Profiling with line_profiler in Data Analysis Python - Step-by-Step Execution

Concept Flow - Profiling with line_profiler

Write Python function

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Add @profile decorator

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Run script with line_profiler

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line_profiler measures time per line

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Output shows time spent on each line

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Analyze output to find slow lines

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Optimize slow lines

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Repeat profiling to check improvements

This flow shows how to add profiling to a Python function, run line_profiler to measure time per line, and use the results to optimize code.

Execution Sample

Data Analysis Python

from time import sleep

@profile
def slow_function():
    sleep(0.1)
    total = 0
    for i in range(3):
        total += i
    return total

This code defines a simple function with a sleep and a loop, decorated for line_profiler to measure time spent on each line.

Execution Table

Step	Line Number	Code Line	Time Spent (s)	Hits	Cumulative Time (s)
1	3	@profile	0.0000	1	0.0000
2	4	def slow_function():	0.0000	1	0.0000
3	5	sleep(0.1)	0.1001	1	0.1001
4	6	total = 0	0.0000	1	0.1001
5	7	for i in range(3):	0.0000	1	0.1001
6	8	total += i	0.0000	3	0.1001
7	9	return total	0.0000	1	0.1001
8	-	End of function	-	-	-

💡 Function completes after line 9; total time mostly spent on sleep(0.1) line.

Variable Tracker

Variable	Start	After sleep(0.1)	After loop iteration 1	After loop iteration 2	After loop iteration 3	Final
total	undefined	0	0	1	3	3
i	undefined	undefined	0	1	2	2

Key Moments - 3 Insights

Why does the sleep(0.1) line take most of the time?

Because sleep pauses the program for 0.1 seconds, line_profiler shows this as the biggest time consumer (see execution_table row 3).

Why does 'total += i' show zero time even though it runs 3 times?

Each addition is very fast, so total time is too small to show; line_profiler sums time but it rounds to zero here (see execution_table row 6).

What does 'Hits' mean in the profiling output?

'Hits' counts how many times a line runs; for example, the loop line runs once, but the addition line runs 3 times (see execution_table rows 5 and 6).

Visual Quiz - 3 Questions

Look at the execution table, which line uses the most time?

A Line 6: total = 0

B Line 5: sleep(0.1)

C Line 8: total += i

D Line 9: return total

💡 Hint

Check the 'Time Spent (s)' column in execution_table row 3.

At which step does the variable 'total' first get a value?

A After loop iteration 1

B After loop iteration 3

C After sleep(0.1)

D At start

💡 Hint

See variable_tracker row for 'total' after 'After sleep(0.1)'.

If the loop ran 5 times instead of 3, what changes in the execution table?

A 'Hits' for line 8 would be 5 instead of 3

B 'Time Spent' for sleep(0.1) would increase

C 'Hits' for line 7 would be 5

D No changes in the table

💡 Hint

Look at how 'Hits' counts executions for line 8 in execution_table row 6.

Concept Snapshot

Profiling with line_profiler:
- Add @profile decorator to functions
- Run script with 'kernprof -l script.py'
- Use 'python -m line_profiler script.py.lprof' to see line times
- Shows time spent per line and hit counts
- Helps find slow lines to optimize

Full Transcript

Profiling with line_profiler helps find which lines in a Python function take the most time. You add @profile above the function, run the script with kernprof, then view detailed timing per line. The example function sleeps for 0.1 seconds and sums numbers in a loop. The profiler shows most time is spent on sleep. Variables like 'total' change as the loop runs. Hits count how many times each line runs. This helps focus optimization on slow parts.