Array Manipulation – NumPy
Reshaping Array: Changing Array Shape
Reshaping an array means changing its dimension. NumPy provides np.reshape() function to reshape or change the dimensions of an array.
Syntax: np.reshape(shape)
Reshaping to 2-D
Example 1:
# Reshape the array into 2D arrays
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
reshaped_arr1 = arr.reshape(2, 6)
reshaped_arr2 = arr.reshape(6, 2)
reshaped_arr3 = arr.reshape(4, 3)
reshaped_arr4 = arr.reshape(3, 4)
print("Original Array:\n", arr, "\n")
print("arr.reshape(2, 6):\n", reshaped_arr1, "\n")
print("arr.reshape(6, 2):\n", reshaped_arr2, "\n")
print("arr.reshape(4, 3):\n", reshaped_arr3, "\n")
print("arr.reshape(3, 4):\n", reshaped_arr4, "\n")
Output,
Original Array:
[ 1 2 3 4 5 6 7 8 9 10 11 12]
arr.reshape(2, 6):
[[ 1 2 3 4 5 6]
[ 7 8 9 10 11 12]]
arr.reshape(6, 2):
[[ 1 2]
[ 3 4]
[ 5 6]
[ 7 8]
[ 9 10]
[11 12]]
arr.reshape(4, 3):
[[ 1 2 3]
[ 4 5 6]
[ 7 8 9]
[10 11 12]]
arr.reshape(3, 4):
[[ 1 2 3 4]
[ 5 6 7 8]
[ 9 10 11 12]]
Reshaping to 3-D
# Reshape the array into 3D arrays
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
reshaped_arr1 = arr.reshape(1, 4, 3)
reshaped_arr2 = arr.reshape(2, 2, 3)
reshaped_arr3 = arr.reshape(3, 2, 2)
print("Original Array:\n", arr, "\n")
print("arr.reshape(1, 4, 3):\n", reshaped_arr1, "\n")
print("arr.reshape(2, 2, 3):\n", reshaped_arr2, "\n")
print("arr.reshape(3, 2, 2):\n", reshaped_arr3, "\n")
Output,
Original Array:
[ 1 2 3 4 5 6 7 8 9 10 11 12]
arr.reshape(1, 4, 3):
[[[ 1 2 3]
[ 4 5 6]
[ 7 8 9]
[10 11 12]]]
arr.reshape(2, 2, 3):
[[[ 1 2 3]
[ 4 5 6]]
[[ 7 8 9]
[10 11 12]]]
arr.reshape(3, 2, 2):
[[[ 1 2]
[ 3 4]]
[[ 5 6]
[ 7 8]]
[[ 9 10]
[11 12]]]
arr.reshape() returns a new view
- arr.reshape() function doesn’t permanently change the shape of the original array — it only returns a new view (or copy) of the array with the specified shape.
Example:
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
print("arr:\n", arr, "\n")
print("arr.reshape(4, 3):\n", arr.reshape(4, 3), "\n")
print("arr:\n", arr, "\n")
Output,
arr:
[ 1 2 3 4 5 6 7 8 9 10 11 12]
arr.reshape(4, 3):
[[ 1 2 3]
[ 4 5 6]
[ 7 8 9]
[10 11 12]]
arr:
[ 1 2 3 4 5 6 7 8 9 10 11 12]
Transposing an Array
Transposing an array means changing/swapping the axis/dimensions of an array. NumPy provides arr.T attribute(?) and np.transpose(arr) function for swapping the dimensions (depth, rows and columns)
arr.T or np.transpose(arr): Swaps the rows and columns (or permutes dimensions for N-D arrays).
Syntax: arr.T
arr-> array to be transposed.T-> attribute to transpose an array
np.transpose(arr): Syntax**: np.transpose(arr)
- arr -> array to be transposed
Transposing 2-D Array
During the transpose of a 2D NumPy array:
- The number of columns becomes the number of rows.
- The number of rows becomes the number of columns.
- Thus, a 2D array with shape
(3, 4)becomes(4, 3)after transposition.
- Thus, a 2D array with shape
Example 1:
# Transposing 2-D array
import numpy as np
arr2d = np.array([
[1, 2, 3, 4, 5, 6],
[7, 8, 9, 10, 11, 12],
[13, 14, 15, 16, 17, 18]
])
arr2d_t = arr2d.T
print("Original Array:\n", arr2d, "\n")
print("Original Array Shape:", arr2d.shape)
print()
print("Transposed Array:\n", arr2d_t, "\n")
print("Transposed Array (shape):", arr2d_t.shape)
Output,
Original Array:
[[ 1 2 3 4 5 6]
[ 7 8 9 10 11 12]
[13 14 15 16 17 18]]
Original Array Shape: (3, 6)
Transposed Array:
[[ 1 7 13]
[ 2 8 14]
[ 3 9 15]
[ 4 10 16]
[ 5 11 17]
[ 6 12 18]]
Transposed Array (shape): (6, 3)
Note: np.transpose(arr) also does the same thing as arr.T does, (i.e. transposition of array).
