Formatting Axes in Python-Matplotlib

Matplotlib is a python library for creating static, animated and interactive data visualizations.

Note: For more information, refer to Introduction to Matplotlib

What is Axes?

This is what you think of as ‘plot’. It is the region of the image that contains the data space. The Axes contains two or three-axis(in case of 3D) objects which take care of the data limits. Below is an image illustrating the different parts of a figure which contains the graph.

The different aspects of the Axes can be changed according to the requirements.

1. Labelling x, y-Axis

Syntax:

for x-axis
Axes.set_xlabel(self, xlabel, fontdict=None, labelpad=None, \*\*kwargs)

for y-axis
Axes.set_ylabel(self, ylabel, fontdict=None, labelpad=None, \*\*kwargs)

These functions are used to name the x-axis and y-axis.

Example:

# importing matplotlib module
import matplotlib.pyplot as plt
import numpy as np

# x-axis & y-axis values
x = [3, 2, 7, 4, 9]
y = [10, 4, 7, 1, 2]

# create a figure and axes
fig, ax = plt.subplots()

# setting title to graph
ax.set_title('Example Graph')

# label x-axis and y-axis
ax.set_ylabel('y-AXIS')
ax.set_xlabel('x-AXIS')

# function to plot and show graph
ax.plot(x, y)
plt.show()

Output:

2. Limits of x, y-Axis

Syntax:

For x-axis:

Axes.set_xlim(self, left=None, right=None, emit=True, auto=False, \*, xmin=None, xmax=None)

Parameters:

• left and right – float, optional
  The left xlim(starting point) and right xlim(ending point) in data coordinates. Passing None leaves the limit unchanged.
• auto – bool or None, optional
  To turn on autoscaling of the x-axis. True turns on, False turns off (default action), None leaves unchanged.
• xmin, xmax : They are equivalent to left and right respectively, and it is an error to pass both xmin and left or xmax and right.

Returns:
right, left – (float, float)

For y-axis:

Axes.set_ylim(self, bottom=None, top=None, emit=True, auto=False, \*, ymin=None, ymax=None)

Parameters:

• bottom and top – float, optional
  The bottom ylim(starting point) and top ylim(ending point) in data coordinates. Passing None leaves the limit unchanged.
• auto – bool or None, optional
  To turn on autoscaling of the y-axis. True turns on, False turns off (default action), None leaves unchanged.
• ymin, ymax : They are equivalent to left and right respectively, and it is an error to pass both ymin and left or ymax and right.

Returns:
bottom, top – (float, float)

Example:

import matplotlib.pyplot as plt
import numpy as np

x = [3, 2, 7, 4, 9]
y = [10, 4, 7, 1, 2]

# create a figure and axes
fig, ax = plt.subplots()

ax.set_title('Example Graph')

ax.set_ylabel('y-AXIS')
ax.set_xlabel('x-AXIS')

# set x, y-axis limits
ax.set_xlim(0, 10)
ax.set_ylim(0, 10)

# function to plot and show graph
ax.plot(x, y)
plt.show()

Output:

3. Major and Minor Ticks

The Ticks are the values/magnitude of the x and y axis. Minor ticks are divisions of major ticks. There are two classes Locator and Formatter. Locators determine where the ticks are and Formatter controls the formatting of the ticks. These two classes must be imported from matplotlib.

• MultipleLocator() places ticks on multiples of some base.

• FormatStrFormatter uses a format string (e.g., ‘%d’ or ‘%1.2f’ or ‘%1.1f cm’ ) to format the tick labels.

Note: Minor ticks are OFF by default and they can be turned ON by without labels by setting the minor locator and minor tick labels can be turned ON by minor formatter.

Example:

# importing matplotlib module and respective classes
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import (MultipleLocator,
                               FormatStrFormatter,
                               AutoMinorLocator)

x = [3, 2, 7, 4, 9]
y = [10, 4, 7, 1, 2]

fig, ax = plt.subplots()

ax.set_title('Example Graph')

ax.set_ylabel('y-AXIS')
ax.set_xlabel('x-AXIS')

ax.set_xlim(0, 10)
ax.set_ylim(0, 10)

# Make x-axis with major ticks that
# are multiples of 11 and Label major
# ticks with '% 1.2f' formatting
ax.xaxis.set_major_locator(MultipleLocator(10))
ax.xaxis.set_major_formatter(FormatStrFormatter('% 1.2f'))

# make x-axis with minor ticks that
# are multiples of 1 and label minor
# ticks with '% 1.2f' formatting
ax.xaxis.set_minor_locator(MultipleLocator(1))
ax.xaxis.set_minor_formatter(FormatStrFormatter('% 1.2f'))

ax.plot(x, y)
plt.show()

Output: