# Python Numpy.mean() - Arithmetic Mean

`Numpy.mean()` function calculates the arithmetic mean, or in layman words - average, of the given array along the specified axis.

## Syntax of `numpy.mean()`

``````numpy.mean(arr, axis=None, dtype=float64)
``````

### Parameters

`arr` array_like
input array to calculate the arithmetic mean
`axis` `None`, `int` or `tuple` of int
Axis along which thearithmetic mean is computed.
`axis=0` means arithmetic mean computed along the column,
`axis=1` means arithmetic mean along the row.
It treats the multiple dimension array as a flattened list if `axis` is not given.
`dtype` `dtype` or `None`
Data type used during the calculation of the arithmetic mean. Default is `float64`

### Return

It returns the arithmetic mean of the given array or an `array` with the arithmetic mean along the specified axis.

## Example Codes: `numpy.mean()` With 1-D Array

``````import numpy as np

arr = [10, 20, 30]
print("1-D array :", arr)
print("Mean of arr is ", np.mean(arr))
``````

Output:

``````1-D array : [10, 20, 30]
Mean of arr is  20.0
``````

## Example Codes: `numpy.mean()` With 2-D Array

``````import numpy as np

arr = [[10, 20, 30],
[3, 50, 5],
[70, 80, 90],
[100, 110, 120]]

print("Two Dimension array :", arr)
print("Mean with no axis :", np.mean(arr))
print("Mean with axis along column :", np.mean(arr, axis=0))
print("Mean with axis aong row :", np.mean(arr, axis=1))
``````

Output:

``````Two Dimension array : [[10, 20, 30], [3, 50, 5], [70, 80, 90], [100, 110, 120]]
Mean with no axis : 57.333333333333336
Mean with axis along column : [45.75 65.   61.25]
Mean with axis aong row : [ 20.          19.33333333  80.         110.        ]
>>
``````

`np.mean(arr)` treats the input array as the flattened array and calculates the arithmetic mean of this 1-D flattened array.

`np.mean(arr, axis=0)` calculates the arithmetic mean along the column.

`np.std(arr, axis=1)` calculates the arithmetic mean along the row.

## Example Codes: `numpy.mean()` With `dtype` Specified

``````import numpy as np

arr = [10.12, 20.3, 30.28]
print("1-D Array :", arr)
print("Mean of arr :", np.mean(arr))
print("Mean of arr with float32 data :", np.mean(arr, dtype = np.float32))
print("Mean of arr with float64 data :", np.mean(arr, dtype = np.float64))
``````

Output:

``````1-D Array : [10.12, 20.3, 30.28]
Mean of arr : 20.233333333333334
Mean of arr with float32 data : 20.233332
Mean of arr with float64 data : 20.233333333333334
``````

If `dtype` parameter is given in the `numpy.mean()` function, it uses the specified data type during the computing of arithmetic mean.

The result has a lower resolution if we use `float32` data type rather than the default `float64`.