matplotlib.axis.Axis.pickable() Function
The Axis.pickable() function in axis module of matplotlib library is used to return whether the artist is pickable or not.
Syntax: Axis.pickable(self)
Parameters: This method does not accept any parameters.
Return value: This method return whether the artist is pickable.
Below examples illustrate the matplotlib.axis.Axis.pickable() function in matplotlib.axis:
Example 1:
Python3
# Implementation of matplotlib function from matplotlib.axis import Axis 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 (Axis.pickable(ax)), fontweight = "bold" , fontsize = 18 ) fig.suptitle('matplotlib.axis.Axis.pickable() \ function Example\n', fontweight = "bold" ) plt.show() |
Output:
Example 2:
Python3
# Implementation of matplotlib function from matplotlib.axis import Axis 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 (Axis.pickable(axes1)), fontweight = "bold" ) fig.suptitle('matplotlib.axis.Axis.pickable() \ function Example\n', fontweight = "bold" ) plt.show() |
Output:
Matplotlib.axis.Axis.pickable() function in Python
Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. It is an amazing visualization library in Python for 2D plots of arrays and used for working with the broader SciPy stack.