Jupyter notebook content for my OReilly book, the Python Data Science Handbook

This repository will contain the full listing of IPython notebooks used to create the book, including all text and code. The code was tested with Python 3.5, though most (but not all) snippets will work correctly in Python 2.7.

See also the free companion project, A Whirlwind Tour of Python: a fast-paced introduction to the Python language aimed at researchers and scientists.

I am currently doing the final edits of these notebooks, and will post them as I make my way through. See the content here:

• 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

• 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

• 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

The notebooks above are still being edited. In the meantime, you can see all the code from an earlier draft of the book in code_listings. The code is in IPython notebooks, organized by book chapter and section. All code from this book was tested with Python 3.4-3.5, though most of it is compatible with Python 2.7 as well.

The code here is released under the MIT license. Read more at the Open Source Initiative.

The text content of the book is released under the CC-BY-NC-ND license. Read more at Creative Commons.