Profiling NumPy operations - Mini Project: Build & Apply

Profiling NumPy operations

📖 Scenario: You are working with numerical data using NumPy. You want to understand how long certain NumPy operations take to run. This helps you find which parts of your code are slow and need improvement.

🎯 Goal: Build a simple program that creates a NumPy array, sets a threshold value, measures the time taken to perform an operation on the array, and prints the elapsed time.

📋 What You'll Learn

Create a NumPy array with specific values

Set a threshold variable

Use time.perf_counter() to measure elapsed time of a NumPy operation

Print the elapsed time in seconds

💡 Why This Matters

🌍 Real World

Profiling helps data scientists find slow parts of their code so they can make it faster and more efficient.

💼 Career

Knowing how to measure and improve code performance is important for data scientists working with large datasets and complex calculations.

1

Create a NumPy array

Import NumPy as np and create a NumPy array called data with these exact values: [10, 20, 30, 40, 50].

NumPy

import numpy as np
# Create the NumPy array called data with values [10, 20, 30, 40, 50]
# Your code here

Need a hint?

Use np.array() to create the array with the given list of numbers.

2

Set a threshold value

Create a variable called threshold and set it to the integer 25.

NumPy

import numpy as np
data = np.array([10, 20, 30, 40, 50])
# Set the variable threshold to 25
# Your code here

Need a hint?

Just assign the number 25 to the variable named threshold.

3

Measure time for a NumPy operation

Import time. Use time.perf_counter() to record the start time in a variable called start_time. Then create a new NumPy array called filtered that contains only the values from data greater than threshold. After that, record the end time in a variable called end_time using time.perf_counter().

NumPy

import numpy as np
data = np.array([10, 20, 30, 40, 50])
threshold = 25
# Import time and record start_time using time.perf_counter()
# Create filtered array with values from data greater than threshold
# Record end_time using time.perf_counter()
# Your code here

Need a hint?

Use boolean indexing data[data > threshold] to filter the array.

4

Print the elapsed time

Calculate the elapsed time by subtracting start_time from end_time and store it in a variable called elapsed. Then print the text Elapsed time: followed by the elapsed time rounded to 6 decimal places and the string seconds.

NumPy

import numpy as np
import time
data = np.array([10, 20, 30, 40, 50])
threshold = 25
start_time = time.perf_counter()
filtered = data[data > threshold]
end_time = time.perf_counter()
# Calculate elapsed time and print it with the text 'Elapsed time:' and 'seconds'
# Your code here

Need a hint?

Use an f-string to format the elapsed time with 6 decimal places.