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Python Data Science Handbook: Tools and Techniques for Developers
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For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.
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Paperback, 500 pages
Published
December 25th 2016
by O'Reilly Media
(first published March 25th 2016)
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This book is not as good as R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, but if you are constrained or committed to using Python, it is the best available alternative as of 2018. Chapters 1 through 3 on ipython, Numpy, and Pandas are very well written, although they do suffer from using mostly small, made-up examples. Chapter 4 on Matplotlib is disappointing, but that's because Matplotlib is itself a weak and obsolete tool; the book acknowledges that fact and cannot fi
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The book is written as a Jupyter notebook, and is available for free on GitHub:
https://github.com/jakevdp/PythonData...
Books written as Jupyter Notebooks are simply wonderful. They should become the default medium for learning new materials related to computer science and mathematics.
Regarding the book itself, it fits more in the "practical knowledge" category, which is totally fine since it's a handbook. Being exposed to the different methods and tools is great. There is however no real theoret ...more
https://github.com/jakevdp/PythonData...
Books written as Jupyter Notebooks are simply wonderful. They should become the default medium for learning new materials related to computer science and mathematics.
Regarding the book itself, it fits more in the "practical knowledge" category, which is totally fine since it's a handbook. Being exposed to the different methods and tools is great. There is however no real theoret ...more
I read this book after having worked as a data scientist for about a year and a half. Most of my work had focused on machine learning, so I had picked up Numpy, Pandas, and Matplotlib along the way. This approach left some glaring holes in my usage of these modules. After having read this book I can see that there has been a couple of things I have been doing wrong -- or at least very ineffectively. So reading this book was definitely a good idea.
I especially appreciated the chapters on Numpy an ...more
I especially appreciated the chapters on Numpy an ...more
It is broad and deep enough for the beginners and experienced users who migrate from other platforms
Mandatory read, did not finish around 50%.
So I'm in my final year of Information Studies and I feel like it wasn't until I read this book that I truly understood computer programming. It covers very useful packages for Data Science (Numpy, Pandas, Matplotlib), and not only explains what the code does, but also provides many code examples that help you to understand it and use it on your own.
I would highly recommend this book to anyone who has some basic knowledge of Python but wants/needs to be ...more
So I'm in my final year of Information Studies and I feel like it wasn't until I read this book that I truly understood computer programming. It covers very useful packages for Data Science (Numpy, Pandas, Matplotlib), and not only explains what the code does, but also provides many code examples that help you to understand it and use it on your own.
I would highly recommend this book to anyone who has some basic knowledge of Python but wants/needs to be ...more
Extremely well written. Just the right level of depth. It was useful to work through bit by bit to gain a general understanding and practice, and I'm sure it will also be useful as a desktop reference. I was inspired throughout to look at my data in new ways and apply new, modern methods to the data in order to obtain more robust results and hopefully uncover things about it that I simply would not have otherwise. Most of that happened in the machine learning (final) chapter. I appreciated the a
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Very good book. Covers many important tools (IPython, Numpy, Pandas, Scikit-Learn) for applied Data Science in Python and breaks them down into logical chunks.
The python data science handbook is the best python tutorial I have read. It is "an overview of python if you want to be a data scientist" - the breadth and depth on specific tools (matplotlib & beyond, pandas, and sci-kit, as well as ipython & jupyter notebooks) is perfect for a data science application. This is definitely addressing the "computer skills" third of the data science Venn diagram (not much on mathematics or subject matter expertise). Recommended for learning python or having as a
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It’s a succinct and well written book in data science using python, one of its greatest weaknesses is its examples, the author didn’t relate subjects with examples well and they are too hard to understand. But in a nutshell, it’s a good book for learning the basics of numpy, pandas, matplotlib and a little bit of machine learning
Liked how it goes in depth into NumPy and then Pandas. Sometimes a "little" too API based but that makes it practical in some respects.
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awesome book,
it does cover the tools nupy, matplotlib, and a bit numpy.
it does cover the tools nupy, matplotlib, and a bit numpy.
While the first four chapters offer a solid, hands-on overview of IPython, Numpy, Pandas and Matplotlib, you can find equivalent tutorials on how to slice arrays and manipulate DataFrames pretty much anywhere. Unless you're a complete beginner in scientific work with Python, these chapters will likely serve as refreshers at best. The chapter that really stood out to me was the last one on Machine Learning, so much so that I almost considered giving this a higher rating. Unfortunately, the lack o
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A rigorous overview of data science tools in Python, combined with an introduction to several machine learning techniques using the sci-kit learn library.
As someone that has approached learning data science and programming on a project-by-project basis, it was wonderfully enlightening to see the author dive deep into the syntax, and reasoning behind libraries such as NumPy, Pandas, and Matplotlib. The chapter on machine learning is surprisingly hefty considering how much has come prior to it.
I r ...more
As someone that has approached learning data science and programming on a project-by-project basis, it was wonderfully enlightening to see the author dive deep into the syntax, and reasoning behind libraries such as NumPy, Pandas, and Matplotlib. The chapter on machine learning is surprisingly hefty considering how much has come prior to it.
I r ...more
This is really an amazing technical resource. Vanderplas manages to keep his content extraordinarily practical and grounded, without being irreverent to the theory like so many lower-quality modern data science texts are. As a contributor to the Python data software libraries such as Scikit-learn, the author is eminently qualified to give a tour of their inner workings. Finally, the book is self-aware of where it lacks depth, and does an excellent job in referring readers to further resources.
As a starter, new to python the first four chapters of the book were very easy to follow, I learned too much from those chapters, except for chapter 5 (Introduction to machine learning) was somehow hard for me to follow because the concept of machine learning was new to me and there was too much code in the chapter that the author assumed you might know so there was no explanation, but someone with a bit knowledge on python would follow it very easily.
I'll just say this:
If I was put into this horrible scenario where I was held at a gunpoint next to a gigantic red button and was told that I must press it and nuke *every* single book publisher in the world bar one and I absolutely must choose which one, I would save O'Reilly. And I would use *this* book as an example to justify why. ...more
If I was put into this horrible scenario where I was held at a gunpoint next to a gigantic red button and was told that I must press it and nuke *every* single book publisher in the world bar one and I absolutely must choose which one, I would save O'Reilly. And I would use *this* book as an example to justify why. ...more
Great resource with excellent examples and useful, well-written Python code. A lot of techniques are introduced here, with the unfortunate exception of neural networks/deep learning, which is beyond the scope of this book. The book is written using Jupyter notebooks and printed in black & white, so for some of the plots you'll have to refer to the online versions to better see what's going on.
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Really good. Starts from blank slate and goes to a good level to all the topics that it touches. The online version is more up to date and the complementary notebooks can be used to run all the examples yourself.
Excellent book for any one interested in understand the fundamentals of scientific computing for data science in Python. I can't recommend this book enough, if you are interested in data science, read it from beginning to end.
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Useful book, especially like pandas package for data manipulation.
To the point and with examples in notebook format, it was easy to follow and understand.
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Great book for learning numpy, pandas, matplotlib and seaborn, it also cover scikit-learn and some of Machine Learning to kickstart your projects
Not exactly bed-time reading, but a very readable overview of the major Python libraries for all things data science. Would be improved with some programming exercises to help the concepts stick.
Excellent read for people that look to improve their knowledge gained i.e. doing basic tutorials on the web. Also good for students in a related field, as food for thought.
Great coverage of the basic tools used in data science by somebody who seems to know the subject well. You don't need to be a python coder for the book to be useful, too.
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Pretty good to get started
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Really good for starters in machine learning.
This book is a good reference book for data science programming.
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