🧠 CorrPY – Correlation Made Easy

PyPI version Downloads License Python

CorrPY is your lightweight buddy for fast, smart correlation analysis.
Forget just numbers — CorrPY tells you what they mean. 📊✨

Built for data scientists who want insights, not just values.

🚀 Install

pip install corrpy

from corrpy import Corrpy

corrpy = Corrpy()
corrpy.getTotalCorrRelation(df)

✅ Analyze correlation across features
✅ Get trends + easy-to-read interpretations
✅ Go deeper with AI explanations (optional)

getTotalCorrRelation(df, features = ["Correlation", "Pearson", "Distance"], feature = "Correlation", short = False): Pass a pandas DataFrame to get correlation analysis across all columns and get trends, interpretations and score with respect to feature u added in parameter.
getGroupInf(objColumn, numColumn, df): Compute the correlation between the given object column and the given numeric column.
getAllGroupInf(df): Compute the correlation between all object columns and all numeric columns.
checkTransit(firstFeature, secondFeature, ThirdFeature): Check for transitive correlation between three features.
checkTransitForColumn(column, df): Check for transitive correlation between a column and all other columns.

explainTC(df, feature="Correlation", prompt="null"): Get AI insights for correlation analysis.
explainShift(num1, num2, shiftValue, df, prompt="Explain like a stand-up comedian"): An AI analyst explains the output of shift() like you're in a meeting with your CEO.
explainTransit(num1, num2, df, prompt="Explain like Angry Professor"): Get AI insights for transitive correlation analysis.
explainTransitForcolumn(column, df, prompt="Explain like Oppenheimer"): An AI analyst explains the output of checkTransitForColumn() like you're in a meeting with your CEO.
explainAI(result, prompt="Explain like angry professor"): Get AI insights for any result.
makeReport(self, method="null", df=None, column=None, feature=None, target=None, prompt="Null", size="short", constant=None, first=None, second=None, third=None): Generate a human-like, well-written paragraph suitable for direct pasting into a PowerPoint slide, based on the output of other methods.

"Age and Fare have a moderate positive correlation.
Pclass has a strong inverse relation with Fare."

✨ Plus visual trends, interpretation tags, and more!

YellowForest
🔗 GitHub

BSD 3-Clause License

#	What CorrPY Gives You
🚀	Quick, meaningful correlation analysis
🤖	AI-driven explanations
🧩	Find hidden patterns
🔥	Detect transitive traps
🎯	Ideal for both beginners and pros

CorrPY isn't just another EDA tool...
It's your data's best storyteller. 📚🚀

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution for Corrpy 0.4.11
File	Size	Uploaded
corrpy-0.4.11.tar.gz	13.4 kB	May 9, 2025	Details

Table of built distributions (wheels) for Corrpy 0.4.11
File	Interpreter	ABI	Platform	Reset
corrpy-0.4.11-py3-none-any.whl 13.6 kB May 9, 2025	Python 3	none	any	Details

Total release size: 27.0 kB

Download URL	corrpy-0.4.11.tar.gz
Size	13.4 kB
Tags	Source
SHA-256 checksum How to use checksums	`cec4d3ef15256bb2c933acfd0357877117a1d3584aa14f98a1d5c239d2ab717f`
BLAKE2b-256 checksum How to use checksums	`3243523c460ba13dab854b1fef1bc0b67aaaa2028ee0f07e7471f62f4183f9c4`
Upload date	May 9, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.1.0 CPython/3.10.11`

Download URL	corrpy-0.4.11-py3-none-any.whl
Size	13.6 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`be0329c4cf5b08957c5e5dd00b632a3016a5af1d46c174bb93f2d2a8bdadee93`
BLAKE2b-256 checksum How to use checksums	`f1a289b1a6e1fd61fc7f599073740231014f64b3169236b1301985b83c1ece3e`
Upload date	May 9, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.1.0 CPython/3.10.11`