How do you create multi-panel figures with Matplotlib subplots?

Multi-panel figures are standard in data science reports — comparing multiple variables or time periods side by side. Matplotlib provides several ways to arrange subplots.

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 200)

# --- Regular grid ---
fig, axes = plt.subplots(2, 2, figsize=(10, 8), sharex=True)
# sharex=True links x-axis zoom/pan across all subplots
axes[0, 0].plot(x, np.sin(x))
axes[0, 0].set_title('sin')
axes[0, 1].plot(x, np.cos(x), color='tomato')
axes[0, 1].set_title('cos')
axes[1, 0].plot(x, np.tan(x))
axes[1, 0].set_title('tan')
axes[1, 1].set_visible(False)   # hide unused subplot
fig.suptitle('Trig functions', fontsize=16)
fig.tight_layout(rect=[0, 0, 1, 0.95])   # leave room for suptitle

# --- Flatten for iteration ---
fig, axes = plt.subplots(2, 3, figsize=(14, 6))
for ax, col in zip(axes.flatten(), df.select_dtypes('number').columns):
    ax.hist(df[col].dropna(), bins=20)
    ax.set_title(col)

# --- GridSpec for irregular layouts ---
from matplotlib.gridspec import GridSpec
fig = plt.figure(figsize=(12, 6))
gs  = GridSpec(2, 3, figure=fig)
ax1 = fig.add_subplot(gs[0, :2])   # spans first two columns of row 0
ax2 = fig.add_subplot(gs[0, 2])    # third column of row 0
ax3 = fig.add_subplot(gs[1, :])    # entire row 1

axes.flatten() is the standard idiom when you want to loop over a 2-D grid of Axes objects as if they were a 1-D list. fig.tight_layout() automatically adjusts spacing to prevent labels overlapping between subplots — call it before plt.show() or fig.savefig().

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