![]() ![]() ![]() This is outside my field, so I cannot comment on its accuracy, let alone understand everything the author writes about in this chapter. The Notebook itself is rarely discussed.Ĭhapter four is all about the pandas project, which is a powerful library for data handling and analytics. This chapter is about creating plots and animations with Matplotlib inside the Notebook. From this point on until we reach the appendices, the book is basically for scientists. I liked this chapter quite a bit.įor chapter three, we change gears dramatically. The chapter ends with a few examples showing how to embed images, Youtube videos and HTML in your Notebook. You will also learn how to run script and load and save data. Here we learn how to edit and navigate the Notebook and IPython magics. In chapter two, we learn more about the Notebook's interface. The chapter ends with a few brief exercises. I also thought it was a good way to demonstrate the Notebook's capabilities. That may sound a little strange, but it was actually pretty interesting to learn about how to calculate how much coffee cools over time. Then you learn how to run a notebook and the author goes into an example using a coffee cooling algorithm. The author recommends that you use the Anaconda distribution of Python and that you sign up for Wakari to make sharing Notebooks easier. The book starts out by giving the reader a tour of the IPython Notebook in chapter one. The book is split up into 5 chapters and 3 appendices. You can get this book in paperback, epub, mobi, or PDF. I’d give it to: Someone who already knows Python and is a data scientist.Why I finished it: I only read the Notebook sections of the book.Why I picked it up: I was asked by the publisher to read the book.I've always been interesting in learning about the IPython Notebook, but had never gotten around to it, so this seemed like a great way to learn more. This week, Packt Publishing asked me to review IPython Notebook Essentials by L.
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