Matplotlib: Customization

Customization

1) Line style and Line width

•  Matplotlib provides different line styles, which you can set using linestyle or Is.

Line style     Code

Solid Line       '‒' or 'solid'

Dashed Line      '‒‒' or 'dashed'

Dotted Line     ':' or 'dotted'

Dash‒Dot Line     ' ‒,' or 'dashdot'

• Similarly we can change the line width using linewidth or lw parameter. Following is a demo example in which we set the line style and line width and color to the lines.

Demo example

import matplotlib.pyplot as plt

import numpy as np

x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

y1 = np.array([2, 3, 5, 7, 6, 8, 10, 9, 11, 13, 12])

y2 = np.array([1, 2, 4, 6, 5, 7, 9, 8, 10, 12, 11])

#Thick solid line

plt.plot(x, y1, linestyle='', linewidth=5, color='blue', label="Thick Line')

#Thin dashed line

plt.plot(x, y2, linestyle='‒‒', linewidth=1, color='black', label="Thin Line')

# Customizations

plt.xlabel("X‒axis")

plt.ylabel("Y‒axis")

plt.title("Line Width and Style Customization")

plt.legend()

plt.grid(True)

plt.show()

Output

Line Width and Style Customization

2) Marker

•  We use markers to highlight data points in a line plot. The marker parameter in the plot() function allows us to customize point styles, sizes and colors.

• There are various marker styles and those are enlisted in the following table.

Marker type        Code

Circle                      'o'

Square                     'g'

Triangle up              '^'

Triangle down          'v'

Diamond                 'D'

Star                          '*'

Plus                          '+'

Cross                       'x'

Demo example

Python code

import numpy as np

y = np.array([5,2,10,8])

plt.plot(y,marker = 'D')

plt.show()

Output

• The diamond shaped marker will be displayed on the line.

3) Grid and Background

• For displaying the Grid in the graph, we use grid() function. The code is as follows‒

from matplotlib import pyplot as plt

import numpy as np

age = np.array([10,22,35,50,40,3,65,54,29,70])

weight = np.array([20,60,75,90,55,8,65,100,64,46])

plt.scatter(age, weight)

plt.title("Age‒Weight Analysis in India")

plt.xlabel("Age")

plt.ylabel("Weight")

plt.grid()

plt.show()

Review Question

1. Discuss various customized markers in scatter plot.