Learn Iterating

Iterating over NumPy arrays can be done in various ways, depending on the specific requirements of your task.

Using Loops : You can iterate over the elements of a NumPy array using loops, such as for loops. However, this method is generally less efficient compared to vectorized operations that NumPy provides.

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
for elem in arr:
print(elem)

Using nditer: : The nditer function provides a way to iterate over elements of arrays in a flexible manner. It's especially useful when dealing with multi-dimensional arrays or when you need more control over the iteration process.

import numpy as np
arr = np.array([[1, 2], [3, 4]])
for elem in np.nditer(arr):
print(elem)

Using Broadcasting: Broadcasting allows you to perform element-wise operations between arrays of different shapes. Instead of explicitly iterating over elements, you can often achieve the desired result using broadcasting, which is more efficient. Here's an example:

import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
result = arr1 + arr2
print(result)

Using vectorized operations:NumPy provides many built-in functions and operations that work efficiently on entire arrays without the need for explicit iteration. These operations are optimized and typically faster than using loops.

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
# Calculate the square of each element
squared_arr = np.square(arr)
print(squared_arr)

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