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Python Data Science Handbook: Essential Tools for Working with Data 1st Edition
There is a newer edition of this item:
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
- ISBN-101491912057
- ISBN-13978-1491912058
- Edition1st
- PublisherO'Reilly Media
- Publication dateJanuary 3, 2017
- LanguageEnglish
- Dimensions5.91 x 0.59 x 9.84 inches
- Print length546 pages
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Product details
- Publisher : O'Reilly Media; 1st edition (January 3, 2017)
- Language : English
- Paperback : 546 pages
- ISBN-10 : 1491912057
- ISBN-13 : 978-1491912058
- Item Weight : 1.47 pounds
- Dimensions : 5.91 x 0.59 x 9.84 inches
- Best Sellers Rank: #290,831 in Books (See Top 100 in Books)
- #90 in Data Modeling & Design (Books)
- #158 in Data Processing
- #270 in Python Programming
- Customer Reviews:
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Learn more how customers reviews work on AmazonCustomers say
Customers find the book an excellent introductory guide for machine learning in Python. They appreciate its rich content and practical examples. The book is described as well-written, easy to follow, and structured well as a handbook.
AI-generated from the text of customer reviews
Customers find this book an excellent introduction to machine learning in the Python programming language. They say it provides a lot of knowledge for the price of a meal, and is the best book on data processing, analysis, and visualization. The book is considered a must-have for any aspiring data scientist. It covers the basics of Python programming for beginners with small, easily executable examples from Pandas, Matplotlib, and Scikit-learn.
"...The author does an excellent job covering broad terrain with enough detail that you are able to apply it to your problems...." Read more
"...It covers everything from Pandas, Matplotlib, and scikit-learn. I would highly recommend it for anyone that is new to Python and/or data science...." Read more
"I've just finished this book. The author gives a well-written introduction into Machine-Learning with Python Scikit-Learn and illustrates each..." Read more
"...are relevant and important to do data science in python, every page is rich in information and provides practical use case, optimization tricks and..." Read more
Customers find the book easy to read and follow. They appreciate the well-designed examples and presentation style. The book is structured well and convenient for them as a handbook. The author explains things clearly and the examples are small and executable. The book is written with Jupyter Notebooks, making it easy to follow along and try code from the book. It has a good balance of details and brevity.
"..."Python for Data Analysis " but because it uses Jupyter it's easy on the eyes to read...." Read more
"...The book is written with Jupyter Notebooks so it is easy to follow along and try code from the book in your own notebook." Read more
"...Machine-Learning with Python Scikit-Learn and illustrates each chapter with well-designed examples that are easy to follow and understand...." Read more
"The book is just great - amazing combination of details and brevity...." Read more
Reviews with images
Intermediate book for reading.
Top reviews from the United States
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- Reviewed in the United States on September 6, 2021Not as easy or straightforward as "Python for Data Analysis " but because it uses Jupyter it's easy on the eyes to read. Highly recommend going to Staples or Office Max and getting the book spiral bound.
5.0 out of 5 starsNot as easy or straightforward as "Python for Data Analysis " but because it uses Jupyter it's easy on the eyes to read. Highly recommend going to Staples or Office Max and getting the book spiral bound.Intermediate book for reading.
Reviewed in the United States on September 6, 2021
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- Reviewed in the United States on August 5, 2017When I first received this book, I was surprised that it didn't get to scikit-learn until the last third of the book. The first third is about numpy and pandas, and the middle third 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 were really essential. I wouldn't be nearly as productive if I had just jumped straight to the sections on scikit-learn. The author does an excellent job covering broad terrain with enough detail that you are able to apply it to your problems. You will find yourself going back to use this book as a reference.
- Reviewed in the United States on June 4, 2017I am currently taking a Machine Learning course from Udacity and this book has proven to be a great reference guide for several projects and quizes. Although it does not go in depth in regards to machine learning (although almost half of the book is dedicated to it), it does give an understanding of essential concepts. For those interested in machine learning I would recommend bying "Hands-On Machine Learning with Scikit-Learn and TensorFlow" by Geron as well as this book.
There is no one book for data science, and this one is no exception. Just keep that in mind before buying it.
Other than that, I am really happy with my purchase.
P.S. For those complaining about black and white graphs and diagrams - check the author's GitHub.
- Reviewed in the United States on June 9, 2017I have used R for a few years and this was my first book that covered Python for data science. Even though it does not go into super great depth in any area, it is definitely a super book. It covers everything from Pandas, Matplotlib, and scikit-learn. I would highly recommend it for anyone that is new to Python and/or data science. The book is written with Jupyter Notebooks so it is easy to follow along and try code from the book in your own notebook.
- Reviewed in the United States on January 11, 2019I've just finished this book. The author gives a well-written introduction into Machine-Learning with Python Scikit-Learn and illustrates each chapter with well-designed examples that are easy to follow and understand. I am very pleased with this book and can definitely recommend to everyone who is a beginner in the field and wants to quickly get hold on the practical approaches to ML.
- Reviewed in the United States on January 29, 2017This is by far the best book out in market to get you started with using python for data science. You will need some basic understanding of python and machine learning to understand concepts here, but this book will definitely take you skill to next level.This is no-nonsense book and goes deep into stuff which are relevant and important to do data science in python, every page is rich in information and provides practical use case, optimization tricks and adds new dimensions to your understanding of topic.
- Reviewed in the United States on April 26, 2019The book is just great - amazing combination of details and brevity. I had programming experience but no experience with Python at all before I started reading this book. Very good fit for my qualification. Recommended for everyone who is going to start a new way into Data Science using Python.
- Reviewed in the United States on June 10, 2017This is an excellent reference book for people working with data science. Remember, 80% of the effort in machine learning, data analysis or data science in general is about processing data and understanding data. This book is for that purpose and I think it's the best book out there about data processing, analysis and visualization using python. If you look for hardcore machine learning, go for other books. Highly recommended!
Top reviews from other countries
- Guillermo Martinez DibeneReviewed in Canada on November 23, 2020
5.0 out of 5 stars Very useful
This book contains introductions, tips and overview of the five more common Python packages for data science. It is clear, concise and quite fun to read. Only one down side, which is quite minor: some graphics needs colour. This is not a big deal because you can check the online version which available for free.
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Ana Isabel Bezerra CavalcantiReviewed in Brazil on October 15, 2020
5.0 out of 5 stars Importante, principalmente para pesquisas futuras.
Atendeu às minhas expectativas atuais e será útil em trabalhos futuros.
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Antonio García GirónReviewed in Spain on February 22, 2021
5.0 out of 5 stars Muy útil
Está escrito de forma muy amena y con ejemplos paso a paso. Requiere que se sepa previamente algo de Python, pero es un libro muy útil para aprender a manejar datos a través de librerías, empezando por NumPy y Pandas, Matplotlib para representar datos, y una sección de modelado.
La única pega, por poner una, es que las figuras están en blanco y negro, pero el libro está muy bien.
- Maddy BurgerReviewed in the United Kingdom on April 11, 2021
5.0 out of 5 stars Happy customer
Item matches product description
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Carlos Rodriguez ContrerasReviewed in Mexico on November 3, 2017
5.0 out of 5 stars La selección ideal para conocer el ecosistema de Python
Si sólo se pudiera tener un libro que sea una buena referencia de Python, este sería el indicado. Expone de manera sencilla los sistemas más utilizados de Python para la Ciencia de datos como pandas, numpy, y scipy. Lo recomiendo ampliamente.