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
- 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
- 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
The code in the book was tested with Python 3.5, though most (but not all) will also work correctly with Python 2.7 and other older Python versions.
The packages required to run the code in the book are listed in requirements.txt. To install these using conda, run the following at the command-line:
$ conda install --file requirements.txt
To create a stand-alone environment named PDSH with Python 3.5 and all the required package versions, run the following:
$ conda create -n PDSH python=3.5 --file requirements.txt
You can read more about using conda environments in the Managing Environments section of the conda documentation.
The notebooks above are still being edited. In the meantime, you can see the code from an earlier draft of the book in code_listings. This will be removed once the full notebooks are posted.
The code in this repository, including all code samples in the notebooks listed above, 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.