# Python prep guidelines

On lecture 1, I informed you that you will be writing Python code as part of
the 3 cases you will need to go through in this course. This means that, the
more you know about Python, the less you will need to focus on *how* to do
things with Python and the *more* you will be able to focus on actually solving
the case.

This preparation is specially important if you have never done any coding. I
highly advise you to take your time to work your Python skills out before case
1 begins. It will pay off down the line.

This document contains two sections: a summary of the skills you are looking
for, and a few suggested contents that can help you. You don't need to use
suggested contents: if you find others that you prefer, go ahead. I do
recommend that you stick to trying to learn the skills I am suggesting, since
that will maximize the return on your effort. There are many other areas of
Python that you can learn but won't be relevant for this course.

## Skills to look for

- Notebooks and Colab
    - How to use your UPF gmail to open
      Colab (https://colab.research.google.com)
    - How to create a new notebook, write code in it and run it
    - How to upload and download CSV or Excel files in Colab notebooks
- Python basics
    - Variables
    - Functions
    - Basic data types: ints, floats, strings
    - Complex data types: lists, dicts, tuples
- Pandas
    - Understanding pandas dataframes
    - How to read and write CSV/Excel files as dataframes
    - How to select data
        - Selecting columns
        - Selecting rows with conditions
    - Grouping and aggregating dataframes
    - Obtaining statistics from columns
- Seaborn
    - Understaning seaborn plots
    - How to plot data from a pandas dataframe with seaborn

## Suggested Materials

- A super brief Colab example
  notebook: https://colab.research.google.com/?utm_source=scs-index
- A more detailed, high quality example Colab
  notebook: https://colab.research.google.com/github/cs231n/cs231n.github.io/blob/master/python-colab.ipynb
- https://www.youtube.com/watch?v=8VFYs3Ot_aA
- https://www.youtube.com/watch?v=oCngVVBSsmA
- A short, 4 hours intro to Python from
  Datacamp: https://www.datacamp.com/courses/intro-to-python-for-data-science
- A self-pace introduction to Python for STEM
  applications: https://www.pythonlikeyoumeanit.com/index.html
- A huge collection of resources, in case you don't like the previous ones or
  want to find more: https://www.reddit.com/r/learnpython/wiki/index