 # 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 a flat array print ( "mean of arr, axis = None: " , np.mean (arr))     # axis mean = 0 print ( " mean of arr, axis = 0: " , np.mean (arr, axis = 0 ))    # average axis = 1 print ( " mean of arr, axis = 1: " , np.mean (arr, axis = 1 ))   out_arr = np.arange ( 3 ) print ( "out_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] `