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
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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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