// Python tips and tutorials

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  • 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]


// Python and the web

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

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


// Algorithms

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

  • Sorting Algorithms [IPython nb]

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

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

  • Sequential Selection Algorithms [IPython nb]

  • Counting points inside a hypercube [IPython nb]


// Benchmarks

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The benchmark category was moved to a separate GitHub repository One-Python-benchmark-per-day

  • 1 - Reversing strings [IPython nb]
  • 2 - Calculating sample means [IPython nb]
  • 3 - 6 different ways to count elements using a dict [IPython nb]
  • 4 - Python vs. Cython vs. Numba [IPython nb]
  • 4.2 - (C)Python compilers - Cython vs. Numba vs. Parakeet [IPython nb]
  • 5 - Comparing 9 ways for flattening lists of sublists [IPython nb]
  • 6 - Determining if a string is a number [IPython nb]
  • 7 - Speeding up NumPy array expressions with Numexpr [IPython nb]
  • 7.2 - Just-in-time compilers for NumPy array expressions [IPython nb]
  • 8 - Calculating square roots and exponents [IPython nb]
  • 9 - The most Pythonic way to check if a string ends with a particular substring [IPython nb]
  • 10 - Cython - Bridging the gap between Python and Fortran [IPython nb]
  • 11 - The deque container data type [IPython nb]
  • 12 - Lightning fast insertion into sorted lists via the bisect module [IPython nb]
  • 13 - Parallel processing via the multiprocessing module [IPython nb]
  • 14 - Python's and NumPy's in-place operator functions [IPython nb]
  • 15 - Array indexing in NumPy: Extracting rows and columns [IPython nb]
  • 16 - Vectorizing a classic for-loop in NumPy [IPython nb]
  • 17 - Stacking NumPy arrays [IPython nb]

// Other

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  • Happy Mother's [IPython nb]

  • Numeric matrix manipulation - The cheat sheet for MATLAB, Python NumPy, R, and Julia [Markdown]

  • Python Book Reviews [Markdown]


// Useful scripts and snippets

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// Links

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// 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

// 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.

  • SciPy Stack - Python packages (NumPy, pandas, SciPy, IPython, Matplotlib) for scientific computing

  • Cython - C-extensions for Python, an optimizing static compiler to combine Python and C code

  • Numba - an 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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