Summary  
This chapter explains how to customize grid lines in a plot by toggling their visibility, selecting which axis they appear on, and adjusting their color and transparency.  

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

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.

Grid

Visibility and Axes

Color and Transparency

Oplossing