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]
