how to reshape numpy array with reorder?

Syntax : array.reshape(shape)Argument : It take tuple as argument, tuple is the new shape to be formedReturn : It returns numpy.ndarray ,Errors Occur during reshaping When we try to reshape a array to a shape which is not mathematically possible then value error is generated saying can not reshape the array. For example when we try to reshape 1-D array with 4 elements into a 2-D array of dimension(3, 3) is not possible as new array requires 9 elements ,Note : We can also use np.reshape(array, shape) command to reshape the arrayReshaping : 1-D to 2D In this example we will reshape the 1-D array of shape (1, n) to 2-D array of shape (N, M) here M should be equal to the n/N there for N should be factor of n. ,Reshaping N-D to 1-D array In this example we will see how we can reshape a 2-D or 3-D array to form a 1-D array. We can also use reshape(-1) to do this, here -1 is the unknown dimension.

Output :

Array: [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16]
First Reshaped Array: [
   [1 2 3 4]
   [5 6 7 8]
   [9 10 11 12]
   [13 14 15 16]
]
Second Reshaped Array: [
   [1 2 3 4 5 6 7 8]
   [9 10 11 12 13 14 15 16]
]

15._

ValueError: cannot reshape array of size 9 into shape(1, 5)

Suggestion : 2

Simply use np.transpose -

a.transpose(2, 0, 1)

Sample run -

In[347]: a
Out[347]:
   array([
      [
         [1, 2, 3],
         [4, 5, 6]
      ]
   ])

In[348]: a.transpose(2, 0, 1)
Out[348]:
   array([
      [
         [1, 4]
      ],

      [
         [2, 5]
      ],

      [
         [3, 6]
      ]
   ])

With np.moveaxis -

np.moveaxis(a, 2, 0)

There are a few ways, but transpose() is probably the easiest:

array.transpose(2, 0, 1)

import einops
einops.rearrange(x, 'x y z -> z x y')

Suggestion : 3

You can think of reshaping as first raveling the array (using the given index order), then inserting the elements from the raveled array into the new array using the same kind of index ordering as was used for the raveling.,The order keyword gives the index ordering both for fetching the values from a, and then placing the values into the output array. For example, let’s say you have an array:,The new shape should be compatible with the original shape. If an integer, then the result will be a 1-D array of that length. One shape dimension can be -1. In this case, the value is inferred from the length of the array and remaining dimensions.,It is not always possible to change the shape of an array without copying the data. If you want an error to be raised when the data is copied, you should assign the new shape to the shape attribute of the array:

>>> a = np.zeros((10, 2))

# A transpose makes the array non - contiguous >>>
   b = a.T

# Taking a view makes it possible to modify the shape without modifying
# the initial object. >>>
   c = b.view() >>>
   c.shape = (20)
Traceback(most recent call last):
   ...
   AttributeError: Incompatible shape
for in - place modification.Use `.reshape()`
to make a copy with the desired shape.

>>> a = np.arange(6).reshape((3, 2)) >>>
   a
array([
   [0, 1],
   [2, 3],
   [4, 5]
])

>>> np.reshape(a, (2, 3)) # C - like index ordering
array([
      [0, 1, 2],
      [3, 4, 5]
   ]) >>>
   np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape
array([
      [0, 1, 2],
      [3, 4, 5]
   ]) >>>
   np.reshape(a, (2, 3), order = 'F') # Fortran - like index ordering
array([
      [0, 4, 3],
      [2, 1, 5]
   ]) >>>
   np.reshape(np.ravel(a, order = 'F'), (2, 3), order = 'F')
array([
   [0, 4, 3],
   [2, 1, 5]
])

>>> a = np.array([
      [1, 2, 3],
      [4, 5, 6]
   ]) >>>
   np.reshape(a, 6)
array([1, 2, 3, 4, 5, 6]) >>>
   np.reshape(a, 6, order = 'F')
array([1, 4, 2, 5, 3, 6])

>>> np.reshape(a, (3, -1)) # the unspecified value is inferred to be 2
array([
   [1, 2],
   [3, 4],
   [5, 6]
])

Suggestion : 4

October 20, 2021

Syntax: numpy.reshape(a, newshape, order=’C’)
Purpose: Gives a new shape to the array without changing the data
Parameters:
- a: _arraylike Array to be reshaped
- newshape:int or tuples of ints Should be compatible with the original shape. If an integer, then the result will be a 1-D array of that length. One shape dimension can be -1. In this case, the value is inferred from the length of the array and remaining dimensions.
- order:{‘C’, ‘F’, ‘A’}, optional Read the elements of a using this index order, and place the elements into the reshaped array using this index order. Detailed usage will be discussed further.
Returns reshaped_array ndarray

# Import Packages
import numpy as np

Step 1: Create a numpy array of shape (8,)

num_array = np.array([1, 2, 3, 4, 5, 6, 7, 8])
num_array

num_array = np.array([1,2,3,4,5,6,7,8])
num_array

array([1, 2, 3, 4, 5, 6, 7, 8])

np.reshape(num_array, (4,2))

array([
   [1, 2],
   [3, 4],
   [5, 6],
   [7, 8]
])

Step 1: Create a numpy array of shape (4,2)

num_array = np.array([
   [1, 2],
   [3, 4],
   [5, 6],
   [7, 8]
])

num_array

Suggestion : 5

Note: There are a lot of functions for changing the shapes of arrays in numpy flatten, ravel and also for rearranging the elements rot90, flip, fliplr, flipud etc. These fall under Intermediate to Advanced section of numpy.,The shape of an array is the number of elements in each dimension.,By reshaping we can add or remove dimensions or change number of elements in each dimension.,Meaning that you do not have to specify an exact number for one of the dimensions in the reshape method.

arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])

newarr = arr.(4, 3)

Suggestion : 6

Remote Work 2022 , Remote Work 2022

Here is the syntax of the function:

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

Input:

import numpy as np

x = np.arange(20)

#prints out the array with its elements
print(x)
print()

#prints out the shape of the array
print(x.shape)
print()

#prints out the dimension value of the array
print(x.ndim)

Output:

[0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]

(20, )

1

Suggestion : 7

The order F means that the elements of the array will be reshaped with the first index changing the fastest. The order F performs column-wise operations on the elements of the array.,The order C means that the elements of the array will be reshaped with the last index changing the fastest. The order C performs row-wise operations on the elements.,The transpose method only changes rows into columns or columns to rows (inverting axes). The reshape method will take an input array and format the array into the given shape.,The reshape() method does not change column data to row, but it changes the shape of an array that is the dimensions of the array.

Syntax:

numpy.reshape(array, shape, order = ‘C’)

Code:

import numpy as npm
import numpy as np
a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
output_array = np.reshape(a, (2, 5))
print("2D array after converting: ", output_array)

8._

import numpy as np
a = np.array([
   [1, 2, 3],
   [6, 7, 8],
   [4, 5, 9],
   [10, 11, 13]
])
output_array = np.reshape(a, (2, 6))
print(output_array)

Axis 1 is the direction along the columns and axis 0 is the direction along rows. For example, if you have an array:

[
   [1, 2],
   [4, 5]
]

newshape_image = np.reshape(image, (800, 1800))
print("After Reshaping", newshape_image.shape)
#After Reshaping(800, 1800)
#Now convert the array back to image.
newshape_image_export = Image.fromarray(newshape_image).save('20-reshape_example.png')