Numpy Ndarray Transpose Method Geeksforgeeks

Leo Migdal

-Nov 18, 2025, 9:08 PM

numpy ndarray transpose method geeksforgeeks

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: 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(). The NumPy ndarray.T attribute finds the view of the transposed Array.

It can transpose any array having a dimension greater than or equal to 2. It works similarly to the numpy.transpose() method but it is easy and concise to use. Let's look at how to use the ndarray.T attribute of the Python's NumPy library. The ndarray.T attribute finds its use in machine learning applications where input data needs to be formatted in a certain way for further processing. It does not modify the original array and only returns the view of the transposed array. ndarray is a short form for N-dimensional array which is a important component of NumPy.

It’s allows us to store and manipulate large amounts of data efficiently. All elements in an ndarray must be of same type making it a homogeneous array. This structure supports multiple dimensions which makes it ideal for handling complex datasets like those used in scientific computing or data analysis. With this we can perform fast and memory efficient operations on data. Let's understand this with a simple example: Understanding the attributes of an ndarray is important while working with NumPy effectively.

Here are the key attributes: NumPy allows indexing and slicing operations on ndarrays which offers more flexibility compared to standard Python lists. Here's a overview: We can access individual elements in an array using square brackets just like Python lists. The indexing starts at 0. 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: 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 Ndarray Transpose Method Geeksforgeeks

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The Ndarray.transpose() Function Returns A View Of The Array With

Parameters: Axes : [None, Tuple Of Ints, Or N Ints]

It Can Transpose Any Array Having A Dimension Greater Than

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Here Are The Key Attributes: NumPy Allows Indexing And Slicing