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]