Python Data Science Handbook

Jake VanderPlas

This is the Jupyter notebook version of the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub.* The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. If you find this content useful, please consider supporting the work by buying the book!

Table of Contents

Preface

1. IPython: Beyond Normal Python

• Help and Documentation in IPython

• Keyboard Shortcuts in the IPython Shell

• IPython Magic Commands

• Input and Output History

• IPython and Shell Commands

• Errors and Debugging

• Profiling and Timing Code

• More IPython Resources

2. Introduction to NumPy

• Understanding Data Types in Python

• The Basics of NumPy Arrays

• Computation on NumPy Arrays: Universal Functions

• Aggregations: Min, Max, and Everything In Between

• Computation on Arrays: Broadcasting

• Comparisons, Masks, and Boolean Logic

• Fancy Indexing

• Sorting Arrays

• Structured Data: NumPy's Structured Arrays

3. Data Manipulation with Pandas

• Introducing Pandas Objects

• Data Indexing and Selection

• Operating on Data in Pandas

• Handling Missing Data

• Hierarchical Indexing

• Combining Datasets: Concat and Append

• Combining Datasets: Merge and Join

• Aggregation and Grouping

• Pivot Tables

• Vectorized String Operations

• Working with Time Series

• High-Performance Pandas: eval() and query()

• Further Resources

4. Visualization with Matplotlib

• Simple Line Plots

• Simple Scatter Plots

• Visualizing Errors

• Density and Contour Plots

• Histograms, Binnings, and Density

• Customizing Plot Legends

• Customizing Colorbars

• Multiple Subplots

• Text and Annotation

• Customizing Ticks

• Customizing Matplotlib: Configurations and Stylesheets

• Three-Dimensional Plotting in Matplotlib

• Geographic Data with Basemap

• Visualization with Seaborn

• Further Resources

5. Machine Learning

• What Is Machine Learning?

• Introducing Scikit-Learn

• Hyperparameters and Model Validation

• Feature Engineering

• In Depth: Naive Bayes Classification

• In Depth: Linear Regression

• In-Depth: Support Vector Machines

• In-Depth: Decision Trees and Random Forests

• In Depth: Principal Component Analysis

• In-Depth: Manifold Learning

• In Depth: k-Means Clustering

• In Depth: Gaussian Mixture Models

• In-Depth: Kernel Density Estimation

• Application: A Face Detection Pipeline

• Further Machine Learning Resources

Appendix: Figure Code