python_reference/README.md

<h3 id="top">A collection of useful scripts, tutorials, and other Python-related things</h3>

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<img src="./Images/python-logo-master-v3-TM-flattened.png" alt="">


<div style="height:100px;"></div>

- [// Python tips and tutorials](#-python-tips-and-tutorials)
- [// Python and the web](#-python-and-the-web)
- [// Algorithms](#-algorithms)
- [// Plotting and Visualization](#-plotting-and-visualization)
- [// Benchmarks](#-benchmarks)
- [// Python and "Data Science"](#-python-and-data-science)
- [// Useful scripts and snippets](#-useful-scripts-and-snippets)
- [// Other](#-other)
- [// Links](#-links)



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<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](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/not_so_obvious_python_stuff.ipynb?create=1)]

- Python's scope resolution for variable names and the LEGB rule [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb?create=1)]

- Key differences between Python 2.x and Python 3.x [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb?create=1)]

- A thorough guide to SQLite database operations in Python [[Markdown](./tutorials/sqlite3_howto/README.md)]  

- Unit testing in Python - Why we want to make it a habit [[Markdown](./tutorials/unit_testing.md)]  

- Installing Scientific Packages for Python3 on MacOS 10.9 Mavericks [[Markdown](./tutorials/installing_scientific_packages.md)]

- Sorting CSV files using the Python csv module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/sorting_csvs.ipynb)]

- Using Cython with and without IPython magic [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/running_cython.ipynb)]

- Parallel processing via the multiprocessing module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb?create=1)]

- Entry point: Data - using sci-packages to prepare data for Machine Learning tasks and other data analyses [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/python_data_entry_point.ipynb?create=1)]

- Awesome things that you can do in IPython Notebooks (in progress) [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/awesome_things_ipynb.ipynb)]

- A collection of useful regular expressions [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/useful_regex.ipynb)]

- Quick guide for dealing with missing numbers in NumPy [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/numpy_nan_quickguide.ipynb)]

- A random collection of useful Python snippets [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/python_patterns/patterns.ipynb)]

- Things in pandas I wish I'd had known earlier [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/things_in_pandas.ipynb)]



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<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](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/table_of_contents_ipython.ipynb)]  

- Converting Markdown to HTML and adding Python syntax highlighting [[Markdown](./tutorials/markdown_syntax_highlighting/README.md)]


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<b id="-algorithms">Algorithms and Data Structures</b> <a href="#top">[back to top]</a>

*This category has been moved to a separate GitHub repository [rasbt/algorithms_in_ipython_notebooks](https://github.com/rasbt/algorithms_in_ipython_notebooks)*


- Sorting Algorithms [[Collection of IPython Notebooks](https://github.com/rasbt/algorithms_in_ipython_notebooks/tree/master/ipython_nbs/sorting)

- Linear regression via the least squares fit method [[IPython nb](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/statistics/linregr_least_squares_fit.ipynb?create=1)]

- Dixon's Q test to identify outliers for small sample sizes [[IPython nb](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/statistics/dixon_q_test.ipynb?create=1)]

- Counting points inside a hypercube [[IPython nb](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/geometry/points_in_hybercube.ipynb)]

- Singly Linked List [[ IPython nbviewer ](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/data-structures/singly-linked-list.ipynb)]

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<b id="-plotting-and-visualization">Plotting and Visualization</b> <a href="#top">[back to top]</a>

*The matplotlib-gallery in IPython notebooks has been moved to a separate GitHub repository [matplotlib-gallery](https://github.com/rasbt/matplotlib-gallery)*

**Featured articles**:  

- Preparing Plots for Publication [[IPython nb](http://nbviewer.ipython.org/github/rasbt/matplotlib-gallery/blob/master/ipynb/publication.ipynb)]





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


- Simple tricks to speed up the sum calculation in pandas [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/pandas_sum_tricks.ipynb)]

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*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**:  



- (C)Python compilers - Cython vs. Numba vs. Parakeet [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_2_cython_numba_parakeet.ipynb)]

- Just-in-time compilers for NumPy array expressions [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day7_2_jit_numpy.ipynb)]

- Cython - Bridging the gap between Python and Fortran [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day10_fortran_lstsqr.ipynb)]

- Parallel processing via the multiprocessing module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb)]

- Vectorizing a classic for-loop in NumPy [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day16_numpy_vectorization.ipynb)]

