Slicing Arrays

Slicing extracts contiguous subsequences from arrays using start:stop:step syntax.

1D Array Slicing

Basic slicing works identically for lists and NumPy arrays.

1. Range Slice

import numpy as np

def main():
    a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
    b = np.array(a)
    print(f"{a = }")
    print(f"{b = }", end="\n\n")

    print(f"{a[1:2] = }")
    print(f"{b[1:2] = }")

if __name__ == "__main__":
    main()

Output:

a[1:2] = [1]
b[1:2] = array([1])

2. From Start

import numpy as np

def main():
    a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
    b = np.array(a)

    print(f"{a[:5] = }")
    print(f"{b[:5] = }")

if __name__ == "__main__":
    main()

Output:

a[:5] = [0, 1, 2, 3, 4]
b[:5] = array([0, 1, 2, 3, 4])

3. To End

import numpy as np

def main():
    a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
    b = np.array(a)

    print(f"{a[1:] = }")
    print(f"{b[1:] = }")

if __name__ == "__main__":
    main()

Output:

a[1:] = [1, 2, 3, 4, 5, 6, 7, 8, 9]
b[1:] = array([1, 2, 3, 4, 5, 6, 7, 8, 9])

Step Slicing

The third parameter specifies the step size.

1. Step from Start

import numpy as np

def main():
    a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
    b = np.array(a)

    print(f"{a[:5:2] = }")
    print(f"{b[:5:2] = }")

if __name__ == "__main__":
    main()

Output:

a[:5:2] = [0, 2, 4]
b[:5:2] = array([0, 2, 4])

2. Step to End

import numpy as np

def main():
    a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
    b = np.array(a)

    print(f"{a[1::2] = }")
    print(f"{b[1::2] = }")

if __name__ == "__main__":
    main()

Output:

a[1::2] = [1, 3, 5, 7, 9]
b[1::2] = array([1, 3, 5, 7, 9])

3. Reverse Array

import numpy as np

def main():
    a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
    b = np.array(a)

    print(f"{a[::-1] = }")
    print(f"{b[::-1] = }")

if __name__ == "__main__":
    main()

Output:

a[::-1] = [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
b[::-1] = array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])

2D Array Slicing

Slicing 2D arrays operates on rows by default.

1. Row Range

import numpy as np

def main():
    a = [[0, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, 6]]
    b = np.array(a)

    print("a[:3]")
    print(a[:3], end="\n\n")
    print("b[:3]")
    print(b[:3])

if __name__ == "__main__":
    main()

2. Row with Step

import numpy as np

def main():
    a = [[0, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, 6]]
    b = np.array(a)

    print("a[:4:2]")
    print(a[:4:2], end="\n\n")
    print("b[:4:2]")
    print(b[:4:2])

if __name__ == "__main__":
    main()

3. Reverse Rows

import numpy as np

def main():
    a = [[0, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, 6]]
    b = np.array(a)

    print("a[::-1]")
    print(a[::-1], end="\n\n")
    print("b[::-1]")
    print(b[::-1])

if __name__ == "__main__":
    main()

Multi-Axis Slicing

NumPy allows simultaneous slicing across multiple axes.

1. Row Slice Only

import numpy as np

def main():
    a = [[0, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, 6]]
    b = np.array(a)

    try:
        print(a[1, :])
    except TypeError as e:
        print(f"List error: {e}")
    print("b[1, :]")
    print(b[1, :])

if __name__ == "__main__":
    main()

Output:

List error: list indices must be integers or slices, not tuple
b[1, :]
[1 2 3]

2. Column Slice Only

import numpy as np

def main():
    a = [[0, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, 6]]
    b = np.array(a)

    try:
        print(a[:, 1])
    except TypeError as e:
        print(f"List error: {e}")
    print("b[:, 1]")
    print(b[:, 1])

if __name__ == "__main__":
    main()

Output:

List error: list indices must be integers or slices, not tuple
b[:, 1]
[1 2 3 4 5]

3. Both Axes

import numpy as np

mat = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(mat[0:2, 1:])

Output:

[[2 3]
 [5 6]]