Python Minifier — Compress Code Instantly
Transform your Python source code into its most compact representation. Remove comments, docstrings, whitespace, and optimize for AWS Lambda, deployments, and distribution.
"""Calculate fibonacci number"""
# Base case
if n <= 1:
return n
return fibonacci(n - 1) + fibonacci(n - 2)
if n<=1:return n
return fibonacci(n-1)+fibonacci(n-2)
Powerful Minification Techniques
Our Python minifier applies multiple optimization strategies to compress your code while maintaining full functionality.
Comment Removal
Strips all single-line (#) comments from your code. Preserves important shebang lines (#!/usr/bin/env python) and encoding declarations.
Docstring Elimination
Removes all triple-quoted docstrings ("""...""" and '''...''') from modules, classes, and functions to significantly reduce size.
Whitespace Optimization
Eliminates unnecessary spaces, tabs, blank lines, and trailing whitespace while preserving Python's indentation syntax.
Statement Joining
Combines short statements on single lines where syntactically valid. For example: if x: return y becomes one compact line.
Indent Minimization
Reduces indentation to single spaces instead of 4 spaces per level. Python only requires consistent indentation, not specific sizes.
Beautify Mode
Reverse mode that formats code according to PEP 8 guidelines with proper indentation, spacing, and line lengths for readability.
Python Performance Optimization Tips
Minification reduces file size, but these techniques improve actual runtime performance.
Use Built-in Functions
Python's built-in functions are implemented in C and run much faster than equivalent Python code.
total = 0
for x in data: total += x
# Fast - built-in
total = sum(data)
List Comprehensions
List comprehensions are faster than traditional loops with append() because they're optimized at the C level.
result = []
for x in items:
result.append(x * 2)
# Fast - comprehension
result = [x * 2 for x in items]
Use Sets for Lookups
Set membership testing is O(1) constant time vs O(n) linear time for lists. Huge speedup for large collections.
if item in my_list: ...
# Fast - O(1) set lookup
my_set = set(my_list)
if item in my_set: ...
Cache with lru_cache
Memoize expensive function results to avoid redundant computation. Perfect for recursive algorithms.
@lru_cache(maxsize=128)
def fib(n):
if n < 2: return n
return fib(n-1) + fib(n-2)
Generators for Large Data
Generators yield items one at a time, using constant memory regardless of data size. Essential for big datasets.
squares = [x**2 for x in range(10**6)]
# Memory efficient - yields
squares = (x**2 for x in range(10**6))
Local Variable Access
Local variables are faster to access than global or class variables. Assign frequently used values locally.
for item in data:
result.append(item)
# Fast - local reference
append = result.append
for item in data:
append(item)
When to Use Python Minifier
Python minification is valuable for deployment optimization, distribution, and specific use cases where file size matters.
AWS Lambda Functions
Lambda has a 4KB limit for inline code in CloudFormation. Minification makes the difference between inline and S3 deployment.
PyPI Package Distribution
Smaller packages mean faster pip installs and reduced bandwidth for users downloading your library.
Embedded Systems & IoT
MicroPython on microcontrollers has severe memory constraints. Every byte counts on ESP32 and Raspberry Pi Pico.
Docker & Container Images
Smaller Python files mean smaller Docker layers, faster builds, and quicker container startup times.
Basic Code Obfuscation
While not security, removing comments and docstrings makes code harder for casual observers to understand.
Code Golf & Challenges
Competitive programming challenges often have character limits. Minification helps squeeze solutions under the threshold.
Jupyter Notebook Cleanup
Clean up Python cells before sharing notebooks by removing unnecessary comments and formatting inconsistencies.
CI/CD Build Pipelines
Faster deployments with smaller artifacts. Integrate minification into your build process for optimized production code.
Ready to Compress Your Python Code?
Transform your Python source into its most compact representation. Free, instant, and runs entirely in your browser — your code never leaves your device.
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