Basic Reference for Learning Python
===================================

There are clearly too many guide and tutorial for Python, some of which
are quite good. Here is just a personal selection.

--------------

Basic
-----

-  `Hitchhiker’s Guide to
   Python <https://github.com/realpython/python-guide>`__

   -  The actual guide book is `here <docs.python-guide.org>`__

-  `A Byte of Python <https://python.swaroopch.com/>`__

   -  “A Byte of Python” is a free book on programming using the Python
      language. It serves as a tutorial or guide to the Python language
      for a beginner audience.

-  `Awesome Python <https://github.com/vinta/awesome-python>`__

   -  A curated list of awesome Python frameworks, libraries, software
      and resources.

-  `Comprehensive Python
   Cheatsheet <https://gto76.github.io/python-cheatsheet/>`__

   -  Made by `Jure Šorn <https://github.com/gto76>`__. The `source
      files are here <https://github.com/gto76/python-cheatsheet>`__

Dig Deeper
----------

-  `All Algorithms implemented in
   Python <https://github.com/TheAlgorithms/Python>`__

   -  These implementations are for learning purposes. They may be less
      efficient than the implementations in the Python standard library.

-  Python Standard Library is a very good place to start learning
   Python:

   -  There are a lot of useful tools in the standard library.
   -  `A brief tour of the standard
      library <https://docs.python.org/3/tutorial/stdlib.html#operating-system-interface>`__
   -  `The Reference of the Python Standard
      Library <https://docs.python.org/3/library/>`__

Managing Python Packages
------------------------

`Anaconda <https://www.anaconda.com/>`__
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-  **Anaconda** distribution is the easiest way to perform Python/R data
   science and machine learning on all platforms. It can help you create
   environments with different Python versions, and manage libraries and
   dependencies in Python.

   -  It is an easy way to start with Python programming without
      worrying too much about installing packages all by yourself.
   -  `Getting started with
      Anaconda <https://docs.anaconda.com/anaconda/user-guide/getting-started/>`__
      and the `Anaconda
      tutorials <https://docs.anaconda.com/anaconda/navigator/tutorials/>`__
      are good places to start.

-  `AstroConda - Conda for
   astronomers <https://astroconda.readthedocs.io/en/latest/>`__

   -  **AstroConda** is a free Conda channel maintained by the Space
      Telescope Science Institute (STScI) ### **pip**: Python Package
      Installer

-  One important reason to use **Python** in science is that there are
   already a huge number of great tools prepared to make your life
   easier.
-  The most useful tool is `pip - The Python Package
   Installer <https://pip.pypa.io/en/stable/>`__.

   -  **pip** is the package installer for Python. You can use pip to
      install packages from the Python Package Index and other indexes
   -  `This complete reference
      guide <https://pip.pypa.io/en/stable/reference/>`__ is very
      useful.
   -  `What Is Pip? A Guide for New
      Pythonistas <https://realpython.com/what-is-pip/>`__

-  `pip-tools - A set of tools to keep your pinned Python dependencies
   fresh <https://github.com/jazzband/pip-tools>`__

   -  A set of command line tools to help you keep your pip-based
      packages fresh, even when you’ve pinned them.

Start-up Package
----------------

-  The most basic packages you want to use on daily bases.

-  **Scipy** ecosystem:

   -  `Numpy - Base N-dimensional array
      package <https://www.numpy.org/>`__
   -  `SciPy - Fundamental library for scientific
      computing <https://github.com/scipy/scipy/>`__
   -  `SymPy - a Python library for symbolic
      mathematics <https://www.sympy.org/en/index.html>`__
   -  `Numpy and Scipy Documentation <https://docs.scipy.org/doc/>`__
   -  `Quickstart tutorial of
      Numpy <https://www.numpy.org/devdocs/user/quickstart.html>`__
   -  `Scipy Lecture Notes - One document to learn numerics, science,
      and data with Python <https://scipy-lectures.org/>`__
   -  `A Visual Intro to NumPy and Data
      Representation <https://jalammar.github.io/visual-numpy/>`__

-  `pandas - Python Data Analysis Library <http://pandas.pydata.org/>`__

   -  **pandas** is an open source, BSD-licensed library providing
      high-performance, easy-to-use data structures and data analysis
      tools for the Python programming language.
   -  `Online document of
      pandas <http://pandas.pydata.org/pandas-docs/stable/>`__

-  `matplotlib - Comprehensive 2D
   Plotting <https://docs.scipy.org/doc/>`__

   -  **Matplotlib** is a Python 2D plotting library which produces
      publication quality figures in a variety of hardcopy formats and
      interactive environments across platforms.
   -  `Online document of
      matplotlib <https://matplotlib.org/users/index.html>`__; and the
      `matplotlib gallery <https://matplotlib.org/gallery/index.html>`__
      are very helpful resources to learn.

-  `Bokeh - <https://bokeh.pydata.org/en/latest/>`__

   -  **Bokeh** is an interactive visualization library that targets
      modern web browsers for presentation. Its goal is to provide
      elegant, concise construction of versatile graphics, and to extend
      this capability with high-performance interactivity over very
      large or streaming datasets.
   -  `Bokeh user
      guide <https://bokeh.pydata.org/en/latest/docs/user_guide.html#userguide>`__
      and the `reference
      guide <https://bokeh.pydata.org/en/latest/docs/reference.html#refguide>`__
      are very useful. The `gallery of
      examples <https://bokeh.pydata.org/en/latest/docs/gallery.html>`__
      is also a good place to start.

-  Interactive Python computing:

   -  `Jupyter environment <https://jupyter.org/>`__

      -  Project **Jupyter** exists to develop open-source software,
         open-standards, and services for interactive computing across
         dozens of programming languages.
      -  The **Jupyter** Notebook App is a server-client application
         that allows editing and running notebook documents via a web
         browser.

   -  `IPython - Interactive computing <http://ipython.org/>`__

      -  **IPython** provides a rich architecture for interactive
         computing
      -  `nbviewer - A simple way to share Jupyter
         notebooks <https://nbviewer.jupyter.org/>`__

   -  `Jupyter/IPython Notebook Quick Start
      Guide <https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/index.html>`__

-  `scikit-learn - Machine learning in
   Python <https://github.com/scikit-learn/scikit-learn>`__

   -  **scikit-learn** is a Python module for machine learning built on
      top of SciPy.
   -  `Online document of
      scikit-learn <https://scikit-learn.org/stable/user_guide.html>`__
      and `scikit-learn
      Tutorials <https://scikit-learn.org/stable/tutorial/index.html>`__

-  `scikit-image - Image processing in
   Python <https://scikit-image.org/>`__

   -  **scikit-image** is a collection of algorithms for image
      processing.
   -  `Tutorials of
      skimage <https://github.com/scikit-image/skimage-tutorials>`__ and
      `gallery of
      examples <https://scikit-image.org/docs/dev/auto_examples/>`__ are
      very useful.

-  `astropy - Community Python library for
   astronomer <https://www.astropy.org/>`__

   -  The **Astropy** Project is a community effort to develop a common
      core package for Astronomy in Python and foster an ecosystem of
      interoperable astronomy packages.
   -  `Online document of astropy is
      here <http://docs.astropy.org/en/stable/index.html>`__
   -  `Learn.Astropy - Tutorials, documentation, and examples of
      astropy <http://learn.astropy.org/>`__

-  `Scrapy - A fast high-level web crawling & scraping framework for
   Python <https://scrapy.org/>`__

   -  An open source and collaborative framework for extracting the data
      you need from websites. In a fast, simple, yet extensible way.

Tricks and Tips
---------------