Further Reading: Python for Sports Analytics

Essential Resources

pandas Documentation

Official pandas Documentation https://pandas.pydata.org/docs/ The comprehensive reference for all pandas functionality. Bookmark the User Guide and API reference.

10 Minutes to pandas https://pandas.pydata.org/docs/user_guide/10min.html Quick introduction covering the essentials. Great for refreshers.

pandas Cookbook https://pandas.pydata.org/docs/user_guide/cookbook.html Practical recipes for common operations. Invaluable problem-solving resource.

NumPy Documentation

NumPy Documentation https://numpy.org/doc/ Official NumPy reference with comprehensive API documentation.

NumPy Quickstart Tutorial https://numpy.org/doc/stable/user/quickstart.html Fast introduction to array operations and broadcasting.

Books

Python for Data Analysis, 3rd Edition by Wes McKinney (2022) The definitive pandas guide by its creator. Chapters 5-10 cover everything from this chapter and more.

Python Data Science Handbook by Jake VanderPlas (2016) Excellent coverage of pandas, NumPy, and matplotlib. Available free online. https://jakevdp.github.io/PythonDataScienceHandbook/

Effective pandas by Matt Harrison (2021) Modern pandas techniques with focus on method chaining and idiomatic code.

High Performance Python by Micha Gorelick and Ian Ozsvald (2020) Deep dive into Python performance. Chapter on pandas optimization is excellent.

Sports Analytics Specific

Online Tutorials

Open Source Football https://opensourcefootball.com/ Community tutorials specifically for football analytics with Python.

The Athletic - Sports Analytics Articles In-depth methodology articles from professional analysts.

Sports Reference Glossary https://www.sports-reference.com/cfb/about/glossary.html Definitions of football statistics helpful when building analysis functions.

Video Courses

DataCamp - pandas Courses https://www.datacamp.com/courses/pandas-foundations Interactive pandas learning with sports-friendly examples.

Coursera - Applied Data Science with Python University of Michigan specialization including extensive pandas coverage.

YouTube - Corey Schafer pandas Tutorial https://www.youtube.com/playlist?list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS Free, high-quality video tutorials on pandas fundamentals.

Advanced Topics

Performance Optimization

"Enhancing Performance" - pandas docs https://pandas.pydata.org/docs/user_guide/enhancingperf.html Official guide to pandas performance optimization.

"Why Your pandas Code is Slow" - Matt Harrison blog posts Practical advice on common performance mistakes.

Apache Arrow and pandas https://arrow.apache.org/docs/python/pandas.html Integration between Arrow and pandas for high-performance data processing.

Method Chaining

"Modern pandas" articles by Tom Augspurger https://tomaugspurger.github.io/modern-1-intro/ Excellent series on idiomatic, modern pandas code style.

"pipe() for Method Chaining" Using the pipe method for custom functions in chains.

Working with Large Data

Dask Documentation https://docs.dask.org/en/stable/ Parallel computing library that scales pandas to larger-than-memory datasets.

"Working with Large CSVs in Python" - Various tutorials Techniques for handling files that don't fit in memory.

Parquet and pandas https://pandas.pydata.org/docs/user_guide/io.html#parquet Documentation for the Parquet format integration.

Reference Cards and Cheat Sheets

pandas Cheat Sheet - DataCamp https://www.datacamp.com/cheat-sheet/pandas-cheat-sheet-for-data-science-in-python Two-page PDF covering common operations.

NumPy Cheat Sheet https://www.datacamp.com/cheat-sheet/numpy-cheat-sheet-data-analysis-in-python Quick reference for array operations.

Python for Data Science Cheat Sheet Comprehensive overview of pandas, NumPy, and visualization.

Community Resources

Forums and Q&A

Stack Overflow [pandas] tag https://stackoverflow.com/questions/tagged/pandas Massive repository of pandas questions and answers.

Reddit r/learnpython Active community for Python learning questions.

Reddit r/datascience Data science community with pandas discussions.

Open Source Projects

cfbfastR GitHub https://github.com/sportsdataverse/cfbfastR R package for college football data - code patterns transferable to Python.

nflfastR GitHub https://github.com/nflverse/nflfastR NFL data package with excellent data processing examples.

Practice Resources

Datasets for Practice

Kaggle College Football Datasets https://www.kaggle.com/search?q=college+football Free datasets for practice with real football data.

FiveThirtyEight Data https://github.com/fivethirtyeight/data Sports analytics datasets with documented methodology.

Coding Practice

HackerRank - Python Practice https://www.hackerrank.com/domains/python Programming challenges to sharpen Python skills.

LeetCode - Database Problems https://leetcode.com/problemset/database/ SQL-like problems that translate to pandas thinking.

Project Euler https://projecteuler.net/ Mathematical programming challenges for NumPy practice.

Tools Mentioned in This Chapter

Development Environment

Jupyter Lab https://jupyter.org/ Interactive development environment for data analysis.

pip install jupyterlab
jupyter lab

VS Code with Python Extension https://code.visualstudio.com/docs/python/python-tutorial Full-featured editor with excellent pandas support.

Package Installation

# Core packages
pip install pandas numpy

# For Parquet support
pip install pyarrow

# For Jupyter
pip install jupyterlab

# For performance profiling
pip install line_profiler memory_profiler

Suggested Learning Path

  1. Foundation (Week 1-2) - Complete pandas 10-minute intro - Work through Chapter 3 exercises Levels 1-2 - Read VanderPlas Chapter 2-3

  2. Intermediate (Week 3-4) - Complete Chapter 3 exercises Levels 3-4 - Read McKinney Chapters 5-8 - Practice with Kaggle datasets

  3. Advanced (Week 5+) - Complete Chapter 3 Level 5 exercises - Read Modern pandas series - Study performance optimization guides - Build Case Study 1 project

Notes

  • Links accurate as of publication date
  • Some resources require free accounts
  • Video quality varies - preview before committing
  • Stack Overflow answers may be outdated - check dates
  • Community resources constantly evolving