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Matplotlib.axes.Axes.set_ylabel() in Python

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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_ylabel() Function

The Axes.set_ylabel() function in axes module of matplotlib library is used to set the label for the y-axis.

Syntax: Axes.set_ylabel(self, xlabel, fontdict=None, labelpad=None, **kwargs)

Parameters: This method accepts the following parameters.

  • ylabel : This parameter is the label text.
  • labelpad : This parameter is the spacing in points from the axes bounding box including ticks and tick labels.

Returns:This method does not returns any value.

Below examples illustrate the matplotlib.axes.Axes.set_ylabel() function in matplotlib.axes:

Example 1:

import matplotlib.pyplot as plt
import numpy as np
t = np.arange(0.01, 5.0, 0.01)
s = np.exp(-t)
fig, ax = plt.subplots()
ax.plot(t, s)
ax.set_ylim(1, 0)
ax.set_ylabel('Display Y-axis Label'
              fontweight ='bold')
ax.set_title('matplotlib.axes.Axes.set_ylabel() \
Examples\n', fontsize = 14, fontweight ='bold')


Example 2:

#Implementation of matplotlib function
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook
with cbook.get_sample_data('goog.npz') as datafile:
    price_data = np.load(datafile)['price_data'].view(np.recarray)
# get the most recent 250 
# trading days
price_data = price_data[-250:]  
delta1 = np.diff(price_data.adj_close) / price_data.adj_close[:-1]
volume = (25 * price_data.volume[:-2] / price_data.volume[0])**(2.2)
close = (0.03 * price_data.close[:-2] / 0.03 *[:-2])**2
fig, ax = plt.subplots()
ax.scatter(delta1[:-1], delta1[1:],
           c=close, s=volume,
ax.set_ylabel(r'Y-axis contains $\Delta_{i+1}$ values',
fig.suptitle('matplotlib.axes.Axes.set_ylabel() Examples\n',
             fontsize=14, fontweight='bold')


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Last Updated : 19 Apr, 2020
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