How to Efficiently Create and Plot in Multiple Matplotlib Subplots?

Plotting in Multiple Subplots

Creating multiple subplots in Matplotlib can be achieved through various methods. Understanding the role of the fig and axes variables is crucial.

The fig, axes Structure

In the code snippet fig, axes = plt.subplots(nrows=2, ncols=2), fig and axes are assigned to the returned figure and a 2D array of Axes objects, respectively. The axes array contains the individual subplots, enabling subsequent plotting operations on specific subplots.

Alternatives to subplots

While the subplots method simultaneously creates a figure and its subplots, it's also possible to create them separately:

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

However, this approach is less preferred because it requires additional steps to plot on each subplot.

Example with Multiple Subplots

Consider the following code that plots a simple curve on each of the four subplots:

import matplotlib.pyplot as plt

x = range(10)
y = range(10)

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

for row in ax:
    for col in row:
        col.plot(x, y)

plt.show()

This code generates a figure with four subplots, each with the same curve. The for loops iterate over the rows and columns of the ax array, assigning each subplot to the col variable for plotting.

Another Alternative Approach

Though not as elegant, one can also manually create and plot on each subplot separately:

fig = plt.figure()

plt.subplot(2, 2, 1)
plt.plot(x, y)

plt.subplot(2, 2, 2)
plt.plot(x, y)

plt.subplot(2, 2, 3)
plt.plot(x, y)

plt.subplot(2, 2, 4)
plt.plot(x, y)

plt.show()

This approach involves creating a figure, manually specifying each subplot's position, and then plotting on them.

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