# Get the Mean of NumPy Array – (With Examples)

The Numpy library in Python comes with a number of useful built-in functions for computing common descriptive statistics like mean, median, standard deviation, etc. In this tutorial, we will look at how to get the mean of a Numpy array with the help of some examples.

## How do you get the mean of an array in Numpy?

You can use the Numpy `mean()` function to get the mean of a Numpy array. Pass the array as an argument.

The following is the syntax –

```# mean of all values in array
numpy.mean(ar)```

It returns the average of the values in the array. For multi-dimensional arrays, you can specify the axis along which you want to compute the mean (see the examples below).

## Examples

Let’s now look at some examples of using the above syntax on single and multi-dimensional arrays.

### Example 1 – Mean of a one-dimensional Numpy array

Let’s first create a one-dimensional Numpy array.

```import numpy as np

# create numpy array
ar = np.array([1, 2, 3, 4])
# display the array
print(ar)```

Output:

`[1 2 3 4]`

Here, we used the `numpy.array()` function to create a one-dimensional array containing some numeric values.

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Let’s now get the average of all the values in the above array.

```# mean of array
print(np.mean(ar))```

Output:

`2.5`

We get the mean as 2.5 which is the correct answer as (1+2+3+4)/4 = 2.5

### Example 2 – Mean of multi-dimensional Numpy array

First, let’s create a 2-D Numpy array.

```# create 2-D numpy array
ar = np.array([[1, 2, 3],
[4, 5, 6]])
# display the array
print(ar)```

Output:

```[[1 2 3]
[4 5 6]]```

Here, we used the `numpy.array()` function to create a Numpy array with two rows and three columns.

If you use the Numpy `mean()` function on an array without specifying the axis, it will return the mean of all the values inside the array.

```# mean of array
print(np.mean(ar))```

Output:

`3.5`

We get the mean of all the values inside the 2-D array.

Use the `numpy.mean()` function with `axis=1` to get the mean value for each row in the array.

```# mean of each row in array
print(np.mean(ar, axis=1))```

Output:

`[2. 5.]`

We get the mean of each row in the above 2-D array. The mean of values in the first row is (1+2+3)/3 = 2 and the mean of values in the second row is (4+5+6)/3 = 3.

Use the `numpy.mean()` function with `axis=0` to get the mean of each column in the array.

```# mean of each column in array
print(np.mean(ar, axis=0))```

Output:

`[2.5 3.5 4.5]`

We get the mean of each column in the above 2-D array. The mean of values – in the first column is (1+4)/2 = 2.5, in the second column is (2+5)/2 = 3.5, and in the third column is (3+6)/2 = 4.5.

## Summary

In this tutorial, we looked at how to use the `numpy.mean()` function to get the average of values in an array. The following are the key takeaways from this tutorial.

• Use the `numpy.mean()` function without any arguments to get the average of all the values inside the array.
• For multi-dimensional arrays, use the `axis` parameter to specify the axis along which to compute the mean. For example, for a 2-D array –
• Pass `axis=1` to get the mean of each row.
• Pass `axis=0` to get the mean of each column.

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