NumPy Exercises

What You'll Learn

In this activity, you'll practice:

• Creating NumPy arrays using different methods
• Accessing and slicing array elements
• Filtering arrays based on conditions
• Performing mathematical operations on arrays
• Working with 2D arrays and random numbers
• Understanding array properties

Estimated Time: 45-60 minutes

:computer: Getting Started

1. Click the link here
2. Make a copy of the Colab Notebook into your Google Drive
3. Import the NumPy package with the short name np:
   python

   import numpy as np
   

:bulb: Tip :bulb: Pro tip: Always import NumPy at the beginning of your notebook to avoid errors later!

Question One: Working with Basic Arrays

Time: 10 minutes

Write the program using variables named array1 and print() to show the outcome for each step.

Step One: Create an array

Create a NumPy array from 11 to 20 using array() in array1. Print array1 as shown below.

:bulb: Tip :bulb: Remember to use a list inside np.array() like this: np.array([11, 12, 13, ...])

Expected output:

[11 12 13 14 15 16 17 18 19 20]

Step 2: Access a single element

Access number 18 in array1.

Expected output:

18

Step 3: Slice the array

Access numbers from 13 to 17 in array1.

:bulb: Tip :bulb: Remember array indexing starts at 0! Count carefully to find the right indices.

Expected output:

[13 14 15 16 17]

Step 4: Filter the array

Filter the data to show only values greater than 15 in array1.

Expected output:

[16 17 18 19 20]

Step 5: Perform array multiplication

Multiply array1 with 10.

Expected output:

[110 120 130 140 150 160 170 180 190 200]

Question 2: Using arange() Function

Time: 10 minutes

Write the program using variables named array2 and print() to show the outcome for each step.

Step One: Create an array with arange()

Create a NumPy array from 0 to 9 using arange() in array2. Print array2 as shown below.

:bulb: Tip :bulb: The arange() function is like Python's range() but creates a NumPy array!

Expected output:

[0 1 2 3 4 5 6 7 8 9]

Step 2: Access the last element

Access number 9 in array2.

Expected output:

9

Step 3: Slice multiple elements

Access numbers from 5 to 8 in array2.

Expected output:

[5 6 7 8]

Step 4: Perform array subtraction

Subtract array2 away from array1.

:bulb: Tip :bulb: NumPy arrays must have the same shape for element-wise operations!

Expected output:

[11 11 11 11 11 11 11 11 11 11]

Question 3: Working with Random Numbers

Time: 10 minutes

Write the program using the built-in function random from package numpy and print() to show the outcome for each step.

Step One: Import random module

From the package numpy, import the built-in function random:

from numpy import random

Step 2: Generate a random float

Generate a random real number from 0 to 1.

:information_source: Info :pushpin: Note: Your answer will be different because it's random! That's expected.

Expected output:

0.9973521728863931

Step 3: Generate a random integer

Generate a random integer from 0 to 20.

:bulb: Tip :bulb: Use random.randint() for generating random integers!

Expected output:

15

Question 4: Working with 2D Arrays

Time: 15 minutes

Write the program using a variable named array2D and print() to show the outcome for each step.

Step One: Create a 2D array

Create a 3x3 2D NumPy array with all ones using ones() in array2D.

:bulb: Tip :bulb: Use np.ones((3, 3)) - notice the double parentheses for the shape!

Expected output:

[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]

Step 2: Check array properties

Check the array properties: dtype, ndim, shape, and size

:information_source: Info :pushpin: These properties tell you:

• dtype: Data type of elements
• ndim: Number of dimensions
• shape: Size of each dimension
• size: Total number of elements

Expected output:

float64
2
(3, 3)
9

Step 3: Modify the center element

Try to make the output as follows by changing the number in the center of array2D to 2.

:bulb: Tip :bulb: For a 3x3 array, the center element is at position [1, 1]!

Expected output:

[[1. 1. 1.]
 [1. 2. 1.]
 [1. 1. 1.]]

:memo: Submission Instructions

:warning: Warning Important: Before submitting, make sure you have:

1. :white_check_mark: Completed all 4 questions
2. :white_check_mark: Tested your code and verified the outputs match the expected results
3. :white_check_mark: Saved your Colab notebook
4. :white_check_mark: Made note of any questions or challenges you encountered

:link: Submit your exercise here

:wrench: Troubleshooting

Common Issues:

• "NameError: name 'np' is not defined" -> Make sure you imported NumPy at the beginning
• "IndexError" -> Check your array indices (remember: Python uses 0-based indexing)
• "ValueError: operands could not be broadcast" -> Ensure arrays have compatible shapes for operations
• Random numbers don't match expected output -> That's normal! Random numbers are different each time

:bulb: Tip :speech_balloon: Need help? Ask your instructor or discuss with your classmates in the forum!