# 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 true

Results :
– 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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