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.

# Python Program illustrating# numpy.mean() methodimportnumpyasnp# 1D arrayarr=[20,2,7,1,34]print("arr : ",arr)print("mean of arr : ",np.mean(arr))

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

# Python Program illustrating# numpy.mean() methodimportnumpyasnp# 2D arrayarr=[[14,17,12,33,44],[15,6,27,8,19],[23,2,54,1,4,]]# mean of the flattened arrayprint("\nmean of arr, axis = None : ",np.mean(arr))# mean along the axis = 0print("\nmean of arr, axis = 0 : ",np.mean(arr,axis=0))# mean along the axis = 1print("\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))

mean of arr, axis = None :  18.6mean 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]