How to Compare Multiple Columns in a DataFrame to a List of Values in Python's pandas

A step-by-step guide on comparing multiple columns in a DataFrame to a list of values using Python's `pandas`. Learn to implement boolean checks for effective data analysis with real-world examples.
---
This video is based on the question https://stackoverflow.com/q/70215022/ asked by the user 'Squid Game' ( https://stackoverflow.com/u/15077259/ ) and on the answer https://stackoverflow.com/a/70215732/ provided by the user 'Daniele Caliari' ( https://stackoverflow.com/u/10679822/ ) 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: Comparing multiple columns to a list with multiple values

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.
---
Comparing Multiple Columns to a List with Multiple Values

When working with large datasets, it's common to encounter situations where you need to compare values across multiple columns. In this post, we'll explore how to compare several columns in a DataFrame against a list of values from another DataFrame, using Python's powerful pandas library.

The Problem

Suppose you have two DataFrames, df1 and df2. df1 contains user data with multiple numeric columns, while df2 contains a list of numbers in square brackets for each corresponding user. The task is to determine if any of the numbers in each row of df1 are present in the corresponding list from df2.

For example:

For df1, you have:

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

And for df2, the data looks like this:

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

The goal is to evaluate each user in df1 and see if any of their numbers (like 1524, 1333, 2021 for the first user) are present in the corresponding list in df2 (e.g., [1123, 2021, 8788]). If there's a match, we would like to output a boolean value (or 1/0) to indicate the result.

The Solution

To compare the rows of df1 against the corresponding lists in df2, we can loop through each row of df1, check for matches in df2, and accumulate the results in a new DataFrame. Here’s how you can do this step-by-step.

Step 1: Setup DataFrames

First, we define our DataFrames df1 and df2. Here's how you can set them up in Python:

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

Step 2: Initialize an Empty DataFrame for Results

Before we start comparing, create an empty DataFrame to store the results.

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

Step 3: Iterate and Compare

Next, we need to go through each row of df1, extract the numeric values, and check if any of them are present in the corresponding row in df2.

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

Resulting Output

By the end of the loop, df3 will contain a column of boolean values (or 1s and 0s) representing the match results for each user.

Enhanced Iteration with NumPy

Here is a more advanced method using NumPy to handle the lists better:

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

Conclusion

In this post, we walked through the process of comparing multiple columns in a DataFrame against a list of values using pandas. This method allows you to efficiently assess and analyze data even when you're dealing with large DataFrames. With the right tools, comparing dataset values becomes much more manageable!

Feel free to experiment with larger datasets and see how this method performs!