Numpy - Indexing, Slicing and Iterating

Photo by Nathan Dumlao on Unsplash

Indexing, slicing, and iterating over arrays are common operations in NumPy, and they work similarly to how they work in Python's built-in list and tuple objects.

To index an array, you can use the square brackets [] and specify the indices of the elements that you want to access. For example:

import numpy as np

# Create a one-dimensional array
x = np.array([1, 2, 3, 4])

# Access the second element of the array
y = x[1]

# Access the second and third elements of the array
z = x[1:3]

In NumPy, you can also use negative indices to index elements from the end of the array. For example:

# Access the last element of the array
y = x[-1]

# Access the last two elements of the array
z = x[-2:]

To slice an array, you can use the colon : operator to specify a range of indices to include in the slice. For example:

# Create a two-dimensional array
x = np.array([[1, 2, 3], [4, 5, 6]])

# Get a slice of the second row of the array
y = x[1,:]

# Get a slice of the second column of the array
z = x[:,1]

# Get a slice of the middle two rows and columns of the array
w = x[1:,1:]

To iterate over the elements of an array, you can use a for loop. For example:

# Iterate over the elements of a one-dimensional array
for element in x:
    print(element)

# Iterate over the elements of a two-dimensional array
for row in x:
    for element in row:
        print(element)