Subplots in Python — Multiple Charts, One Figure

Sometimes one chart isn't enough. You've got sales data and profit data covering the same six months, and putting both on one graph makes it unreadable. Subplots solve this — each dataset gets its own panel, but everything lives inside one figure. Cleaner to look at, easier to compare, and more impressive to show.

This guide covers everything from a basic two-chart layout to a full 2×2 dashboard grid. Three working examples, a parameter reference, common mistakes to avoid, and a mini project at the end.

How plt.subplots() works

One function call sets up the entire layout. Here's the sequence:

Key parameters

Everything you can pass to plt.subplots() and related functions:

Example 1 — two charts side by side

The simplest subplot setup. One row, two columns — a line graph on the left and a bar chart on the right, both showing the same six months of data:

import matplotlib.pyplot as plt

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
sales  = [120, 145, 160, 130, 175, 190]
profit = [30,  40,  45,  35,  55,  60]

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

# Left panel — line graph
ax1.plot(months, sales, color='#e86c2f', marker='o', linewidth=2)
ax1.set_title('Monthly Sales')
ax1.set_xlabel('Month')
ax1.set_ylabel('Units Sold')
ax1.grid(True, linestyle='--', alpha=0.5)

# Right panel — bar chart
ax2.bar(months, profit, color='#3d5afe', width=0.5)
ax2.set_title('Monthly Profit')
ax2.set_xlabel('Month')
ax2.set_ylabel('Profit (₹ thousands)')
ax2.grid(True, linestyle='--', alpha=0.5)

plt.suptitle('Sales & Profit Overview — 2025', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.show()

What each part is doing

• plt.subplots(1, 2) — 1 row, 2 columns. Returns the canvas as fig and unpacks the axes as (ax1, ax2).
• figsize=(12, 5) — total figure size. Both panels share this space, so going wider is better for side-by-side layouts.
• ax1.plot() and ax2.bar() — each axis gets its own chart type. They're completely independent.
• ax.set_title() / ax.set_xlabel() — labels go on the individual panel, not the whole figure.
• plt.suptitle() — the headline above both panels.
• plt.tight_layout() — fixes padding so labels don't overlap between panels.

Example 2 — stacked charts with a shared x-axis

When both charts track the same time period, stacking them vertically and sharing the x-axis removes duplicate labels and makes comparison much cleaner:

import matplotlib.pyplot as plt

days     = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
steps    = [4200, 8100, 6500, 9300, 7800, 11200, 5400]
calories = [1800, 2100, 1950, 2300, 2050, 2600,  1700]

fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 7), sharex=True)

ax1.plot(days, steps, color='#e86c2f', marker='o', linewidth=2)
ax1.set_ylabel('Steps')
ax1.set_title('Daily Steps')
ax1.grid(True, linestyle='--', alpha=0.5)

ax2.bar(days, calories, color='#00b894', width=0.5)
ax2.set_ylabel('Calories Burned')
ax2.set_title('Calories Burned')
ax2.grid(True, linestyle='--', alpha=0.5)

plt.suptitle('Weekly Fitness Summary', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.show()

Example 3 — the 2×2 dashboard grid

Four charts, one figure. This is the layout that makes your output look like a proper data dashboard. With a 2×2 grid, axes becomes a 2D array — you access each panel with axes[row, col]:

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)

fig, axes = plt.subplots(2, 2, figsize=(12, 8))

# Top-left — sine wave
axes[0, 0].plot(x, np.sin(x), color='#e86c2f')
axes[0, 0].set_title('Sine Wave')
axes[0, 0].grid(True, linestyle='--', alpha=0.4)

# Top-right — cosine wave
axes[0, 1].plot(x, np.cos(x), color='#3d5afe')
axes[0, 1].set_title('Cosine Wave')
axes[0, 1].grid(True, linestyle='--', alpha=0.4)

# Bottom-left — scatter plot
axes[1, 0].scatter(x, np.random.rand(100), color='#00b894', s=15, alpha=0.7)
axes[1, 0].set_title('Random Scatter')
axes[1, 0].grid(True, linestyle='--', alpha=0.4)

# Bottom-right — bar chart
categories = ['A', 'B', 'C', 'D', 'E']
values     = [23, 45, 31, 60, 42]
axes[1, 1].bar(categories, values, color='#e86c2f', width=0.5)
axes[1, 1].set_title('Category Values')
axes[1, 1].grid(True, linestyle='--', alpha=0.4)

plt.suptitle('Chart Dashboard — 4 Panels', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.show()

Think of axes like a table. Top-left is axes[0, 0], top-right is axes[0, 1], bottom-left is axes[1, 0], bottom-right is axes[1, 1]. Once that mental model clicks, any grid size works the same way.

Save the figure to a file

Instead of displaying the chart on screen, you can save it directly as an image — useful for reports, presentations, or sharing:

plt.suptitle('My Dashboard', fontsize=14, fontweight='bold')
plt.tight_layout()

# Save instead of show
plt.savefig('dashboard.png', bbox_inches='tight', dpi=150)
print("Saved as dashboard.png")

• bbox_inches='tight' — crops the output to the figure content so nothing gets clipped at the edges
• dpi=150 — higher resolution for sharper output. Use 300 for print quality.
• Supported formats: .png, .jpg, .pdf, .svg — just change the file extension

Common mistakes beginners make

These are the things that silently break subplot layouts:

• Calling plt.xlabel() instead of ax.set_xlabel() — the global function only labels the last active panel, not all of them
• Forgetting plt.tight_layout() — titles and labels from adjacent panels overlap and everything looks broken
• Using subplot() (no 's') when you mean subplots() — the older API is more error-prone and harder to manage
• Accessing a 2×2 grid with axes[0] instead of axes[0, 0] — gives you a row of axes, not a single panel
• Setting figsize too small for the number of panels — charts get squashed and labels overlap even with tight_layout
• Adding plt.show() inside a loop — renders multiple incomplete figures instead of one complete one

When to use subplots

Mini project — build your personal dashboard

Take the 2×2 grid from Example 3 and make it yours. Use real numbers from your phone's health or screen time app — the chart tells a different story when the data is actually about you.

Frequently asked questions