Learn Subplots | Plots Customization

The subplots() function from pyplot serves this specific purpose. It was previously used to create a canvas in the first section, and now it will be examined in more detail.

Rows and Columns

The most important arguments of this function are nrows and ncolumns, which set the number of rows and columns in the subplot grid. By default, both are 1, so you get a single plot (Axes).

The function subplots() returns a Figure object and either one Axes object or an array of Axes objects, depending on the grid size.


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import matplotlib.pyplot as plt

fig, axs = plt.subplots(2, 2)

plt.show()

A 2 by 2 subplot grid was created.

Note

Since there are multiple subplots, subplots returns an array of Axes objects, usually stored in a variable called axs (singular ax is for one plot).

In this case, axs is a two-dimensional array, so you need both a row and a column index to access a specific subplot.


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import matplotlib.pyplot as plt
import numpy as np

data_linear = np.arange(1, 11)
data_squared = data_linear ** 2

# Creating a 2x2 subplot grid
fig, axs = plt.subplots(2, 2)

# Creating a different plot for each Axes object
axs[0, 0].plot(data_linear)
axs[0, 1].plot(data_squared)
axs[1, 0].scatter(data_linear, data_linear)
axs[1, 1].scatter(data_linear, data_squared)

plt.show()

The first row (row 0) has two line plots, and the second row (row 1) has two scatter plots.

Since each plot is placed on a separate subplot, plt.plot() or plt.scatter() cannot be used directly. The appropriate approach is to call the corresponding method on each individual Axes object.

Converting to 1D Array

It is also possible to use the .ravel() method to convert 2D Axes array to 1D contiguous flattened array:


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import matplotlib.pyplot as plt
import numpy as np

data_linear = np.arange(1, 11)
data_squared = data_linear ** 2

# Creating a 2x2 subplot grid
fig, axs = plt.subplots(2, 2)

# Flattening axs to a 1D array for easier indexing
axs = axs.ravel()

# Creating a different plot for each Axes object
axs[0].plot(data_linear)
axs[1].plot(data_squared)
axs[2].scatter(data_linear, data_linear)
axs[3].scatter(data_linear, data_squared)

plt.show()

With a 2x2 array, axs.ravel() converts it into a 1D array containing four elements.

Sharing an Axis

The subplots() function also has sharex and sharey parameters. They control whether the x or y axes are shared across subplots. Both are set to False by default.


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import matplotlib.pyplot as plt
import numpy as np

data_linear = np.arange(1, 11)
data_squared = data_linear ** 2

# Create a 2x2 subplot grid with shared x-axis across all subplots
fig, axs = plt.subplots(2, 2, sharex=True)

# Flatten axs array for easier indexing
axs = axs.ravel()

# Plotting different data on each subplot
axs[0].plot(data_linear)
axs[1].plot(data_squared)
axs[2].scatter(data_linear, data_linear)
axs[3].scatter(data_linear, data_squared)

plt.show()

Setting sharex=True shares the x-axis across all subplots, which is useful here because all subplots use the same x-axis values.

You can also set sharex or sharey to 'row' to share the axis within each subplot row, or 'col' to share it within each subplot column.

Study More

As usual feel free to explore more in the subplots() documentation in case you want to.

Task

Swipe to start coding

Use the correct function to create a subplot grid.
The grid should have 3 rows and 1 column (specify the first two parameters).
Specify the rightmost keyword argument, so that x-axis will be shared among all the subplots.
Store the result of the function for creating subplots in the fig and axs variables (from left to right).
Place the first line plot for data_linear on the first row (row 0) of the subplot grid.
Place the second line plot for data_squared on the second row (row 1) of the subplot grid.
Place the third line plot for data_exp on the third row (row 2) of the subplot grid.

Solution

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Section 3. Chapter 6

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