Example 2:
# Transposing 2-D array
import numpy as np
arr2d = np.array([
[10, 20, 30, 40],
[50, 60, 70, 80],
[90, 100, 110, 120],
[130, 140, 150, 160],
[170, 180, 190, 200]
])
arr2d_t = np.transpose(arr2d)
print("Original Array:\n", arr2d, "\n")
print("Original Array Shape:", arr2d.shape)
print()
print("Transposed Array:\n", arr_trans, "\n")
print("Transposed Array (shape):", arr_trans.shape)
Output,
Original Array:
[[ 10 20 30 40]
[ 50 60 70 80]
[ 90 100 110 120]
[130 140 150 160]
[170 180 190 200]]
Original Array Shape: (5, 4)
Transposed Array:
[[ 10 50 90 130 170]
[ 20 60 100 140 180]
[ 30 70 110 150 190]
[ 40 80 120 160 200]]
Transposed Array (shape): (4, 5)
Transposing 3-D Array
During the transpos of a 3D NumPy array:
- The number of columns becomes the number of blocks (or depth).
- The number of rows becomes the number of columns.
- The number of blocks becomes the number of rows.
- Thus, a 3D array with shape
(2, 3, 4)becomes(4, 3, 2)after transposition.
- Thus, a 3D array with shape
Explanation:
In a 3D array, the dimensions are typically represented as (depth, rows, columns).
When you apply a transpose operation, NumPy rearranges these axes — by default, it reverses them (e.g., (2, 3, 4) becomes (4, 3, 2)).
So, if you want to describe it conceptually:
Transposing a 3D array swaps the roles of blocks, rows, and columns — the structure is reoriented along different axes.
Example 1:
# Transposing a 3-D array
import numpy as np
arr3d = np.array([
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12]
]
])
arr3d_t = arr3d.T
print("Original 3-D Array:\n", arr3d, "\n")
print("Original Array Shape:", arr3d.shape)
print()
print("Transposed 3-D Array:\n", arr3d_t, "\n")
print("Transposed Array Shape:", arr3d_t.shape)
Output,
Original 3-D Array:
[[[ 1 2 3 4]
[ 5 6 7 8]
[ 9 10 11 12]]]
Original Array Shape: (1, 3, 4)
Transposed 3-D Array:
[[[ 1]
[ 5]
[ 9]]
[[ 2]
[ 6]
[10]]
[[ 3]
[ 7]
[11]]
[[ 4]
[ 8]
[12]]]
Transposed Array Shape: (4, 3, 1)
Example 2:
# Transposing a 3-D array
import numpy as np
arr3d = np.array([
[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20]
],
[
[21, 22, 23, 24, 25],
[26, 27, 28, 29, 30],
[31, 32, 33, 34, 35],
[36, 37, 38, 39, 40]
],
[
[41, 42, 43, 44, 45],
[46, 47, 48, 49, 50],
[51, 52, 53, 54, 55],
[56, 57, 58, 59, 50]
]
])
arr3d_t = np.transpose(arr3d)
print("Original 3-D Array:\n", arr3d, "\n")
print("Original Array Shape:", arr3d.shape)
print()
print("Transposed 3-D Array:\n", arr3d_t, "\n")
print("Transposed Array Shape:", arr3d_t.shape)
Output,
Original 3-D Array:
[[[ 1 2 3 4 5]
[ 6 7 8 9 10]
[11 12 13 14 15]
[16 17 18 19 20]]
[[21 22 23 24 25]
[26 27 28 29 30]
[31 32 33 34 35]
[36 37 38 39 40]]
[[41 42 43 44 45]
[46 47 48 49 50]
[51 52 53 54 55]
[56 57 58 59 50]]]
Original Array Shape: (3, 4, 5)
Transposed 3-D Array:
[[[ 1 21 41]
[ 6 26 46]
[11 31 51]
[16 36 56]]
[[ 2 22 42]
[ 7 27 47]
[12 32 52]
[17 37 57]]
[[ 3 23 43]
[ 8 28 48]
[13 33 53]
[18 38 58]]
[[ 4 24 44]
[ 9 29 49]
[14 34 54]
[19 39 59]]
[[ 5 25 45]
[10 30 50]
[15 35 55]
[20 40 50]]]
Transposed Array Shape: (5, 4, 3)