line_profiler

The line_profiler tool profiles code at the line level, showing exactly which lines consume the most time. It requires installation via pip.

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Installation and Setup

pip install line_profiler

Using line_profiler

Mark functions with @profile decorator and run with kernprof:

# example.py
@profile
def process_data(n):
    result = 0
    for i in range(n):
        result += i ** 2  # Expensive operation

    total = sum(range(n))  # Another loop
    return result, total

if __name__ == "__main__":
    process_data(10000)

Run with profiler:

kernprof -l -v example.py

Output Interpretation

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
     1                                           @profile
     2                                           def process_data(n):
     3         1            2      2.0      0.0      result = 0
     4     10001          456     0.0      5.2      for i in range(n):
     5     10000         8234     0.8     94.2          result += i ** 2
     6         1          122    122.0      1.4      total = sum(range(n))
     7         1            1      1.0      0.0      return result, total

Advanced Features

# Profiling without @profile decorator
from line_profiler import LineProfiler

def expensive_function():
    data = []
    for i in range(100):
        data.append(i ** 2)
    return data

profiler = LineProfiler()
profiler.add_function(expensive_function)
profiler.enable()
expensive_function()
profiler.disable()
profiler.print_stats()

Combining with cProfile

For a complete picture, use both tools: - cProfile identifies which functions are slow - line_profiler identifies which lines within those functions are slow