Numpy Transpose Ndarray Swap Rows And Columns Rearrange Axes

Leo Migdal

-Nov 19, 2025, 12:03 AM

numpy transpose ndarray swap rows and columns rearrange axes

To transpose NumPy array ndarray (swap rows and columns), use the T attribute (.T), the ndarray method transpose() and the numpy.transpose() function. With ndarray.transpose() and numpy.transpose(), you can not only transpose a 2D array (matrix) but also rearrange the axes of a multi-dimensional array in any order. This article describes the following contents. If you want to swap rows and columns of pandas.DataFrame or a two-dimensional list (list of lists), see the following article. You can get the transposed matrix of the original two-dimensional array (matrix) with the T attribute. Returns a view of the array with axes transposed.

Refer to numpy.transpose for full documentation. None or no argument: reverses the order of the axes. tuple of ints: i in the j-th place in the tuple means that the array’s i-th axis becomes the transposed array’s j-th axis. n ints: same as an n-tuple of the same ints (this form is intended simply as a “convenience” alternative to the tuple form). Communities for your favorite technologies. Explore all Collectives

Ask questions, find answers and collaborate at work with Stack Overflow Internal. Ask questions, find answers and collaborate at work with Stack Overflow Internal. Explore Teams Find centralized, trusted content and collaborate around the technologies you use most. Connect and share knowledge within a single location that is structured and easy to search. Transpose is a special form of reshaping that also provides a view of the underlying data without copying anything.

Arrays have the Transpose method and also the special T attribute: numpy.dot returns the scalar product of two arrays, for example: The @ infix operator is another way to perform matrix multiplication. It implements the semantics of the @ operator introduced in Python 3.5 with PEP 465 and is an abbreviation of np.matmul. For higher dimensional arrays, transpose accepts a tuple of axis numbers to swap the axes: Here the axes have been reordered with the second axis in first place, the first axis in second place and the last axis unchanged.

NumPy, short for Numerical Python, is an essential library in the Python data science ecosystem. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to perform operations on these arrays. One such powerful operation is the ndarray.transpose() method, which rearranges the dimensions of an array. In this tutorial, we’ll demystify the workings of transpose() through illustrative examples, spanning from basic to advanced use cases. To start with the basics, let’s consider a simple 2D array. Transposing this array essentially swaps its rows with its columns.

Moving ahead to slightly more complex structures, let’s consider transposing a 3-dimensional array. Here, we have more flexibility in how we want to rearrange the dimensions. For a more in-depth manipulation, transpose() allows for the specification of axes to define exactly how the dimensions should be swapped. This ability is particularly useful for higher-dimensional data manipulation. Transposing arrays plays a crucial role in data analysis and preprocessing. Let’s go through an advanced example where we utilize transposition within a data processing context.

Swapping columns of a NumPy array means exchanging the positions of two specified columns across all rows. For example, if you have a 3x3 array with values like [[0, 1, 2], [3, 4, 5], [6, 7, 9]] and you swap column 0 with column 2, the array becomes [[2, 1, 0],... Let’s explore different ways to do this efficiently using NumPy. This is the simplest method, where we use slicing to specify the column indices to swap like a[:, [0, 2]] = a[:, [2, 0]]. It swaps the columns in-place, making it fast and ideal for quick operations on the original array. Explanation: a[:, [0, 2]] = a[:, [2, 0]] selects all rows and swaps columns 0 and 2.

It replaces column 0 with column 2 and vice versa, modifying the original array in-place without creating a new one. This method uses list comprehension to create a new column order, offering flexibility for dynamic or reusable functions. Define i and j, build the new index list and reindex the array. It requires slightly more code but provides greater control. Explanation: List comprehension swaps columns 0 and 2 by generating a new column order. It selects all rows and returns a new array with the specified columns swapped, leaving the original array unchanged.

