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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. Working scientists 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. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms


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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. Working scientists 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. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

30 review for Python Data Science Handbook: Tools and Techniques for Developers

  1. 5 out of 5

    Terran M

    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 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 fix it. I do not care for Chapter 5, which attempts too much and delivers too little (for example, the “in depth” treatment of linear regression is all of 2 pages). I suggest that you stop at the end of Chapter 4 and instead move on to Introduction to Machine Learning with Python: A Guide for Data Scientists. As an alternative to this book, also consider Python for Data Analysis by Wes McKinney, which includes more verbose coverage of Pandas, at the expense of removing the ML section that you probably don't want to read anyway. The two are about equally good and share the same strengths (good writing) and weaknesses (dry references with mostly made-up data in the example, and use of Matplotlib for graphics). Update in late 2018: I now recommend Altair as the best native-Python graphics library, or plotnine, a clone of ggplot. Either way, you should skip most of Chapter 4 on matplotlib and learn one of these other libraries instead.

  2. 4 out of 5

    Jeremy

    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 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 theoretical explanations behind the tools themselves or details about their implementation, but the reader can freely refer to extra materials if needed.

  3. 4 out of 5

    Mikkel Hansen

    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 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 and Pandas (~180 pages). Particularly the proper usage of indexing (eg. timestamps as indices) and multi-indexing for hierarchal structure. Both chapters also contain advice on how to speed up the code when needed. Generally, I really liked this book and will definitely add it to our library at work so I can reference it and lend it to our students and interns.

  4. 4 out of 5

    Ye Lin Kyaw

    It is broad and deep enough for the beginners and experienced users who migrate from other platforms

  5. 4 out of 5

    Gabri

    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 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 able to understand and execute the process of Data Science.

  6. 4 out of 5

    James Mason

    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 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 attention to aesthetics in visualizations in earlier chapters, especially the one on matplotlib. And I also really appreciated the first chapter on IPython and the various ways you can write your code, though I wish it had a little more breadth in terms of the available options and justifications for why you might use, e.g., Jupyter notebooks as opposed to Atom/Ipython console. I also wish that there were more astronomy examples since that is the author's and my area of study. Despite those minor qualms, 5 stars! Note that the goodreads subtitle is incorrect. It should be: Essential Tools for Working with Data.

  7. 4 out of 5

    Moeen Sahraei

    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

  8. 4 out of 5

    Oleg Shevelyov

    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.

  9. 4 out of 5

    Matt Heavner

    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 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 reference.

  10. 4 out of 5

    Hays Hutton

    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. 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.

  11. 4 out of 5

    Iurie Cojocari

    awesome book, it does cover the tools nupy, matplotlib, and a bit numpy.

  12. 5 out of 5

    Kainé

    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 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 of exercises makes this book not as useful as it could be, so I find it hard to award it the full 5 stars. That said, as a primer on some of the key concepts of Machine Learning and how to get started with Scikit-Learn, this was rather excellent. It's really nice to have a collection of clean, bite-sized notebooks that succinctly and intuitively illustrate common models and algorithms such as Naive Bayes Classification, Support Vector Machines, Gaussian Mixture Models, and Kernel Density Estimation. Even though I have had peripheral exposure to most of these techniques before, pretty much all of them managed to low-key blow my mind when I saw them in action here, leaving me acutely hungry for more. It's incredible how oftentimes simple ideas can be pushed and combined to produce results that are almost uncanny in their effectiveness. And with a high-level library like Scikit-Learn, all you need is a handful lines of code to implement them. Truly, it feels like there is black magic happening under the hood. (Or maybe it's just C++.) Of course, data science out in the real world is a lot muddier than these kinds of elegant textbook examples might lead you to believe, which Vanderplas doesn't try to hide. A lot of the real work consists of potentially tedious data wrangling, not to mention that choosing the optimal model (rather than implementing it) is where the real difficulty lies. This book won't suffice to get you started on serious work in ML, but as a warm-up and appetizer to a more in-depth treatment of the subject, I can definitely recommend this. The notebook version of this book can be found for free here.

  13. 5 out of 5

    Sebastian

    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 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 read this book for free on the author's GitHub however I will be going back and purchasing it, as it truly is a handbook. I have already gone back and referred to work in this book on several projects, and I know that I'll be using it in the future to flick through to refresh my ideas, or think about how I would structure my own code.

  14. 5 out of 5

    Ray

    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.

  15. 5 out of 5

    Hadiana Sliwa

    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.

  16. 4 out of 5

    Lukas Rubikas

    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. 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.

  17. 5 out of 5

    Ravi

    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. 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.

  18. 4 out of 5

    George

    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.

  19. 5 out of 5

    Alvaro Fuentes

    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. 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.

  20. 5 out of 5

    Loc Nguyen

    Useful book, especially like pandas package for data manipulation.

  21. 5 out of 5

    Shashank Shrivastava

    To the point and with examples in notebook format, it was easy to follow and understand.

  22. 4 out of 5

    Faheemsadiki

    This review has been hidden because it contains spoilers. To view it, click here. its amazing book

  23. 4 out of 5

    Miguel Veliz

    Great book for learning numpy, pandas, matplotlib and seaborn, it also cover scikit-learn and some of Machine Learning to kickstart your projects

  24. 4 out of 5

    Carlos Martinez

    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.

  25. 5 out of 5

    Julius

    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.

  26. 4 out of 5

    Todd

    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. 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.

  27. 5 out of 5

    Gerardo Alonso

    Pretty good to get started

  28. 5 out of 5

    Mlv Prasad

    This review has been hidden because it contains spoilers. To view it, click here. Overall idea

  29. 4 out of 5

    Subhodip Panda

    Really good for starters in machine learning.

  30. 4 out of 5

    Megan

    This book is a good reference book for data science programming.

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