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# sciPy function stats.histogram () | python

` 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] `

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