Convert a 1D array to a 2D Numpy array

Numpy is a Python package that consists of multidimensional array objects and a collection of operations or routines to perform various operations on the array and processing of the array. This package consists of a function called numpy.reshape which is used to convert a 1-D array into a 2-D array of required dimensions (n x m).  This function gives a new required shape without changing the data of the 1-D array.

Syntax: numpy.reshape(array, new_shape, order)

Parameters:

• array: is the given 1-D array that will be given a new shape or converted into 2-D array
• new_shape: is the required shape or 2-D array having int or tuple of int
• order: ‘C’ for C style, ‘F’ for Fortran style, ‘A’ if data is in Fortran style then Fortran like order else C style.

Examples 1:

Python3

import numpy as np
# 1-D array having elements [1 2 3 4 5 6 7 8]
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])

# Print the 1-D array
print ('Before reshaping:')
print (arr)
print ('\n')

# Now we can convert this 1-D array into 2-D in two ways

# 1. having dimension 4 x 2
arr1 = arr.reshape(4, 2)
print ('After reshaping having dimension 4x2:')
print (arr1)
print ('\n')

# 2. having dimension 2 x 4
arr2 = arr.reshape(2, 4)
print ('After reshaping having dimension 2x4:')
print (arr2)
print ('\n')

Output:

Before reshaping:
[1 2 3 4 5 6 7 8]
After reshaping having dimension 4x2:
[[1 2]
 [3 4]
 [5 6]
 [7 8]]
After reshaping having dimension 2x4:
[[1 2 3 4]
 [5 6 7 8]]

Example 2: Let us see an important observation that whether we can reshape a 1-D array into any 2-D array.

Python3

import numpy as np
# 1-D array having elements [1 2 3 4 5 6 7 8]
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])

# Print the 1-D array
print('Before reshaping:')
print(arr)
print('\n')

# let us try to convert into 2-D array having dimension 3x3
arr1 = arr.reshape(3, 3)
print('After reshaping having dimension 3x3:')
print(arr1)
print('\n')

Output:

This concludes that the number of elements should be equal to the product of dimension i.e. 3×3=9 but total elements = 8;

Example 3: Another example is that we can use the reshape method without specifying the exact number for one of the dimensions. Just pass -1 as the value and NumPy will calculate the number.

Python3

import numpy as np
# 1-D array having elements [1 2 3 4 5 6 7 8]
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])

# Print the 1-D array
print('Before reshaping:')
print(arr)
print('\n')


arr1 = arr.reshape(2, 2, -1)
print('After reshaping:')
print(arr1)
print('\n')

Output:

Before reshaping:
[1 2 3 4 5 6 7 8]
After reshaping:
[[[1 2]
 [3 4]]
[[5 6]
 [7 8]]]