Understanding %mprun Output in Python 2026 with Efficient Code

Understanding %mprun Output in Python 2026 with Efficient Code

The %mprun magic from memory_profiler is one of the most powerful tools for line-by-line memory usage analysis. In 2026, understanding its output correctly is essential for identifying memory hotspots, reducing peak memory consumption, and preventing memory leaks.

TL;DR — Key Takeaways 2026

• %mprun shows memory usage and increment per line
• Focus on the Increment and % Increment columns
• High increment lines are the real memory consumers
• Use it after identifying slow functions with cProfile or %lprun

1. How to Use %mprun

%load_ext memory_profiler

@mprun
def process_large_data(n=100000):
    a = [i**2 for i in range(n)]           # Memory spike
    b = sum(a)
    c = [x / 2 for x in a]                 # Another big allocation
    del a                                  # Explicit cleanup
    return b + sum(c)

process_large_data()

2. Understanding %mprun Output

Typical Output:

Line #    Mem usage    Increment   Occurrences   Line Contents
==============================================================
     3     45.2 MiB      0.0 MiB           1       a = [i**2 for i in range(n)]
     5     92.7 MiB     47.5 MiB           1       c = [x / 2 for x in a]
     6     68.3 MiB    -24.4 MiB           1       del a

3. Best Practices for Reading %mprun Output

• Focus on lines with large positive Increment
• Watch for big temporary objects created inside loops
• Look for negative increments after explicit cleanup
• Always compare before and after optimization

Conclusion — Understanding %mprun Output in 2026

%mprun gives you precise, line-level visibility into memory consumption. In 2026, mastering this tool allows you to find and fix memory bottlenecks with surgical precision, leading to more efficient and scalable Python applications.

Next steps:

• Install memory_profiler and start using %mprun on your memory-heavy functions
• Related articles: Code Profiling for Memory Usage 2026 • Efficient Python Code 2026