## Flatten to a 1D NumPy Array

To flatten any NumPy array to a one-dimensional array, use the `array.flatten()`

method that returns a new flattened 1D array.

Here’s a simple example:

import numpy as np arr = np.array([[1, 2, 3], [4, 5, 6]]) print(arr.flatten()) # [1 2 3 4 5 6]

## Flatten NumPy Array Along Axis with reshape()

To “flatten” a NumPy array along an axis, it’s often better to use the `array.reshape()`

function. You can pass the new `shape`

tuple as an argument.

Here’s an example:

>>> arr = np.array([[1, 2, 3], [4, 5, 6]]) >>> arr.reshape(2, 3) array([[1, 2, 3], [4, 5, 6]]) >>> arr.reshape(3, 2) array([[1, 2], [3, 4], [5, 6]]) >>> arr.reshape(6, -1) array([[1], [2], [3], [4], [5], [6]])

π‘ **Recommended Tutorial**: Reshaping a NumPy Array

*There are more ways to flatten a NumPy array along an axis.*

## Flatten NumPy Array of Lists

To convert the list of arrays (in variable `lst`

) to a flat array, you can use any of the following functions:

`np.concatenate(lst).ravel()`

`np.array(lst).ravel()`

`np.array(lst).flatten()`

`np.array(lst).reshape(-1)`

The following shows the first approach—you can replace the highlighted line with any of the given approaches.

import numpy as np lst = [np.array([1, 2, 3]), np.array([4, 5, 6]), np.array([7, 8, 9])] print(lst) # [array([1, 2, 3]), array([4, 5, 6]), array([7, 8, 9])] # Convert the List of Array to a Flat Array arr = np.concatenate(lst).ravel() print(arr) # [1 2 3 4 5 6 7 8 9]

A great performance analysis was performed by SO user “ayorgo” that shows that `reshape()`

and `ravel()`

are much faster because they operate on a view of the original array rather than returning a copy like `flatten()`

:

## Flatten NumPy Array of Arrays

To flatten a NumPy array of arrays, say `arr`

, use the `np.concatenate(arr).ravel()`

function call. The result will be a one-dimensional (1D) flattened NumPy array of values.

Here’s an example:

import numpy as np arr = np.array([np.array([1, 2, 3]), np.array([4, 5, 6]), np.array([7, 8, 9])]) print(arr) ''' [[1 2 3] [4 5 6] [7 8 9]] ''' # Convert the Array of Array to a Flat Array arr = np.concatenate(arr).ravel() print(arr) # [1 2 3 4 5 6 7 8 9]

## Flatten NumPy Array of Tuples

To convert the tuple of arrays (in variable `t`

) to a flat array, you can use any of the following functions:

`np.concatenate(t).ravel()`

`np.array(t).ravel()`

`np.array(t).flatten()`

`np.array(t).reshape(-1)`

The following shows all those approaches and how they result in the same output:

import numpy as np t = (np.array([1, 2, 3]), np.array([4, 5, 6]), np.array([7, 8, 9])) print(t) # (array([1, 2, 3]), array([4, 5, 6]), array([7, 8, 9])) # Convert the Tuple of Arrays to a Flat Array print(np.concatenate(t).ravel()) print(np.array(t).ravel()) print(np.array(t).flatten()) print(np.array(t).reshape(-1)) # [1 2 3 4 5 6 7 8 9]

## NumPy Flatten Array – Only Some Dimensions (Row, Column, etc.)

To flatten only some dimensions in a NumPy array, use the `arr.reshape()`

function and pass the shape tuple of the desired array. This way, you can flatten rows and columns easily.

For example, to flatten an array with shape `(10, 20, 30)`

, you can call `array.reshape(200, 30)`

that collapses (i.e., *flattens*) the first two dimensions into one.

import numpy as np arr = np.zeros((10, 20, 30)) flat = arr.reshape(200, 30) print(flat.shape) # (200, 30)

## Where to Go From Here?

Thanks for reading through this whole tutorial. If you feel like you’re in need of some more NumPy education, check out our full 8000-word mega tutorial on the Finxter blog:

π **Recommended Tutorial**: NumPy — Everything You Need to Know to Get Started

While working as a researcher in distributed systems, Dr. Christian Mayer found his love for teaching computer science students.

To help students reach higher levels of Python success, he founded the programming education website Finxter.com that has taught exponential skills to millions of coders worldwide. He’s the author of the best-selling programming books Python One-Liners (NoStarch 2020), The Art of Clean Code (NoStarch 2022), and The Book of Dash (NoStarch 2022). Chris also coauthored the Coffee Break Python series of self-published books. He’s a computer science enthusiast, freelancer, and owner of one of the top 10 largest Python blogs worldwide.

His passions are writing, reading, and coding. But his greatest passion is to serve aspiring coders through Finxter and help them to boost their skills. You can join his free email academy here.