Python Data Science Handbook | Python Data Science Handbook

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The Wayback Machine - http://web.archive.org/web/20211025075045/https://jakevdp.github.io/PythonDataScienceHandbook/ Jake VanderPlas Book Cover

Book Cover

This website contains the full text of the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub in the form of Jupyter notebooks. 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 ¶