Previous edition: 2016

Part I : Jupyter : Beyond normal Python -- Chapter 1 : Getting started in in IPython and Jupyter -- Chapter 2 : Enhanced interactive features -- Chapter 3 : Debugging and profiling -- Part II : Introduction to NumPy -- Chapter 4 : Understanding data types in Python -- Chapter 5 : The basics of NumPy arrays -- Chapter 6 : Computation on NumPy arrays : Universal functions -- Chapter 7 : Aggregations : min, max, and everything in between -- Chapter 8 : Computation on arrays : broadcasting -- Chapter 9 : Comparisons, masks, and boolean logic -- Chapter 10 : Fancy indexing -- Chapter 11 : Sorting arrays -- Chapter 12 : Structured data : NumPy's structured arrays -- Part III : Data manipulation with Pandas -- Chapter 13 : Introducing Pandas objects -- Chapter 14 : Data indexing and selection -- Chapter 15 : Operating on data in Pandas -- Chapter 16 : Handling missing data -- Chapter 17 : Hierarchial indexing -- Chapter 18 : Combining datasets : concat and append -- Chapter 19 : Combining datasets : merge and join -- Chapter 20 : Aggregation and grouping -- Chapter 21 : Pivot tables -- Chapter 22 : Vectorized string operations -- Chapter 23 : Working with time series -- Chapter 24 : High-performace Pandas : eval and query -- Part IV : Visualization with Matplotlib -- Chapter 25 : General Matplotlib tips -- Chapter 26 : Simple line plots -- Chapter 27 : Simple scatter plots -- Chapter 28 : Density and contour plots -- Chapter 29 : Customizing plot legends -- Chapter 30 : Customizing colorbars -- Chapter 31 : Multiple subplots -- Chapter 32 : Text and annotation -- Chapter 33 : Customizing ticks -- Chapter 34 : Customizing Matplotlib : Configurations and stylesheets -- Chapter 35 : Three-dimensional plotting in Matplotlib -- Chapter 36 : Visualization with Seaborn -- Part V : Machine learning -- Chapter 37 : What is machine learning? -- Chapter 38 : Introducing Scikit-Learn -- Chapter 39 : Hyperparameters and model validation -- Chapter 40 : Feature engineering -- Chapter 41 : In depth : Naive bayes classification -- Chapter 42 : In depth : Linear regression -- Chapter 43 : In depth : Support vector machines -- Chapter 44 : In depth : Decision trees and random forests -- Chapter 45 : In depth : Principal component analysis -- Chapter 46 : In depth : Manifold learning -- Chapter 47 : In depth : k-means clustering -- Chapter 48 : In depth : Gaussian mixture models -- Chapter 49 : In depth : Kernel density estimation -- Chapter 50 : Application : a face detection pipeline

Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all--IPython, NumPy, pandas, Matplotlib, scikit-learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find the second edition of this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. -- Provided by publisher