How to add a grid on a figure in Matplotlib ?

Matplotlib library is widely used for plotting graphs. In many graphs, we require to have a grid to improve readability. Grids are created by using grid() function in the Pyplot sublibrary. In this article, we will see how to add grid in Matplotlb.

Add a Grid on a Figure in Matplotlib

Below are the ways by which we can see how to add grid in Matplotlib in Python:

• Using scatter plot
• Using Plot()
• Using add_gridspec()

Add a Grid on a Figure in Matplotlib Using scatter()

In this example, the code uses the Matplotlib library to create a scatter plot of y = x^2 with points generated using NumPy. The first part uses the pyplot interface to create a scatter plot and grid on the y-axis. The second part creates a figure and axis explicitly, sets ticks on both the x and y axes, plots the scatter graph, and specifies the default grid on the figure.

import matplotlib.pyplot as plt
import numpy

# Define x and y
x = numpy.arange(0, 1, 0.1)
y = numpy.power(x, 2)

# Plot graph
plt.scatter(x, y)

# Define grid with axis='y'
plt.grid(axis='y')
plt.show()

# Define a figure
fig = plt.figure()
ax = fig.gca()

# Set labels on x and y axis of figure
ax.set_xticks(numpy.arange(0, 1, 0.1))
ax.set_yticks(numpy.arange(0, 1, 0.1))

# Plot the graph
ax.scatter(x, y)

# Specify default grid on figure
ax.grid()
ax.show()

Output:

Matplotlib Adding Grid Lines Using Plot()

In this example, the given code uses the Matplotlib library to create a line graph of the sine function. It defines an array 'x' from -5 to 5 with a step size of 0.01 and calculates 'y' as the sine of 2π times 'x'. The code then plots the line graph, sets a red dashed grid, and displays the plot.

import matplotlib.pyplot as plt
import numpy as np

# Define x and y
x = np.arange(-5, 5, 0.01)
y = np.sin(2*np.pi*x)

# Plot line graph
plt.plot(x, y)

# Specify grid with line attributes
plt.grid(color='r', linestyle='--')

# Display the plot
plt.show()

Output:

Add a Matplotlib Grid on a Figure Using add_gridspec()

In this example, the code uses Matplotlib and add_gridspec() to create a figure with a 2x2 grid of subplots. It defines three subplots (line plot, scatter plot, and bar plot) within this grid and plots data on each. Additionally, it adds a dashed grid to all subplots, enhancing visualization. Finally, the `plt.show()` command displays the figure with the configured subplots.

import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec

# Creating a grid of subplots
fig = plt.figure()

# Define a 2x2 grid
gs = GridSpec(2, 2)

# Creating subplots
ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1])
ax3 = fig.add_subplot(gs[1, :])

# Plotting some data on the subplots
ax1.plot([1, 2, 3], [4, 5, 6])
ax2.scatter([1, 2, 3], [4, 5, 6])
ax3.bar([1, 2, 3], [4, 5, 6])

# Adding grid to all subplots
for ax in [ax1, ax2, ax3]:
    ax.grid(True, linestyle='--', linewidth=0.5, color='gray')

plt.show()

Output: