Download Python Data Visualization Cookbook (2nd Edition) by Igor Milovanovic, Dimitry Foures, Giuseppe Vettigli PDF

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By Igor Milovanovic, Dimitry Foures, Giuseppe Vettigli

Over 70 recipes to get you began with well known Python libraries according to the central recommendations of knowledge visualization

About This Book
Learn how you can manage an optimum Python setting for information visualization
Understand tips on how to import, fresh and manage your data
Determine diverse methods to information visualization and the way to settle on the main applicable on your needs
Who This publication Is For
If approximately Python programming and need to appreciate information, info codecs, information visualization, and the way to exploit Python to imagine info then this publication is for you.

What you are going to Learn
Introduce your self to the basic tooling to establish your operating environment
Explore your information utilizing the functions of ordinary Python information Library and Panda Library
Draw your first chart and customise it
Use the preferred info visualization Python libraries
Make 3D visualizations more often than not utilizing mplot3d
Create charts with photos and maps
Understand the main applicable charts to explain your data
Know the matplotlib hidden gems
Use plot.ly to proportion your visualization online

In Detail
Python facts Visualization Cookbook will growth the reader from the purpose of putting in and constructing a Python setting for information manipulation and visualization all of the option to 3D animations utilizing Python libraries. Readers will reap the benefits of over 60 designated and reproducible recipes that may advisor the reader in the direction of a greater knowing of information suggestions and the development blocks for next and occasionally extra complicated concepts.
Python info Visualization Cookbook starts off via displaying the way to organize matplotlib and the comparable libraries which are required for many components of the e-book, sooner than relocating directly to speak about many of the lesser-used diagrams and charts resembling Gantt Charts or Sankey diagrams. at first it makes use of uncomplicated plots and charts to extra complicated ones, to make it effortless to appreciate for readers. because the readers will battle through the e-book, they are going to get to understand in regards to the 3D diagrams and animations. Maps are irreplaceable for showing geo-spatial information, so this e-book also will exhibit how you can construct them. within the final bankruptcy, it comprises rationalization on tips to contain matplotlib into various environments, corresponding to a writing method, LaTeX, or the right way to create Gantt charts utilizing Python.

Style and approach
A step by step recipe established method of information visualization. the subjects are defined sequentially as cookbook recipes inclusive of a code snippet and the ensuing visualization.

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Additional resources for Python Data Visualization Cookbook (2nd Edition)

Sample text

Tab. split('\t') We also see that there is another approach to do this—using the split('\t') function. Sniffer class). 26 Chapter 2 Importing data from a JSON resource This recipe will show us how we can read the JSON data format. Moreover, we'll be using a remote resource in this recipe. It will add a tiny level of complexity to the recipe, but it will also make it much more useful because in real life we will encounter more remote resources than local ones. JavaScript Object Notation (JSON) is widely used as a platform-independent format to exchange data between systems or applications.

44 Chapter 2 After we have concluded what is present in the data, we can write Python code to group those similar values and replace them with the one value that is going to make further processing consistent. The usual way of doing this is to read in lines of the file using readlines() and use standard Python string manipulation functions to perform manipulations. There's more... com/OpenRefine) that provide some automation around transformation on "dirty" live datasets. Manual work is still involved, depending on how noisy the data is and how great our understanding of that data is.

It is very popular in the web application domain as it is native to JavaScript, the language used for most of today's rich Internet applications. The Python JSON module has more capabilities than we have displayed here; for example, we could specialize the basic JSONEncoder/JSONDecoder class to transform our Python data into JSON format. The classical example uses this approach to JSON-ify the Python built-in type for complex numbers. For simple customization, we don't have to subclass the JSONDecoder/JSONEncoder class as some of the parameters can solve our problems.

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