PythonDataScienceHandbook/notebooks at master · jakevdp/PythonDataScienceHandbook
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Python Data Science Handbook: full text in Jupyter Notebooks - jakevdp/PythonDataScienceHandbook
  • 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
  1. 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
  1. 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
  1. 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
  1. 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