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Tutorial in Python targeted at Epidemiologists. Will discuss the basics of analysis in Python 3
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1_Python-Basics
2_Data-Basics
3_Epidemiology_Analysis
4_Hernan-Robins
5_Random_Demonstrations
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README.md

README.md

Python-for-Epidemiologists

Join the chat at https://gitter.im/zEpid/community DOI

This repository is an introduction to epidemiology analyses in Python. Additionally, the tutorials for my library zEpid are hosted here. For more information on zEpid, see GitHub or ReadTheDocs.

The directory of this guide is

  1. Python Basics
  2. Basics of pandas (data management library)
  3. Epidemiology analyses in Python
    1. Basics
    2. Missing data
    3. Causal inference
      1. Time-fixed treatments
      2. Time-varying treatments
    4. Sensitivity analyses

Required packages for tutorial

To complete the tutorial, user must have the following packages installed: numpy, pandas, zepid, matplotlib, statsmodels, lifelines, and sklearn

IDE (Integrated Development Environment)

No IDE is required to complete the tutorial. All files are available in ipynb also known as jupyter notebooks. Code can be either downloaded or copied from the notebooks.

Here are some IDEs I have used in the past (and what I believe to be their advantages and disadvantages

Rodeo

This is the IDE I used for a long time. It is set up like RStudio

Advantages:

Basically RStudio but for Python, decent interface, easy to run line-by-line, easy to visualize plots (although it encourage bad habits)

Disadvantages:

Does not have all the features of RStudio (will delete changes if closed without saving), sucks up a lot of memory, sometimes the auto-complete would stop working if I hit more than 300+ lines of code, the environment tab is not great (don't expect it to open anything like RStudio)

Aside: their website has great tutorials how to run some basic stuff in Python if you are new to analysis in Python https://rodeo.yhat.com/

jupyter notebooks

Designed to be like a lab notebook, or like R markdown. Supports a pseudo-line-by-line concept Good for writing, since it allows for MarkDown. While I know a lot of people like jupyter, I only really use it for examples of code, not my personal programming. I never liked how it had to open via a Web Browser. I would rather have it be separate program. However, all guides were made using this IDE

PyCharm

This is the IDE I currently use

Advantages:

Easily set up virtual environments, interacts natively with Git, supports different file formats with plug-ins (e.g. .md), enforces certain coding conventions, better debug code features, organization of files under the project tab are convenient

Disadvantages:

Not great for running line-by-line code (it can do it, just not as elegantly), little more hardcore (I wouldn't really consider it a beginner's IDE. It requires some knowledge of set-up of Python)

IDLE

Ships with the basic Python 3.x installation. It is very basic and does not support line-by-line. Wouldn't recommend unless you are just starting with Python and don't want to commit to an IDE yet

Spyder

Ships with conda. Not bad but I didn't use it that much (I couldn't get the hang of it). Similarly it is an RStudio copy. Can't say too much since I haven't used it extensively

Basic Introduction to Python

If you have never used Python before, I have created some introductory materials to Python and the data management library I use, pandas. These are basic guides, but they also point to other resources. Please READ ALL OF THE BELOW BEFORE PROCEEDING.

Installing Python

To install, Python 3.x, we can download it directly from: https://www.python.org/downloads/

The installer provides an option to add Python3 to PATH, it is highly recommended you do this, since it allows you to avoid having to do it manually

Open Command Prompt / Terminal. When opened, type python and this should open Python in the same window. From here, you can quit by typing 'quit()' or closing the window. If this does NOT work, make sure your environmental variable was created properly

Installing Python Packages

Packages are what stores Python functions that we will use. These packages are contributed by various members of the community (including me)) and there is a wide array. To be able to download packages, we need to make sure we have an environmental variable created for python. We will discuss how to install packages

Python 3.x conveniently comes with a package manager. Basically it stores all the packages and we can use it to download new ones or update already downloaded ones.

To download a new package: Open Command Prompt/Terminal and use the following code (we will be installing pandas)

pip install pandas

To update a Python package, type the following command into Command Prompt. For example, we will update our pandas package

pip install pandas --upgrade

That concludes the basics. Please review parts 1 and 2 of the tutorials next

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