Customizing Styles in Matplotlib

Customizing styles in Matplotlib refers to the process of modifying the visual appearance of plots such as colors, fonts, line styles and background themes to create visually appealing and informative data visualizations. Using built-in styles, custom style files you can control the appearance of your plots to match reports, presentations or personal preferences.

Figure Class

The Figure class in Matplotlib represents the entire plotting area where one or more charts (axes) are drawn. It acts as the main container for all plots in a figure. Below is the syntax used to create a Figure object:

class matplotlib.figure.Figure( figsize=None, dpi=None, facecolor=None, edgecolor=None, linewidth=0.0, frameon=None, subplotpars=None, tight_layout=None, constrained_layout=None)

Example 1: This example creates a figure and adds one plotting area (axes) to it. A simple line graph is then drawn inside that figure.

Python

import matplotlib.pyplot as plt 
from matplotlib.figure import Figure 

# new figure with width = 5 inches and height = 4 inches
fig = plt.figure(figsize =(5, 4)) 

# new axes for the figure
ax = fig.add_axes([1, 1, 1, 1]) 

# Adding the data to be plotted
ax.plot([2, 3, 4, 5, 5, 6, 6], [5, 7, 1, 3, 4, 6 ,8])
plt.show()

Output

Example 2: This example creates one figure and adds two different axes inside it. Each axes displays its own line plot within the same figure window.

Python

import matplotlib.pyplot as plt 
from matplotlib.figure import Figure 

# new figure with width = 5 inches and height = 4 inches
fig = plt.figure(figsize =(5, 4)) 

# first axes for the figure
ax1 = fig.add_axes([1, 1, 1, 1]) 

# second axes for the figure
ax2 = fig.add_axes([1, 0.5, 0.5, 0.5])

# Adding the data to be plotted
ax1.plot([2, 3, 4, 5, 5, 6, 6], [5, 7, 1, 3, 4, 6 ,8])
ax2.plot([1, 2, 3, 4, 5], [2, 3, 4, 5, 6])
plt.show()

Output

Pyplot Class

The Pyplot module in Matplotlib is used to create and control plots using simple plotting functions. It allows you to quickly draw and display graphs. Below is the syntax of the plot() function used in Pyplot:

matplotlib.pyplot.plot(*args, scalex=True, scaley=True, data=None, **kwargs)

Example: This example uses Pyplot to draw a line graph by providing x and y values and then displays it on the screen.

Python

import matplotlib.pyplot as plt 
plt.plot([1, 2, 3, 4], [1, 4, 9, 16]) 
plt.axis([0, 6, 0, 20]) 
plt.show()

Output

Axes Class

The Axes class represents the plotting area inside a figure where data is drawn. Each figure can contain multiple axes, but each axes belongs to only one figure. Below is the syntax used to create and control an axes object:

matplotlib.pyplot.axes(*args, **kwargs)

Example 1: This example creates an empty axes region on the figure by specifying its position and size.

Python

import matplotlib.pyplot as plt 
from matplotlib.figure import Figure 

# Creating the axes object with argument as [left, bottom, width, height]
ax = plt.axes([1, 1, 1, 1])

Output

Example 2: This example creates a figure, adds an axes to it and plots two line datasets inside the same axes.

Python

import matplotlib.pyplot as plt 
from matplotlib.figure import Figure 
fig = plt.figure(figsize = (5, 4))

# Adding the axes to the figure
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])

# plotting 1st dataset to the figure
ax1 = ax.plot([1, 2, 3, 4], [1, 2, 3, 4])

# plotting 2nd dataset to the figure
ax2 = ax.plot([1, 2, 3, 4], [2, 3, 4, 5])
plt.show()

Output

Setting Colors

Colors in Matplotlib control the appearance of lines, markers and plot elements to visually distinguish data on a graph.

Example 1: This example draws a line plot and applies a custom color to the line using the color parameter.

Python

import matplotlib.pyplot as plt

# Define the Color
color = 'green'
plt.plot([1, 2, 3, 4], color=color)
plt.show()

Output

Example 2: This example plots points with circular markers and applies a different color to the marker fill.

Python

import matplotlib.pyplot as plt
x = [1, 2, 3, 4]
y = [1, 4, 9, 16]
plt.plot(x, y, marker='o', markerfacecolor='r')
plt.show()

Output

Adding Text, Font and Grid lines

Text and grid lines in Matplotlib are used to label plots and show reference lines that make values easier to read. Below is the common syntax used to add text and grid lines to a plot:

plt.title(label, **style)
plt.grid(**style)

Example: This example creates a line chart and adds a title at the top along with grid lines in the background to make the plot easier to read.

Python

import matplotlib.pyplot as plt 
X = [12, 34, 23, 45, 67, 89] 
Y = [1, 3, 67, 78, 7, 5] 
plt.plot(X, Y) 

# Add gridlines to the plot 
plt.grid(color = 'green', linestyle = '--', linewidth = 0.5)

# displaying the title
plt.title(label='Number of Users of a particular Language', fontweight=10, pad='2.0')
plt.show()

Output

download-(1)

Adding Legends

Legends in Matplotlib are used to identify different plotted data by showing their labels inside a small box on the chart. Below is the basic syntax used to display a legend on a plot:

plt.legend(labels=None, loc='best', bbox_to_anchor=None, ncol=1)

Example: This example plots two lines and adds a legend so each line can be identified using its label.

