Data Science in Python, Volume 2: Data I/O, Jupyter Notebook, GUI, Deployment, Numeric Programming, High Performance Python

by Alexander Stepanov

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 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 formatUse 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 computersUse Numpy for number crunchingMake the Python program run as fast as compiled codeUse 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.

66 pages, Kindle Edition

Published April 25, 2016

Reviews

[Rajiv Agarwal · Rating 2 out of 5]

Very poorly written book

Only saving grace is that it costs only $2.99 for the kindle edition. Code examples and text don’t match. All kinds of typos throughout the book. Look for another book.

[Stephen · Rating 1 out of 5]

This is volume 2 of a three book series. However volume 1 is 18 pages long, and even for just 99p seemed like a waste of time when it apparently just contains the setup instructions for iPython, which can be easily found on the Internet. I skipped volume 1 and tried volume 2, as this was still reasonably priced, but a longer work purporting to teach data analysis through iPython and Jupyter notebooks.

For the uninitiated, Jupyter notebooks are a wonderful web based alternative to doing your data analysis in a spreadsheet, allowing you to leverage all the power of the python programming language and a set of extensions that can display your analysis instantly in a kind of web based notebook.

However if you think that may be worth a try, let me be quite clear that this is not the book that you need. The author simply launches into a set of instructions on how to use Jupyter notebooks, without spending any time explaining why they may be a good idea. The instructions take you through a series of activities that could be instructional, if they are not so tedious that you soon give up. It is also evident that English is not his first language, and although his English is excellent, he should have got a native speaker to read through his work and put all the missing articles back in that are presumably not present in his own language (I think maybe Russian).

This book is cheap because it is just some guide that someone decided to put together and self publish. It is not terrible, and the price is good, but when buying a book on some technical subject, there are a whole range of things the author must put into the text to sell the subject, add interest, and ensure a good learning experience.

I won’t bother with the third volume.