Summary  
This chapter explains how to customize grid lines in plots by setting their visibility, axis direction, color, and transparency using the `pyplot.grid()` function.

General domain of usage  
Data visualization

Another important part of the customization is **grid** customization. `pyplot` module has a `grid()` function for this purpose. 

## Visibility and Axes

Its first parameter `visible` specifies whether to show the **grid lines** (by default, they are not shown). 

The `axis` parameter in grid customization allows you to control the direction in which grid lines appear on a plot:

* `'x'` — displays vertical grid lines aligned with the **x-axis**;
* `'y'` — displays horizontal grid lines aligned with the **y-axis**;
* `'both'` — displays grid lines in **both** directions (this is the default behavior).

This parameter is useful when you want to emphasize data alignment along a specific axis or reduce visual clutter by limiting grid lines to one direction.



import matplotlib.pyplot as plt
import numpy as np

data_linear = np.arange(0, 11)
data_squared = data_linear ** 2

plt.plot(data_linear, label='linear function', color='red', alpha=0.5)
plt.plot(data_squared, '-o', label='quadratic function', color='blue')

plt.xticks(data_linear)
plt.xlabel('x', loc='right')
plt.ylabel('y', loc='top', rotation=0)

# Setting the horizontal grid lines to be visible
plt.grid(True, axis='x')

plt.legend()
plt.show()

In this example, `visible=True` and `axis='x'` were set to enable only the vertical grid lines. This enhances the plot by adding useful reference lines while avoiding unnecessary horizontal elements.


## Color and Transparency

It is also possible to change the color of the grid lines using the `color` parameter and their transparency using the `alpha` parameter.

import matplotlib.pyplot as plt
import numpy as np

data_linear = np.arange(0, 11)
data_squared = data_linear ** 2

plt.plot(data_linear, label='linear function', color='red', alpha=0.5)
plt.plot(data_squared, '-o', label='quadratic function', color='blue')

plt.xticks(data_linear)
plt.xlabel('x', loc='right')
plt.ylabel('y', loc='top', rotation=0)

# Customizing the horizontal grid lines
plt.grid(True, axis='x', alpha=0.2, color='black')

plt.legend()
plt.show()

Now our grid lines are **black** (`color='black'`) and are **more transparent** (`alpha=0.2`) which makes the plot look even better. 

There are still more possible parameters for the `grid()` functions (they are not used so often), so here is its <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.grid.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>grid()</code> documentation</a> in case you want to explore more.

Study More

import unittest
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import user_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 TestGridCustomization(unittest.TestCase):

    def test_grid_visible(self):
        # Test if grid is enabled
        gridlines = plt.gca().get_ygridlines()
        _dynamic_test(
            self,
            all(line.get_visible() for line in gridlines),
            "Grid lines are visible",
            "Grid lines are not visible"
        )

    def test_grid_axis(self):
        # Test if grid is applied only to y-axis (horizontal grid lines)
        ax = plt.gca()
        y_lines = ax.get_ygridlines()
        x_lines = ax.get_xgridlines()

        _dynamic_test(
            self,
            all(line.get_visible() for line in y_lines) and not any(line.get_visible() for line in x_lines),
            "Grid is applied only to the y-axis",
            "Grid is not correctly restricted to the y-axis"
        )

    def test_grid_color(self):
        # Test if grid color is slategrey
        expected_color = mcolors.to_rgba('slategrey')
        y_lines = plt.gca().get_ygridlines()

        _dynamic_test(
            self,
            all(mcolors.to_rgba(line.get_color())[:3] == expected_color[:3] for line in y_lines),
            "Grid line color is correctly set to 'slategrey'",
            "Grid line color is not set to 'slategrey'"
        )

    def test_grid_alpha(self):
        # Test if grid transparency is 0.5
        y_lines = plt.gca().get_ygridlines()

        _dynamic_test(
            self,
            all(round(line.get_alpha(), 2) == 0.5 for line in y_lines),
            "Grid line transparency (alpha) is correctly set to 0.5",
            "Grid line transparency (alpha) is not set to 0.5"
        )


test_grid.py

Master the foundational skills of data visualization using the object-oriented approach in Matplotlib. Learn to control every aspect of your figures, from axes and legends to colors and styles. Create professional-quality static charts, handle time-series data, and export high-resolution graphics for reports and publications.

Adding Grid

Visibility and Axes

Color and Transparency

解答