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Python Data Visualization Cookbook (Englisch) Taschenbuch – 25. November 2013

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  • Taschenbuch: 280 Seiten
  • Verlag: Packt Publishing (25. November 2013)
  • Sprache: Englisch
  • ISBN-10: 1782163360
  • ISBN-13: 978-1782163367
  • Größe und/oder Gewicht: 19 x 1,6 x 23,5 cm
  • Durchschnittliche Kundenbewertung: 4.0 von 5 Sternen  Alle Rezensionen anzeigen (1 Kundenrezension)
  • Amazon Bestseller-Rang: Nr. 225.393 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)


Über den Autor und weitere Mitwirkende

Igor Milovanovic

Igor Milovanovic is an experienced developer with a strong background in Linux system knowledge and software engineering. He is skilled at building scalable, data-driven, distributed-software-rich systems.

He is an Evangelist for high-quality systems design who holds strong interests in software architecture and development methodologies. He is always persistent on advocating methodologies that promote high-quality software, such as test-driven development, one-step builds, and continuous integration.

He also possesses a solid knowledge of product development. Having field experience and official training, he is capable of transferring knowledge and communication flow from business to developers and vice versa.

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Von pszu am 25. Januar 2014
Format: Taschenbuch
I was quite sceptical before reading the book as I suspected it will not bring anything new to me but I was pleasantly mistaken. The book provides decent introduction and recipes for handling different data sets, contains information on how to prepare, structure and apply different data for different kinds of plots. It also gives hints on using the right plot to understand raw data. I also liked a chapter on plotting data on a map using Google Map API.

I tried to find not only pros and found three missing points (at least for me).

* most of the examples in the book NumPy & Matplotlib, I was quite surprised that I did not see PANDAS library being mentioned which becomes a standard for handling data in Python nowadays.
* there's one recipe on how to access a typical sql database from python and extract data using sql. I missed another example for recently popular nosql databases like MongoDB.
* I missed a chapter on d3py. D3PY is a Python frontend for D3.js - Data Driven documents for the web.

Still I would like to recommend reeading this book. It is beneficial for intermediate+ Python developers, students or scientists that already know Python basics and is a good collection of recipes. If one needs to dive in quickly to present data on a chart, then this book is for you. For absolute beginners a Python introduction is a must.
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Die hilfreichsten Kundenrezensionen auf (beta) 6 Rezensionen
9 von 9 Kunden fanden die folgende Rezension hilfreich
What software documentation should be 29. Januar 2014
Von Amazon Customer - Veröffentlicht auf
Format: Kindle Edition
The majority of software documentation is similar to a remark made by the developer of a well-known, difficult language; "Maybe you are not smart enough?". In contrast, this book has made sure that nothing is implied without being oversimplified. The book covers; installing and customizing libraries, reading in data, extensive information on 2D and 3D plots, using images and maps, determining the right plots for specified data types, and additional information for matplotlib. References are provided to other sources throughout the book.
7 von 7 Kunden fanden die folgende Rezension hilfreich
Some great things about data visualization 21. Januar 2014
Von Yury - Veröffentlicht auf
Format: Taschenbuch
I'd like to recommend this book, for people, who makes their first steps in data visualization. Good compilation of free Python stuff for data analysis at one place. From the enviroment creating to the complicated plots.
There no any comments about Python as a language, so be sure that you know it quite good. No need to be a senior developer, but strong junior, would be nice.
4 von 4 Kunden fanden die folgende Rezension hilfreich
good book to learn data visualization 19. März 2014
Von carl - Veröffentlicht auf
Format: Taschenbuch
I am an intermedium python developer. My past python experience is on system admin, DevOps, deployment and web management. Data visualization is a fairly new area to me. So this book is a perfect fit for me.

Author uses lots of examples to demonstrate different visualization terminology, which really helps people to understand the abstract image processing technology. This book also shows you how to setup the virtual env to isolate development environment. Although the main purpose of this book is to teach how to visualize data, many of the example programs also show the best python development practice. Majority of the code is runnable without touch-up. Some typos are pretty easy to be spotted. I would recommend it to people who already have python experience and would like to extend their experience to data visualization area.
4 von 5 Kunden fanden die folgende Rezension hilfreich
Good introduction for Scientists to learn more about Data Visualization 25. Februar 2014
Von Jack Golding - Veröffentlicht auf
Format: Kindle Edition
Python Data Visualization Cookbook introduces the process of doing data visualisation with the Python programming language. The book uses the Scipy stack for data visualisation (however was published before the new Bokeh package was released) and introduces how to install the libraries in multiple operating systems which can be a task in itself for those unfamiliar with Python. The book covers the basics of data visualization and touches on exploratory data analysis, mostly in a scientific context. Given the size of the field of data visualization, it is unrealistic to expect that a book can introduce the semantics of a programming language as well as all of its applications. In conclusion this book is recommended to professionals who are interested in scientific data visualisation with a novice level understanding of both mathematics and programming.
1 von 1 Kunden fanden die folgende Rezension hilfreich
Gets you started right away 29. November 2014
Von Bernie Ongewe - Veröffentlicht auf
Format: Kindle Edition
This is a nice tour of modules and techniques for importing and scrubbing data from various sources (CSV, databases, Excel, etc), manipulating said data and presenting it in an intuitive manner. The author is generous with examples, which allows you to start right away.

While this is not a rigorous tutorial, the author goes into exactly the right depth to allow you to make a decision on methodology and begin implementing right away.

If, rather than becoming a NumPy scholar, you expect to have to deliver results from varied species of data, having this in your back pocket will help you accomplish that.
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