Programming Part 3

Question 1

What is NumPy used for?

Answer 1

Efficient numerical computing with arrays.

Question 2

What is the main data structure in NumPy?

Answer 2

The ndarray (n-dimensional array).

Question 3

How do you import NumPy?

Answer 3

import numpy as np

Question 4

How do you create a NumPy array from a list?

Answer 4

np.array([1, 2, 3])

Question 5

How do you create an array of zeros?

Answer 5

np.zeros((rows, cols))

Question 6

How do you get the shape of a NumPy array?

Answer 6

Using array.shape

Question 7

How do you reshape an array?

Answer 7

Using array.reshape(new_shape)

Question 8

What does np.arange() do?

Answer 8

Creates an array with regularly spaced values.

Question 9

What does np.linspace() do?

Answer 9

Generates evenly spaced values over a specified range.

Question 10

What is broadcasting in NumPy?

Answer 10

Automatic expansion of arrays with different shapes for element-wise operations.

Question 11

How do you slice a NumPy array?

Answer 11

Using array[start:stop:step]

Question 12

How do you index a 2D array?

Answer 12

Using array[row, column]

Question 13

What is boolean indexing in NumPy?

Answer 13

Using a boolean array to filter values.

Question 14

How do you access a single row in a 2D array?

Answer 14

Using array[row_index, :]

Question 15

How do you access a single column in a 2D array?

Answer 15

Using array[:, col_index]

Question 16

How do you compute the sum of an array?

Answer 16

Using np.sum(array)

Question 17

How do you compute the mean of an array?

Answer 17

Using np.mean(array)

Question 18

How do you compute the standard deviation?

Answer 18

Using np.std(array)

Question 19

How do you find the max value in an array?

Answer 19

Using np.max(array)

Question 20

How do you compute element-wise multiplication?

Answer 20

Using array1 * array2

Question 21

How do you generate random numbers in NumPy?

Answer 21

Using np.random

Question 22

How do you set the random seed in NumPy?

Answer 22

np.random.seed(42)

Question 23

Why is NumPy faster than regular Python lists?

Answer 23

Because it uses fixed-typed, contiguous memory and vectorized operations.

Question 24

What is vectorization?

Answer 24

Replacing explicit loops with array operations.

Question 25

What does np.dot() do?

Answer 25

Computes the dot product of two arrays.