Plt.subplots with rows and columns in Matplotlib

Introduction

We use plt.subplots to create multiple plots in one figure easily. It helps us compare data side by side.

When you want to show different charts together for comparison.

When you have several related datasets to visualize at once.

When you want to organize plots in a grid layout.

When you want to save space by combining plots in one image.

Syntax

Matplotlib

fig, axes = plt.subplots(nrows=number_of_rows, ncols=number_of_columns)

fig is the whole figure (the window or image).

axes is an array of plots arranged in rows and columns.

Examples

This creates a 2 by 2 grid of plots (4 plots total).

Matplotlib

fig, axes = plt.subplots(nrows=2, ncols=2)

This creates 1 row and 3 columns of plots (3 plots in a row).

Matplotlib

fig, axes = plt.subplots(1, 3)

This creates a single plot (1 row, 1 column by default).

Matplotlib

fig, axes = plt.subplots()

Sample Program

This code creates a 2x2 grid of plots showing different math functions. Each plot has a title. The tight_layout() makes sure plots don't overlap.

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

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

fig, axes = plt.subplots(nrows=2, ncols=2)

axes[0, 0].plot(x, np.sin(x))
axes[0, 0].set_title('sin(x)')

axes[0, 1].plot(x, np.cos(x), 'r')
axes[0, 1].set_title('cos(x)')

axes[1, 0].plot(x, np.tan(x), 'g')
axes[1, 0].set_title('tan(x)')
axes[1, 0].set_ylim(-10, 10)  # limit y to avoid extreme values

axes[1, 1].plot(x, np.log1p(x), 'm')
axes[1, 1].set_title('log1p(x)')

plt.tight_layout()
plt.show()

Important Notes

If you create only one plot, axes is not an array but a single object.

You can access each plot by axes[row, column] when you have multiple rows and columns.

Use plt.tight_layout() to avoid overlapping labels and titles.

Summary

plt.subplots helps create multiple plots in a grid.

You get a figure and an array of axes to draw on.

Use rows and columns to organize your plots clearly.

Practice

1. What does plt.subplots(2, 3) create in matplotlib?

easy

A. A figure with 2 rows and 3 columns of subplots

B. A figure with 3 rows and 2 columns of subplots

C. A single plot with size 2x3 inches

D. A figure with 6 rows and 1 column of subplots

Solution

1. Step 1: Understand plt.subplots parameters

   The first parameter is the number of rows, the second is columns.

2. Step 2: Apply to given code

   plt.subplots(2, 3) means 2 rows and 3 columns of subplots.

3. Final Answer:

   A figure with 2 rows and 3 columns of subplots -> Option A

4. Quick Check:

   rows=2, columns=3 = B [OK]

Hint: First number = rows, second = columns in plt.subplots [OK]

Common Mistakes:

• Confusing rows and columns order
• Thinking parameters set figure size
• Assuming single plot created

2. Which of the following is the correct syntax to create a 3x1 subplot grid using plt.subplots?

easy

A. fig, axes = plt.subplots(1, 3)

B. fig, axes = plt.subplots(3)

C. fig, axes = plt.subplots(3, 1)

D. fig, axes = plt.subplots(rows=3, cols=1)

Solution

1. Step 1: Recall plt.subplots parameter order

   The first argument is rows, second is columns.

2. Step 2: Match to 3 rows and 1 column

   plt.subplots(3, 1) creates 3 rows and 1 column.

3. Final Answer:

   fig, axes = plt.subplots(3, 1) -> Option C

4. Quick Check:

   3 rows, 1 column = C [OK]

Hint: Remember: plt.subplots(rows, columns) order matters [OK]

Common Mistakes:

• Swapping rows and columns
• Using single number for both dimensions
• Using keyword arguments incorrectly

3. What is the shape of axes returned by fig, axes = plt.subplots(2, 2)?

medium

A. A 1D array with 4 axes objects

B. A 2D array with shape (2, 2) of axes objects

C. A list of 2 axes objects

D. A single axes object

Solution

1. Step 1: Understand axes shape for multiple rows and columns

   When rows and columns are more than 1, axes is a 2D array matching shape.

2. Step 2: Apply to plt.subplots(2, 2)

   axes will be a 2x2 array of axes objects.

3. Final Answer:

   A 2D array with shape (2, 2) of axes objects -> Option B

4. Quick Check:

   Multiple rows and columns = 2D axes array [OK]

Hint: Multiple rows & columns -> axes is 2D array [OK]

Common Mistakes:

• Assuming axes is always 1D
• Confusing number of axes with shape
• Expecting a single axes object

4. Given the code:

fig, axes = plt.subplots(2, 2)
axes[2, 0].plot([1, 2, 3])

What error will occur?

medium

A. No error, plot will display

B. TypeError: axes object is not subscriptable

C. AttributeError: 'AxesSubplot' object has no attribute 'plot'

D. IndexError: index 2 is out of bounds for axis 0 with size 2

Solution

1. Step 1: Check axes shape from plt.subplots(2, 2)

   axes is a 2x2 array with indices 0 and 1 for rows and columns.

2. Step 2: Analyze axes[2, 0] access

   Index 2 for rows is out of bounds since max index is 1.

3. Final Answer:

   IndexError: index 2 is out of bounds for axis 0 with size 2 -> Option D

4. Quick Check:

   axes shape (2,2), index 2 invalid [OK]

Hint: Check axes array shape before indexing [OK]

Common Mistakes:

• Using invalid indices beyond shape
• Confusing axes as list instead of array
• Expecting no error on wrong index

5. You want to create a 2x3 grid of subplots and plot a line on each subplot with data from a list of 6 arrays. Which code correctly accesses each subplot to plot inside a nested loop?

hard

A. for i in range(2):
for j in range(3):
axes[i][j].plot(data[i*3 + j])

B. for i in range(2):
for j in range(3):
axes[i, j].plot(data[i + j])

C. for i in range(3):
for j in range(2):
axes[i][j].plot(data[i*2 + j])

D. for i in range(2):
for j in range(3):
axes[j][i].plot(data[i*3 + j])

Solution

1. Step 1: Understand axes shape and indexing

   plt.subplots(2, 3) creates axes as a 2x3 array, indexed as axes[row][col].

2. Step 2: Correctly map data list to subplot indices

   Data index = row * number_of_columns + column = i*3 + j matches subplot position.

3. Final Answer:

   for i in range(2): for j in range(3): axes[i][j].plot(data[i*3 + j]) -> Option A

4. Quick Check:

   axes[row][col], data index = row*cols + col [OK]

Hint: Use axes[row][col] and data index = row*cols + col [OK]

Common Mistakes:

• Swapping row and column loops
• Incorrect data indexing
• Using axes[j][i] instead of axes[i][j]