Summary  
This chapter explains how to use the subplots() function to create a grid of Axes in a single Figure, access and manipulate individual plots via array indexing or array flattening, and share axes across subplots.

General domain of usage  
Data visualization

The `subplots()` function from `pyplot` is used to create multiple plots in a single figure. You have already seen it when creating a canvas; now we examine it in more detail.

## Rows and Columns

The key arguments are `nrows` and `ncolumns`, which define the subplot grid. By default, both are `1`, producing a single `Axes`.
`subplots()` returns a `Figure` and either one `Axes` object or an array of them, depending on the layout.

import matplotlib.pyplot as plt

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

plt.show()

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).

Note

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


import matplotlib.pyplot as plt
import numpy as np

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

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

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 contains two line plots; the second row contains two scatter plots.
Each plot must be drawn using the method of the corresponding `Axes` object, not `plt.plot()` or `plt.scatter()`.

## Converting to 1D Array

You can flatten the 2D `Axes` array using `.ravel()` to simplify indexing:

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

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.


fig, axs = plt.subplots(2, 2, sharex=True)
axs = axs.ravel()

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.
You may also pass `'row'` or `'col'` to share axes only within rows or columns.

As usual feel free to explore more in the 
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html" target="_blank"><svg width="1em" height="1em" viewBox="0 0 30 32" fill="none" xmlns="http://www.w3.org/2000/svg"><path fill="none" stroke="#098f67" style="stroke: var(--color1, #098f67)" stroke-linejoin="miter" stroke-linecap="round" stroke-miterlimit="4" stroke-width="2.4156" d="M17.289 17.305v0c-1.754 1.754-4.597 1.754-6.351 0l-7.76-7.759c-1.755-1.755-1.755-4.601 0-6.356v0c1.753-1.753 4.595-1.756 6.351-0.005l6.208 6.187"></path><path fill="none" stroke="#098f67" style="stroke: var(--color1, #098f67)" stroke-linejoin="miter" stroke-linecap="round" stroke-miterlimit="4" stroke-width="2.4156" d="M12.504 13.97v0c1.754-1.754 4.597-1.754 6.351 0l7.762 7.762c1.754 1.754 1.754 4.597 0 6.351v0c-1.754 1.754-4.597 1.754-6.351 0l-5.953-5.953"></path></svg> <code>subplots()</code> documentation</a> in case you want to.

Study More

import unittest
import matplotlib.pyplot as plt
import numpy as np
import user_code  # Your solution code

def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)

class TestSubplotsGrid(unittest.TestCase):

    def test_fig_and_axs_exist(self):
        _dynamic_test(
            self,
            hasattr(user_code, 'fig') and hasattr(user_code, 'axs'),
            "Variables 'fig' and 'axs' are defined",
            "Variables 'fig' and/or 'axs' are not defined"
        )

    def test_fig_type(self):
        _dynamic_test(
            self,
            isinstance(getattr(user_code, 'fig', None), plt.Figure),
            "'fig' is a matplotlib Figure object",
            "'fig' is not a matplotlib Figure object"
        )

    def test_axs_type_and_shape(self):
        axs = getattr(user_code, 'axs', None)
        is_valid_type = isinstance(axs, (np.ndarray, list)) or hasattr(axs, 'plot')
        
        if isinstance(axs, np.ndarray):
            correct_shape = axs.shape == (3,)
        elif isinstance(axs, list):
            correct_shape = len(axs) == 3
        else:
            correct_shape = False

        _dynamic_test(
            self,
            is_valid_type,
            "'axs' is an array or Axes object",
            "'axs' is not an array or Axes object"
        )
        _dynamic_test(
            self,
            correct_shape,
            "'axs' has correct shape/length (3,)",
            "'axs' does not have correct shape/length (3,)"
        )

    def test_shared_x_axis(self):
        axs = getattr(user_code, 'axs', None)
        if axs is None:
            self.fail("'axs' variable is not defined")
            return

        # Check if x-axis is shared among all subplots
        shared_x = True
        for i in range(1, len(axs)):
            if not axs[0].get_shared_x_axes().joined(axs[0], axs[i]):
                shared_x = False
                break

        _dynamic_test(
            self,
            shared_x,
            "x-axis is shared among all subplots",
            "x-axis is not shared among all subplots"
        )

    def test_first_plot(self):
        line = user_code.axs[0].lines[0]
        _dynamic_test(
            self,
            line.get_color() == 'red',
            "First plot color is red",
            "First plot color is not red"
        )

        ydata = line.get_ydata()
        expected_y = np.arange(1, 11)
        _dynamic_test(
            self,
            np.array_equal(ydata, expected_y),
            "First plot data matches data_linear",
            "First plot data does not match data_linear"
        )

    def test_second_plot(self):
        line = user_code.axs[1].lines[0]
        _dynamic_test(
            self,
            line.get_color() == 'blue',
            "Second plot color is blue",
            "Second plot color is not blue"
        )

        ydata = line.get_ydata()
        expected_y = np.arange(1, 11) ** 2
        _dynamic_test(
            self,
            np.array_equal(ydata, expected_y),
            "Second plot data matches data_squared",
            "Second plot data does not match data_squared"
        )

    def test_third_plot(self):
        line = user_code.axs[2].lines[0]
        _dynamic_test(
            self,
            line.get_color() == 'orange',
            "Third plot color is orange",
            "Third plot color is not orange"
        )

        ydata = line.get_ydata()
        expected_y = np.exp(np.arange(1, 11))
        _dynamic_test(
            self,
            np.allclose(ydata, expected_y),
            "Third plot data matches data_exp",
            "Third plot data does not match data_exp"
        )


if __name__ == "__main__":
    unittest.main()


test_subplots.py

Learn how to explore and communicate data through visual analysis. The course covers creating common plots, customizing visualizations, and using statistical charts to examine data distributions, relationships, and patterns. Exploratory data analysis techniques are applied to identify trends, anomalies, and insights in real-world datasets.

Subplots

Rows and Columns

Converting to 1D Array

Sharing an Axis

Oplossing