# IPython Minibook

### Learning IPython for Interactive Computing and Data Visualization, second edition

Packt Publishing (175 pages, 25$, 10/2015)

This book is a beginner-level introduction to Python for data analysis:

- Interactive Python with IPython
- Data analysis with pandas
- Numerical computing with NumPy/SciPy
- Data visualization with matplotlib

This book is a **beginner-friendly introduction to IPython/Jupyter** for *data analysis*, *interactive visualization*, *numerical computing*, and *high-performance computing*. It is perfectly suitable to beginners with no programming experience (an introduction to the Python language is provided in the first chapter).

It targets students, teachers, researchers, engineers, analysts, journalists, hobbyists, and all data enthusiasts who are interested in analyzing and visualizing real-world datasets.

## Table of contents

All code examples are freely available on GitHub as Jupyter Notebooks.

### 1. Getting started with IPython

- 1.1. What are Python, IPython, and Jupyter?
- 1.2. Installing Python with Anaconda
- 1.3. Introducing the Notebook
- 1.4. A crash course on Python
- 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
- 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