Numpy reshape()

Numpy

numpy.reshape(a, newshape, order='C')
Return reshaped array using the newshape as given.
aarray input
newshapeNew shape for the original array. Integer for 1-D array, tulp for multi Dimensional.
order{‘C’, ‘F’, ‘A’}, optional
Read more about shape().

Ones array of shape(3,4)Ones array of shape(4,3)
Shape: (3, 4)
Dimension 2
Shape: (4, 3)
Dimension 2
  1. While reshaping data of the original array will not be lost.
  2. The reshape array must be compatible with original shape , otherwise value error will be raised.
We will try to use reshape() with different dimensional arrays. To Understand the difference we will first use the shape() to get the tuple or integer about the Array and then use reshape() and get the details of the updated array. You can read more on how to create arrays by using ones(). We will input different shape() to create the array by using ones().

Along with shape() we will display the dimainsion of the array by using ndim

One dimensional array

import numpy as np
ar=np.ones((6,))
print(ar)
print("Shape: ", ar.shape)
print("Dimension ", ar.ndim)
ar=ar.reshape((2,3))
print("##After using reshpae()##")
print(ar)
print("Shape: ", ar.shape)
print("Dimension ", ar.ndim)
Output
[1. 1. 1. 1. 1. 1.]
Shape:  (6,)
Dimension  1
##After using reshpae()##
[[1. 1. 1.]
 [1. 1. 1.]]
Shape:  (2, 3)
Dimension  2

Two dimensional array

import numpy as np
ar=np.ones((3,2))
print(ar)
print("Shape: ", ar.shape)
print("Dimension ", ar.ndim)
ar=ar.reshape((2,3))
print("##After using reshpae()##")
print(ar)
print("Shape: ", ar.shape)
print("Dimension ", ar.ndim)
Output
[[1. 1.]
 [1. 1.]
 [1. 1.]]
Shape:  (3, 2)
Dimension  2
##After using reshpae()##
[[1. 1. 1.]
 [1. 1. 1.]]
Shape:  (2, 3)
Dimension  2

Three dimensional array

import numpy as np
ar=np.ones((3,2,3))
print(ar)
print("Shape: ", ar.shape)
print("Dimension ", ar.ndim)

ar=ar.reshape((2,3,3))
print("##After using reshpae()##")
print(ar)
print("Shape: ", ar.shape)
print("Dimension ", ar.ndim)
Output
[[[1. 1. 1.]
  [1. 1. 1.]]

 [[1. 1. 1.]
  [1. 1. 1.]]

 [[1. 1. 1.]
  [1. 1. 1.]]]
Shape:  (3, 2, 3)
Dimension  3
##After using reshpae()##
[[[1. 1. 1.]
  [1. 1. 1.]
  [1. 1. 1.]]

 [[1. 1. 1.]
  [1. 1. 1.]
  [1. 1. 1.]]]
Shape:  (2, 3, 3)
Dimension  3

Four dimensional array

import numpy as np
ar=np.ones((3,2,3,4))
print(ar)
print("Shape: ", ar.shape)
print("Dimension ", ar.ndim)

ar=ar.reshape((9,8))
print("##After using reshpae()##")
print(ar)
print("Shape: ", ar.shape)
print("Dimension ", ar.ndim)
Output
[[[[1. 1. 1. 1.]
   [1. 1. 1. 1.]
   [1. 1. 1. 1.]]

  [[1. 1. 1. 1.]
   [1. 1. 1. 1.]
   [1. 1. 1. 1.]]]


 [[[1. 1. 1. 1.]
   [1. 1. 1. 1.]
   [1. 1. 1. 1.]]

  [[1. 1. 1. 1.]
   [1. 1. 1. 1.]
   [1. 1. 1. 1.]]]


 [[[1. 1. 1. 1.]
   [1. 1. 1. 1.]
   [1. 1. 1. 1.]]

  [[1. 1. 1. 1.]
   [1. 1. 1. 1.]
   [1. 1. 1. 1.]]]]
Shape:  (3, 2, 3, 4)
Dimension  4
##After using reshpae()##
[[1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1.]
 [1. 1. 1. 1. 1. 1. 1. 1.]]
Shape:  (9, 8)
Dimension  2
Numpy eye() bincount() arange() linspace()


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