Why NumPy performance matters - See It in Action

Why NumPy Performance Matters

📖 Scenario: You work as a data analyst. You often handle large lists of numbers. Sometimes, your calculations take too long. You want to see how using NumPy can make your work faster.

🎯 Goal: You will create a list of numbers, then create a NumPy array with the same numbers. You will measure and compare the time taken to add 1 to each number in both cases. This shows why NumPy performance matters.

📋 What You'll Learn

Create a Python list called numbers with integers from 0 to 9999

Create a variable called increment and set it to 1

Use a for loop to add increment to each number in numbers and store results in result_list

Use NumPy to create an array called np_numbers from numbers

Add increment to np_numbers using NumPy vectorized operation and store in result_np

Print the time taken for list addition and NumPy addition

💡 Why This Matters

🌍 Real World

Data scientists and analysts often work with large datasets. Using NumPy speeds up calculations and saves time.

💼 Career

Knowing how to use NumPy efficiently is a key skill for data science jobs, improving performance and productivity.

1

Create the list of numbers

Create a Python list called numbers with integers from 0 to 9999 using range(10000).

NumPy

# Create the list called numbers with integers 0 to 9999
# Your code here

Need a hint?

Use list(range(10000)) to create numbers from 0 to 9999.

2

Set the increment value

Create a variable called increment and set it to 1.

NumPy

numbers = list(range(10000))
# Create a variable increment and set it to 1
# Your code here

Need a hint?

Just assign 1 to increment.

3

Add increment to each number using a for loop

Use a for loop with variable num to add increment to each number in numbers. Store the results in a new list called result_list.

NumPy

numbers = list(range(10000))
increment = 1
# Use a for loop to add increment to each number and store in result_list
# Your code here

Need a hint?

Start with an empty list result_list. Loop over numbers and append num + increment.

4

Use NumPy to add increment to numbers

Import NumPy as np. Create a NumPy array called np_numbers from numbers. Add increment to np_numbers using vectorized addition and store the result in result_np. Then print both result_list[0] and result_np[0] to check they match.

NumPy

numbers = list(range(10000))
increment = 1
result_list = []
for num in numbers:
    result_list.append(num + increment)
# Import numpy as np
# Create np_numbers from numbers
# Add increment to np_numbers and store in result_np
# Print result_list[0] and result_np[0]
# Your code here

Need a hint?

Use np.array(numbers) to create the array. Add increment directly to the array.