackoverflow.com/a/62593189/ provided by the user 'Umar.H' ( https://stackoverflow.com/u/9375102/ ) 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 groupby and filter by conditional check on rows

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.
---
How to Use Pandas groupby and Conditional Filtering to Organize Your Data

When working with large datasets in Python, efficiently managing and organizing your data is essential. One common problem faced by data analysts is how to filter groups based on certain conditions, especially when using the Pandas library. In this guide, we’ll address a specific scenario where we want to group data by multiple columns and filter out groups that do not meet certain criteria.

The Problem

Imagine you have a Pandas dataframe containing different variable types (high and low) measured over a period of years, identified by unique IDs. Here's a sample of the dataframe:

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

In this dataframe, each ID-year combination contains two rows: one for the high variable and one for the low variable. The objective is to filter these groups to only include rows where the value of the high variable is greater than the value of the low variable. In essence, we want to segregate the acceptable groups from the unacceptable and store them in two different dataframes.

The expected output would be:

df (Accepted Groups):

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

df2 (Rejected Groups):

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

The Solution

To achieve this separation, we can use Pandas’ groupby method in conjunction with a few other techniques. Let’s break down the steps involved:

Step 1: Group By and Unstack

First, we’ll group the dataframe by id, year, and variable. We can then unstack the variable column to reshape our dataframe. This way, we will have the high and low values side by side.

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

Step 2: Calculate Differences

Using the diff function, we can check the differences across the columns, specifically to establish whether the high value is greater than the low value:

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

Step 3: Finalizing the Dataframes

The above operation will provide us with a dataframe (df_new) containing the accepted groups (where high is greater than low). The remaining data will be in the original dataframe minus the accepted groups. To get the rejected groups, you can use the ~ operator:

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

Example Code

Putting it all together, your complete code will look like this:

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

Conclusion

In summary, using Pandas allows you to efficiently manage and filter complex datasets with conditional checks. Through the combination of groupby and diff methods, we can easily isolate groups that meet specific criteria, making our data analysis both effective and straightforward.

Feel free to adapt the code snippets and techniques used here for your unique datasets, and happy coding!