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.

Compito

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Manipulate arrays to change their shape and get desired elements.

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)

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Sezione 2. Capitolo 2

single

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)

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