Skip to content
Related Articles

Related Articles

Improve Article
Save Article
Like Article

Matplotlib.pyplot.hist() in Python

  • Last Updated : 21 Apr, 2020

Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. Pyplot is a state-based interface to a Matplotlib module which provides a MATLAB-like interface.

matplotlib.pyplot.hist() Function

 Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.  

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning - Basic Level Course

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

Syntax: matplotlib.pyplot.hist(x, bins=None, range=None, density=False, weights=None, cumulative=False, bottom=None, histtype=’bar’, align=’mid’, orientation=’vertical’, rwidth=None, log=False, color=None, label=None, stacked=False, \*, 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.pyplot.hist() function in matplotlib.pyplot:

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
   
n, bins, patches = plt.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))
  
plt.plot(bins, y, '--', color ='black')
  
plt.xlabel('X-Axis')
plt.ylabel('Y-Axis')
  
plt.title('matplotlib.pyplot.hist() function Example\n\n',
          fontweight ="bold")
  
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)
    
colors = ['green', 'blue', 'lime']
  
plt.hist(x, n_bins, density = True
         histtype ='bar',
         color = colors,
         label = colors)
  
plt.legend(prop ={'size': 10})
  
plt.title('matplotlib.pyplot.hist() function Example\n\n',
          fontweight ="bold")
  
plt.show()

Output:




My Personal Notes arrow_drop_up
Recommended Articles
Page :

Start Your Coding Journey Now!