Скачать или смотреть How to Filter DataFrame Rows in Pandas Based on Conditions from Another DataFrame

Learn how to efficiently filter rows in a DataFrame using conditions derived from another DataFrame in Pandas. This guide explains the process step-by-step.
---
This video is based on the question https://stackoverflow.com/q/62649671/ asked by the user 'Mike' ( https://stackoverflow.com/u/13838014/ ) and on the answer https://stackoverflow.com/a/62649712/ provided by the user 'Henry Yik' ( https://stackoverflow.com/u/9284423/ ) 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: How to return the row from dataframe having same row values by using column value of other dataframe?

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 DataFrames in Pandas: A Step-by-Step Guide

When working with data in Python, especially using the Pandas library, you may often find yourself needing to filter rows from one DataFrame based on conditions defined in another DataFrame. This is a common scenario that can help you manipulate and analyze your data more efficiently. In this post, we'll break down the problem and provide a clear, concise approach to solving it using a practical example.

The Problem

Imagine you have two DataFrames. The first one, df1, contains various details about financial instruments, including columns like 'country', 'inst', and 'frequency'. The second DataFrame, df2, contains a list of 'tenor' values that you want to use to filter df1. The goal here is to retrieve the 'country' values from df1 that do not correspond to any 'frequency' listed in df2.

Example DataFrames

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

From df1, we want to filter out the 'country' values where the 'frequency' corresponds to any 'tenor' from df2.

The Solution

To achieve this filtering, we can make use of the isin() method provided by Pandas. This allows us to check if elements in one Series are present in another Series.

Step-by-step Breakdown

Identify Matching Frequencies: First, we want to locate all 'frequency' values in df1 that match with any 'tenor' values in df2.

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

Here, s will hold the 'country' values associated with the 'frequency' values that are present in df2.

Filter Out the Desired Countries: Next, we will filter the 'country' column in df1 using the values we've just identified. We want all unique 'country' values that do not appear in s.

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

This code does the following:

~ negates the condition, so we get countries not in s.

.unique() ensures we only get distinct values.

.tolist() converts the result into a Python list.

Final Output: We can print the final filtered list of countries.

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

This will yield the following output:

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

Conclusion

By utilizing the isin() function and logical negation, we can efficiently filter DataFrames in Pandas. This method is versatile and can be applied in numerous data manipulation scenarios within Python. By mastering this technique, you can enhance your data analysis skills and increase your productivity when working with large datasets.

If you're new to Pandas, practicing with such filtering tasks will help you grasp the library's capabilities and streamline your data workflows.

Happy coding!