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<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](https://github.com/rasbt/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](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/python_data_entry_point.ipynb)]

- About Feature Scaling: Standardization and Min-Max-Scaling (Normalization) [[IPython nb](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/preprocessing/about_standardization_normalization.ipynb)]

- Principal Component Analysis (PCA) [[IPython nb](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/dimensionality_reduction/projection/principal_component_analysis.ipynb)]

- Linear Discriminant Analysis (LDA) [[IPython nb](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/dimensionality_reduction/projection/linear_discriminant_analysis.ipynb)]

- Kernel density estimation via the Parzen-window technique [[IPython nb](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/parameter_estimation_techniques/parzen_window_technique.ipynb)]


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

- [watermark](https://github.com/rasbt/watermark) - An IPython magic extension for printing date and time stamps, version numbers, and hardware information.

- [Shell script](./useful_scripts/prepend_python_shebang.sh) For prepending Python-shebangs to .py files.

- A random string generator [function](./useful_scripts/random_string_generator.py).

- [Converting large CSV files](https://github.com/rasbt/python_reference/blob/master/useful_scripts/large_csv_to_sqlite.py) to SQLite databases using pandas.

- [Sparsifying a matrix](https://github.com/rasbt/python_reference/blob/master/useful_scripts/sparsify_matrix.py) by zeroing out all elements but the top k elements in a row using NumPy.

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

- [Python book reviews](./other/python_book_reviews.md)
- [Happy Mother's Day Plot](./other/happy_mothers_day.ipynb)

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



- [PyPI - the Python Package Index](https://pypi.python.org/pypi) - The official repository for all open source Python modules and packages.  

- [PEP 8](https://www.python.org/dev/peps/pep-0008/) - The official style guide for Python code.

- [PEP 257](https://www.python.org/dev/peps/pep-0257/) - Python's official docstring conventions;  [pep257 - Python style guide checker](https://pypi.python.org/pypi/pep257)


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**// News**

- [Python subreddit](http://www.reddit.com/r/Python/) - My favorite resource to catch up with Python news and great Python-related articles.

- [Python community on Google+](https://plus.google.com/communities/103393744324769547228) - A nice and friendly community to share and discuss everything about Python.

- [Python Weekly](http://www.pythonweekly.com) - A free weekly newsletter featuring curated news, articles, new releases, jobs etc. related to Python.


<br>

**// Resources for learning Python**

- [Dive Into Python](http://www.diveintopython.net) / [Dive Into Python 3](http://getpython3.com/diveintopython3/) - A free Python book for experienced programmers.

- [The Hitchhiker’s Guide to Python](http://docs.python-guide.org/en/latest/) - A free best-practice handbook for both novices and experts.

- [Think Python - How to Think Like a Computer Scientist](http://www.greenteapress.com/thinkpython/) - An introduction for beginners starting with basic concepts of programming.

- [A Byte of Python](https://python.swaroopch.com/) - a free book on programming using the Python language.

- [Python Patterns](http://matthiaseisen.com/pp/) -  A directory of proven, reusable solutions to common programming problems.

- [Intro to Computer Science - Build a Search Engine & a Social Network](https://www.udacity.com/course/intro-to-computer-science--cs101) - A great, free course for learning Python if you haven't programmed before.

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**// My favorite Python projects and packages**

- [The IPython Notebook](http://ipython.org/notebook.html) - An interactive computational environment for combining code execution, documentation (with Markdown and LateX support), inline plots, and rich media all in one document.

- [matplotlib](http://matplotlib.org) - Python's favorite plotting library.

- [NumPy](http://www.numpy.org) - A library for multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays.

- [SciPy](http://www.scipy.org) - A library that provides various useful functions for numerical computing, such as modules for optimization, linear algebra, integration, interpolation, ...


- [pandas](http://pandas.pydata.org) - High-performance, easy-to-use data structures and data analysis tools build on top of NumPy.

- [Cython](http://cython.org) - C-extensions for Python, an optimizing static compiler to combine Python and C code.

- [Numba](http://numba.pydata.org) - A just-in-time specializing compiler which compiles annotated Python and NumPy code to LLVM (through decorators)

- [scikit-learn](http://scikit-learn.org/stable/) - A powerful machine learning library for Python and tools for efficient data mining and analysis.