Python Numpy Numpy Transpose Geeksforgeeks

Leo Migdal

-Nov 18, 2025, 9:06 PM

python numpy numpy transpose geeksforgeeks

With the help of Numpy numpy.transpose(), We can perform the simple function of transpose within one line by using numpy.transpose() method of Numpy. It can transpose the 2-D arrays on the other hand it has no effect on 1-D arrays. This method transpose the 2-D numpy array. Parameters: axes : [None, tuple of ints, or n ints] If anyone wants to pass the parameter then you can but it's not all required. But if you want than remember only pass (0, 1) or (1, 0). Like we have array of shape (2, 3) to change it (3, 2) you should pass (1, 0) where 1 as 3 and 0 as 2.Returns: ndarray

Example #1 : In this example we can see that it's really easy to transpose an array with just one line. Example #2 : In this example we demonstrate the use of tuples in numpy.transpose(). matrix.transpose() method in NumPy is used to find the transpose of a matrix that is, it flips the matrix over its diagonal, turning rows into columns and columns into rows. Returns: A new matrix that is the transposed version of the original. Example 1: This creates a 2×3 matrix and finds its transpose using the transpose() method. Example 2: Here, a 3×3 matrix is created and transposed using the same method.

Example 3: Transpose in Matrix Multiplication Numpy is a general-purpose array-processing package. It provides a high-performance multidimensional array object, and tools for working with these arrays. It is the fundamental package for scientific computing with Python. Besides its obvious scientific uses, Numpy can also be used as an efficient multi-dimensional container of generic data. Array in Numpy is a table of elements (usually numbers), all of the same type, indexed by a tuple of positive integers.

In Numpy, number of dimensions of the array is called rank of the array. A tuple of integers giving the size of the array along each dimension is known as shape of the array. An array class in Numpy is called as ndarray. Elements in Numpy arrays are accessed by using square brackets and can be initialized by using nested Python Lists. Arrays in Numpy can be created by multiple ways, with various number of Ranks, defining the size of the Array. Arrays can also be created with the use of various data types such as lists, tuples, etc.

The type of the resultant array is deduced from the type of the elements in the sequences.Note: Type of array can be explicitly defined while creating the array. In a numpy array, indexing or accessing the array index can be done in multiple ways. To print a range of an array, slicing is done. Slicing of an array is defining a range in a new array which is used to print a range of elements from the original array. Since, sliced array holds a range of elements of the original array, modifying content with the help of sliced array modifies the original array content. NumPy is a powerful library for numerical computing in Python.

It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays. NumPy’s array objects are more memory-efficient and perform better than Python lists, which is essential for tasks in scientific computing, data analysis, and machine learning. This NumPy tutorial will cover core features, and all concept from basic to advanced divided in 10 sections. The numpy array also called ndarray is a grid of values, all of the same types. They can be one-dimensional (like a list), two-dimensional (like a matrix) or multi-dimensional (like a table with rows and columns). To understand all the basics of Numpy Arrays - explaining their types (one-dimensional and multi-dimensional), key attributes (axis, shape, rank, dtype): Basics of Numpy Arrays

NumPy arrays are created using the np.array() function, which converts lists, tuples, or other sequences into a NumPy array. You can create different types of arrays, such as 1D arrays from a simple list of elements, 2D arrays from nested lists representing rows and columns, and multi-dimensional arrays by further nesting lists. Example of a simple one-dimensional array: The ndarray.transpose() function returns a view of the array with axes transposed. Return : [ndarray] View of arr, with axes suitably permuted. Let's look at some examples to of transpose() method of the NumPy library to find transpose of a ndarray:

Recently in a project for USA clients, I got a scenario to transpose an array in Python, that deals with matrices and two-dimensional arrays. Python provides several ways to transpose arrays, Let us explore different methods and provide examples to help you understand. Before we get into the code, let us understand what is Python array transposition. In simple words, I can say, transposing an array is swapping its rows and columns. Consider you have a 2D array with 3 rows and 4 columns, transposing would result in an array with 4 rows and 3 columns. The elements in the original array are rearranged such that the element at position (i, j) in the original array becomes the element at position (j, i) in the transposed array.

After transposing, the array would become: Check out How to Find the Number of Elements in a Python Array The easiest and most efficient way to transpose an array in Python is by using the NumPy library. NumPy provides transpose() function that allows you to easily transpose arrays of any dimension. Let’s see how it works. matrix.transpose() method in NumPy is used to find the transpose of a matrix that is, it flips the matrix over its diagonal, turning rows into columns and columns into rows.

Returns: A new matrix that is the transposed version of the original. Example 1: This creates a 2×3 matrix and finds its transpose using the transpose() method. Example 2: Here, a 3×3 matrix is created and transposed using the same method. Example 3: Transpose in Matrix Multiplication

Python Numpy Numpy Transpose Geeksforgeeks

People Also Search

With The Help Of Numpy Numpy.transpose(), We Can Perform The

Example #1 : In This Example We Can See That

Example 3: Transpose In Matrix Multiplication Numpy Is A General-purpose

In Numpy, Number Of Dimensions Of The Array Is Called

The Type Of The Resultant Array Is Deduced From The