Python Learning Materials fun tech researching

Python Learning Materials

A short list for anyone who want to learn Python.

You do want to learn (more) about Python?

Since I have also been learning this programming languages and have found its awesomeness so I decided to write this post and hoped that you will find a little bit help by using this.

And since you are learning, then I suggest that you should go with Python 3. Python 3 maybe lacks of library support but things have been changed, the only problem that you should consider is the compatibility of your application with the existing systems which are using Python 2 by default, but that is not a very big problem nowadays.

I will update this list frequently. There is also my recommendation libraries/frameworks at the end, please also have a look at that section and give me your feedback 😁.

Learning materials

1. E-learning courses

2. Guides

  • The Hitchhiker’s Guide to Python: The site contains almost every things that you want to know about Python
    from setup working environment to options for libraries/frameworks.
  • Full Stack Python: More advantage topics and also best practice for Python.
  • Awesome Python: A awesome list for Python’s solutions, libraries and frameworks.

3. Books

Below books are free for you to read in your browser, very good ones 😄.

Programming objectives

When studying programming languages, practicing make you learn faster. But if you somehow are bored with text books and its examples, you should find something for achievements, those site below will help you. Further more, you can learn more about algorithm just by practicing, have fun programming!

  • Project Euler: The project’s main aim is to help learners solve problems with the combination of math, computer and programming.
  • Exercism: The name tell it all, you level up whenever you practice with those exercises.
  • HackerRank: Complete the tutorial, solve problems, receive points, get the chances for jobs. Try this site.

Recommendation for readings/libraries/frameworks:

  • PEP 8 – Style Guide for Python Code: This is the coding convention for Python, together with PEP 257 (for docstring), you need to follow these convention for better code reading and maintenance.
  • Pylint: This is the tool to check your Python code, and rate it. Using this you can keep your code clean and follow the best practices.
  • PyTest and PyUnit: these are testing frameworks for Python. Testing keep your code works right and also prevent faults coming from modifying your code.
  • Flask: A web microframework for Python.
  • Jupyter Notebook: A web application can be used to create and share document having live code and also interacting, data visualization. You can use it with other programing languages like R, Julia…
  • NumPy, SciPy and Pandas: libraries used for mathematics, science, engineering and further more, data analysis. If you want to work with machine learning, please also have a look at scikit-learn.
  • NLTK: a toolkit for Natural Language Processing.

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