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chapters/Ch.02.DataTypes.ipynb (40702, 2023-11-09)
chapters/Ch.03.Collections.ipynb (35179, 2023-11-09)
chapters/Ch.04.ConditionalsLoopsErrors.ipynb (57124, 2023-11-09)
chapters/Ch.05.FunctionsClasses.ipynb (26437, 2023-11-09)
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latex/build_book.py (1501, 2023-11-09)
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pdf/IntroducingPython.pdf (417269, 2023-11-09)

# Introducing Python An introductory course on Python Programming from [Bernie Hogan](https://www.oii.ox.ac.uk/people/profiles/bernie-hogan/) at the [Oxford Internet Institute](https://www.oii.ox.ac.uk/). This course goes through some of the basics of Python using Jupyter Lab. The course can be considered as a [prinable PDF document](https://github.com/berniehogan/introducingpython/blob/main/pdf/IntroducingPython.pdf) or as a series of Jupyter Lab notebooks. The notebooks will allow you to run the code yourself and tinker with it. These can be run on your own machine by cloning this repository to your desktop. You can also save the raw files as `*.ipynb` (such as `Ch.00.Prologue.ipynb`) and run them locally. Then you can run them using Jupyter Lab. You can alternatively run them online using the buttons at the top of each file that signal "Run in Colab" or "launch binder", like the following: [](https://mybinder.org/v2/gh/berniehogan/introducingpython/main?filepath=chapters%2FCh.00.Prologue.ipynb) [](https://colab.research.google.com/github/berniehogan/introducingpython/blob/main/chapters/Ch.00.Prologue.ipynb) Jupyter Lab is covered in Chapter 1. So if you do not have it installed, you can read this via the PDF, the GitHub repository, or via Colab / Binder until you get it sorted. ## Table of contents - **Prologue**. A short introduction and welcome; - **Chapter 1**. An orientation to Jupyter Lab and programming in Python; - **Chpater 2**. Introducing simple data types: from characters to numbers to strings; - **Chapter 3**. Collections: ordering data by position, index, and membership; - **Chapter 4**. Conditionals, loops, and errors: How to change the flow of a program; - **Chapter 5**. Functions and object-oriented programming: Using abstraction to limit redundancy; - **Chpater 6**. The file system; - **Exercises**. Short exercises, answers to the short exerciess, and some longer exercises which might not have a specific "goal" but are more creative. This course does not have a large number of exercises. Instead, there are at the end a small number of projects that you might want to complete in order to get a feel for the material and show some of your creativity along the way. This book runs through pretty well-worn territory and despite its inclusion in a social science degree, there is not much social science here. Instead, these are the basic grammar of Python that you would use regardless of your eventual destination. This should cover a lot of the same material as other Introducing Python courses broadly. That said, I hope my pacing, language, and resources bring some value add to this. For social science, computational social science, and some data science students, I would then recommend my forthcoming book, "From Social Science to Data Science", which goes through the next set of skills. These include how to wrangle data in DataFrames, collect data using an API, merge data, and look at social data as text, networks, and geographies. It is due out in December 2022. The companion [GitHub archive is here](https://www.github.com/berniehogan/fsstds) [Note Forthcoming prior to December 2022].

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