numpy.mean () in Python

numpy.mean (arr, axis = None) : compute the arithmetic mean (average) of the given data (array elements) along the specified axis.

Parameters:
arr: [array_like] input array.
axis: [int or tuples of int] axis along which we want to calculate the arithmetic mean. Otherwise, it will consider arr to be flattened (works on all
the axis). axis = 0 means along the column and axis = 1 means working along the row.
out: [ndarray, optional] Different array in which we want to place the result. The array must have the same dimensions as expected output.
dtype: [data-type, optional] Type we desire while computing mean.

Results: Arithmetic mean of the array (a scalar value if axis is none) or array with mean values ​​along specified axis.

Code # 1:

# Python program illustrating
# numpy.mean () method

import numpy as np

 
# 1D array

arr = [ 20 , 2 , 7 , 1 , 34 ]

 

print ( "arr:" , arr) 

print ( "mean of arr:" , np.mean (arr))

  

Output:

 arr: [20, 2, 7, 1, 34] mean of arr: 12.8 

Code # 2:

# Python program illustrating
# numpy.mean () method

import numpy as np

 

 
# 2D array

arr = [[ 14 , 17 , 12 , 33 , 44 ], 

  [ 15 , 6 , 27 , 8 , 19 ], 

[ 23 , 2 , 54 , 1 , 4 ,]] 

 
# mean of a flat array

print ( "mean of arr, axis = None: " , np.mean (arr)) 

  
# axis mean = 0

print ( " mean of arr, axis = 0: " , np.mean (arr, axis = 0 )) 

 
# average axis = 1

print ( " mean of arr, axis = 1: " , np.mean (arr, axis = 1 ))

 

out_arr = np.arange ( 3 )

print ( "out_arr:" , out_arr) 

print ( "mean of arr, axis = 1:"

np.mean (arr, axis = 1 , out = out_arr))

Output:

 mean of arr, axis = None: 18.6 mean of arr, axis = 0: [17.33333333 8.33333333 31. 14. 22.33333333] mean of arr , axis = 1: [24. 15.16.8] out_arr: [0 1 2] mean of arr, axis = 1: [24 15 16]