Challenge 2: Array Manipulation
NumPy offers a comprehensive set of tools for modifying and reshaping arrays. The key benefits include:
- Diverse Capabilities: From reshaping to splitting, NumPy offers a wide range of functions to modify arrays as per requirements.
- In-place Operations: Many NumPy functions can perform modifications in-place, meaning without creating a copy of the array, ensuring efficient memory usage.
- Intuitive Syntax: Array manipulations often have a very readable and straightforward syntax, making the code self-explanatory.
The ability to seamlessly manipulate arrays is a cornerstone of many numerical and data science tasks, making these functions indispensable.
Завдання
Swipe to start coding
Manipulate arrays to change their shape and get desired elements.
- Extract the diagonal elements.
- Retrieve the elements in the second row.
- Extract the 2nd and 3rd rows and change their shape to (5, 2).
Рішення
import numpy as np
# Given the following array
arr = np.arange(0, 25).reshape(5, 5)
print(arr, '\n')
# 1. Extract the diagonal elements.
diagonal_elements = np.diag(arr)
print(diagonal_elements)
# 2. Retrieve the elements in the second row.
second_row = arr[1]
print(second_row)
# 3. Extract the 2nd and 3rd rows and change their shape to (5, 2).
reshaped_rows = arr[1:3].reshape(5, 2)
print(reshaped_rows)
Все було зрозуміло?
Дякуємо за ваш відгук!
Секція 2. Розділ 2
import numpy as np
# Given the following array
arr = np.arange(0, 25).reshape(5, 5)
# 1. Extract the diagonal elements.
diagonal_elements = ___
print(diagonal_elements)
# 2. Retrieve the elements in the second row.
second_row = ___
print(second_row)
# 3. Extract the 2nd and 3rd rows and change their shape to (5, 2).
reshaped_rows = ___
print(reshaped_rows)
