sciPy stats.histogram() function | Python
scipy.stats.histogram(a, numbins, defaultreallimits, weights, printextras)
works to segregate the range into several bins and then returns the number of instances in each bin. This function is used to build the 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 trueResults :
– cumulative frequency binned values
– width of each bin
– lower real limit
– extra points.
Code #1:
# building the 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 the results print ( "No. of points in each bin : " , hist) print ( "Size of the bins : " , bin_edges) # plotting the 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]
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