python_reference/README.md

### A collection of useful scripts, tutorials, and other Python-related things

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


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- [// 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)
- [// Other](#-other)
- [// Useful scripts and snippets](#-useful-scripts-and-snippets)
- [// Links](#-links)



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

- 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)]

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

- 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)] 


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

*The algorithms category was moved to a separate GitHub repository [rasbt/algorithms_in_ipython_notebooks](https://github.com/rasbt/algorithms_in_ipython_notebooks)*



- Sorting Algorithms [[IPython nb](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/sorting/sorting_algorithms.ipynb?create=1)]

- 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)]

- Sequential Selection Algorithms [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/sorting_csvs.ipynb)]

- 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)]

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

- a matplotlib gallery in IPython notebooks [[GitHub repo](https://github.com/rasbt/matplotlib-gallery)]


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

*The benchmark category was moved to a separate GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)*

- **1** - Reversing strings [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day1_string_reverse.ipynb)]
- **2** - Calculating sample means [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day2_mean_values.ipynb)]
- **3** - 6 different ways to count elements using a dict [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day3_dictionary_counting.ipynb)]
- **4** - Python vs. Cython vs. Numba [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_python_cython_numba.ipynb)]
- **4.2** - (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)]
- **5** - Comparing 9 ways for flattening lists of sublists [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day5_flattening_lists.ipynb)]
- **6** - Determining if a string is a number [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day6_string_is_number.ipynb)]
- **7** - Speeding up NumPy array expressions with Numexpr [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day7_numpy_numexpr.ipynb)]
- **7.2** - 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)] 
- **8** - Calculating square roots and exponents [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day8_sqrt_and_exp.ipynb)] 
- **9** - The most Pythonic way to check if a string ends with a particular substring [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day9_string_endswith.ipynb)]
- **10** - 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)]
- **11** - The `deque` container data type [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day11_deque_container.ipynb)]
- **12** - Lightning fast insertion into sorted lists via the `bisect` module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day12_insert_into_sorted_list.ipynb)]
- **13** - Parallel processing via the multiprocessing module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb)]
- **14** - Python's and NumPy's in-place operator functions [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day14_inplace_operators.ipynb)]
- **15** - Array indexing in NumPy: Extracting rows and columns [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day15_array_indexing_numpy.ipynb)] 
- **16** - 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)]
- **17** - Stacking NumPy arrays [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day17_numpy_stacking.ipynb)]


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

- Happy Mother's [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/other/happy_mothers_day.ipynb?create=1)]

- Numeric matrix manipulation - The cheat sheet for MATLAB, Python NumPy, R, and Julia [[Markdown](./tutorials/matrix_cheatsheet.md)]

- Python Book Reviews [[Markdown](./other/python_book_reviews.md)]

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

- [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.

- convert 'tab-delimited' to 'comma-separated' CSV files [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/useful_scripts/fix_tab_csv.ipynb?create=1)]

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



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



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

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



**// 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



**// Resources for learning Python**

- [Learn Python The Hard Way](http://learnpythonthehardway.org/book/) - one of the most popular and recommended 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

**// 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) - an 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