A collection of design patterns/idioms in Python. Contribute to faif/python-patterns development by creating an account on GitHub.

These Python libraries make it easy to scratch that personal project itch.

Natural language processing (NLP) is an exciting field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages.

“Don’t think of the overwhelming majority of the impossible.”
“Grew up your bliss and the world.”
“what we would end create, creates the ground and you are the one to warm it”
“look and give up in miracles”

All the quotes above have been generated by a computer, using a program that consists of less than 20 lines of python code.

Although Python is an easy to learn and powerful programming language as it is known in common parlance, there is nevertheless need of a good introduction and tutorial on the Python language.

Why yet another documentation and tutorial on Python? Aren't there enough websites with tutorials and books dealing with Python? Isn't there already everything said about this great programming language?

These were the questions which came to our mind, when we started this website in June 2010. Yes, there are lots of tutorials and introductions, but we wanted to present a different approach, with other - more interesting - examples, better explanatory diagrams and so on. We had a lot to build on, above all the longstanding experience of Bernd Klein as a computer scientist and Python lecturer. Actually, this online course is based on the material from the classroom training courses of Bodenseo and his book on Python.

A short course that will teach you how to write Python scripts that can take advantage of the processing power of multicore processors and large compute clusters. While this course is based on Python, the core ideas of functional programming and parallel functional programming are applicable to a wide range of languages.

Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.

• is a simple way to create a CLI in Python.
• is a helpful tool for developing and debugging Python code.
• helps with exploring existing code or turning other people's code into a CLI.
• makes transitioning between Bash and Python easier.
• makes using a Python REPL easier by setting up the REPL with the modules and variables you'll need already imported and created.

Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.

• is a simple way to create a CLI in Python.
• is a helpful tool for developing and debugging Python code.
• helps with exploring existing code or turning other people's code into a CLI.
• makes transitioning between Bash and Python easier.
• makes using a Python REPL easier by setting up the REPL with the modules and variables you'll need already imported and created.

What are wheels?

Wheels are the new standard of Python distribution and are intended to replace eggs. Support is offered in pip >= 1.4 and setuptools >= 0.8.

Advantages of wheels

• Faster installation for pure Python and native C extension packages.
• Avoids arbitrary code execution for installation. (Avoids setup.py)
• Installation of a C extension does not require a compiler on Windows or macOS.
• Allows better caching for testing and continuous integration.
• Creates .pyc files as part of installation to ensure they match the Python interpreter used.
• More consistent installs across platforms and machines

Purpose of this Collection

This collection is a presentation of fairly small Python programs. They are aimed at intermediate programmers; people who have studied Python and are fairly comfortable with basic recursion and object oriented techniques. Most programs are very short, not more than a couple of pages and all projects are accompanied with a write-up.

I have found Python to be an excellent language to express algorithms clearly. Some of the ideas here originated in other programs in other languages. But in most cases I developed code from scratch from just an outline of an idea. But one, Lisp in Python was almost a translation exercise from John McCarthy's original Evalquote in Lisp.

From many years of programming these are some of my favorite programs. I hope you enjoy them as much as I do. I look forward to hearing from readers, especially those with suggestions for improvements, ideas for new projects, or people who are doing similar things. You can email me at mailme.html

Many thanks to Paul Carduner and Jeff Elkner for their work on this page, especially for Paul's graphic of Psyltherin (apologies to Harry Potter) and to the teams behind reStructured text and Sphinx to which the web pages in this collection have been adapted.

Introduction to sparse matrices.

A sparse matrix is just a matrix that is mostly zero. Typically, when people talk about sparse matrices in numerical computations, they mean matrices that are mostly zero.

Machine learning methods can be used for classification and forecasting on time series problems. Before exploring machine learning methods for time series, it is a good idea to ensure you have exhausted classical linear time series forecasting methods. Classical time series forecasting methods may be focused on linear relationships, nevertheless, they are sophisticated and perform …

Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.

Twitter's Anomaly Detection in Pure Python

In this article, we offer several different projects, resources, and tutorials that you can use to start building things with Python.

Based on marisa-trie C++ library.

Simple Python Motion Jpeg (mjpeg server) from webcam. Using: OpenCV,BaseHTTPServer

Most Python developers have written at least one tool, script, library or framework that others would find useful. My goal in this article is to make the process of open-sourcing existing Python code as clear and painless as possible. And I don't simply mean, "create GitHub repo, git push, post on Reddit, and call it a day." By the end of this article, you'll be able to take an existing code base and transform it into an open source project that encourages both use and contribution.

While every project is different, there are some parts of the process of open-sourcing existing code that are common to all Python projects. In the vein of another popular series I've written, "Starting a Django Project The Right Way," I'll outline the steps I've found to be necessary when open-sourcing a Python project.