Скачать или смотреть How to Calculate Euclidean Distance Between Rows in a Pandas DataFrame

Learn how to efficiently compute the `Euclidean distance` between clusters in a Pandas DataFrame, and output results in a matrix format.
---
This video is based on the question https://stackoverflow.com/q/71114788/ asked by the user 'quant' ( https://stackoverflow.com/u/5868293/ ) and on the answer https://stackoverflow.com/a/71114948/ provided by the user 'not_speshal' ( https://stackoverflow.com/u/9857631/ ) 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: How to calculate euclidian distance between combinations of rows in pandas 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.
---
Understanding Euclidean Distance in Pandas

Calculating the Euclidean distance between data points is a common task, especially in data science fields like clustering. When working with data in a Pandas DataFrame, it can be tricky to calculate these distances effectively, especially when you need a structured output. In this guide, we'll walk through the problem of computing the Euclidean distance between combinations of rows in a DataFrame and show you how to create a correlation-like distance matrix for your clusters.

The Initial Setup

Let’s start with a given DataFrame that represents clusters, with each cluster having specific features (var1, var2, var3).

Here’s the code to create the DataFrame:

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

In this DataFrame:

Each row corresponds to a different cluster.

The values for var1, var2, and var3 specify the position of the cluster centers.

The Challenge

The goal is to calculate the Euclidean distance between each cluster and all other clusters. You’ll want to output these distances in a symmetric matrix format, similar to a correlation matrix. The expected output should look like this:

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

Solution Using SciPy

Performing these calculations manually can become cumbersome. Thankfully, you can utilize the scipy library to make the process much simpler. Below are the steps you need to follow.

Step 1: Install SciPy

Before using the library, ensure you have it installed. You can do so using pip:

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

Step 2: Import Necessary Functions

You will need to import the pdist and squareform functions from scipy.spatial.distance:

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

Step 3: Calculate Distances

Now, we can compute the distances and format them into a DataFrame. Here’s the code:

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

Explanation of the Code

pdist: Computes the pairwise distances (in this case, Euclidean distance) between the rows of the DataFrame.

squareform: Converts the condensed distance vector into a square matrix form, making it easier to visualize.

Output

When you run the above code, the output will be as expected, giving you a symmetric distance matrix:

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

Conclusion

Calculating the Euclidean distance between clusters in a Pandas DataFrame can be straightforward with scipy. Not only does it save time, but it also provides a clean output format. With these steps, you can apply similar techniques to any clustering analysis or distance-based computation you may encounter in your data science projects.

Now, go ahead and try it out! With these tools, you'll have a clearer understanding of how your clusters relate to one another based on their feature values.