Jul 15, 2025 · In this example, two NumPy arrays arr1 and arr2 are created, representing a 2D array and a 3D array, respectively. The shape of each array is printed, revealing their dimensions and sizes …

The shape property is usually used to get the current shape of an array, but may also be used to reshape the array in-place by assigning a tuple of array dimensions to it.

The shape of an array is the number of elements in each dimension. NumPy arrays have an attribute called shape that returns a tuple with each index having the number of corresponding elements. Print …

The shape of a NumPy array is a tuple of integers representing the size of the array along each dimension. It describes the number of elements in each axis, defining the array’s structure.

Oct 31, 2022 · This tutorial explains how to get the shape of a Numpy array with Numpy shape. It explains the syntax of np.shape and shows clear examples.

Nov 30, 2017 · For a 1D array, the shape would be (n,) where n is the number of elements in your array. For a 2D array, the shape would be (n,m) where n is the number of rows and m is the number of …

May 8, 2025 · Learn how to use NumPy shape in Python to understand and manipulate array dimensions. Examples with real-world data, reshaping techniques, and common solutions.

Aug 28, 2025 · When you're working with NumPy, numpy.shape () is a super handy function for getting the dimensions of an array. Think of it like a quick way to find out how big your data is in each direction.

Understanding how NumPy array shape works is essential when working with multidimensional data. Whether you're manipulating datasets, feeding input into a machine learning model, or transforming …

In this blog post, we will explore the concept of NumPy array shape in detail, covering its fundamental concepts, usage methods, common practices, and best practices.