Preface
https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/00.00-Preface.ipynb
1. IPython: Beyond Normal Python
https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.00-IPython-Beyond-Normal-Python.ipynb
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
https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.00-Introduction-to-NumPy.ipynb
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
https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.00-Introduction-to-Pandas.ipynb
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
https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.00-Introduction-To-Matplotlib.ipynb
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
https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.00-Machine-Learning.ipynb
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
https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb