The Python Numpy module has shape function, which helps us to find the shape or size of an array or matrix. Apart from this, Python Numpy module has reshape, resize, transpose, swapaxes, flatten, ravel and squeeze functions to alter the matrix of an array to required shape.
Python Numpy Array Shape
The Python Numpy module has one important property called shape. The Python array shape property is used to get or find the shape of an array.
import numpy as np
arr = np.array([10, 20, 30, 40, 50, 60, 70, 80])
print(arr)
print('Array Shape = ', np.shape(arr))OUTPUT
Let me show you, one more example of Python Numpy array shape. Here, we used arrays of different sizes and then finding their shape using Python array shape property.
import numpy as np
x = np.array([[10, 20, 30], [40, 50, 60]])
print(x)
print('Array Shape = ', np.shape(x))
print()
arr = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
print(arr)
print('Array Shape = ', np.shape(arr))
arr1 = np.random.randint(5, 50, size = (5, 8))
print('\n-----Two Dimensional Random Array----')
print(arr1)
print('Array Shape = ', np.shape(arr1))
arr2 = np.array([[[ 7, 24, 24, 22], [24, 10, 16, 2], [ 1, 16, 7, 16]],
[[22, 23, 12, 39], [16, 30, 37, 15],[16, 17, 3, 19]]])
print('\n-----Three Dimensional Array----')
print(arr2)
print('Array Shape = ', np.shape(arr2))OUTPUT
Python Numpy Array reshape
The Python Numpy array reshape function accepts array as the first argument and shape or matrix size as the second argument.
import numpy as np
arr = np.array([[10, 20, 30], [40, 50, 60]])
print(arr)
print('Array Shape = ', np.shape(arr))
print('\n---New Array---')
new_arr = np.reshape(arr, (6,))
print(new_arr)
print('Array Shape = ', np.shape(new_arr))OUTPUT
This is an another example of array reshape function. Here, we used a one dimensional array and reshaped into different dimensions. For your reference, we are using the Python Numpy array shape function to return the array shape after reshaping them.
import numpy as np
arr = np.array([10, 20, 30, 40, 50, 60, 70, 80])
print(arr)
print('Array Shape = ', np.shape(arr))
print('\n---New Array---')
new_arr = np.reshape(arr, (2,4))
print(new_arr)
print('Array Shape = ', np.shape(new_arr))
print('\n---New Array---')
new_arr1 = np.reshape(arr, (4, 2))
print(new_arr1)
print('Array Shape = ', np.shape(new_arr1))
print('\n---New Array---')
new_arr2 = np.reshape(arr, (8,1))
print(new_arr2)
print('Array Shape = ', np.shape(new_arr2))OUTPUT
If you don’t know or don’t want to use the second value of the shape then you can assign -1. The array reshape function will automatically pick the size and replaces with -1.
import numpy as np
arr = np.array([[10, 20, 30, 40, 50, 60, 70, 80]])
print(arr)
print('Array Shape = ', np.shape(arr))
print('\n---New Array---')
new_arr = np.reshape(arr, (2,-1))
print(new_arr)
print('Array Shape = ', np.shape(new_arr))
print('\n---New Array---')
new_arr1 = np.reshape(arr, (4, -1))
print(new_arr1)
print('Array Shape = ', np.shape(new_arr1))OUTPUT
Python Numpy Array resize
The Python resize function is useful to resize the existing array to desired shape. This function accepts array as the first argument and desired shape size as a second argument. If you specify the desired shape larger than the original, resize function will replicate the values in the base array to create a big array.
import numpy as np
arr = np.array([1, 2, 3])
print(arr)
print('\n---New Array---')
new_arr = np.resize(arr, (4, 3))
print(new_arr)
print('\n---New Array---')
new_arr1 = np.resize(arr, (9, 8))
print(new_arr1)OUTPUT
Let us see, what happens when we resize array to smaller size
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print('---Original Array---')
print(arr)
print('\n---New Array---')
new_arr = np.resize(arr, (1, 3))
print(new_arr)
print('\n---New Array---')
new_arr1 = np.resize(arr, (4, 1))
print(new_arr1)OUTPUT
Python Numpy Array transpose
The Python transpose function helps you to transpose the given matrix or 2D array.
import numpy as np
arr = np.random.randint(5, 50, size = (5, 8))
print('\n-----Two Dimensional Random Array----')
print(arr)
print('\n-----Transposed Two Dimensional Array----')
print(np.transpose(arr))OUTPUT
Python Transpose Three Dimensional Array
Let me use this Python array transpose function to transpose 3D array
import numpy as np
arr2 = np.random.randint(1, 20, size = (2, 3, 4))
print('\n-----Three Dimensional Random Array----')
print(arr2)
print('\n-----Transposed Three Dimensional Array----')
print(np.transpose(arr2))OUTPUT
Python Numpy Array swapaxes
The Python array swapaxes function is used to interchange the given two axis of an array. It accepts three arguments – array_name, first_axis and second_axis. Next, it will swap the first_axis and second_axis.
