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