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Matplotlib.axes.Axes.hist() in Python
  • Last Updated : 13 Apr, 2020

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.hist() Function

The Axes.hist() function in axes module of matplotlib library is used to plot a histogram.

Syntax: Axes.hist(self, x, bins=None, range=None, density=None, weights=None, cumulative=False, bottom=None, histtype=’bar’, align=’mid’, orientation=’vertical’, rwidth=None, log=False, color=None, label=None, stacked=False, normed=None, *, data=None, **kwargs)

Parameters: This method accept the following parameters that are described below:

  • x : This parameter are the sequence of data.
  • bins : This parameter is an optional parameter and it contains the integer or sequence or string.
  • range : This parameter is an optional parameter and it the lower and upper range of the bins.
  • density : This parameter is an optional parameter and it contains the boolean values.
  • weights : This parameter is an optional parameter and it is an array of weights, of the same shape as x.
  • bottom : This parameter is the location of the bottom baseline of each bin.
  • histtype : This parameter is an optional parameter and it is used to draw type of histogram. {‘bar’, ‘barstacked’, ‘step’, ‘stepfilled’}
  • align : This parameter is an optional parameter and it controls how the histogram is plotted. {‘left’, ‘mid’, ‘right’}
  • rwidth : This parameter is an optional parameter and it is a relative width of the bars as a fraction of the bin width
  • log : This parameter is an optional parameter and it is used to set histogram axis to a log scale
  • color : This parameter is an optional parameter and it is a color spec or sequence of color specs, one per dataset.
  • label : This parameter is an optional parameter and it is a string, or sequence of strings to match multiple datasets.
  • normed : This parameter is an optional parameter and it contains the boolean values.It uses the density keyword argument instead.

Returns: This returns the following:



  • n :This returns the values of the histogram bins.
  • bins :This returns the edges of the bins.
  • patches :This returns the list of individual patches used to create the histogram.

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

Example-1:




# Implementation of matplotlib function
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
  
np.random.seed(10**7)
mu = 121  
sigma = 21
x = mu + sigma * np.random.randn(1000)
  
num_bins = 100
fig, ax = plt.subplots()
  
n, bins, patches = ax.hist(x, num_bins,
                           density = 1
                           color ='green'
                           alpha = 0.7)
  
y = ((1 / (np.sqrt(2 * np.pi) * sigma)) *
     np.exp(-0.5 * (1 / sigma * (bins - mu))**2))
ax.plot(bins, y, '--', color ='black')
ax.set_xlabel('X-Axis')
ax.set_ylabel('Y-Axis')
  
ax.set_title('matplotlib.axes.Axes.hist() Example')
plt.show()

Output:

Example-2:




# Implementation of matplotlib function
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
  
np.random.seed(10**7)
n_bins = 20
x = np.random.randn(10000, 3)
  
fig, [(ax0, ax1), (ax2, ax3)] = plt.subplots(nrows = 2,
                                             ncols = 2)
  
  
colors = ['green', 'blue', 'lime']
  
ax0.hist(x, n_bins, density = True
         histtype ='bar',
         color = colors, 
         label = colors)
  
ax0.legend(prop ={'size': 10})
  
ax1.hist(x, n_bins, density = True,
         histtype ='barstacked',
         stacked = True
         color = colors)
  
ax2.hist(x, n_bins, histtype ='step',
         stacked = True,
         fill = False
         color = colors)
  
x_multi = [np.random.randn(n) for n in [100000,
                                        80000,
                                        1000]]
  
ax3.hist(x_multi, n_bins, 
         histtype ='stepfilled',
         color = colors)
  
plt.show()

Output:

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