10 Amazing Python Open Source Projects
Discover 10 amazing Python open source projects that can help you automate tasks, solve complex problems, and build innovative applications. Python open source projects to learn and use, from data science to machine learning to web development.
1. Pyray
This is a rendering library project that was written in python by Rohit Pandey to help solve issues related to dependency and complex APIs.
Installation
To install the library, run (pyray was taken on pypi):
pip install raypy
Make sure you have all the requirements (requirements.txt) installed. If not, you can run:
pip install -r requirements.txt
Alternately, you can fork/download the code and run from the main folder:
python setup.py install2. Python-fire
The open-source Python project that is Python-fire will generate CLIs for you by just simply calling the Fire function on any Python code or program. The whole process is done automatically and there will be no need to either define arguments or define the running of your code through a written main function.
Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.
Installation
To install Python Fire with pip, run: pip install fire
To install Python Fire with conda, run: conda install fire -c conda-forge
To install Python Fire from source, first clone the repository and then run: python setup.py install
3. Pipenv
Pipenv is a packaging and environment tool that python programmers use to create virtual environments for projects while also updating package dependencies. Combining Pipfile, virtualenv, and pip, Pipenv gives you an advanced workflow with easy to understand commands.
Python Development Workflow for Humans.
Installation
Pipenv can be installed with Python 3.7 and above.
For most users, we recommend installing Pipenv using pip:
pip install --user pipenv
Or, if you're using Fedora:
sudo dnf install pipenv
Or, if you're using FreeBSD:
pkg install py39-pipenv
Or, if you're using Gentoo:
sudo emerge pipenv
Or, if you're using Void Linux:
sudo xbps-install -S python3-pipenv
Alternatively, some users prefer to use Pipx:
pipx install pipenv4. MicroPython
For those working with microcontrollers or environments that are constrained MicroPython offers a leeway. With its efficiently implemented and lean Python3 programming language you will be able to transfer your code easily between desktop and microcontroller systems.
This is the MicroPython project, which aims to put an implementation of Python 3.x on microcontrollers and small embedded systems. You can find the official website at micropython.org.
5. Grumpy
When the need for translation of python code into Go programs arose, the best solution came in the form of Grumpy. Grumpy is in an experimental stage and its builders aim at it being the drop-in alternative runtime for any of the pure-Python projects out there.
Grumpy is a Python to Go source code transcompiler and runtime that is intended to be a near drop-in replacement for CPython 2.7. The key difference is that it compiles Python source code to Go source code which is then compiled to native code, rather than to bytecode. This means that Grumpy has no VM. The compiled Go source code is a series of calls to the Grumpy runtime, a Go library serving a similar purpose to the Python C API (although the API is incompatible with CPython's).
6. Scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy, It features various classification, regression and clustering algorithms including support vector machines, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
User installation
If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is using pip:
pip install -U scikit-learn
or conda:
conda install -c conda-forge scikit-learn
The documentation includes more detailed installation instructions.
7. Pythorch
This machine learning library comes in handy when programming needs such as tensor computation and deep neural networking arise. Pytorch is based on the scientific computing framework and scripting language torch.
8. Home Assistant
Home Assistant is anOpen source home automation that puts local control and privacy first. Powered by a worldwide community of tinkerers and DIY enthusiasts. Perfect to run on a Raspberry Pi or a local server.
9. Whatwaf
WhatWaf is an advanced firewall detection tool who's goal is to give you the idea of "There's a WAF?". WhatWaf works by detecting a firewall on a web application, and attempting to detect a bypass (or two) for said firewall, on the specified target.
Detect and bypass web application firewalls and protection systems
10. TensorFlow
TensorFlow is an open-source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them.
Google built the underlying TensorFlow software with the C++ programming language. But in developing applications for this AI engine, coders can use either C++ or Python, the most popular language among deep learning researchers.
An Open Source Machine Learning Framework for Everyone
Conclusion
Open source projects will continue to be important and ever useful to programmers. One of them could even spark your creative programming juices.
moreover, if you ever work on one, please share it with us on the comment section, we would love to know about it and I am sure other Python developers would like to contribute when they see it.
Open Source is amazing, Create open-source projects, contribute to others, and join communities around Python and open-source. It would definitely help grow your portfolio.
Do well to leave a comment about this article.
#python #programming #development
