Matplotlib.axes.Axes.set_ymargin() in Python

Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. The Axes Class contains most of the figure elements: Axis, Tick, Line2D, Text, Polygon, etc., and sets the coordinate system. And the instances of Axes supports callbacks through a callbacks attribute.

matplotlib.axes.Axes.set_ymargin() Function

The Axes.set_ymargin() function in axes module of matplotlib library is used to set padding of Y data limits prior to autoscaling.

Syntax: Axes.set_ymargin(self, m)
Parameters: This method accepts the following parameters. 

• m: This parameter is used to specific margin values for the y-axis.

Return value: This method does not return any value. 

Below examples illustrate the matplotlib.axes.Axes.set_ymargin() function in matplotlib.axes:
Example 1:

Python3

# Implementation of matplotlib function
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button, RadioButtons

fig, (ax, ax1) = plt.subplots(1, 2)
plt.subplots_adjust(bottom = 0.25)
t = np.arange(0.0, 1.0, 0.001)
a0 = 5
f0 = 3
delta_f = 5.0
s = a0 * np.sin(2 * np.pi * f0 * t)
ax.plot(t, s, lw = 2, color = 'green')

ax1.plot(t, s, lw = 2, color = 'green')
ax1.set_ymargin(0.5)

ax.set_title("Without set_ymargin() Function")
ax1.set_title("With set_ymargin value = 0.5")

fig.suptitle('matplotlib.axes.Axes.set_ymargin() \
function Example\n', fontweight ="bold")
fig.canvas.draw()
plt.show()

Output:

Example 2:

Python3

# Implementation of matplotlib function
import numpy as np
import matplotlib.pyplot as plt

t = np.arange(0, 3, 1)
t1 = np.cos(np.pi * t) + np.sin(np.pi * t)

fig, [ax1, ax2, ax3] = plt.subplots(nrows = 3)
ax1.plot(t1, color ="green")
ax1.text(0.75, 0.65, 'Original window')

ax2.set_ymargin(2)
ax2.plot(t1, color ="green")
ax2.text(0.8, 3, 'Zoomed out')

ax3.set_ymargin(-0.45)
ax3.plot(t1, color ="green")
ax3.text(0.8, 0.05, 'Zoomed in')

fig.suptitle('matplotlib.axes.Axes.set_ymargin() \
function Example\n', fontweight ="bold")
fig.canvas.draw()
plt.show()

Output: