Hello, Python!

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Day 1 - Summary

1. Installation

   1. Plain Python - The Official Python Installation
   2. Anaconda Python Distribution [Recommended] as it contains most of the required modules
   3. Intel Distribution - A super set of Anaconda with various optimization for Intel Architecture

2. Syntax

   1. No semicolons for line separation!!
   2. No parenthesis for blocks!!!
   3. Indentation

3. Variables and types

   1. Numeric - Integers, Long, Float , Complex
   2. Boolean - True , False
   3. Strings and operations on strings
   4. Conversion from one type to another - int(), float() , str() , bool()

4. Keywords

5. Sequences

   1. Lists
   2. Tuples
   3. Sets
   4. Dictionaries

6. Control flow

   1. if elif else
   2. NO switch!! - either use if elif else OR use dictionary mappings

7. Loops - for , while ; break , continue

8. Functions

9. Files

10. Modules - os , subprocess , random , sys

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Day2 - Summary

1. Files - reading and writing

2. Using the with keyword - context managers

3. Operations on dictionaries , looping through contents of a dictionary

4. json - reading and writing json documents

5. Exception Handling - try except finally raise

6. Classes

   1. Creating a class
   2. Object creation
   3. __init__ method - to initialize attributes
   4. Dunders methods to be uploaded for operator overloading ex : __str__ (Dunders is short form for Double Underscores)

7. datetime module to deal with date,time,datetime objects

8. requests module to make HTTP Requests

9. Connecting to a database and querying - using pyodbc/pypyodbc

10. Creating our own modules and packages.

11. Revisiting functions

    1. Lambdas
    2. Functions in python as first class citizens - can be passed , returned as any other objects.
    3. Decorators - To do some pre-work before a function is called. Using the @ construct for decorating
    4. Functional programming constructs - map(),reduce(),filter() and the operator module
    5. Functional Programming With Python

12. Mini project - py_shell - Implementing our own mini shell using the module os

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Installing packages

• Using pip

  pip install [pkg_name]

  pip --proxy [proxy_url] install [pkg_name]

  example : pip --proxy <proxy_url> install requests

• Using conda
  conda install [pkg_name]

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Must Reads

1. PEP-8 Python Style Guide -
2. Doing things the Pythonic way -Idiomatic Python
3. Standard Library Functions for Python2.x
4. Standard Library Functions for Python3.x
5. LearnPython - A good online code playground to learn the language.
6. Learn Python the Hard Way
7. The Hitchhiker’s Guide to Python! - A fully comprehensive site on most of the things required on the journey to be an awesome Python developer
8. Python for you and me - Great content to get started with python development
9. Python_syntax_and_semantics

References

1. PEP - Python Enhancement Proposals
2. Python Course.eu - Very good collection on basic and advanced topics.
3. IntoPython - CPython Code Walkthrough
4. 2 vs 3 - Difference between Python 2 and 3
5. datetime - Datetime formating
6. conda - Conda cheatsheet
7. Anandalogy Python Practice book - A good resource to refresh your knowledge.Also has a good content related to functional programming.
8. DiveIntoPython

Advanced and/or interesting

1. Call by Reference or value - Neither
2. Decorators

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Additional Resources

Resources for Data Science

1. Awesomestats.in - Concise jupyter notebook examples to do data science
2. Analytics Vidhya Python Learning Path

Automation

• Automate The Boring Stuff

Miscellaneous packages which could be useful

• For creating GUI Applications
  • Tkinter - The standard module for GUI's
  • PyQt - Python bindings to the popular Qt Framework
  • Kivy - A cross platform UI framework
• For doing backend web development
  • flask
  • Django
  • CheeryPy
• For web scrapping
  • Beautiful Soup
  • Scarpy
• Testing frameworks
  • unittest
  • doctest
  • nose
• For Machine Learning and Data Science
  • numpy, scipy
  • scikit-learn
  • pandas
• For visulization
  • matplotlib
  • bokeh
  • seaborn
• For text analysis and NLP
  • nltk
  • gensim
  • pyparsing

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-- Umesh N Rao