cnanalysis 0.1.0

A Python package for statistical and EDA analysis

CNAnalysis

CNAnalysis is a Python package for comprehensive statistical and exploratory data analysis (EDA). It provides tools for performing ANOVA, Chi-square tests, correlation matrices, outlier handling, encoding categorical features, and visualizing distributions.

📦 Installation

pip install cnanalysis

📚 Modules Overview

1. AnovaTest – One-Way ANOVA

   from cnanalysis import AnovaTest
   anova = AnovaTest(data=df, num_col='score',cat_col='group')
   result = anova.test()
   print(result)
   

2. ChiSquareTest – Chi-Square Test with Cramér’s V

   from cnanalysis import ChiSquareTest
   chi_test = ChiSquareTest(data=df, col1='gender', col2='purchase')
   result = chi_test.test()
   print(result)
   

3. CombineAnalysis – Grouped Bar Plots with Stats

   from cnanalysis import CombineAnalysis
   
   plotter = CombineAnalysis(data=df, number_col='sales', categorical_col='region')
   summary_df = plotter.PlotGroupedData()
   

4. CorrelationMat – Correlation Matrix Plot

   from cnanalysis import CorrelationMat
   
   corr = CorrelationMat(data=df)
   corr.plotCM()`
   

5. DescriptiveSAT – Descriptive Statistics + Outlier Count

   from cnanalysis import DescriptiveSAT
   
   desc = DescriptiveSAT(data=df)
   stats = desc.get_descriptive_statistics(columns=['height', 'weight'])
   print(stats)
   

6. DistributionOutViz – Histogram + Boxplot for Outlier

   from cnanalysis import DistributionOutViz
   
   dist_viz = DistributionOutViz(data=df, num_cols=['price','income'])
   dist_viz.PlotDAO()
   

7. EncodeCat – Label, One-Hot, and Ordinal Encoding

   from cnanalysis import EncodeCat
   
   encoder = EncodeCat(data=df, method='label')
   encoded_df = encoder.encode()
   

8. HandleOutlier – Winsorization (Cap Outliers)

   from cnanalysis import HandleOutlier
   
   out = HandleOutlier(data=df, lower=0.05, upper=0.95)
   winsorized_df = out.winsorize()
   

9. CardinalityAndRareCategoryAnalyzer – Rare Category

   from cnanalysis import CardinalityAndRareCategoryAnalyzer
   
   analyzer = CardinalityAndRareCategoryAnalyzer(data=df, thresh=0.01)
   report = analyzer.get_cardinality_n_rare_cat()
   print(report)
   

🧑‍💻 Author

Roshan Kumar
🎓 Student, B.Sc. in Computer Science and Data Analytics (CSDA)
🏫 Indian Institute of Technology Patna (IITP)
📧 [e-mail]

📝 License

This project is licensed under the MIT License – see the LICENSE file for details.