Profiling with line_profiler in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of using line_profiler in Python?

line_profiler helps you find out how much time each line of your Python code takes to run. It shows where your program spends most of its time, so you can make it faster.

beginner

How do you mark a function to be profiled using line_profiler?

You add the @profile decorator just above the function definition. This tells line_profiler to measure the time spent on each line inside that function.

beginner

What command do you run in the terminal to profile a Python script with line_profiler?

You run kernprof -l -v your_script.py. The -l option enables line-by-line profiling, and -v shows the results after the script finishes.

beginner

What kind of output does line_profiler provide after profiling?

It shows a table with each line of the profiled function, how many times it ran, the time spent on that line, and the percentage of total time. This helps you see slow parts clearly.

intermediate

Why is it better to profile code line-by-line instead of just timing the whole function?

Line-by-line profiling shows exactly which lines take the most time. Timing the whole function only gives total time, hiding slow spots inside. This helps you fix specific slow lines.

Which decorator do you use to mark a function for line_profiler?

A @timeit

B @measure

C @profile

D @timer

The @profile decorator is used to mark functions for profiling with line_profiler.

What command runs a Python script with line-by-line profiling?

A python -m line_profiler your_script.py

B kernprof -l -v your_script.py

C python -profile your_script.py

D line_profiler your_script.py

kernprof -l -v your_script.py runs the script with line-by-line profiling and shows results.

What does the -l option do in the kernprof command?

A Enables line-by-line profiling

B Logs output to a file

C Runs the script in low memory mode

D Limits the runtime

The -l option tells kernprof to profile the code line-by-line.

What information does line_profiler NOT provide?

A Time spent on each line

B Number of times each line runs

C Percentage of total time per line

D Memory used by each line

line_profiler does not measure memory usage, only time spent per line.

Why is line-by-line profiling useful?

A It shows which lines are slow inside a function

B It automatically fixes slow code

C It measures disk usage

D It runs code faster

Line-by-line profiling helps identify slow lines so you can improve them.

Explain how to use line_profiler to find slow parts in a Python function.

Think about marking functions and running a special command.

Describe the benefits of line-by-line profiling compared to timing the whole function.

Consider what details you get from each method.