Let’s learn how to calculate mean in Numpy Python library.

## Mean in Numpy

To calculate mean in Numpy it is enough to use mean built-in function offered by Numpy library.

import numpy as np my_array = np.array([1, 56, 55, 15, 0]) mean = np.mean(my_array) print(f"Mean equals: {mean}")

Mean function returned mean value of every array elements.

The `mean` function will return the mean value of all the elements in the array.

You can also use the `axis` parameter to calculate the mean along a specific axis of the array. For example, to calculate the mean of each column in the array, you would use the following code:

mean_by_column = np.mean(my_array, axis=0) print(mean_by_column)

This code will return an array with two elements, the mean of the first column and the mean of the second column.

## Other parameters of Numpy mean function

The `mean` function in Numpy has several other parameters that can be used to customize the output.

- `axis`: The axis along which the mean will be calculated. The default value is `None`, which means that the mean will be calculated over the entire array.
- `dtype`: The data type of the output array. The default value is the same as the data type of the input array.
- `keepdims`: If set to True, the output array will have the same dimensions as the input array, except along the axis dimension. The default value is False.

For example, the following code will calculate the mean of each column in the array and return an array with the same dimensions as the input array:

import numpy as np my_array = np.array([ [1, 56, 55], [15, 0, 9] ]) mean_by_column = np.mean(my_array, axis=0, keepdims=True) print(mean_by_column)

This code will return an array with two elements, the mean of the first column and the mean of the second column, in the same order as the original array.