https://jakevdp.github.io/PythonDataScienceHandbook/

Python Data Science Handbook

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Python Data Science Handbook

Jake VanderPlas

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 ContentsP

PrefaceP

1. IPython: Beyond Normal PythonP

  * 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 NumPyP

  * 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 PandasP

  * 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 MatplotlibP

  * 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 LearningP

  * 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 CodeP