my-eda-helper 0.1.1

A helper package for Exploratory Data Analysis (EDA)

EDA Helper Function - Boost your Exploretory Data Analysis Process!

Credit

• I first came to this idea of using helper function from a course by @MisbahullahSheriff and it was brilliant. This EDA helper function originally was created by @MisbahullahSheriff. Now I have edited the file and organized it for easy to use.Extra functions has been added by me.

Installation

Use in Google Colab: put the fill in the notebook's directory

import google.colab.drive as drive
drive.mount('/content/drive', force_remount=True)

import sys
import os
sys.path.append('<your_directory>')
import eda_helper_functions as ehf

Step-by-Step EDA

• Import Libraries

• Read training dataset, we perform EDA only on training dataset

• High-level Analysis

  • Data Summary:

    • .info() method
    • .describe() method on numeric and categorical features separately

  • Missing Data:

    • find missing value with number and percentages

          ehf.missing_info(df)
      

    • bar plot for better visualization of missing data

         ehf.plot_missing_info(df)
      

  • Outliers:

    • Isolation forest

  • Pair plots:

        ehf.pair_plots(df)
    

  • Correlation Analysis(heatmaps):

    • Numeric(Pearson's/Spearman's)
    • Categorical(Cramer's V)

        ehf.correlation_heatmap(df)
    

        ehf.cramersV_heatmap(df)
    

• Detailed Analysis of each Columns

      df.columns # find all columns   
  

  • Summary

        ehf.cat_summary(df, "<cat_feature>")
    

  • Univariate plots

        ehf.cat_univar_plots(df, "<cat_feature>")
    

  • Bivariate plots

        ehf.num_cat_bivar_plots(
        data=train,
        num_var="<num_feature>",
        cat_var="<cat_feature>"
        )
    

  • Hypothesis Testing(normality, strength of association)

        ehf.num_cat_bivar_plots(
        data=train,
        num_var="<num_feature>",
        cat_var="<cat_feature>"
        )