Active September 19, 2021    /    Viewed 312    /    Comments 0    /    Edit

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`
```

### References

##### Daidalos

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