[GitHub]Python Data Science Handbook

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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
  

[/url][url=http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.00-Introduction-to-Pandas.ipynb]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
  

[/url][url=http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.00-Introduction-To-Matplotlib.ipynb]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
  

[/url][url=http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.00-Machine-Learning.ipynb]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
  

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