Examples of how to calculate the mean along a given axis with numpy in python

### Create a random matrix with numpy

`import numpy as np`

`data = np.random.randint(0,10,size=(3,3))`

gives for exaple

`array([[4, 1, 9],`

`[1, 6, 5],`

`[9, 9, 5]])`

### Calculate the mean along an axis with numpy

To calculate the mean along an axis with numpy, a solution is to use numpy.mean, example along axis=0

`data.mean(axis=0)`

gives

`array([4.66666667, 5.33333333, 6.33333333])`

Note: to round alement of a matrix with numpy a solution is to use numpy.matrix.round

`np.round( data.mean(axis=0) , 2)`

gives then

`array([4.67, 5.33, 6.33])`

Note: same as doing

`data.sum(axis=0) / data.shape[0]`

gives

`array([4.66666667, 5.33333333, 6.33333333])`

Another example along axis=1:

`data.mean(axis=1)`

gives

`array([4.66666667, 4. , 7.66666667])`

and

`np.round( data.mean(axis=1) , 2)`

gives

`array([4.67, 4. , 7.67])`

Note: same as doing

`data.sum(axis=1) / data.shape[1]`

gives

`array([4.66666667, 4. , 7.66666667])`

### Calculate the mean using all elements of a matrix

`data.mean()`

gives

`5.444444444444445`

Note: same as doing

`data.sum() / data.size`

also returns

`5.444444444444445`