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Python companion guides for Quantitative Social Science: An Introduction.

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# Python code for *Quantitative Social Science: An Introduction*

Welcome! This repository contains Python companion guides for Kosuke Imai's [Quantitative Social Science](https://press.princeton.edu/books/paperback/9780691175461/quantitative-social-science) (QSS).

The `qsspy` code is available for all book chapters (in `.py`, `.ipynb`, and `.pdf` file formats):

1. [Introduction](https://github.com/jeffallen13/qsspy/tree/main/INTRO)
2. [Causality](https://github.com/jeffallen13/qsspy/tree/main/CAUSALITY)
3. [Measurement](https://github.com/jeffallen13/qsspy/tree/main/MEASUREMENT)
4. [Prediction](https://github.com/jeffallen13/qsspy/tree/main/PREDICTION)
5. [Discovery](https://github.com/jeffallen13/qsspy/tree/main/DISCOVERY)
6. [Probability](https://github.com/jeffallen13/qsspy/tree/main/PROBABILITY)
7. [Uncertainty](https://github.com/jeffallen13/qsspy/tree/main/UNCERTAINTY)

## Setup

The companion guides focus on replicating the QSS analysis in Python. Currently, they do not contain detailed instructions on setting up a Python environment. Fortunately, there are many excellent and free resources available online that provide guidance in this respect. Below are a few recommendations.

### Installation and Package Management

A good option is to follow the [Installation and Setup](https://wesmckinney.com/book/preliminaries#installation_and_setup) instructions in Wes McKinney's [Python for Data Analysis, 3E](https://wesmckinney.com/book/). The instructions walk readers through a few important steps:

- Installing Python via [miniconda](https://docs.conda.io/projects/miniconda/en/latest/) 
- Working with [conda environments](https://conda.io/projects/conda/en/latest/user-guide/index.html)
- Installing packages from the [conda-forge](https://conda-forge.org/) channel

The approach outlined above is one among many options. Other popular ways to install Python and manage packages include:

- Installing Python from the [Python Software Foundation](https://www.python.org/downloads/), using the [pip](https://pip.pypa.io/en/stable/) package manager to install packages from the [Python Package Index](https://pypi.org/) (PyPI), and managing packages with Python [virtual environments](https://docs.python.org/3/tutorial/venv.html). This approach is similar to installing miniconda and using conda, conda-forge, and conda environments. Note that these approaches are not mutually exclusive. For example, you can use pip to install packages from PyPI into a conda environment.
- Installing an [Anaconda](https://www.anaconda.com/) distribution of Python. Anaconda comes pre-loaded with many data analysis packages. Anaconda offers a free individual version. 
- Using container-based approaches, such as [Docker](https://www.docker.com/). This is a more advanced workflow that is widely used for deploying applications.

### Required Packages

After setting up a Python environment, you will need to install the packages that are necessary for running the `qsspy` code. The following packages are used extensively:

- [pandas](https://pandas.pydata.org/)
- [numpy](https://numpy.org/) 
- [matplotlib](https://matplotlib.org/)
- [seaborn](https://seaborn.pydata.org/)
- [statsmodels](https://www.statsmodels.org/stable/index.html)
- [scikit-learn](https://scikit-learn.org/stable/)
- [scipy](https://www.scipy.org/)

Chapter 5, `Discovery`, also makes targeted use of nltk, wordcloud, python-igraph, and geopandas.

If you are working with conda, you can use the `environment.yml` file contained in this repository to [create a conda environment](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-from-an-environment-yml-file) that includes the required packages.

### Integrated Development Environments

There are a variety of popular integrated development environments (IDEs) for conducting data analysis in Python, including:

- [VS Code](https://code.visualstudio.com/)
- [PyCharm](https://www.jetbrains.com/pycharm/)
- [Spyder](https://www.spyder-ide.org/) 
- [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/)

The companion guides were built using the VS Code IDE and VS Code's Jupyter notebooks integration. If you elect to use VS Code, helpful documentation pages include:

- [Getting Started with Python in VS Code](https://code.visualstudio.com/docs/python/python-tutorial)
- [Jupyter Notebooks in Visual Studio Code](https://code.visualstudio.com/docs/datascience/jupyter-notebooks)
- [Data Science in VS Code Tutorial](https://code.visualstudio.com/docs/datascience/data-science-tutorial)
- [Python Interactive Window](https://code.visualstudio.com/docs/python/jupyter-support-py)

For those coming from an R background, Spyder is similar to RStudio.

## References

>Imai, Kosuke (2017). *Quantitative Social Science: An Introduction.* Princeton University Press. 
>
>McKinney, Wes (2022). *Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter, 3rd Edition.* O'Reilly Media.

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