Changelog

Important

Any bug/typo reports/fixes are appreciated. The most up-to-date version of this book can be found at https://datawranglingpy.gagolewski.com/.

Below is the list of the most noteworthy changes.

• under development (v1.0.3.9xxx):

  • New HTML theme (includes the light and dark mode).

  • Not using seaborn where it can easily be replaced by 1–3 calls to the lower-level matplotlib, especially in the numpy chapters. This way, we can learn how to create some popular charts from scratch. In particular, we are now using own functions to display a heat map and a pairs plot.

  • Use numpy.genfromtxt more eagerly.

  • A few more examples of using f-strings for results’ pretty-printing.

  • Bug fixes and loads of other minor extensions.

  • (…) to do (…) work in progress (…) more to come (…)

• 2023-02-06 (v1.0.3):

  • Numeric reference style; updated bibliography.

  • Reduce the file size of the screen-optimised PDF at the cost of a slight decrease of the quality of some figures.

  • The print-optimised PDF now uses selective rasterisation of parts of figures, not whole pages containing them. This should increase the quality of the printed version of this book.

  • Bug fixes.

  • Minor extensions, including: pandas.Series.dt.strftime, more details how to avoid pitfalls in data frame indexing, etc.

• 2022-08-24 (v1.0.2):

  • The first printed (paperback) version can be ordered from Amazon.

  • Fixed page margin and header sizes.

  • Minor typesetting and other fixes.

• 2022-08-12 (v1.0.1):

  • Cover.

  • ISBN 978-0-6455719-1-2 assigned.

• 2022-07-16 (v1.0.0):

  • Preface complete.

  • Handling tied observations.

  • Plots now look better when printed in black and white.

  • Exception handling.

  • File connections.

  • Other minor extensions and material reordering: more aggregation functions, pandas.unique, pandas.factorize, probability vectors representing binary categorical variables, etc.

  • Final proofreading and copyediting.

• 2022-06-13 (v0.5.1):

  • The Kolmogorov–Smirnov Test (one and two sample).

  • The Pearson Chi-Squared Test (one and two sample and for independence).

  • Dealing with round-off and measurement errors.

  • Adding white noise (jitter).

  • Lambda expressions.

  • Matrices are iterable.

• 2022-05-31 (v0.4.1):

  • The Rules.

  • Matrix multiplication, dot products.

  • Euclidean distance, few-nearest-neighbour and fixed-radius search.

  • Aggregation of multidimensional data.

  • Regression with k-nearest neighbours.

  • Least squares fitting of linear regression models.

  • Geometric transforms; orthonormal matrices.

  • SVD and dimensionality reduction/PCA.

  • Classification with k-nearest neighbours.

  • Clustering with k-means.

  • Text Processing and Regular Expression chapters merged.

  • Unidimensional Data Aggregation and Transformation chapters merged.

  • pandas.GroupBy objects are iterable.

  • Semitransparent histograms.

  • Contour plots.

  • Argument unpacking and variadic arguments (*args, **kwargs).

• 2022-05-23 (v0.3.1):

  • More lightweight mathematical notation.

  • Some equalities related to the mathematical functions we rely on (the natural logarithm, cosine, etc.).

  • A way to compute the most correlated pair of variables.

  • A note on modifying elements in an array and on adding new rows and columns.

  • An example seasonal plot in the time series chapter.

  • Solutions to the SQL exercises added; to ignore small round-off errors, use pandas.testing.assert_frame_equal instead of pandas.DataFrame.equals.

  • More details on file paths.

• 2022-04-12 (v0.2.1):

  • Many chapters merged or relocated.

  • Added captions to all figures.

  • Improved formatting of elements (information boxes such as note, important, exercise, example).

• 2022-03-27 (v0.1.1):

  • The first public release: most chapters are drafted, more or less.

  • Using Sphinx for building.

• 2022-01-05 (v0.0.0):

  • Project started.