Minimalist Data Wrangling with Python

Minimalist Data Wrangling with Python is envisaged as a student’s first introduction to data science, providing a high-level overview as well as discussing key concepts in detail. We explore methods for cleaning data gathered from different sources, transforming, selecting, and extracting features, performing exploratory data analysis and dimensionality reduction, identifying naturally occurring data clusters, modelling patterns in data, comparing data between groups, and reporting the results.

Although available online, it is a whole course, and should be read from the beginning to the end. In particular, refer to the Preface for general introductory remarks.

For many students around the world, educational resources are hardly affordable. Therefore, I have decided that this book should remain an independent, non-profit, open-access project (available both in PDF and HTML forms). Whilst, for some people, the presence of a “designer tag” from a major publisher might still be a proxy for quality, it is my hope that this publication will prove useful to those who seek knowledge for knowledge’s sake.

You can also order a paper copy.

Any bug/typo reports/fixes are appreciated. Please submit them via this project’s GitHub repository. Thank you.

Citation: Gagolewski M. (2024), Minimalist Data Wrangling with Python, Melbourne, DOI: 10.5281/zenodo.6451068, ISBN: 978-0-6455719-1-2, URL: https://datawranglingpy.gagolewski.com/.

Make sure to check out Deep R Programming [36] too.

Copyright (C) 2022–2024 by Marek Gagolewski. Some rights reserved. This material is published under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

Start here

• Preface
  • The art of data wrangling
  • Aims, scope, and design philosophy
  • Structure
  • The Rules
  • About the author
  • Acknowledgements
  • You can make this book better

Introducing Python

• 1. Getting started with Python
  • 1.1. Installing Python
  • 1.2. Working with Jupyter notebooks
  • 1.3. The best note-taking app
  • 1.4. Initialising each session and getting example data
  • 1.5. Exercises
• 2. Scalar types and control structures in Python
  • 2.1. Scalar types
  • 2.2. Calling built-in functions
  • 2.3. Controlling program flow
  • 2.4. Defining functions
  • 2.5. Exercises
• 3. Sequential and other types in Python
  • 3.1. Sequential types
  • 3.2. Working with sequences
  • 3.3. Dictionaries
  • 3.4. Iterable types
  • 3.5. Object references and copying (*)
  • 3.6. Further reading
  • 3.7. Exercises

Unidimensional data

• 4. Unidimensional numeric data and their empirical distribution
  • 4.1. Creating vectors in numpy
  • 4.2. Some mathematical notation
  • 4.3. Inspecting the data distribution with histograms
  • 4.4. Exercises
• 5. Processing unidimensional data
  • 5.1. Aggregating numeric data
  • 5.2. Vectorised mathematical functions
  • 5.3. Arithmetic operators
  • 5.4. Indexing vectors
  • 5.5. Other operations
  • 5.6. Exercises
• 6. Continuous probability distributions
  • 6.1. Normal distribution
  • 6.2. Assessing goodness-of-fit
  • 6.3. Other noteworthy distributions
  • 6.4. Generating pseudorandom numbers
  • 6.5. Further reading
  • 6.6. Exercises

Multidimensional data

• 7. From uni- to multidimensional numeric data
  • 7.1. Creating matrices
  • 7.2. Reshaping matrices
  • 7.3. Mathematical notation
  • 7.4. Visualising multidimensional data
  • 7.5. Exercises
• 8. Processing multidimensional data
  • 8.1. Extending vectorised operations to matrices
  • 8.2. Indexing matrices
  • 8.3. Matrix multiplication, dot products, and Euclidean norm (*)
  • 8.4. Pairwise distances and related methods (*)
  • 8.5. Exercises
• 9. Exploring relationships between variables
  • 9.1. Measuring correlation
  • 9.2. Regression tasks (*)
  • 9.3. Finding interesting combinations of variables (*)
  • 9.4. Further reading
  • 9.5. Exercises

Heterogeneous data

• 10. Introducing data frames
  • 10.1. Creating data frames
  • 10.2. Aggregating data frames
  • 10.3. Transforming data frames
  • 10.4. Indexing Series objects
  • 10.5. Indexing data frames
  • 10.6. Further operations on data frames
  • 10.7. Exercises
• 11. Handling categorical data
  • 11.1. Representing and generating categorical data
  • 11.2. Frequency distributions
  • 11.3. Visualising factors
  • 11.4. Aggregating and comparing factors
  • 11.5. Exercises
• 12. Processing data in groups
  • 12.1. Basic methods
  • 12.2. Plotting data in groups
  • 12.3. Classification tasks (*)
  • 12.4. Clustering tasks (*)
  • 12.5. Further reading
  • 12.6. Exercises
• 13. Accessing databases
  • 13.1. Example database
  • 13.2. Exporting data to a database
  • 13.3. Exercises on SQL vs pandas
  • 13.4. Closing the database connection
  • 13.5. Common data serialisation formats for the Web
  • 13.6. Working with many files
  • 13.7. Further reading
  • 13.8. Exercises

Other data types

• 14. Text data
  • 14.1. Basic string operations
  • 14.2. Working with string lists
  • 14.3. Formatted outputs for reproducible report generation
  • 14.4. Regular expressions (*)
  • 14.5. Exercises
• 15. Missing, censored, and questionable data
  • 15.1. Missing data
  • 15.2. Censored and interval data (*)
  • 15.3. Incorrect data
  • 15.4. Outliers
  • 15.5. Exercises
• 16. Time series
  • 16.1. Temporal ordering and line charts
  • 16.2. Working with date-times and time-deltas
  • 16.3. Basic operations
  • 16.4. Further reading
  • 16.5. Exercises

Appendix

• Changelog
• References