Boost Python Performance: 24 Proven Techniques to Speed Up Code
This guide presents 24 practical methods—including timing measurements, faster data structures, loop optimizations, vectorization, and parallel processing—to dramatically accelerate Python code, each illustrated with clear before‑and‑after performance screenshots.
1. Analyzing Code Execution Time
Formula 1 – Measure single run time
Ordinary method
and quick Jupyter method
.
Formula 2 – Average time over multiple runs
Ordinary method
and quick Jupyter method
.
Formula 3 – Profile by function calls
Ordinary method
and quick Jupyter method
.
Formula 4 – Line‑by‑line analysis
Ordinary method
; quick Jupyter method
.
2. Accelerating Lookups
Formula 5 – Use set instead of list
Low‑speed method
; high‑speed method
.
Formula 6 – Use dict instead of two lists
Low‑speed method
; high‑speed method
.
3. Accelerating Loops
Formula 7 – Prefer for over while
Low‑speed method
; high‑speed method
.
Formula 8 – Avoid repeated calculations inside the loop
Low‑speed method
; high‑speed method
.
4. Accelerating Functions
Formula 9 – Replace recursion with loops
Low‑speed method
; high‑speed method
.
Formula 10 – Cache results to speed up recursion
Low‑speed method
; high‑speed method
.
Formula 11 – Use numba to JIT‑compile functions
Low‑speed method
; high‑speed method
.
5. Using Standard‑Library Functions
Formula 12 – collections.Counter for fast counting
Low‑speed method
; high‑speed method
.
Formula 13 – collections.ChainMap for fast dict merging
Low‑speed method
; high‑speed method
.
6. Numpy Vectorization
Formula 14 – Replace list with np.array
Low‑speed method
; high‑speed method
.
Formula 15 – Use np.ufunc instead of math functions
Low‑speed method
; high‑speed method
.
Formula 16 – Use np.where instead of if
Low‑speed method
; high‑speed method
.
7. Pandas Optimizations
Formula 17 – Use np.ufunc instead of applymap
Low‑speed method
; high‑speed method
.
Formula 18 – Pre‑allocate storage instead of dynamic expansion
Low‑speed method
; high‑speed method
.
Formula 19 – Use CSV instead of Excel for I/O
Low‑speed method
; high‑speed method
.
Formula 20 – Parallelize with pandarallel
Low‑speed method
; high‑speed method
.
8. Dask for Large‑Scale Computation
Formula 21 – Accelerate DataFrames with dask
Low‑speed method
; high‑speed method
.
Formula 22 – Use dask.delayed for task parallelism
Low‑speed method
; high‑speed method
.
9. Multithreading & Multiprocessing
Formula 23 – Threads for I/O‑bound tasks
Low‑speed method
; high‑speed method
.
Formula 24 – Processes for CPU‑bound tasks
Low‑speed method
; high‑speed method
.
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