Awesome

• // Python tips and tutorials
• // Python and the web
• // Algorithms
• // Plotting and Visualization
• // Benchmarks
• // Python and "Data Science"
• // Useful scripts and snippets
• // Other
• // Links

<b id="-python-tips-and-tutorials">Python tips and tutorials</b> <a href="#top">[back to top]</a>

• 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]

<b id="-python-and-the-web">Python and the web</b> <a href="#top">[back to top]</a>

• Creating internal links in IPython Notebooks and Markdown docs [IPython nb]

• Converting Markdown to HTML and adding Python syntax highlighting [Markdown]

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

• Sorting Algorithms [Collection of IPython Notebooks

• Linear regression via the least squares fit method [IPython nb]

• Dixon's Q test to identify outliers for small sample sizes [IPython nb]

• Counting points inside a hypercube [IPython nb]

• Singly Linked List [ IPython nbviewer ]

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

Featured articles:

• Preparing Plots for Publication [IPython nb]

• Simple tricks to speed up the sum calculation in pandas [IPython nb]

Featured articles:

• (C)Python compilers - Cython vs. Numba vs. Parakeet [IPython nb]

• Just-in-time compilers for NumPy array expressions [IPython nb]

• Cython - Bridging the gap between Python and Fortran [IPython nb]

• Parallel processing via the multiprocessing module [IPython nb]

• Vectorizing a classic for-loop in NumPy [IPython nb]

<b id="-python-and-data-science">Python and "Data Science"</b> <a href="#top">[back to top]</a>

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]

<b id="-useful-scripts-and-snippets">Useful scripts and snippets</b> <a href="#top">[back to top]</a>

• 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.

<b id="-other">Other</b> <a href="#top">[back to top]</a>

• Python book reviews
• Happy Mother's Day Plot

<b id="-links">Links</b> <a href="#top">[back to top]</a>

• 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

• 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.

• A Byte of Python - a free book on programming using the Python language.

• Python Patterns - A directory of proven, reusable solutions to common programming problems.

• Intro to Computer Science - Build a Search Engine & a Social Network - A great, free course for learning Python if you haven't programmed before.

// 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.