The .transpose() method returns a view of the array with axes permuted. It rearranges the dimensions of a NumPy array by swapping or reordering its axes, effectively changing how data is organized without modifying the underlying data values. For a 2-D array, this performs the standard matrix transpose operation by swapping rows and columns. The .transpose() method is essential for data manipulation tasks in scientific computing, machine learning, and data analysis. Common use cases include preparing data for matrix operations, reshaping arrays for different algorithms, converting between row-major and column-major formats, adapting image data for different processing libraries, and optimizing memory access patterns for performance-critical... This example demonstrates the fundamental transpose operation on a 2-D array, showing how rows and columns are swapped:

This example shows how the .transpose() method converts a 2×3 matrix into a 3×2 matrix by swapping rows and columns. The element at position [0, 1] (value 2) moves to position [1, 0] in the transposed array. This example demonstrates transposing image data arrays, a common operation in computer vision and image processing workflows: NumPy is a fundamental library for scientific computing in Python. It provides a powerful ndarray object, which is a multi - dimensional array. One of the important operations that can be performed on these arrays is transposing.

Transposing an array can be crucial in various applications such as matrix operations in linear algebra, data reshaping for machine learning algorithms, and image processing. This blog post will dive deep into the concept of NumPy array transpose, its usage, common scenarios, and best practices. In the context of NumPy arrays, transposing is the operation of interchanging the axes of an array. For a two - dimensional array (a matrix), the transpose swaps the rows and columns. For higher - dimensional arrays, the concept is extended to reorder the axes. Mathematically, if we have a matrix (A) with dimensions (m \times n), its transpose (A^T) has dimensions (n \times m).

In NumPy, this operation can be applied to arrays of any number of dimensions. The simplest way to transpose a NumPy array is by using the transpose method or the T attribute of the ndarray object. In the above code: 1. We first create a 2D NumPy array arr_2d. 2. Then we use the transpose method to transpose the array and store the result in transposed_1.

3. Finally, we use the T attribute to achieve the same result and store it in transposed_2. Both transposed_1 and transposed_2 will be the transpose of arr_2d. In data science, multidimensional arrays are important because they allow us to work efficiently with complex data structures. Imagine you’re handling data from a 3D scan, an image with multiple color channels, or a large dataset with various features over time. These are all examples of multidimensional arrays.

Manipulating these arrays, such as swapping axes, is important for several reasons: In NumPy, arrays can have multiple dimensions, and each dimension is referred to as an “axis.” Understanding how these axes work helps in manipulating and analyzing data effectively. Here’s a breakdown of what axes are and how they relate to multidimensional arrays: A one-dimensional array is essentially a simple list of values. It has only one axis. A two-dimensional array can be visualized as a grid or matrix with rows and columns.

It has two axes where axis 0 refers to the rows and axis 1 refers to the columns. For a 1-D array, this returns an unchanged view of the original array, as a transposed vector is simply the same vector. To convert a 1-D array into a 2-D column vector, an additional dimension must be added, e.g., np.atleast_2d(a).T achieves this, as does a[:, np.newaxis]. For a 2-D array, this is the standard matrix transpose. For an n-D array, if axes are given, their order indicates how the axes are permuted (see Examples). If axes are not provided, then transpose(a).shape == a.shape[::-1].

If specified, it must be a tuple or list which contains a permutation of [0, 1, …, N-1] where N is the number of axes of a. Negative indices can also be used to specify axes. The i-th axis of the returned array will correspond to the axis numbered axes[i] of the input. If not specified, defaults to range(a.ndim)[::-1], which reverses the order of the axes. a with its axes permuted. A view is returned whenever possible.

Return the indices that would sort an array. Use transpose(a, argsort(axes)) to invert the transposition of tensors when using the axes keyword argument.

Numpy Transpose Ndarray Swap Rows And Columns Rearrange Axes

People Also Search

To Transpose NumPy Array Ndarray (swap Rows And Columns), Use

Refer To Numpy.transpose For Full Documentation. None Or No Argument:

Ask Questions, Find Answers And Collaborate At Work With Stack

Arrays Have The Transpose Method And Also The Special T

NumPy, Short For Numerical Python, Is An Essential Library In