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‎README.md

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# Python Deliberate Practice
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First of all, don't be afraid, read [Plateau of Productivity]. More importantly, be patient, a good read from Peter Norvig, titled [Teach Yourself Programming in 10 years].
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## Motivation
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[Language war] between Python and R is one of the most frequently discussed topics among the Data Scientist, and there doesn't seem to be a consensus on which one is better. Personally, I used both R and Python, but for very different purposes. I mainly use tidyverse packages (dplyr + ggplot2) to carry out data analyses and data visualization, while using Python for web scraping, task automations, and building [basic web applications in Flask].
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[Language war] between Python and R is one of the most frequently discussed topics among the Data Scientists, and there doesn't seem to be a consensus on which one is better. Personally, I used both R and Python, but for very different purposes. I mainly use tidyverse packages (dplyr + ggplot2) to carry out data analyses and data visualization, while using Python for web scraping, task automations, and building [basic web applications in Flask].
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By now, I have a pretty good working knowledge of the R language. There are obviously many more things that I can learn - in particular building and maintaining R packages as well as more [advanced R materials]. Yet, the appeal of Python has always been there for me for a few reasons:
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* **Outcome**: I want to move my data stack to Python completely. This means my day-to-day data analysis work will be done in Python instead of R, make my code as pythonic as possible. Become a Contributor to Airpy / tools, and take on one bigger Python project (ML, Data Viz ...etc).
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* **Curriculum**: I want do everything that I can to go through all the basic materials in Pandas/Matplotlib combo. Expose yourself to functional programming, OOP, testing in Python, or even make tools. Get feedbacks from Airpy team members.
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* **Curriculum**: I want do everything that I can to go through all the basic materials in Pandas/Matplotlib combo. Expose myself to functional programming, OOP, testing in Python, or even making command tools. Get feedbacks from Airpy team members.
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* **Timeframe**: Efficiency parity by end of October. One contribution to Airpy by Mid November. One ongoing big project touching different stacks in Python by the end of 2016.
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## Project Milestones
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* **Learning Python & Best Practices**
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* [Plateau of Productivity]
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* [Build On Top of the Basics: Python Progression]
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* [Drastically Improve Your Understanding: Jeff Knupp: Python's Execution Mode]
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* [Nate Batchelder: Loop like a native]
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* [Columbia Data Scientist Style Guide]
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* **[Build On Top of the Basics]: Writing Pythonic Code**
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* Guidelines of Writing Pythonic Code
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* **Writing Pythonic Code**
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* Guidelines For Writing Pythonic Code
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* Function: Use *args and **kwargs to accept arbitrary arguments in function definition
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* Tuples: effective unpacking, use _ for placeholder, swap values without tmp variables
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* List/Dict/Set: list comprehension, dict comprehension. dict.get, set comprehension
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* Formatting: pep8 standards
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* Executable script: __name__ = __main__
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* Import: The right way to do imports
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* [Drastically Improve Your Understanding: Jeff Knupp: Python's Execution Mode]
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* [Nate Batchelder: Loop like a native]
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* [Writing Idiomatic Python - Jeff Knupp]
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* [Stanford CS 41: Idiomatic Python]
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* [Another Tutorial On How To Write Pythonic Code]
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* **iPython Notebook**
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* Just start writing all your practices as iPython notebooks
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* [BIDS: Python Bootcamp: IPython Notebook]
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* [Jupyter Notebook tips, tricks and shortcuts]
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* [iPython Notebook Keybinding]
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* **Pandas For Data Analysis**
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* [Brandon Rhode's Pandas From The Groud Up]: good explanation on index; (row) set_index, sort_index; (column) stack, unstack
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* [dplyr/pandas vignette comparison]
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* [Data School Pandas Tutorials]
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* [Data School Pandas Github iPython notebook]