Python

import matplotlib.pyplot as plt
x = [3, 1, 3] 
y = [3, 2, 1] 
plt.plot(x, y)
plt.plot(y, x)

# Adding the legends
plt.legend(["blue", "orange"])
plt.show()

Output

Customizing Ticks and Tick Labels

Ticks and tick labels control how values are displayed along the X-axis and Y-axis in a Matplotlib plot. Below are the main methods used to control axis limits and tick labels:

ax.set_xlim(min, max)
ax.set_ylim(min, max)
ax.set_xticklabels(labels)
ax.set_yticklabels(labels)

Example: This example creates a line plot, restricts the X-axis range and assigns custom text labels to the X-axis ticks.

Python

import matplotlib.pyplot as plt 
from matplotlib.figure import Figure 

x = [3, 1, 3] 
y = [3, 2, 1]

fig = plt.figure(figsize =(5, 4)) 
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8]) 
ax.plot(x, y)

ax.set_xlim(1, 2)
ax.set_xticklabels(["one", "two", "three", "four", "five", "six"]) 
plt.show()

Output

Applying Styles to Plots

Matplotlib styles change the visual theme of a plot such as colors, fonts and background. Below is the syntax used to apply a style to a plot:

plt.style.use(style_name)

Example: This example applies a predefined Matplotlib style and plots random data using that theme.

Python

import numpy as np 
import matplotlib.pyplot as plt 
from matplotlib import style 

# creating an array of data for plot 
data = np.random.randn(50) 

# using the style for the plot 
plt.style.use('Solarize_Light2') 

plt.plot(data) 
plt.show()

Output

download-(2)

Creating Multiple Subplots

Multiple subplots allow you to display more than one plot inside the same figure for side-by-side or stacked comparisons. Below are the main Matplotlib methods used to create multiple subplots:

1. add_axes() method: The add_axes() method figure module of matplotlib library is used to add an axes to the figure. Below is the syntax:

fig.add_axes([left, bottom, width, height])

Example: This example creates one figure and places two plots inside it by defining their exact positions.

Python

import matplotlib.pyplot as plt 
from matplotlib.figure import Figure 

fig = plt.figure(figsize =(5, 4)) 

ax1 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) 
ax2 = fig.add_axes([0.5, 0.5, 0.3, 0.3])

ax1.plot([5, 4, 3, 2, 1], [2, 3, 4, 5, 6])
ax2.plot([1, 2, 3, 4, 5], [2, 3, 4, 5, 6])
plt.show()

Output

2. subplot() method: subplot() method adds another plot to the current figure at the specified grid position. Below is the syntax:

plt.subplot(nrows, ncols, index)

Example: This example splits the figure into two columns and places one plot in each.

Python

import matplotlib.pyplot as plt 
x = [3, 1, 3] 
y = [3, 2, 1] 
z = [1, 3, 1] 

plt.figure() 

# adding first subplot 
plt.subplot(121) 
plt.plot(x, y) 

# adding second subplot 
plt.subplot(122) 
plt.plot(z, y)

Output

Note: Subplot() function have disadvantages like - It does not allow adding multiple subplots at the same time and It deletes the preexisting plot of the figure.

3. subplots() method: subplots() function is used to create figure and multiple subplots at the same time. Below is the syntax:

plt.subplots(nrows, ncols)

Example: This example creates one row with two subplots and plots data in each.

Python

import matplotlib.pyplot as plt 
fig, axes = plt.subplots(1, 2)

axes[0].plot([1, 2, 3, 4], [1, 2, 3, 4])
axes[0].plot([1, 2, 3, 4], [4, 3, 2, 1])

axes[1].plot([1, 2, 3, 4], [1, 1, 1, 1])

Output

4. subplot2grid() method: subplot2grid() function give additional flexibility in creating axes object at a specified location inside a grid. Below is the syntax:

plt.subplot2grid(shape, location, rowspan, colspan)

Example: This example creates three vertically stacked plots inside a grid layout.

Python

import matplotlib.pyplot as plt

x = [3, 1, 3]
y = [3, 2, 1]
z = [1, 3, 1]

ax1 = plt.subplot2grid((7, 1), (0, 0), rowspan=2)
ax2 = plt.subplot2grid((7, 1), (2, 0), rowspan=2)
ax3 = plt.subplot2grid((7, 1), (4, 0), rowspan=2)

ax1.plot(x, y)
ax2.plot(x, z)
ax3.plot(z, y)
plt.show()

Output

Working with Images

Matplotlib provides image functions that allow you to read and display image files directly inside a plot window. Below is the syntax commonly used for working with images:

matplotlib.image.imread(filename)
matplotlib.pyplot.imshow(image)

Example: This example reads an image file from disk and displays it using Matplotlib.

Python

import matplotlib.pyplot as plt
import matplotlib.image as img

im = img.imread("g4g.png")
plt.imshow(im)
plt.show()

Output

Customizing Styles in Matplotlib

Figure Class

Related Articles

Pyplot Class

Axes Class

Related articles:

Setting Colors

Related articles:

Adding Text, Font and Grid lines

Related articles:

Adding Legends

Related articles:

Customizing Ticks and Tick Labels

Related articles:

Applying Styles to Plots

Creating Multiple Subplots

Working with Images

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