IPython minibook, second edition

This repository contains all the code examples as IPython notebooks.

Table of contents

1. Getting started with IPython

• 1.1. What are Python, IPython, and Jupyter?
• 1.2. Installing Python with Anaconda (Complete sample!)
• 1.3. Introducing the Notebook (Complete sample!)
• 1.4. A crash course on Python (Complete sample!)
• 1.5. Ten Jupyter/IPython essentials
• 1.6. Summary

2. Interactive data analysis with pandas

• 2.1. Exploring a dataset in the Notebook
• 2.2. Manipulating data
• 2.3. Complex operations
• 2.4. Summary

3. Numerical computing with NumPy

• 3.1. A primer to vector computing
• 3.2. Creating and loading arrays
• 3.3. Basic array manipulations
• 3.4. Computing with NumPy arrays (Complete sample!)
• 3.5. Summary

4. Interactive plotting and Graphical Interfaces

• 4.1. Choosing a plotting backend
• 4.2. matplotlib and seaborn essentials
• 4.3. Image processing
• 4.4. Further plotting and visualization libraries
• 4.5. Summary

5. High-performance and parallel computing

• 5.1. Accelerating Python code with Numba
• 5.2. Writing C in Python with Cython
• 5.3. Distributing tasks on several cores with IPython.parallel
• 5.4. Further high-performance computing techniques
• 5.5. Summary

6. Customizing IPython

• 6.1. Creating a custom magic command in an IPython extension
• 6.2. Writing a new Jupyter kernel
• 6.3. Displaying rich HTML elements in the Notebook
• 6.4. Customizing the Notebook interface with JavaScript
• 6.5. Summary