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:

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# 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))
   

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Output :



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

 
Code #2:

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# 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))

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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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