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 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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