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* [More Pandas Questions Answered]
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* [Other resources]
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* [Tom Augspurgur: Pandas]
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* [Coursera: Introduction to Data Science in Python]: This course is basically about Pandas
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* Introduction to Numpy
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* [BIDS: Python Bootcamp: Intro to Numpy]
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* creating ndarray, data type of ndarray
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* indexing and slicing: basic, boolean, fancy
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* element wise (vectorized) operations
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* [Stanford ICME 193: Scientific Python]
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* Introduction to Pandas
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* [Dplyr/pandas Vignette Comparison]
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* [Data School Pandas Tutorials]
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* [Data School Pandas Github iPython notebook]
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* [More Pandas Questions Answered]
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* [Other Resources]
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* [Brandon Rhode's Pandas From The Groud Up]
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* [Tom Augspurgur: Pandas]
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* [BIDS: Python Bootcamp: Scipy Pandas]
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* DataFrame
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* Reading in Data into Dataframe
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* Indexing, Selection, Filtering, Sorting, Ranking
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* Combine and merge DataFrame
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* Reshape and Pivot DataFrame
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* Group By + Data Aggregation
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* [Coursera: Introduction to Data Science in Python]
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* [Chris Albon's notes]
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* **Matplotlib For Data Visualization**
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* **Data Visualization**
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* [BIDS: Python Bootcamp: Intro to Matplotlib]: The 800 pound gorilla, everything is customizable, but very low level
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* [Seaborn]: Good for statistical visualization. I still find it a bit limited on the type of simple plots it can do
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* **Writing Object Oriented Programming Python Code**
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* [Objected Oriented Programming For Scientist]
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* [Jeff Knupp's OOP Post]
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* [Computational Biology: OOP For Scientist]
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* [Improve Your Python: Jeff Knupp: OOP]
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* [BIDS: Python Bootcamp: OOP]
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* Simeon Franklin's Twitter University Class (not available to the public)
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* [BIDS: Python Bootcamp: Higher order functions]
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* [Improve Your Python: Jeff Knupp: Yield & Generator Explained]
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* [Improve Your Python: Jeff Knupp: Decorator Explained]
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* [Improve Your Python: Jeff Knupp: Context Manager]
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* **[Scikit-learn Machine Learning Library]**
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* **Machine Learning In Python**
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* [Scikit-learn Machine Learning Library]
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* [Scikit-learn metrics]
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* [Scikit-learn Pipeline]
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* **Testing Codes In Python**
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* **Testing In Python**
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* [Four Tools For Testing Your Python Code]
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* [Testing For Scientist]
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* [Improve Your Python: Understanding Unit Testing: Jeff Knupp]
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* [Computational Biology: Four Tools For Testing Your Python Code]
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* [Computational Biology: Testing For Scientist]
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* [Improve Your Python: Jeff Knupp: Understanding Unit Testing]
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* [BIDS: Python Bootcamp: Test Driven Development]
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* [Software Carpentry: Testing]
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* Logging
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* https://pymotw.com/2/logging/index.html#module-logging
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* http://docs.python-guide.org/en/latest/writing/logging/
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## Next Steps / Level In 2017
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* Writing Command Line Tool
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Once mastered all the above, the next natural step is to create public work that other people can use so you can democratize your useful tool to others. A great introduction to how to get started is from Tim Hopper's talk, titled [Sharing Your Side Projects].
