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Quickly uncover potential relationships in a CSV dataset by getting an overview of correlation coefficients between several pairs of metrics.

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💡 Batch correlations

A simple python app to quickly uncover potential relationships in a CSV dataset by getting an overview of correlation coefficients between several pairs of metrics. Made with Streamlit.

https://batch-correlations.streamlit.app/

Usage:

  1. Uploade a CSV file from the left sidebar. The format expected is:
item,metric_1,metric_2,metric_3,etc

For example, for a GA4 export:

Address,Word Count,GA4 Sessions,GA4 Views,GA4 Engaged sessions,GA4 Bounce rate,Performance Score,First Contentful Paint Time (ms),Speed Index Time (ms),Largest Contentful Paint Time (ms),Time to Interactive (ms),Total Blocking Time (ms),Cumulative Layout Shift,Image Count
https://www.example.com/article-1/,692,13,13,7,0.3,60,2294,3864.021,6255,6716,537,0.013,5
https://www.example.com/article-2/,114,5,5,1,0,61,2264,3316.94,8505,6850.5,418.5,0.059,6
https://www.example.com/article-3/,1780,20,21,11,0.27,52,2704,3881.909,9030,7943,712,0.001,8
...
  1. The chart automatically appears under "Results:"

How to read the chart:

alt text

Coming soon!

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Quickly uncover potential relationships in a CSV dataset by getting an overview of correlation coefficients between several pairs of metrics.

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