Aggregate and Statistical Functions In Numpy

In this tutorial, we will learn about the aggregate and statistical functions in Numpy. Numpy has fast built-in aggregate and statistical for working on arrays. By using these function or if we have good knowledge of these functions than we will play with arrays.

Aggregate and Statistical Functions in Numpy – Python

First, we have to import Numpy as import numpy as np. To make a Numpy array, you can just use the np.array() function. The aggregate and statistical functions are given below:

  1. np.sum(m): Used to find out the sum of the given array.
  2. np.prod(m): Used to find out the product(multiplication) of the values of m.
  3. np.mean(m): It returns the mean of the input array m.
  4. np.std(m): It returns the standard deviation of the given input array m.
  5. np.var(m): Used to find out the variance of the data given in the form of array m.
  6. np.min(m): It returns the minimum value among the elements of the given array m.
  7. np.max(m): It returns the maximum value among the elements of the given array m.
  8. np.argmin(m): It returns the index of the minimum value among the elements of the array m.
  9. np.argmax(m): It returns the index of the maximum value among the elements of the array m.
  10. np.median(m): It returns the median of the elements of the array m.

The code using the above all the function is given below:

import numpy as np
a=np.array([1,2,3,4,5])
print("a :",a)
sum=np.sum(a)
print("sum :",sum)
product=np.prod(a)
print("product :",product)
mean=np.mean(a)
print("mean :",mean)
standard_deviation=np.std(a)
print("standard_deviation :",standard_deviation)
variance=np.var(a)
print("variance :",variance)
minimum=np.min(a)
print("minimum value :",minimum)
maximum=np.max(a)
print("maximum value :",maximum)
minimum_index=np.argmin(a)
print("minimum index :",minimum_index)
maximum_index=np.argmax(a)
print("maximum-index :",maximum_index)
median=np.median(a)
print("median :",median)

Output is:

a : [1 2 3 4 5]
sum : 15
product : 120
mean : 3.0
standard_deviation : 1.4142135623730951
variance : 2.0
minimum value : 1
maximum value : 5
minimum index : 0
maximum-index : 4
median : 3.0

You can also see:

Multiplication of two matrices in Python using NumPy

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