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.grid(True) ax.set_title('matplotlib.axes.Axes.set_ylabel() \ Examples\n', fontsize = 14, fontweight ='bold') plt.show() |
Output:
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 * price_data.open[:-2])**2 fig, ax = plt.subplots() ax.scatter(delta1[:-1], delta1[1:], c=close, s=volume, alpha=0.5) ax.set_ylabel(r'Y-axis contains $\Delta_{i+1}$ values', fontweight='bold') ax.grid(True) fig.suptitle('matplotlib.axes.Axes.set_ylabel() Examples\n', fontsize=14, fontweight='bold') plt.show() |
Output:
