Data Science in Python, Volume 1: Data I/O, Jupyter Notebook, GUI, Deployment, Numeric Programming, High Performance Python

This volume covers the fundamentals of scientific Python programming, and I assume you are familiar with Python 3. If you need help with obtaining and setting up a scientific Python 3 distribution, make sure to check out volume 0 of this series.

In this volume I will show how to:

• Read data from a tab delimited text file, sort, filter, and recover from errors


• Save data in a tab delimited text or in rich Microsoft Excel format


• Use an IPython notebook to quickly prototype your program, explore your data interactively, document, and share your research.


• Give your program a Graphic User Interface (GUI) to make it useful for non-programmers.


• Package a Python program for deployment on other computers


• Use Numpy for number crunching


• Make the Python program run as fast as compiled code


• Use multiple cores or processors for parallel execution of a Python program

You might also want to look at volume 2 describing plotting with Matplotlib and using Python together with SQLite database for data analysis.