import numpy as np
arr1 = np.array([[10, 20, 30, 40]])
print(arr1)
print('swapaxes Result of arr1')
print(np.swapaxes(arr1, 0, 1))
arr2 = np.array([[10, 20, 30], [40, 50, 60]])
print(arr2)
print('swapaxes Result of arr2')
print(np.swapaxes(arr2, 0, 1))
arr3 = np.array([[[10, 20], [30, 40], [50, 60]]])
print('swapaxes Result of arr3')
print(np.swapaxes(arr3, 0, 1))
print('swapaxes Result of arr3 - 0, 2')
print(np.swapaxes(arr3, 0, 2))
print('swapaxes Result of arr3 - 2, 1')
print(np.swapaxes(arr3, 2, 1))OUTPUT
This time, we using this Python swapaxes function on three dimensional random array generated by the randint function
import numpy as np
arr3 = np.random.randint(1, 40, size = (2, 3, 4))
print(arr3)
print('swapaxes Result of arr3, 0, 1')
print(np.swapaxes(arr3, 0, 1))
print('swapaxes Result of arr3, 0, 2')
print(np.swapaxes(arr3, 0, 2))
print('swapaxes Result of arr3, 2, 1')
print(np.swapaxes(arr3, 2, 1))OUTPUT
Python Numpy Array flatten
The Python array flatten function collapses the given array into a one dimensional array. This function accepts order parameters to decide the order of flatten array items.
order = {C, F, A, K} – You can use either one of them or it will consider C because it is the default one. C means array items will flatten in row-major order. F means Fortran style or column major order. If array is Fortrain contiguous, A flattens in column major order otherwise, row major order. K flattens in the order of array elements occurred in memory.
import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
print('flatten Result of arr Order C = ', arr.flatten())
print('flatten Result of arr Order F = ', arr.flatten('F'))
print('flatten Result of arr Order A = ', arr.flatten('A'))
print('flatten Result of arr Order K = ', arr.flatten('K'))
arr1 = np.array([[10, 20], [30, 40], [50, 60], [70, 80]])
print('\nflatten Result of arr1 Order C')
print(arr1.flatten())
print('\nflatten Result of arr1 - Order = F')
print(arr1.flatten('F'))
print('\nflatten Result of arr1 - Order = A')
print(arr1.flatten('A'))
print('\nflatten Result of arr1 - Order = K')
print(arr1.flatten('K'))OUTPUT
This time, we generated a random three dimensional integer array using randint function. Next, we used this Python Numpy flatten function to flatten the array to single dimensional array.
import numpy as np
arr2 = np.random.randint(1, 40, size = (2, 3, 4))
print(arr2)
print('flatten Result of arr2')
print(arr2.flatten())
print('\nflatten Result of arr2 - Order = F')
print(arr2.flatten('F'))
print('\nflatten Result of arr2 - Order = A')
print(arr2.flatten('A'))
print('\nflatten Result of arr2 - Order = K')
print(arr2.flatten('K'))OUTPUT
Python Array ravel
The Python array ravel function returns a contiguous flattened one dimensional array. The basic syntax of this Python Numpy ravel function is
numpy.ravel(array_name, order = {C, F, A, K})This function accepts order parameters to decide the order of flatten array items. C means index the array items in row-major order. F means index items in Fortran style or column major order. If array is Fortrain contiguous, A reads indexes in column major order otherwise, row major order. K reads array indexes in the order of array elements occurred in memory.
import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
print('arr ravel Result of Order C = ', np.ravel(arr))
print('arr ravel Result of Order F = ', np.ravel(arr, order = 'F'))
print('arr ravel Result of Order A = ', np.ravel(arr, order = 'A'))
print('arr ravel Result of Order K = ', np.ravel(arr, order = 'K'))
arr1 = np.array([[11, 20], [33, 40], [55, 60], [77, 80]])
print('\narr1 ravel Result of Order C = ', np.ravel(arr1))
print('arr1 ravel Result of Order F = ', np.ravel(arr1, order = 'F'))
print('arr1 ravel Result of Order A = ', np.ravel(arr1, order = 'A'))
print('arr1 ravel Result of Order K = ', np.ravel(arr1, order = 'K'))OUTPUT
In this example, we declared a three dimensional integer array of random numbers using randint function. Next, we used this Python Numpy ravel function to flatten this random array to contiguous single dimensional array.
import numpy as np
arr = np.random.randint(1, 40, size = (2, 3, 4))
print(arr)
print('\narr ravel Result of Order C')
print(np.ravel(arr))
print('arr ravel Result of Order F')
print(np.ravel(arr, order = 'F'))
print('arr ravel Result of Order A')
print(np.ravel(arr, order = 'A'))
print('arr ravel Result of Order K')
print(np.ravel(arr, order = 'K'))
print('\nTransposed arr ravel Result of Order C')
print(np.ravel(arr.T))OUTPUT
Python Array squeeze
The Python array squeeze function removes single dimensional entries from the given array shape.The basic syntax of this Python Numpy squeezeunction is numpy.squeeze(array_name, axis = {None, 0, 1, 2 ..})
import numpy as np
arr = np.arange(12).reshape(1, 3, 4)
print(arr)
print('\narr squeeze Result')
a = np.squeeze(arr)
print(a)
print('Shapes = ', arr.shape, a.shape)
b = np.squeeze(arr, axis = 0)
print(b)
print('Shapes = ', arr.shape, b.shape)
arr1 = np.array([[[[11, 20], [33, 40], [55, 60], [77, 80]]]])
x = np.squeeze(arr1)
print(x)
print('Shapes = ', arr1.shape, x.shape)
y = np.squeeze(arr1, axis = 1)
print(y)
print('Shapes = ', arr1.shape, y.shape)OUTPUT
