🧠 CorrPY – Correlation Made Easy

---

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

---

📦 Quickstart

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)

---

🔥 Key Features

• Numerical vs Numerical — Classic correlations + strength.
• Object vs Numerical — Category impacts, clear trends.
• Object vs Object — Categorical association (Chi2).
• Transitive Trap Alerts — Detect hidden indirect links. 🚨
• AI-Generated Insights — Explain data like a boss 🧠📜

---

Methods

1. 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.
2. getGroupInf(objColumn, numColumn, df): Compute the correlation between the given object column and the given numeric column.
3. getAllGroupInf(df): Compute the correlation between all object columns and all numeric columns.
4. checkTransit(firstFeature, secondFeature, ThirdFeature): Check for transitive correlation between three features.
5. checkTransitForColumn(column, df): Check for transitive correlation between a column and all other columns.

AI-Generated Insights

1. explainTC(df, feature="Correlation", prompt="null"): Get AI insights for correlation analysis.
2. 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.
3. explainTransit(num1, num2, df, prompt="Explain like Angry Professor"): Get AI insights for transitive correlation analysis.
4. explainTransitForcolumn(column, df, prompt="Explain like Oppenheimer"): An AI analyst explains the output of checkTransitForColumn() like you're in a meeting with your CEO.
5. explainAI(result, prompt="Explain like angry professor"): Get AI insights for any result.
6. 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.

---

🧠 Example Insights

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

✨ Plus visual trends, interpretation tags, and more!

---

👨‍💻 Author

YellowForest
🔗 GitHub

---

📄 License

BSD 3-Clause License

---

⚡ TL;DR

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

---

📢 FINAL NOTE:

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

---

🧹 How to use:

• README for Quick Start 📑
• Full GUIDE.md for Deep Dive 📚