Skip to main content

FastPyX 🚀

FastPyX is a high-performance C extension for Python, designed to optimize memory usage and significantly improve execution speed.

Why FastPyX?

🔥 Faster than Python's built-in list for fappend operations
📉 Uses up to 56% less memory than Python lists
⚡ Ideal for high-performance applications requiring optimized data structures

Features

  • 🚀 Written in C for extreme performance
  • 📌 Optimized FastList implementation (efficient fappend method)
  • 🔹 Supports multiple data types seamlessly
  • 🔧 Efficient memory allocation to reduce fragmentation
  • 🔄 Convert to Python list (to_list) for compatibility
  • 📊 Fast random access (fget) and optimized length retrieval (len())

Installation

To install FastPyX:

pip install fastpyx  

Usage

import fastpyx  

fl = fastpyx.FastList()

# Append values  
for i in range(10):  
    fl.fappend(i)

# Print all elements  
fl.fprint()

# Access an element  
index = 5  
value = fl.fget(index)  
print(f"Index {index}: {value}")

# Convert to Python list  
py_list = fl.to_list()  
print("Converted list:", py_list)

# Get length  
print("Length:", len(fl))

Benchmark Results

Operation FastList Python List Speedup Memory Usage (10M items)
fappend 0.56s 0.67s 🔥 17% Faster 30.5MB (FastList) vs. 69MB (Python List)
fget 0.0067s 0.0062s Slightly slower (needs optimization) -
to_list 0.8999s 0.124s Needs improvement -

✅ FastList is both faster and more memory-efficient than Python's built-in list!


📜 License

MIT License. Feel free to contribute and improve FastPyX!


🔗 Future Roadmap

✅ Optimize to_list performance
✅ Add slicing support
✅ Implement iterator support for better compatibility
✅ Advanced memory management optimizations for Django and other frameworks
✅ Additional performance improvements for lists, dictionaries, and sets
✅ More Pythonic API and integration with NumPy/Pandas

Release files for fastpyx 0.5.4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for fastpyx 0.5.4
File Size Uploaded
fastpyx-0.5.4.tar.gz 4.1 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for fastpyx 0.5.4
File Interpreter ABI Platform
fastpyx-0.5.4-cp312-cp312-win_amd64.whl CPython 3.12 CPython 3.12 Windows x86-64 Details

Total release size: 12.8 kB

Release files / fastpyx-0.5.4.tar.gz

Download URL fastpyx-0.5.4.tar.gz
Size 4.1 kB
Tags Source
SHA-256 checksum
How to use checksums
047092bfaf6d8fdeee2eb266035b33b64118ea8c99ee60d63e5c4541cfb7579a
BLAKE2b-256 checksum
How to use checksums
55788f7e13089069da231723f8eeb9c238beb9ea937bd4835fe07bc580d0c50e
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.12.3

Release files / fastpyx-0.5.4-cp312-cp312-win_amd64.whl

Download URL fastpyx-0.5.4-cp312-cp312-win_amd64.whl
Size 8.7 kB
Tags CPython 3.12 Windows x86-64
SHA-256 checksum
How to use checksums
32ab1b4b5b166ccccb2a77584d4fb458605f5f13eecfb053084aea2b5491d3f6
BLAKE2b-256 checksum
How to use checksums
82ac7860594c8b0dde55c2194c24cc311286c2961bc1b8a91d748bb368e4e66e
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.12.3

Release history Release notifications | RSS feed

This release

0.5.4 This release

2 release files

0.2.1

2 release files

0.1.1

2 release files

0.1.0

1 release file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page