# numpy.mean() in Python | np.mean() in Python

Numpy `mean()` function is used to calculate arithmetic mean of the values along the specified axis.

## Syntax of Numpy mean()

`np.mean(a, axis=None, dtype=None)`

Here, `a` parameter is used to pass Numpy array to `mean()` function.

`axis` parameter is optional and is used to specify the axis along which we want to perform arithmetic mean. When no axis value is passed then arithmetic mean of entire value is computed and a single value is returned.

`dtype` parameter controls the data type of the resultant mean.

## Python program to calculate mean using numpy.mean() on 1-Dimensional Numpy Array

```import numpy as np

a = np.array([32, 4, 8, 12, 20])
print('Original array is ', a)
print('Mean is ', np.mean(a))```

Output of the above program

```Original array is  [32  4  8 12 20]
Mean is  15.2```

As you can see in the above output, `np.mean()` has computed mean for this array. In 1-dimensional array, it is optional to provide `axis` parameter to `mean()` function. ## Python program to calculate mean using numpy.mean() on 2-Dimensional Numpy Array

```import numpy as np

a = np.array([[32, 4, 8, 12, 20], [35, 5, 15, 10, 30]])
print('Mean of arr is ', np.mean(a))
print('Mean of arr along axis0 is ', np.mean(a, axis=0))
print('Mean of arr along axis1 is ', np.mean(a, axis=1))```

Output of the above program

```Mean of arr is  17.1
Mean of arr along axis0 is  [33.5  4.5 11.5 11.  25. ]
Mean of arr along axis1 is  [15.2 19. ]```

When axis value is not provided, then `mean()` function works like this- When axis=0 is passed to `mean()` function, then it works like this- When axis=1 is passed to `mean()` function, then it works like this- 