Data Science in Python, Volume 2: 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 1 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 3 describing plotting with Matplotlib and using Python together with SQLite database for data analysis.

Author: Alexander Stepanov

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