What do you think?
Rate this book
Learning IPython for Interactive Computing and Data Visualization: Learn Ipython for Interactive Python Programming, High-performance Numerical Computing, and Data Visualization
Learning IPython for Interactive Computing and Data Visualization
123 pages, Paperback
First published January 1, 2013
18 people are currently reading
67 people want to read
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 - 14 of 14 reviews
November 7, 2013
The book introduces the IPython basics and then focuses on how to combine IPython with some of the most useful libraries for data analysis such as Numpy, Matplotlib, Basemap and Pandas. Every topic is covered with examples and the code presented is also available online. The references proposed are always up-to-date and give the reader the opportunity to discovery resources not covered in the book.
In conclusion, this book definitely achieves its goal to provide a technical introduction to IPython. It is intended for Python users who want an easy to follow introduction to IPython, but also experienced users will find this book useful. It is to notice that, at the moment, this is the only book about IPython.
In conclusion, this book definitely achieves its goal to provide a technical introduction to IPython. It is intended for Python users who want an easy to follow introduction to IPython, but also experienced users will find this book useful. It is to notice that, at the moment, this is the only book about IPython.
May 23, 2013
This is a concise book (only 138 pages) that introduce you Ipython, a very powerful tool for computing and data visualization. The book easily exaplain: 1) how to manipulate arrays using python numerical libraries, 2) how to plot data, maps and create animations and 3) how to parallelize codes using Ipython. The book is easy to read and full of practical examples, It does not require to be a python "guru", even if the reader it is supposed to have a basic knowledge of the language. The large number of exampels within the book allow to learn Ipython basics quikly and without much efford. The book is a must for who would like to learn python for scientific applications.
April 5, 2022
It worked as a refresher/introduction to new but very specific algorithms.
If you are looking for an introductory text, this is clearly not it (it is for interactive computing and visualization, so it assumes some base knowledge to begin with)
If you are looking for an introductory text, this is clearly not it (it is for interactive computing and visualization, so it assumes some base knowledge to begin with)
June 26, 2017
Buen libro sobre ipython en general, pero se queda un poco corto en el tema de visualización.
May 6, 2024
A quick survey of many ideas, but not much good for learning how to use those ideas.
May 10, 2015
The book falls short as it fails to focus on IPython. [Interestingly, this seems to be a recurring issue across IPython books from PacktPub.]
The book does a good job introducing IPython in Chapter 2. In chapters 3, the book describes how to use NumPy from within IPython. It is not clear if this chapter is intended to make the reader proficient with NumPy or IPython or the combination. The exposition about NumPy (and Pandas) is very limited; of course, NumPy is rich. Further, this chapter neither introduces new IPython features specific to computing nor describes nuances of previously introduced IPython features in the context of computing. This treatment continues in chapter 4 where the book talks about IPython and visualization.
When I picked the book, I wanted to learn about IPython features along with its facets specific to computation and visualization. When I put it down, I had learned about the basic features of IPython and was unclear if there were features specific to computation and visualization.
An ideal IPython book would talk about the features of IPython (shell and notebook), when to use these features (shell vs notebook), and the workflow to adopt when using IPython (and let other books focus on technologies such as NumPy and Matplotlib).
The book does a good job introducing IPython in Chapter 2. In chapters 3, the book describes how to use NumPy from within IPython. It is not clear if this chapter is intended to make the reader proficient with NumPy or IPython or the combination. The exposition about NumPy (and Pandas) is very limited; of course, NumPy is rich. Further, this chapter neither introduces new IPython features specific to computing nor describes nuances of previously introduced IPython features in the context of computing. This treatment continues in chapter 4 where the book talks about IPython and visualization.
When I picked the book, I wanted to learn about IPython features along with its facets specific to computation and visualization. When I put it down, I had learned about the basic features of IPython and was unclear if there were features specific to computation and visualization.
An ideal IPython book would talk about the features of IPython (shell and notebook), when to use these features (shell vs notebook), and the workflow to adopt when using IPython (and let other books focus on technologies such as NumPy and Matplotlib).
November 17, 2014
I don't see the point of iPython
The more I learn about it, the more it seems like iPython is a fad. there is nothing in this book that convinces me iPython is anything more than a hodgepodge of ideas that is billed as some sort of soft and easy to learn tool for scientists. Instead learning the ins and outs of the iPython environment for scientific computing, it may just be better to stick with the bash shell, master R for more powerful statistical packages and better graphics. If you want a notebook, use Markdown. I'm not sure anyone will be talking about iPython seriously in a few years.
The more I learn about it, the more it seems like iPython is a fad. there is nothing in this book that convinces me iPython is anything more than a hodgepodge of ideas that is billed as some sort of soft and easy to learn tool for scientists. Instead learning the ins and outs of the iPython environment for scientific computing, it may just be better to stick with the bash shell, master R for more powerful statistical packages and better graphics. If you want a notebook, use Markdown. I'm not sure anyone will be talking about iPython seriously in a few years.
July 13, 2015
Fast read. Went from all the coolness of iPython to the gritty details of interacting with it to leverage Numpy, Scipy, Pandas Matplotlib. Not bad. A bit fast, but I wanted a quick read. You will get instant value with a few of the magic commands that make you go "Aha." Gave it 4 stars even though it suited my needs. I'm learning data science, so I think it works well if you want to use iPython to do quant work. If you are a casual programmer, it may be overkill and you will end up skimming after chapter 3.
February 1, 2014
A decent introduction; the quality was good, the quantity was low. This was much more than a blog post but felt less than a book-level treatment. I appreciated the pointers to further reading on topics, though, and certainly left knowing more about IPython than when I arrived.
January 28, 2015
Fantastic primer on getting started with IPython. Only a couple of hours, and I already feel like an expert. Kudos, Rossant!
February 5, 2016
It is very brief book. The author tried to cover a lot of topics in only a few pages.
June 27, 2014
very crisp overview. looks like a great tool... now let's build something wonderful!
January 30, 2015
A solid introduction to Ipython.
Displaying 1 - 14 of 14 reviews
Can't find what you're looking for?
Get help and learn more about the design.