ISBN: 1491912057
EAN13: 9781491912058
Language: English
Release Date: Dec 20, 2016
Pages: 548
Dimensions: 1.3" H x 9.1" L x 7" W
Weight: 1.85 lbs.
Format: Paperback
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Book Overview

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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Book Reviews (9)

5
  |   9  reviews
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5
   Great book for beginners !
Before diving into the programming language, you must know how to program a python program. The. Net framework is extremely small and easily executable. The remaining 15 days are spent with two hours a day spent on the book. It's a great book.
 
3
   excellent book but disappointing physical print/copy
Amazon's own printed book, "The Age of Enlightenment," is off-brand. The book was printed on black and white paper, rendering many of the illustrations useless. The book is excellent and the contents are excellent, a great course with thorough and useful detail, said Dr. Paul LiCalsi. Amazon's own printed version is cheaper and more flexible, even if the original was never sold.
 
5
   Must have book for Machine Learning
I love the presentation style and the treatment of the subject in this book, Mary. The book could have been organized better by adding more chapters.
 
5
   An excellent primer on data science tools
I really enjoyed the book, Morgenthau said. I had not really picked up the python programming language prior to reading the book, but I was able to pick it up quickly. Before that I was plotting distributions of real-time statistics and developing a predictive modeling service. This book is a must-have for any aspiring data scientist.
 
5
   Great coverage of essential topics
The first three are about pandas, the middle one is about matplotlib. Now that I've been applying it at work, however, I've found that the items covered in the first two thirds are really essential. If I had just jumped straight to the sections on scikit-learn, I would have been nearly as productive. The author does an excellent job covering broad terrain with enough detail that you can apply it to your problems, he said. If you have any questions, you can find yourself going back to use this book as a reference.
 
5
   Best book for python data analysis
This is an excellent reference book for people working in data analysis. 80% of the effort in machine learning, data analysis or data science is about processing data, not about understanding data. If you're looking for hardcore machine learning, you're out of luck. Highly recommend.
 
5
   This book is well written and easy to follow
The book is well written and easy to follow, said Dr. Brian McBride, director of the federal Centers for Disease Control and Prevention. It has saved me from spending hours on the internet to find the books I need.
 
5
   Excllent Introduction to Python for Data Science
This was my first book that covered all the Python for data science. Even though it doesn't go into super great depth in any area, it is definitely a super book. It covers everything from. Net to Web 2.0, including everything from pandas to scikit-learn. Anyone new to Python or data science will find this useful.
 
4
   Not the panacea for data science challenges, but a pretty good resource nevertheless
I am currently taking a Machine Learning course from Udacity and this book has proven to be a great reference guide for several projects and quizzes. Although it does not go into great depth of machine learning, it does give an understanding of essential concepts. For those interested in machine learning I would recommend bying hands-on TensorFlow by Geron and bying the book, "Machine Learning with Scikit-Learn." Just keep that in mind before buying it, said Jennette Tamayo, a New Jersey resident. For those complaining about black and white graphs and diagrams, check the author's GitHub page.
 
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