scipy.stats.histogram (a, numbins, defaultreallimits, weights, printextras) works to split a range into multiple bins and then returns the number of instances in each bin. This function is used to build a histogram.

Parameters:
arr: [array_like] input array.
numbins: [int] number of bins to use for the histogram. [Default = 10]
defaultlimits: (lower, upper) range of the histogram.
weights: [array_like] weights for each array element.
printextras: [array_like] to print the no, if extra points to the standard output, if true

Results:
- cumulative frequency binned values ​​
- width of each bin
- lower real limit
- extra points.

Code # 1:

# building histogram import scipy import numpy as np  import matplotlib.pyplot as plt   hist, bin_edges = scipy.histogram ([ 1 , 1 , 2 , 2 , 2 , 2 , 3 ], bins = range ( 5 ))   # Checking results print ( "No. of points in each bin: " , hist) print ( "Size of the bins :" , bin_edges)   # building a histogram plt.bar (bin_edges [: - 1 ], hist, width = 1 ) plt.xlim ( min (bin_edges), max (bin_edges)) plt.show ()

Output:

 No. of points in each bin: [0 2 4 1] Size of the bins: [0 1 2 3 4]