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]
Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.
To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.