Python Quick Reference

Python Quick Reference is a working desk reference, not a course. It assumes you already know what a mean and a standard deviation are, and gets straight to the Python syntax you need to compute them, plot them, and reproduce the result six months later.

Ten chapters cover the data stack a statistician, data scientist, student, or researcher reaches for daily: NumPy and pandas for numerical and tabular data, reading and writing every common file format, fitting and sampling from probability distributions with SciPy, running the standard battery of hypothesis tests, regression and econometrics with statsmodels, the core scikit-learn machine-learning workflow, publication-quality graphics with Matplotlib and Seaborn, and the reproducibility habits -- environments, profiling, debugging -- that keep an analysis rerunnable. A one-page syntax appendix closes the book for the fastest possible lookup.

Every code listing in this book has been run and its output shown exactly as produced -- if your own output differs, something in your environment differs too, and that is worth tracking down before moving on.