Exploratory Data Analysis

In statistics, exploratory data analysis is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task

Techniques for performing EDA

Some of the most common methods for performing the exploratory data analysis are given below. Definitely there are a lot of other methods.

A - Univariate and bivariate analysis
B - Missing value analysis
C - Outlier detection analysis
D - Percentile based outlier removal
E - Correlation analysis
F - Covariance analysis

This is one example result after the exploratory data analysis on a sample dataset

Proposed methods

Here I am discussing mainly four advanced python libraries for performing the exploratory data analysis. They are

1 - pandas-profiling
2 - streamlit
3 - sweetviz
4 - wordcloud