Python for Social Scientists: A Practical Guide

This guide provides a comprehensive, beginner-friendly introduction to Python programming for social science researchers. It is designed to help researchers move beyond tools like Excel and SPSS, offering more scalable, flexible, and reproducible approaches to data analysis and statistical work. Written specifically with social scientists in mind, the guide walks readers through the entire research workflow using Python—from installing the right tools and setting up the Python environment, to handling tabular data with pandas, performing statistical tests with statsmodels, and creating clear, publication-ready visualisations using matplotlib and seaborn. The guide also introduces essential programming concepts (variables, data types, functions), explains how to clean and transform real-world datasets, and demonstrates how to automate repetitive tasks. Each section is supported by examples and annotated code, and the entire guide was developed using Jupyter Notebooks to encourage interactivity and hands-on learning. No prior programming experience is required. This resource is intended for researchers, postgraduate students, and support staff in the social sciences who want to explore data more deeply, improve transparency and reproducibility, and build confidence with coding in a practical, applied context. This guide is part of a set of deliverables coming out of the Research Software Practices in the Social Sciences project, which received additional funding as part of the UK Software Sustainability Institute: Phase 4, supported through the UKRI Digital Research Infrastructure Programme (grant number AH/Z000114/1)