# [NumPy] How to Calculate The Average Along an Axis?

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This article explains how to calculate basic statistics such as average, standard deviation, and variance along an axis. We use the NumPy library for linear algebra computations. These three ways are very similar — if you understand one of them, you’ll understand all of them.

TLDR;

To average a NumPy array `x` along an axis, call `np.average()` with arguments `x` and the axis identifier. For example, `np.average(x, axis=1)` averages along axis 1. The outermost dimension has axis identifier “0”, the second-outermost dimension has identifier “1”. Python collapses the identified axis and replaces it with the axis average, which reduces dimensionality of the resulting array by one.

Feel free to watch the video while skimming over the article for maximum learning efficiency:

## Graphical Explanation

Here’s what you want to achieve:

Extracting basic statistics such as average, variance, standard deviation from NumPy arrays and 2D matrices is a critical component for analyzing a wide range of data sets such as financial data, health data, or social media data. With the rise of machine learning and data science, your proficient education of linear algebra operators with NumPy becomes more and more valuable to the marketplace

## Code Solution

Here is how you can accomplish this task in NumPy:

```import numpy as np

x = np.array([[1, 3, 5],
[1, 1, 1],
[0, 2, 4]])

print(np.average(x, axis=1))
# [3. 1. 2.]

print(np.var(x, axis=1))
# [2.66666667 0.  2.66666667]

print(np.std(x, axis=1))
# [1.63299316 0.  1.63299316]```

## Slow Explanation

Next, I’ll explain you how this works in a step-by-step manner.

NumPy internally represents data using NumPy arrays (`np.array`). These arrays can have an arbitrary number of dimensions. In the figure above, we show a two-dimensional NumPy array but in practice, the array can have much higher dimensionality. You can quickly identify the dimensionality of a NumPy array by counting the number of opening brackets “[“ when creating the array. (The more formal alternative would be to use the `ndim` property.)

Each dimension has its own axis identifier.

Rule of thumb: The outermost dimension has the identifier “0”, the second-outermost dimension has the identifier “1”, and so on.

By default, the NumPy average, variance, and standard deviation functions aggregate all the values in a NumPy array to a single value.

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## Simple Average, Variance, Standard Deviation

What happens if you don’t specify any additional argument apart from the NumPy array on which you want to perform the operation (average, variance, standard deviation)?

```import numpy as np

x = np.array([[1, 3, 5],
[1, 1, 1],
[0, 2, 4]])

print(np.average(x))
# 2.0

print(np.var(x))
# 2.4444444444444446

print(np.std(x))
# 1.5634719199411433
```

For example, the simple average of a NumPy array is calculated as follows:

`(1+3+5+1+1+1+0+2+4)/9 = 18/9 = 2.0`

## Calculating Average, Variance, Standard Deviation Along an Axis

However, sometimes you want to calculate these functions along an axis.

For example, you may work at a large financial corporation and want to calculate the average value of a stock price — given a large matrix of stock prices (rows = different stocks, columns = daily stock prices).

Here is how you can do this by specifying the keyword “`axis`” as an argument to the average, variance, and standard deviation functions:

```import numpy as np

## Stock Price Data: 5 companies
# (row=[price_day_1, price_day_2, ...])
x = np.array([[8, 9, 11, 12],
[1, 2, 2, 1],
[2, 8, 9, 9],
[9, 6, 6, 3],
[3, 3, 3, 3]])

avg, var, std = np.average(x, axis=1), np.var(x, axis=1), np.std(x, axis=1)

print("Averages: " + str(avg))
print("Variances: " + str(var))
print("Standard Deviations: " + str(std))

"""
Averages: [10.   1.5  7.   6.   3. ]
Variances: [2.5  0.25 8.5  4.5  0.  ]
Standard Deviations: [1.58113883 0.5        2.91547595 2.12132034 0.        ]
"""```

Note that you want to perform these three functions along the axis=1, i.e., this is the axis that is aggregated to a single value. Hence, the resulting NumPy arrays have a reduced dimensionality.

## High-Dimensional Averaging Along An Axis

Of course, you can also perform this averaging along an axis for high-dimensional NumPy arrays. Conceptually, you’ll always aggregate the axis you specify as an argument.

Here is an example:

```import numpy as np

x = np.array([[[1,2], [1,1]],
[[1,1], [2,1]],
[[1,0], [0,0]]])

print(np.average(x, axis=2))
print(np.var(x, axis=2))
print(np.std(x, axis=2))

"""
[[1.5 1. ]
[1.  1.5]
[0.5 0. ]]
[[0.25 0.  ]
[0.   0.25]
[0.25 0.  ]]
[[0.5 0. ]
[0.  0.5]
[0.5 0. ]]
"""
```

## Where to Go From Here?

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