Using NumPy reshape() to Change the Shape of an Array

The main data structure you’ll use in NumPy, Python’s core library for numerical computing, is the N-dimensional array. Sometimes you need to reorganize the values in a NumPy array into a different layout. That’s what NumPy’s reshape() is for. It changes an array’s shape without changing its data, so you can fit the same values into whatever configuration your program needs.

By the end of this tutorial, you’ll understand that:

• You reshape a NumPy array by passing a compatible shape to .reshape(), which rearranges the data without changing its values.
• A new shape is compatible only when its total number of elements matches the original array.
• reshape() can add or remove dimensions, so the new array doesn’t need the same number of dimensions as the original.
• The order parameter sets whether the data is read in row-major ("C") or column-major ("F") order.
• Passing -1 for one dimension lets reshape() infer that dimension’s length automatically.

The shape of an array describes the number of dimensions in the array and the length of each dimension. In this tutorial, you’ll learn how to reshape a NumPy array to place all its data in a different configuration. When you complete this tutorial, you’ll be able to alter the shape of any array to suit your application’s needs.

For this tutorial, you should be familiar with the basics of NumPy and N-dimensional arrays. You can read NumPy Tutorial: Your First Steps Into Data Science in Python to learn more about NumPy before diving in.

Install NumPy

You’ll need to install NumPy to your environment to run the code in this tutorial and explore reshape(). You can install the package using pip within a virtual environment. Select either the Windows or Linux + macOS tab below to see instructions for your operating system:

• Windows
• Linux + macOS

PS> python -m venv venv
PS> .\venv\Scripts\activate
(venv) PS> python -m pip install numpy

$ python -m venv venv
$ source venv/bin/activate
(venv) $ python -m pip install numpy

It’s a convention to use the alias np when you import NumPy. To get started, you can import NumPy in the Python REPL:

>>> import numpy as np

Now that you’ve installed NumPy and imported the package in a REPL environment, you’re ready to start working with NumPy arrays.

Understand the Shape of NumPy Arrays

You’ll use NumPy’s ndarray in this tutorial. In this section, you’ll review the key features of this data structure, including an array’s overall shape and number of dimensions.

You can create an array from a list of lists:

>>> import numpy as np
>>> numbers = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
>>> numbers
array([[1, 2, 3, 4],
       [5, 6, 7, 8]])

The function np.array() returns an object of type np.ndarray. This data structure is the main data type in NumPy.

You can describe the shape of an array using the length of each dimension of the array. NumPy represents this as a tuple of integers. The array numbers has two rows and four columns. Therefore, this array has a (2, 4) shape:

>>> numbers.shape
(2, 4)

You can represent the same data using a different shape:

Both of these arrays contain the same data. The array with the shape (2, 4) has two rows and four columns, and the array with the shape (4, 2) has four rows and two columns. You can check the number of dimensions of an array using .ndim:

>>> numbers.ndim
2