###// Python tips and tutorials [back to top]

A collection of not so obvious Python stuff you should know! [IPython nb]
Python's scope resolution for variable names and the LEGB rule [IPython nb]
Key differences between Python 2.x and Python 3.x [IPython nb]
A thorough guide to SQLite database operations in Python [Markdown]
Unit testing in Python - Why we want to make it a habit [Markdown]
Installing Scientific Packages for Python3 on MacOS 10.9 Mavericks [Markdown]
Sorting CSV files using the Python csv module [IPython nb]
Using Cython with and without IPython magic [IPython nb]
Parallel processing via the multiprocessing module [IPython nb]
Entry point: Data - using sci-packages to prepare data for Machine Learning tasks and other data analyses [IPython nb]
Awesome things that you can do in IPython Notebooks (in progress) [IPython nb]
A collection of useful regular expressions [IPython nb]
Quick guide for dealing with missing numbers in NumPy [IPython nb]
A random collection of useful Python snippets [IPython nb]
Things in pandas I wish I'd had known earlier [IPython nb]

###// Python and the web [back to top]

###// Algorithms [[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]

The algorithms category has been moved to a separate GitHub repository rasbt/algorithms_in_ipython_notebooks

###// Plotting and Visualization [[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]

The matplotlib-gallery in IPython notebooks has been moved to a separate GitHub repository matplotlib-gallery

Featured articles:

###// Benchmarks [[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]

*More benchmarks can be found in the separate GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)*

Featured articles:

###// Python and "Data Science" [back to top]

The "data science"-related posts have been moved to a separate GitHub repository pattern_classification

Featured articles:

Entry Point: Data - Using Python's sci-packages to prepare data for Machine Learning tasks and other data analyses [IPython nb]
About Feature Scaling: Standardization and Min-Max-Scaling (Normalization) [IPython nb]
Principal Component Analysis (PCA) [IPython nb]
Linear Discriminant Analysis (LDA) [IPython nb]
Kernel density estimation via the Parzen-window technique [IPython nb]

###// Useful scripts and snippets [back to top]

watermark - An IPython magic extension for printing date and time stamps, version numbers, and hardware information.
Shell script For prepending Python-shebangs to .py files.
A random string generator function.
Converting large CSV files to SQLite databases using pandas.
Sparsifying a matrix by zeroing out all elements but the top k elements in a row using NumPy.

###// Links [back to top]

PyPI - the Python Package Index - The official repository for all open source Python modules and packages.
PEP 8 - The official style guide for Python code.
PEP 257 - Python's official docstring conventions; pep257 - Python style guide checker

// News

Python subreddit - My favorite resource to catch up with Python news and great Python-related articles.
Python community on Google+ - A nice and friendly community to share and discuss everything about Python.
Python Weekly - A free weekly newsletter featuring curated news, articles, new releases, jobs etc. related to Python.

// Resources for learning Python

Learn Python The Hard Way - The popular and probably most recommended resource for learning Python.
Dive Into Python / Dive Into Python 3 - A free Python book for experienced programmers.
The Hitchhiker’s Guide to Python - A free best-practice handbook for both novices and experts.
Think Python - How to Think Like a Computer Scientist - An introduction for beginners starting with basic concepts of programming.
Python Patterns - A directory of proven, reusable solutions to common programming problems.

// My favorite Python projects and packages

The IPython Notebook - An interactive computational environment for combining code execution, documentation (with Markdown and LateX support), inline plots, and rich media all in one document.
matplotlib - Python's favorite plotting library.
NumPy - A library for multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays.
SciPy - A library that provides various useful functions for numerical computing, such as modules for optimization, linear algebra, integration, interpolation, ...
pandas - High-performance, easy-to-use data structures and data analysis tools build on top of NumPy.
Cython - C-extensions for Python, an optimizing static compiler to combine Python and C code.
Numba - A just-in-time specializing compiler which compiles annotated Python and NumPy code to LLVM (through decorators)
scikit-learn - A powerful machine learning library for Python and tools for efficient data mining and analysis.

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