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.
#Sample Code
# Implementation of matplotlib function import matplotlib.pyplot as plt import numpy as np # make an agg figure fig, ax = plt.subplots() ax.plot([1, 2, 3]) ax.set_title('matplotlib.axes.Axes function') fig.canvas.draw() plt.show() |
Output:
Violinplot using Axes Class
The Axes.violinplot() function in axes module of matplotlib library is used to make a violin plot for each column of dataset or each vector in sequence dataset.
Syntax:
Axes.violinplot(self, dataset, positions=None, vert=True, widths=0.5, showmeans=False, showextrema=True, showmedians=False, points=100, bw_method=None, *, data=None)
Parameters: This method accept the following parameters that are described below:
- dataset: This parameter is a sequence of data.
- positions : This parameter is used to sets the positions of the violins.
- vert: This parameter is an optional parameter and contain boolean value. It makes the vertical violin plot if true.Otherwise horizontal.
- widths: This parameter is used to sets the width of each violin either with a scalar or a sequence.
- showmeans : This parameter contain boolean value. It is used to toggle rendering of the means.
- showextrema : This parameter contain boolean value. It is used to toggle rendering of the extrema.
- showmedians : This parameter contain boolean value. It is used to toggle rendering of the medians.
- points : This parameter is used to defines the number of points to evaluate each of the gaussian kernel density estimations at.
Returns: This returns the following:
- result :This returns the dictionary which maps each component of the violinplot to a list of the matplotlib.collections instances.
Below examples illustrate the matplotlib.axes.Axes.violinplot() function in matplotlib.axes:
Example-1:
# Implementation of matplotlib function import matplotlib.pyplot as plt import numpy as np # create test data np.random.seed(10**7) data = np.random.normal(0, 5, 100) fig, ax1 = plt.subplots() val = ax1.violinplot(data) ax1.set_title('matplotlib.axes.Axes.violinplot() Example') plt.show() |
Output:
Example-2:
# Implementation of matplotlib function import matplotlib.pyplot as plt import numpy as np # create test data np.random.seed(10**7) data = [sorted(np.random.normal(0, std, 100)) for std in range(1, 5)] fig, ax1 = plt.subplots() val = ax1.violinplot(data) ax1.set_ylabel('Result') ax1.set_xlabel('Domain Name') for i in val['bodies']: i.set_facecolor('green') i.set_alpha(1) ax1.set_title('matplotlib.axes.Axes.violinplot() Example') plt.show() |
Output:
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