Estimating Pearson's Correlation Coefficient and P-Value from DataFrames in Python

Discover how to efficiently estimate `correlation` and `p-values` from two DataFrames using Python's `pandas` and `scipy`. Learn to loop through columns correctly and save outputs!
---
This video is based on the question https://stackoverflow.com/q/66740083/ asked by the user 'Ali Ajaz' ( https://stackoverflow.com/u/9513405/ ) and on the answer https://stackoverflow.com/a/66748468/ provided by the user 'AirSquid' ( https://stackoverflow.com/u/10789207/ ) 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: Iterating over columns from two dataframes to estimate correlation and p-value

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/licensing
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/by-sa/4.0/ ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/by-sa/4.0/ ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Estimating Pearson's Correlation Coefficient and P-Value from DataFrames in Python

When working with data analysis, understanding the correlation between different datasets is crucial. Specifically, using Pearson's correlation coefficient allows you to gauge the strength and direction of a linear relationship between two variables. However, if you're trying to do this with multiple columns from two pandas DataFrames, you might encounter a few hiccups along the way. This guide will guide you through a common scenario where you need to iterate over the columns of two DataFrames to compute the correlation and P-value for each pair.

The Problem

Imagine you have two DataFrames, each containing sets of data represented in columns. You want to calculate the correlation coefficient and P-value for the corresponding columns in both DataFrames. However, the code you've written seems to only provide results from the last columns due to incorrect looping and variable naming.

Here's a snippet of the code that depicts the problem you might be facing:

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

Issues with the Existing Code

Variable Naming: Using the same variable name (column) for both loops leads to confusion and overwriting, which results in only the last column being processed.

Placement of Calculation: The calculation of the correlation coefficient (correl) is placed outside of the inner loop, which means you are not capturing results for each corresponding column pair.

The Solution

To correctly estimate the Pearson’s correlation coefficient and P-value from each corresponding column in the two DataFrames, follow these structured steps:

Step 1: Ensure Column Names Match

Before iterating, it’s essential to confirm that both DataFrames share the same column names. If they do not match, you'll need to identify the common names.

Step 2: Set-Up the Loop Structure

You will only need one loop over the columns, assuming that both DataFrames have identical columns. Here's how you can set it up:

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

Step 3: Analyzing the Results

The results dictionary will store the correlation coefficients along with their corresponding P-values for each column pair from the two DataFrames. This structured output can now be used for further analysis or stored in a new DataFrame if necessary.

Conclusion

By fixing the loop structure and ensuring proper handling of variable names, you can easily compute the correlation and P-values for corresponding columns in two DataFrames using Python. Remember, leveraging libraries like pandas and scipy not only simplifies the process but also enhances your data analysis capabilities significantly. Happy coding!

Видео Estimating Pearson's Correlation Coefficient and P-Value from DataFrames in Python канала vlogize