Data Visualization with Matplotlib

Plot first, model later

Visualizing data reveals outliers, skew, and relationships that summary stats hide.

import matplotlib.pyplot as plt

fig, ax = plt.subplots(1, 2, figsize=(10, 4))
ax[0].hist(df["age"], bins=30)
ax[0].set_title("Age distribution")
ax[1].scatter(df["income"], df["spend"], alpha=0.4)
ax[1].set_title("Income vs. spend")
plt.tight_layout(); plt.show()

A few habits

• Always label axes and titles.
• Use histograms for distributions, scatter for relationships, line for trends.
• For quick statistical plots, seaborn sits on top of matplotlib and does more with less code.