Matplotlib.axes.Axes.pickable() 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.pickable() Function

The Axes.pickable() function in axes module of matplotlib library is used to return whether the artist is pickable or not.

Syntax: Axes.pickable(self)

Parameters: This method does not accept any parameters.

Returns: This method return whether the artist is pickable.

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

Example 1:

# Implementation of matplotlib function
import numpy as np
np.random.seed(19680801)
import matplotlib.pyplot as plt

volume = np.random.rayleigh(27, size = 40)
amount = np.random.poisson(10, size = 40)
ranking = np.random.normal(size = 40)
price = np.random.uniform(1, 10, size = 40)

fig, ax = plt.subplots()

scatter = ax.scatter(volume * 2, amount * 3,
                     c = ranking * 3,
                     s = 0.3*(price * 3)**2,
                     vmin = -4, vmax = 4,
                     cmap = "Spectral")

legend1 = ax.legend(*scatter.legend_elements(num = 5),
                    loc ="upper left",
                    title ="Ranking")

ax.add_artist(legend1)

ax.text(60, 30, "Value return : " + str(ax.pickable()),
        fontweight ="bold",
        fontsize = 18)

fig.suptitle('matplotlib.axes.Axes.pickable() function\
Example', fontweight ="bold")

plt.show()

Output:

Example 2:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook

np.random.seed(10**7)
data = np.random.lognormal(size =(10, 4),
                           mean = 4.5,
                           sigma = 4.75)

labels = ['G1', 'G2', 'G3', 'G4']

result = cbook.boxplot_stats(data,
                             labels = labels,
                             bootstrap = 1000)

for n in range(len(result)):
    result[n]['med'] = np.median(data)
    result[n]['mean'] *= 0.1

fig, axes1 = plt.subplots()
axes1.bxp(result)

axes1.text(2, 30000,
           "Value return : " + str(axes1.pickable()),
           fontweight ="bold")

fig.suptitle('matplotlib.axes.Axes.pickable() function \
Example', fontweight ="bold")

plt.show()

Output: