1. Introduction

When you need to display multiple plots side by side (or in rows and columns), you can use Matplotlib’s subplots() function. It automatically creates both a Figure object (the container for your plots) and an array of Axes objects (the individual plots themselves). This makes it easier to manage multiple plots at once.

---

2. Basic Setup

import matplotlib.pyplot as plt
import numpy as np
 
# Create some example data
a = np.linspace(0, 10, 11)  # 0 to 10, 11 points
b = a ** 4                 # a^4
x = np.arange(0, 10)
y = 2 * x

• a, b: Show a non-linear relationship (b = a^4).
• x, y: Show a simple linear relationship (y = 2x).

Note:

• If you are running this in a non-Notebook environment (e.g., PyCharm, Sublime Text), remember to call plt.show() at the end to actually see your figures in a pop-up window.

---

3. Single Subplot with plt.subplots()

fig, axes = plt.subplots()  
# By default, subplots() with no arguments gives you 1 row and 1 column.
 
axes.plot(x, y, 'r')
axes.set_xlabel('x')
axes.set_ylabel('y')
axes.set_title('Single Subplot Example')
 
# Display the plot
plt.show()

• fig: The Figure object, a container holding everything.
• axes: The Axes object, which you call .plot(), .set_xlabel(), etc. on.
• 'r': The line color (red) in the example above.

---

4. Multiple Subplots in Rows and Columns

4.1 Creating a 1×2 Grid of Subplots

fig, axes = plt.subplots(nrows=1, ncols=2)
 
# axes here is a 1D NumPy array of two Axes objects: [axes0, axes1]
axes[0].plot(a, b)        # left subplot
axes[0].set_title('Left Plot')
 
axes[1].plot(x, y)        # right subplot
axes[1].set_title('Right Plot')
 
plt.tight_layout()        # adjusts spacing to prevent overlap
plt.show()

• axes[0] and axes[1] are your two separate subplots.

4.2 Creating a 2×2 Grid of Subplots

fig, axes = plt.subplots(nrows=2, ncols=2)
 
# axes is now a 2D NumPy array: 
# [[axes00, axes01],
#  [axes10, axes11]]
 
axes[0][0].plot(a, b)
axes[0][0].set_title('Plot (0,0)')
 
axes[0][1].plot(b, a)
axes[0][1].set_title('Plot (0,1)')
 
axes[1][0].plot(x, y)
axes[1][0].set_title('Plot (1,0)')
 
axes[1][1].plot(y, x)
axes[1][1].set_title('Plot (1,1)')
 
plt.tight_layout()
plt.show()

Tip: If the subplots overlap, use plt.tight_layout() or fig.tight_layout() to automatically adjust spacing.

---

5. Adjusting Figure Size and Titles

You can specify the figure size using the figsize=(width, height) argument in inches. You can also add a main title for the entire figure (sometimes called a “super title”).

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 8))
 
# Plot on individual Axes:
axes[0][0].plot(a, b)
axes[0][0].set_title('0,0 Plot')
 
axes[0][1].plot(y, x)
axes[0][1].set_title('0,1 Plot')
 
axes[1][0].plot(b, a)
axes[1][0].set_title('1,0 Plot')
 
axes[1][1].plot(x, y)
axes[1][1].set_title('1,1 Plot')
 
# Set an overall figure title
fig.suptitle("Figure-Level Title", fontsize=16)
 
plt.tight_layout()  # Use this first
plt.show()

---

6. Manually Adjusting Spacing

Sometimes tight_layout() doesn’t give you exactly what you want. You can manually adjust spacing with fig.subplots_adjust(...):

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 8))
 
axes[0][0].plot(a, b)
axes[0][1].plot(y, x)
axes[1][0].plot(b, a)
axes[1][1].plot(x, y)
 
# Manually adjust the layout
fig.subplots_adjust(
    left=0.1,    # Default is 0.125
    right=0.9,   # Default is 0.9
    bottom=0.1,  # Default is 0.1
    top=0.9,     # Default is 0.9
    wspace=0.9,  # Width padding between subplots
    hspace=0.1   # Height padding between subplots
)
 
plt.show()

• left, right, bottom, top “stretch” the margins of your entire figure.
• wspace, hspace control the blank space between subplots.

---

7. Saving a Figure

To save your figure to a file, call fig.savefig():

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 8))
 
axes[0][0].plot(a, b)
axes[0][1].plot(y, x)
axes[1][0].plot(b, a)
axes[1][1].plot(x, y)
 
# Save before or after plt.show()
fig.savefig('subplots.png', bbox_inches='tight')
 
plt.show()

• bbox_inches='tight' crops any extra whitespace around your figure.

---

Key Takeaways

1. fig, axes = plt.subplots() provides both the figure and axes automatically.
2. You can create multiple rows and columns by setting nrows and ncols.
3. Access individual Axes via indexing:
   • 1×2 subplots: axes[0], axes[1]
   • 2×2 subplots: axes[row][col]
4. Use tight_layout() for automatic spacing or fig.subplots_adjust() for more control.
5. The Figure is the overall container; each Axes is an individual plotting area.

---

Additional Reading

• Official Matplotlib Subplots Documentation
• Adjusting Spacing with subplots_adjust()

---

Analogy: Think of the Figure as a large sheet of paper, and each Axes is a smaller “panel” or “box” on that paper. Each panel can contain one or more plots. The subplots() function helps you quickly cut that sheet into a grid of these panels.