Exploratory Data Analysis with Python

A walkthrough of a simple EDA workflow using pandas and matplotlib.

Overview

This example post demonstrates how a typical Exploratory Data Analysis (EDA) blog post looks on this site. It generates synthetic data so the post renders without external dependencies.

Generate Data

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

np.random.seed(123)

n = 300
df = pd.DataFrame({
    "age": np.random.randint(18, 65, n),
    "income": np.random.exponential(50000, n).round(2),
    "score": np.random.beta(2, 5, n).round(4),
})
df.head()

	age	income	score
0	63	42639.29	0.0089
1	20	5414.64	0.4655
2	46	60170.99	0.2125
3	52	54112.52	0.1904
4	56	2517.17	0.2594

Distributions

fig, axes = plt.subplots(1, 3, figsize=(12, 3.5))
for ax, col in zip(axes, df.columns):
    ax.hist(df[col], bins=30, edgecolor="white")
    ax.set_title(col.capitalize())
fig.tight_layout()
plt.show()

Figure 1: Distribution of age, income, and score.

Correlations

df.corr().round(3)

	age	income	score
age	1.000	0.096	0.012
income	0.096	1.000	-0.077
score	0.012	-0.077	1.000

Next Steps

• Handle outliers
• Feature engineering
• Modelling