numpy.reshape() in Python (original) (raw)

Last Updated : 13 Jan, 2025

In Python, **numpy.reshape() function is used to give a new shape to an existing NumPy array without changing its data. It is important for manipulating array structures in Python.

Let's understand with an example:

Python `

import numpy as np

Creating a 1D NumPy array

arr = np.array([1, 2, 3, 4, 5, 6])

Reshaping the 1D array into a 2D array with 2 rows and 3 columns

reshaped_arr = np.reshape(arr, (2, 3)) print(reshaped_arr)

`

**Explanation:

• array arr is reshaped into a 2x3 matrix, where 2 is number of rows and 3 is number of columns.
• Each element from the original array is rearranged into the new shape while maintaining the order.

Table of Content

• Syntax of numpy.reshape() :
• Using -1 to infer a dimension
• Reshaping with column-major order

**Syntax of numpy.reshape() :

numpy.reshape(array, shape, order = 'C')

**Parameters :

• **array : [array_like]Input array
• **shape : [int or tuples of int] e.g. The desired shape of the array. If one dimension is -1, the value is inferred from the length of the array and the remaining dimensions.
• **order : [C-contiguous, F-contiguous, A-contiguous; optional]
  • 'C' (default): Row-major order.
  • 'F': Column-major order.
  • 'A': Fortran-like index order if the array is Fortran-contiguous; otherwise, C-like order.
  • 'K': Keeps the array's order as close to its original as possible.

**Return Type:

• Array which is reshaped without changing the data.

Using -1 to infer a dimension

It allows to automatically calculate the dimension that is unspecified as long as the total size of the array remains consistent.

Python `

import numpy as np

Creating a 1D NumPy array

arr = np.array([1, 2, 3, 4, 5, 6])

Reshaping the array into a 2D array

'-1' allows to calculate the number of rows based on the total number of elements

reshaped_arr = np.reshape(arr, (-1, 2))
print(reshaped_arr)

`

Output

[[1 2] [3 4] [5 6]]

**Explanation:

• -1 allows NumPy to automatically calculate the number of rows needed based on the total size and the other given dimension.
• resulting array has 3 rows and 2 columns, as NumPy calculates the required number of rows.

Reshaping with column-major order

We can specify the order in which the elements are read from the original array and placed into the new shape.

Python `

import numpy as np

Creating a 1D NumPy array

arr = np.array([1, 2, 3, 4, 5, 6])

Reshaping the array into a 2D array with 2 rows and 3 columns

reshaped_arr = np.reshape(arr, (2, 3), order='F') print(reshaped_arr)

`

**Explanation:

• order='F' argument reshapes the array in a column-major (Fortran-style) order, meaning the elements are filled by columns instead of rows.
• The result is a 2x3 matrix where the data is arranged column-wise.