# How to Average a List of Lists in Python?

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Problem: You have a list of lists and you want to calculate the average of the different columns.

Example: Given the following list of lists with four rows and three columns.

```data = [[0, 1, 0],
[1, 1, 1],
[0, 0, 0],
[1, 1, 0]]```

You want to have the average values of the three columns:

`[average_col_1, average_col_2, average_col_3]`

There are three methods that solve this problem. You can play with them in the interactive shell and read more details below:

## Method 1: Average in Python (No Library)

A simple one-liner with list comprehension in combination with the `zip()` function on the unpacked list to transpose the list of lists does the job in Python.

```data = [[0, 1, 0],
[1, 1, 1],
[0, 0, 0],
[1, 1, 0]]

# Method 1: Pure Python
res = [sum(x) / len(x) for x in zip(*data)]
print(res)
# [0.5, 0.75, 0.25]```

Do you love Python one-liners? I do for sure—I’ve even written a whole book about it with San Francisco Publisher NoStarch. Click to check out the book in a new tab:

You can visualize the code execution and memory objects of this code in the following tool (just click “Next” to see how one step of the code unfolds).

## Method 2: Average with NumPy Library

You create a NumPy array out of the data and pass it to the np.average() function.

```data = [[0, 1, 0],
[1, 1, 1],
[0, 0, 0],
[1, 1, 0]]

# Method 2: NumPy
import numpy as np
a = np.array(data)
res = np.average(a, axis=0)
print(res)
# [0.5  0.75 0.25]```

The `axis` argument of the average function defines along which axis you want to calculate the average value. If you want to average columns, define `axis=0`. If you want to average rows, define `axis=1`. If you want to average over all values, skip this argument.

## Method 3: Mean Statistics Library + Map()

Just to show you another alternative, here’s one using the `map()` function and our `zip(*data)` trick to transpose the “matrix” `data`.

```data = [[0, 1, 0],
[1, 1, 1],
[0, 0, 0],
[1, 1, 0]]

# Method 3: Statistics + Map()
import statistics
res = map(statistics.mean, zip(*data))
print(list(res))
# [0.5, 0.75, 0.25]```

The `map(function, iterable)` function applies `function` to each element in `iterable`. As an alternative, you can also use list comprehension as shown in method 1 in this tutorial. In fact, Guido van Rossum, the creator of Python and Python’s benevolent dictator for life (BDFL), prefers list comprehension over the `map()` function.

## Where to Go From Here?

Enough theory. Let’s get some practice!

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