Length of each dimension: The integers in.

For example, (2, 4) contains two items, which means that an array with this shape is a 2D array. shape shows you how many dimensions are in the array.

Number of dimensions: The length of the tuple.

shape to understand the array’s characteristics: Both have the same data as the original array, numbers. The array with the shape (8,) is one-dimensional (1D), and the array with the shape (2, 2, 2) is three-dimensional (3D).

You can arrange the same data contained in numbers in arrays with a different number of dimensions:

The array numbers is two-dimensional (2D). You can read NumPy Tutorial: Your First Steps Into Data Science in Python to learn more about NumPy before diving in.

Use a wildcard value of -1 for one of the dimensions in reshape()įor this tutorial, you should be familiar with the basics of NumPy and N-dimensional arrays.

Control how data is rearranged when reshaping an array with the order parameter.

Add and remove dimensions in a NumPy array.

Change the shape of a NumPy array without changing its number of dimensions.

When you complete this tutorial, you’ll be able to alter the shape of any array to suit your application’s needs. In this tutorial, you’ll learn how to change the shape of a NumPy array to place all its data in a different configuration. The shape of an array describes the number of dimensions in the array and the length of each dimension. You can use NumPy’s reshape() to rearrange the data. In some programs, you may need to change how you organize your data within a NumPy array. An array can have one or more dimensions to structure your data. The main data structure that you’ll use in NumPy is the N-dimensional array.

Install numpy