python.rst

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Getting started

1. Find the name of your home directory

   On windows:

   1. start a cmd shell by typing "cmd" in the windows search bar and executing the dos command prompt

   2. Note the location that is listed when you type

      echo %userprofile%

      in the command window

   On macs:

2. Start a terminal by typing terminal in spotlight

   1. Note the location that is listed when you type

      echo $HOME

3. Install Miniconda Python version 3.6 from https://conda.io/miniconda.html into a a folder called mini36 in your home directory. When prompted, choose to install for just yourself, but make miniconda python the default python

4. To see if your installation is working, start a terminal and type

   where python (on windows)

   or

   which python (on macs)

   You should see that mini36 python is the version that is found on your path

5. Install the git version control package (which we will learn more about later). To do this, type

   conda install git

   at a prompt and answer yes to permit the install

6. Clone the course software into a directory of your choosing. Create a folder somewhere under your home directory and cd into that folder. Then type the following at the prompt:

   git clone https://github.com/phaustin/numeric.git

7. cd into the numeric folder and install the course software by typing (note single minus sign in front of the e):

   pip install -e .

8. cd into the numeric/utilities folder and install the required python packages by typing (note two minus signs in front of the file flag):

   conda install --file class_specs.txt

9. If the packages have installed correctly then typing

   jupyter notebook

   at a prompt should launch jupyter.

Books and tutorials

• If you are new to python, I would recommend you go over the following ebook in detail:

  • Jake Vanderplas' Whirlwind tour of Python is available both as a set of notebooks which you can clone from github or as a free ebook: http://www.oreilly.com/programming/free/a-whirlwind-tour-of-python.csp

    • to get the notebooks do:

      git clone https://github.com/jakevdp/WhirlwindTourOfPython

• We will be referencing chapters from:

  • A Springer ebook from the UBC library: Numerical Python

    • with code on github:

      git clone https://github.com/jrjohansson/numerical-python-book-code

• Two other texts that are available as a set of notebooks you can clone with git:

  • https://github.com/fangohr/introduction-to-python-for-computational-science-and-engineering
  • https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/Index.ipynb

• Our version of David Pine's Introduction to Python: http://clouds.eos.ubc.ca/~phil/djpine_python/

• My favorite O'Reilly book is:

  • Python for Data Analysis

• Some other resources:

  • If you know Matlab, there is Numpy for Maltab users
  • Here is a python translation by Don MacMillen of Chapter 1 of his matlab guide
  • Python data structure cheat sheet
  • Numpy beginners guide
  • Learning Ipython
  • The official Python tutorial
  • Numpy cookbook
  • A general computing introduction: How to think like a computer scientist with an interactive version
  • Think Stats
  • Think Bayes