Creating arrays

Question 1

What is NumPy?

Answer 1

NumPy (Numerical Python) is a library for numerical computations in Python, providing support for arrays, matrices, and mathematical functions.

Question 2

Why use NumPy over Python lists?

Answer 2

NumPy is faster, more memory-efficient, and provides advanced functionality like vectorized operations and mathematical functions.

Question 3

How to install NumPy?

Answer 3

Use pip install numpy.

Question 4

How to import NumPy?

Answer 4

import numpy as np.

Question 5

What is the purpose of the np alias?

Answer 5

It is a convention to make the code more concise when using NumPy functions.

Question 6

How to create a 1D array from a Python list?

Answer 6

arr = np.array([1, 2, 3, 4]).

Question 7

How to create a 2D array from a Python list?

Answer 7

matrix = np.array([[1, 2], [3, 4]]).

Question 8

How to create an array filled with zeros?

Answer 8

zeros_arr = np.zeros((3, 3)).

Question 9

How to create an array filled with ones?

Answer 9

ones_arr = np.ones((2, 4)).

Question 10

How to create an uninitialized array?

Answer 10

empty_arr = np.empty((2, 2)).

Question 11

What does arr.shape return?

Answer 11

The dimensions of the array (e.g., (3, 3) for a 3x3 array).

Question 12

What does arr.ndim return?

Answer 12

The number of dimensions (e.g., 2 for a 2D array).

Question 13

What does arr.dtype return?

Answer 13

The data type of the array elements (e.g., int32, float64).

Question 14

What does arr.size return?

Answer 14

The total number of elements in the array (e.g., 9 for a 3x3 array).

Question 15

What are common data types in NumPy?

Answer 15

int32, float64, bool, complex.

Question 16

How to specify the data type when creating an array?

Answer 16

arr = np.array([1, 2, 3], dtype=np.float64).

Question 17

How to create an array with values from 0 to 10 with a step of 2?

Answer 17

arr = np.arange(0, 10, 2) → [0, 2, 4, 6, 8].

Question 18

How to create an array with 5 evenly spaced values between 0 and 1?

Answer 18

arr = np.linspace(0, 1, 5) → [0.0, 0.25, 0.5, 0.75, 1.0].

Question 19

How to create an array of random numbers between 0 and 1?

Answer 19

rand_arr = np.random.rand(3, 3).

Question 20

How to create an identity matrix?

Answer 20

identity_matrix = np.eye(3) or identity_matrix = np.identity(3).

Question 21

How to create a diagonal matrix?

Answer 21

diag_matrix = np.diag([1, 2, 3]) → [[1, 0, 0], [0, 2, 0], [0, 0, 3]].

Question 22

What is the difference between np.eye() and np.identity()?

Answer 22

np.eye() can create non-square matrices, while np.identity() always creates square matrices.

Question 23

What is vectorization in NumPy?

Answer 23

Performing operations on entire arrays without explicit loops, making code faster and more concise.

Question 24

How to check the shape of an array?

Answer 24

Use arr.shape.

Question 25

How to check the number of dimensions of an array?

Answer 25

Use `arr.ndim`.

Question 26

How to check the data type of an array?

Answer 26

Use `arr.dtype`.

Question 27

How to check the total number of elements in an array?

Answer 27

Use `arr.size`.

Question 28

How to create a 3x3 array of zeros?

Answer 28

`zeros_arr = np.zeros((3, 3))`.

Question 29

How to create a 2x4 array of ones?

Answer 29

`ones_arr = np.ones((2, 4))`.

Question 30

How to create a 2x2 uninitialized array?

Answer 30

`empty_arr = np.empty((2, 2))`.

Question 31

How to create an array with values from 0 to 9?

Answer 31

`arr = np.arange(10)`.

Question 32

How to create an array with 10 evenly spaced values between 0 and 1?

Answer 32

`arr = np.linspace(0, 1, 10)`.

Question 33

How to create a 3x3 array of random numbers?

Answer 33

`rand_arr = np.random.rand(3, 3)`.

Question 34

How to create a 3x3 identity matrix?

Answer 34

`identity_matrix = np.eye(3)`.

Question 35

How to create a diagonal matrix with values [1, 2, 3]?

Answer 35

`diag_matrix = np.diag([1, 2, 3])`.