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* **Building Packages In Python (Next Year?)**
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* **Logging In Python (Next Year?)**
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* [Basic Python Logging - Code Session]
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* [Logging HOWTO]
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* [Become A Logging Expert In 30 Minutes]
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* **Writing Command-Line Tool (Next Year?)**
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* [Click Documentation]
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* [Writing A Command-Line Tool In Python]
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* [Using cookiecutter to set up a project]
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* [Creating a clean Pytyon development environment]
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* [How to generate beautiful technical documentation]
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* [Five steps to add the bling factor your Python package]
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* **Building Packages In Python (Next Year?)**
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* [Computational Biology: Using Cookiecutter To Set Up A Project]
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* [Computational Biology: Creating A Clean Pytyon Development Environment]
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* [Computational Biology: How To Generate Beautiful Technical Documentation]
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* [Computational Biology: Five Steps To Add The Bling Factor Your Python Package]
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## Reference
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* [Berkeley BIDS Python bootcamp]
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* [Josh Bloom's Python Computing For Data Science]
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* [Writing Idiomatic Python - Jeff Knupp]
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* [Another tutorial on how to write pythonic code]
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* [Another Tutorial On How To Write Pythonic Code]
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* [Pandas Cookbook]
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* [Udemy course]
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* [Chris Albon's notes]
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[Teach Yourself Programming in 10 years]:http://norvig.com/21-days.html
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[Plateau of Productivity]:http://pbpython.com/plateau-of-productivity.html
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[Language war]:http://www.dataschool.io/python-or-r-for-data-science/
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[advanced R materials]:http://adv-r.had.co.nz/
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[basic web applications in Flask]:https://github.com/robert8138/flask-google-calendar-api-project
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[data stacks]:https://lab.getbase.com/productive-data-science-python/
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[Plateau of Productivity]:http://pbpython.com/plateau-of-productivity.html
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[Build On Top of the Basics: Python Progression]:http://stackoverflow.com/questions/2573135/python-progression-path-from-apprentice-to-guru
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[Drastically Improve Your Understanding: Jeff Knupp: Python's Execution Mode]:https://www.jeffknupp.com/blog/2013/02/14/drastically-improve-your-python-understanding-pythons-execution-model/
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[Nate Batchelder: Loop like a native]:https://www.youtube.com/watch?time_continue=14&v=EnSu9hHGq5o
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[Columbia Data Scientist Style Guide]:http://columbia-applied-data-science.github.io/pages/lowclass-python-style-guide.html
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[Build On Top of the Basics]:http://stackoverflow.com/questions/2573135/python-progression-path-from-apprentice-to-guru
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[Drastically Improve Your Understanding: Jeff Knupp: Python's Execution Mode]:https://www.jeffknupp.com/blog/2013/02/14/drastically-improve-your-python-understanding-pythons-execution-model/
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[Writing Idiomatic Python - Jeff Knupp]:https://jeffknupp.com/writing-idiomatic-python-ebook/
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[Stanford CS 41: Idiomatic Python]:https://drive.google.com/file/d/0B-eHIhYpHrGDNGZCYUN6SVB1OGc/view
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[Another Tutorial On How To Write Pythonic Code]:http://safehammad.com/downloads/python-idioms-2014-01-16.pdf
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[iPython Notebook Keybinding]:https://www.webucator.com/blog/wp-content/uploads/2015/07/IPython-Notebook-Shortcuts.pdf
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[BIDS: Python Bootcamp: IPython Notebook]:https://www.youtube.com/watch?v=HrylK8I1ALs&index=3&list=PLKW2Azk23ZtSeBcvJi0JnL7PapedOvwz9
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[Jupyter Notebook tips, tricks and shortcuts]:https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/
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[iPython Notebook Keybinding]:https://www.webucator.com/blog/wp-content/uploads/2015/07/IPython-Notebook-Shortcuts.pdf
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[Nate Batchelder: Loop like a native]:https://www.youtube.com/watch?time_continue=14&v=EnSu9hHGq5o
