Practical Exploratory Data Analysis with Python

Exploratory data analysis is the process of understanding a dataset before asking a model to make predictions from it.

Inspect the shape

Start with rows, columns, data types, missingness, and unique values. These basic checks often reveal the biggest issues quickly.

import pandas as pd

df = pd.read_csv("data.csv")
print(df.shape)
print(df.dtypes)
print(df.isna().mean().sort_values(ascending=False).head(10))

Ask distribution questions

Look at ranges, skew, outliers, and category frequency. Do not assume a column means what its name suggests until you inspect actual values.

Connect variables to the problem

EDA should be hypothesis-driven. Explore relationships that could affect the business or scientific question rather than generating dozens of disconnected charts.

Document what you learn

The most valuable output of EDA is not the notebook itself. It is the set of findings, caveats, and decisions that make the next analytical step more rigorous.