Array Reshaping

Reshaping allows you to change the shape of an array without altering the data. Below is an example demonstrating how to reshape a NumPy array:

import numpy as np
arr = np.arange(12)  # Create an array with 12 elements
reshaped_arr = arr.reshape(3, 4)  # Reshape it into a 3x4 matrix

print("Original Array:", arr)
print("Reshaped Array:\n", reshaped_arr)
    

auto_reshaped = arr.reshape(4, -1)  # NumPy automatically calculates the missing dimension
print(auto_reshaped.shape)  # Output: (4, 3)
    

Array Iteration

Iterating over arrays is a fundamental operation in NumPy. You can iterate over 1D and 2D arrays efficiently:

1. Iterating Over a 1D Array

arr_1d = np.array([10, 20, 30, 40])
for num in arr_1d:
    print(num)
    

2. Iterating Over a 2D Array

You can iterate over a 2D array row by row or element by element using np.nditer():

arr_2d = np.array([[1, 2, 3], [4, 5, 6]])

# Iterating row by row
for row in arr_2d:
    print("Row:", row)

# Iterating element by element
for element in np.nditer(arr_2d):
    print("Element:", element)