Profiling with line_profiler in Data Analysis Python - Mini Project: Build & Apply

Profiling Python Code with line_profiler

📖 Scenario: You are working on a small Python function that processes a list of numbers. You want to understand which parts of your code take the most time so you can make it faster.

🎯 Goal: Learn how to use line_profiler to find out which lines in a Python function take the most time to run.

📋 What You'll Learn

Create a simple Python function that processes a list of numbers

Set up a variable to hold the list of numbers

Use line_profiler to profile the function line by line

Print the profiling results to see which lines take the most time

💡 Why This Matters

🌍 Real World

Profiling helps developers find slow parts in their code so they can improve performance, which is important in data analysis and software development.

💼 Career

Knowing how to profile code is a valuable skill for data scientists and developers to optimize programs and make them run faster.

1

Create a Python function to process numbers

Create a function called process_numbers that takes a list called numbers and returns a new list with each number squared.

Data Analysis Python

# Your code here

Hint

Use a for loop to go through each number and append its square to a new list.

2

Create a list of numbers to process

Create a variable called numbers and set it to the list [1, 2, 3, 4, 5].

Data Analysis Python

def process_numbers(numbers):
    result = []
    for number in numbers:
        result.append(number ** 2)
    return result

# Your code here

Hint

Just assign the list [1, 2, 3, 4, 5] to the variable numbers.

3

Use line_profiler to profile the function

Import LineProfiler from line_profiler. Create a LineProfiler object called lp. Add the function process_numbers to lp. Run lp.runcall(process_numbers, numbers) and save the result in a variable called result.

Data Analysis Python

def process_numbers(numbers):
    result = []
    for number in numbers:
        result.append(number ** 2)
    return result

numbers = [1, 2, 3, 4, 5]

# Your code here

Hint

Use LineProfiler() to create the profiler, add your function, then run it with runcall.

4

Print the profiling results

Use lp.print_stats() to print the line-by-line profiling results.

Data Analysis Python

from line_profiler import LineProfiler

def process_numbers(numbers):
    result = []
    for number in numbers:
        result.append(number ** 2)
    return result

numbers = [1, 2, 3, 4, 5]

lp = LineProfiler()
lp.add_function(process_numbers)
result = lp.runcall(process_numbers, numbers)

# Your code here

Hint

Call lp.print_stats() to see the profiling output in the console.