Matplotlib: Controlling Axes

Controlling Axes

•  When we create a plot using Matplotlib, the axes (the X‒axis and Y‒axis) define the scale, limits and layout of our graph.

• Controlling Axes helps us‒

i) To focus on specific data ranges.

ii) Improves readability.

iii) Highlight important trends.

1. Setting Axis Limits

• We can set the limits of X‒axis and Y‒axis.

Syntax

plt.xlim(xmin,xmax)

plt.ylim(ymin,ymax)

•  Following is a simple Python program that illustrates how to set limits to both the x‒axis and y‒axis.

Python program

import matplotlib.pyplot as plt

x = [1,2,3,4,5]

y = [2,4,6,8,10]

plt.plot(x,y)

plt.xlim(0,6)

plt.ylim(0,12)

plt.show()

Output

2. Titles and Labels

• We can give the labels to x‒axis and y‒axis. For that purpose we use the functions xlabel(label_name) and ylabel(label_name). Similarly the title to the graph can be given using the function title(title_name)

• For example ‒

In [11]: 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.show()

Output

3. Ticks

• Ticks are the small marks and numbers along each axis.

Syntax

plt.xticks([list of tick values])

plt.yticks([list of tick values])

Python program

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]

y = [10, 20,30, 40,50]

plt.plot(x, y, label='Sample Line')

plt.xticks([1, 2, 3, 4, 5], ['A', 'B', 'C', 'D', 'E'],color='red')

plt.yticks([10, 20, 30, 40, 50], ['Ten', 'Twenty', 'Thirty', 'Forty', 'fifty'])

plt.xlabel('X‒axis Label')

plt.ylabel('Y‒axis Label')

plt.title('Customized Ticks Demo')

plt.show()

Output

• This code creates a simple line plot with customized x‒tick and y‒tick labels., The xticks are red colored ticks.