reshape()

The reshape() function in NumPy is used to give a new shape to an array without changing its data. It returns a new array with the same data but a different shape. It is one of the most commonly used functions in data wrangling with NumPy.

Syntax

The basic syntax for using reshape() is as follows:

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

Here, array is the array that needs to be reshaped, newshape is the integer or tuple that represents the new shape to which the array should be reshaped, and order is the optional parameter that specifies the order in which the elements of the array should be read.

Example

Consider the following example:

import numpy as np

a = np.array([[1, 2, 3], [4, 5, 6]])
print("Original array:\n", a)

b = np.reshape(a, 6)
print("\nReshaped array:\n", b)

Output:

Original array:
 [[1 2 3]
  [4 5 6]]

Reshaped array:
 [1 2 3 4 5 6]

In this example, the reshape() function is used to reshape the original 2-dimensional array a into a 1-dimensional array b.

Explanation

The reshape() function in NumPy is used to reshape an array to a new shape without changing its data. This function is useful in cases where you need to change the structure of the array to fit a particular analysis or computation.

The reshape() function returns a new array with the same data but a different shape. The newshape argument specifies the new shape of the array as an integer or tuple of integers. The order argument is an optional parameter that specifies the order in which the elements of the array should be read.

Use

The reshape() function is commonly used in data wrangling and data analysis with NumPy. It can be used to reorganize arrays to fit specific analysis requirements, such as converting a 1-dimensional array into a multi-dimensional array, or vice versa.

Important Points

• The product of the dimensions in the newshape argument must be equal to the size of the original array.
• The elements of the original array are read in row-major order (C-style), unless the order parameter is set to 'F' (Fortran-style).
• The reshape() function returns a new array with the same data but a different shape, so it does not modify the original array.

Summary

The reshape() function in NumPy is used to reshape an array to a new shape without changing its data. It returns a new array with the same data but a different shape. The new shape is specified as the newshape argument, which can be an integer or tuple of integers. The order parameter is used to specify the order in which elements should be read from the original array. It is commonly used in data wrangling and data analysis with NumPy and can be used to reorganize arrays to fit specific analysis requirements.