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[Improve Your Python: Jeff Knupp: Yield & Generator Explained]:https://jeffknupp.com/blog/2013/04/07/improve-your-python-yield-and-generators-explained/
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[Improve Your Python: Jeff Knupp: Decorator Explained]:https://jeffknupp.com/blog/2013/11/29/improve-your-python-decorators-explained/
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[Simeon Franklin's higher order function]:http://simeonfranklin.com/blog/2013/jun/17/higher-order-functions-python/
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[BIDS: Python Bootcamp: Higher order functions]:https://www.youtube.com/watch?v=ob797BA49ZQ
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[Brandon Rhode's Pandas From The Groud Up]:https://www.youtube.com/watch?v=5JnMutdy6Fw
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[dplyr/pandas vignette comparison]:http://nbviewer.jupyter.org/gist/TomAugspurger/6e052140eaa5fdb6e8c0
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[BIDS: Python Bootcamp: Intro to Numpy]:https://www.youtube.com/watch?v=PDOsOcG0m-Q
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[Stanford ICME 193: Scientific Python]:http://stanford.edu/~arbenson/cme193.html
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[Dplyr/pandas Vignette Comparison]:http://nbviewer.jupyter.org/gist/TomAugspurger/6e052140eaa5fdb6e8c0
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[Data School Pandas Tutorials]:http://www.dataschool.io/easier-data-analysis-with-pandas/
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[Data School Pandas Github iPython notebook]:https://github.com/justmarkham/pandas-videos
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[More Pandas Questions Answered]:https://www.youtube.com/watch?v=CWRKgBtZN18&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=31
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[Other resources]:http://www.dataschool.io/best-python-pandas-resources/
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[Other Resources]:http://www.dataschool.io/best-python-pandas-resources/
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[Brandon Rhode's Pandas From The Groud Up]:https://www.youtube.com/watch?v=5JnMutdy6Fw
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[Tom Augspurgur: Pandas]:https://www.youtube.com/watch?v=otCriSKVV_8
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[BIDS: Python Bootcamp: Intro to Numpy]:https://www.youtube.com/watch?v=PDOsOcG0m-Q
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[BIDS: Python Bootcamp: Scipy Pandas]:https://www.youtube.com/watch?v=bgIZAeNpL1U
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[Coursera: Introduction to Data Science in Python]:https://www.coursera.org/learn/python-data-analysis/home/welcome
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[Chris Albon's notes]:http://chrisalbon.com/
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[BIDS: Python Bootcamp: Intro to Matplotlib]:https://www.youtube.com/watch?v=j5P822TSCKs
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[Seaborn]:https://stanford.edu/~mwaskom/software/seaborn/
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[Bokeh]:http://bokeh.pydata.org/en/latest/
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[A Dramatic Tour through Python’s Data Visualization Landscape (including ggplot and Altair)]:https://dansaber.wordpress.com/2016/10/02/a-dramatic-tour-through-pythons-data-visualization-landscape-including-ggplot-and-altair/
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[Objected Oriented Programming For Scientist]:http://tjelvarolsson.com/blog/object-oriented-programming-for-scientists/
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[Jeff Knupp's OOP Post]:https://jeffknupp.com/blog/2014/06/18/improve-your-python-python-classes-and-object-oriented-programming/
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[Computational Biology: OOP For Scientist]:http://tjelvarolsson.com/blog/object-oriented-programming-for-scientists/
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[Improve Your Python: Jeff Knupp: OOP]:https://jeffknupp.com/blog/2014/06/18/improve-your-python-python-classes-and-object-oriented-programming/
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[BIDS: Python Bootcamp: OOP]:https://www.youtube.com/watch?v=HQ0q6oMpOEs
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[Simeon Franklin's higher order function]:http://simeonfranklin.com/blog/2013/jun/17/higher-order-functions-python/
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[BIDS: Python Bootcamp: Higher order functions]:https://www.youtube.com/watch?v=ob797BA49ZQ
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[Improve Your Python: Jeff Knupp: Yield & Generator Explained]:https://jeffknupp.com/blog/2013/04/07/improve-your-python-yield-and-generators-explained/
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[Improve Your Python: Jeff Knupp: Decorator Explained]:https://jeffknupp.com/blog/2013/11/29/improve-your-python-decorators-explained/
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[Improve Your Python: Jeff Knupp: Context Manager]:https://jeffknupp.com/blog/2016/03/07/improve-your-python-the-with-statement-and-context-managers/
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[Scikit-learn Machine Learning Library]:http://www.dataschool.io/machine-learning-with-scikit-learn/
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[Scikit-learn metrics]:http://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics
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[Scikit-learn Pipeline]:http://scikit-learn.org/stable/modules/classes.html#module-sklearn.pipeline
