# How to calculate mean in Numpy?

Let’s learn how to calculate mean in Numpy Python library. Calculating the mean is a fundamental operation in statistics and data analysis, and NumPy provides efficient tools for this purpose.

## 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}")

```

The mean function calculates the average value of all the elements in the array.

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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.
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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 calculates the mean of each column in a two-dimensional array and returns a row vector with these mean values, maintaining the order from the original array.

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