Array Manipulation

Array manipulation covers the techniques used to reshape and reorganize NumPy arrays - joining multiple arrays together, splitting one array into several, and changing its structure without altering the underlying data.

Joining Arrays

import numpy as np

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

print(np.concatenate([a, b]))   # [1 2 3 4 5 6]
print(np.vstack([a, b]))        # stack vertically (rows)
print(np.hstack([a, b]))        # stack horizontally (columns)

Splitting Arrays

import numpy as np

arr = np.arange(9)   # [0 1 2 3 4 5 6 7 8]

parts = np.split(arr, 3)
print(parts)   # [array([0,1,2]), array([3,4,5]), array([6,7,8])]

Transposing an Array

import numpy as np

matrix = np.array([[1, 2, 3], [4, 5, 6]])
print(matrix.T)
# [[1 4]
#  [2 5]
#  [3 6]]

Adding and Removing Elements

import numpy as np

arr = np.array([1, 2, 3])

print(np.append(arr, [4, 5]))     # [1 2 3 4 5]
print(np.insert(arr, 1, 99))      # [1 99 2 3]
print(np.delete(arr, 0))          # [2 3]

Reshaping and stacking operations are used constantly when preparing data for Machine Learning models, which often expect input arrays in a very specific shape, such as (number_of_samples, number_of_features).

Coming Up Next