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[Software Carpentry: Testing]:http://katyhuff.github.io/python-testing/
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[Computational Biology: Four Tools For Testing Your Python Code]:http://tjelvarolsson.com/blog/four-tools-for-testing-your-python-code/
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[Computational Biology: Testing For Scientist]:http://tjelvarolsson.com/blog/test-driven-develpment-for-scientists/
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[Improve Your Python: Jeff Knupp: Understanding Unit Testing]:https://jeffknupp.com/blog/2013/12/09/improve-your-python-understanding-unit-testing/
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[BIDS: Python Bootcamp: Test Driven Development]:https://www.youtube.com/watch?v=hrj8Wo34nvw
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[Four Tools For Testing Your Python Code]:http://tjelvarolsson.com/blog/four-tools-for-testing-your-python-code/
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[Improve Your Python: Understanding Unit Testing: Jeff Knupp]:https://jeffknupp.com/blog/2013/12/09/improve-your-python-understanding-unit-testing/
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[Testing For Scientist]:http://tjelvarolsson.com/blog/test-driven-develpment-for-scientists/
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[Automation And Make]:http://swcarpentry.github.io/make-novice/
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[Software Carpentry: Testing]:http://katyhuff.github.io/python-testing/
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[Sharing Your Side Projects]:https://www.youtube.com/watch?v=uRul8QdYvqQ
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[Basic Python Logging - Code Session]:https://www.youtube.com/watch?v=PX_xd2YjrsU
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[Logging HOWTO]:https://docs.python.org/2/howto/logging.html
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[Become A Logging Expert In 30 Minutes]:https://www.youtube.com/watch?v=24_4WWkSmNo
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[Click Documentation]:http://click.pocoo.org/5/quickstart/
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[Writing A Command-Line Tool In Python]:http://nvie.com/posts/writing-a-cli-in-python-in-under-60-seconds/
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[Using cookiecutter to set up a project]:http://tjelvarolsson.com/blog/using-cookiecutter-a-passive-code-generator/
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[Creating a clean Pytyon development environment]:http://tjelvarolsson.com/blog/begginers-guide-creating-clean-python-development-environments/
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[How to generate beautiful technical documentation]:http://tjelvarolsson.com/blog/how-to-generate-beautiful-technical-documentation/
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[Five steps to add the bling factor your Python package]:http://tjelvarolsson.com/blog/five-steps-to-add-the-bling-factor-to-your-python-package/
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[Computational Biology: Using Cookiecutter To Set Up A Project]:http://tjelvarolsson.com/blog/using-cookiecutter-a-passive-code-generator/
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[Computational Biology: Creating A Clean Pytyon Development Environment]:http://tjelvarolsson.com/blog/begginers-guide-creating-clean-python-development-environments/
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[Computational Biology: How To Generate Beautiful Technical Documentation]:http://tjelvarolsson.com/blog/how-to-generate-beautiful-technical-documentation/
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[Computational Biology: Five Steps To Add The Bling Factor Your Python Package]:http://tjelvarolsson.com/blog/five-steps-to-add-the-bling-factor-to-your-python-package/
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[Improve Your Python: Jeff Knupp: Context Manager]:https://www.jeffknupp.com/blog/2016/03/07/improve-your-python-the-with-statement-and-context-managers/
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[Python Tutor Visualizer]:http://www.pythontutor.com/visualize.html#mode=edit
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[Python For Data Analysis]:http://www3.canisius.edu/~yany/python/Python4DataAnalysis.pdf
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[Harvard CS 109: Data Science]:http://cs109.github.io/2015/
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[Berkeley BIDS Python bootcamp]:https://bids.berkeley.edu/news/python-boot-camp-fall-2016-training-videos-available-online
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[Josh Bloom's Python Computing For Data Science]:https://github.com/profjsb/python-seminar
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[Writing Idiomatic Python - Jeff Knupp]:http://share.sm3.su/writing_idiomatic_python_3.pdf
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[Another tutorial on how to write pythonic code]:http://safehammad.com/downloads/python-idioms-2014-01-16.pdf
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[Pandas Cookbook]:http://pandas.pydata.org/pandas-docs/stable/cookbook.html
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[Udemy course]:https://www.udemy.com/learning-python-for-data-analysis-and-visualization/?ccManual=&couponCode=DEAL19
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[Chris Albon's notes]:http://chrisalbon.com/
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[Udemy course]:https://www.udemy.com/learning-python-for-data-analysis-and-visualization/?ccManual=&couponCode=DEAL19

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