numpy.mean() in Python

Difficulty Level : Basic
Last Updated : 28 Nov, 2018

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 the flattened array  
print("\nmean of arr, axis = None : ", np.mean(arr))  
    
# mean along the axis = 0  
print("\nmean of arr, axis = 0 : ", np.mean(arr, axis = 0))  
   
# mean along the axis = 1  
print("\nmean of arr, axis = 1 : ", np.mean(arr, axis = 1)) 
  
out_arr = np.arange(3) 
print("\nout_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]

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