Mastering Python Data Visualization

Generate effective results in a variety of visually appealing charts using the plotting packages in Python
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Mastering Python Data Visualization

Kirthi Raman

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Generate effective results in a variety of visually appealing charts using the plotting packages in Python

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Book Details

ISBN 139781783988327
Paperback372 pages

Book Description

Python has a handful of open source libraries for numerical computations involving optimization, linear algebra, integration, interpolation, and other special functions using array objects, machine learning, data mining, and plotting. Pandas have a productive environment for data analysis. These libraries have a specific purpose and play an important role in the research into diverse domains including economics, finance, biological sciences, social science, health care, and many more. The variety of tools and approaches available within Python community is stunning, and can bolster and enhance visual story experiences.

This book offers practical guidance to help you on the journey to effective data visualization. Commencing with a chapter on the data framework, which explains the transformation of data into information and eventually knowledge, this book subsequently covers the complete visualization process using the most popular Python libraries with working examples. You will learn the usage of Numpy, Scipy, IPython, MatPlotLib, Pandas, Patsy, and Scikit-Learn with a focus on generating results that can be visualized in many different ways. Further chapters are aimed at not only showing advanced techniques such as interactive plotting; numerical, graphical linear, and non-linear regression; clustering and classification, but also in helping you understand the aesthetics and best practices of data visualization. The book concludes with interesting examples such as social networks, directed graph examples in real-life, data structures appropriate for these problems, and network analysis.

By the end of this book, you will be able to effectively solve a broad set of data analysis problems.

Table of Contents

Chapter 1: A Conceptual Framework for Data Visualization
Data, information, knowledge, and insight
The transformation of data
Data visualization history
How does visualization help decision-making?
Visualization plots
Summary
Chapter 2: Data Analysis and Visualization
Why does visualization require planning?
The Ebola example
A sports example
Creating interesting stories with data
Perception and presentation methods
Some best practices for visualization
Visualization tools in Python
Interactive visualization
Summary
Chapter 3: Getting Started with the Python IDE
The IDE tools in Python
Visualization plots with Anaconda
Interactive visualization packages
Summary
Chapter 4: Numerical Computing and Interactive Plotting
NumPy, SciPy, and MKL functions
Scalar selection
Slicing
Array indexing
Other data structures
Visualization using matplotlib
The visualization example in sports
Summary
Chapter 5: Financial and Statistical Models
The deterministic model
The stochastic model
The threshold model
An overview of statistical and machine learning
Creating animated and interactive plots
Summary
Chapter 6: Statistical and Machine Learning
Classification methods
Understanding linear regression
Linear regression
Decision tree
The Bayes theorem
The Naïve Bayes classifier
The Naïve Bayes classifier using TextBlob
Viewing positive sentiments using word clouds
k-nearest neighbors
Logistic regression
Support vector machines
Principal component analysis
k-means clustering
Summary
Chapter 7: Bioinformatics, Genetics, and Network Models
Directed graphs and multigraphs
The clustering coefficient of graphs
Analysis of social networks
The planar graph test
The directed acyclic graph test
Maximum flow and minimum cut
A genetic programming example
Stochastic block models
Summary
Chapter 8: Advanced Visualization
Computer simulation
Summary

What You Will Learn

  • Gather, cleanse, access, and map data to a visual framework
  • Recognize which visualization method is applicable and learn best practices for data visualization
  • Get acquainted with reader-driven narratives and author-driven narratives and the principles of perception
  • Understand why Python is an effective tool to be used for numerical computation much like MATLAB, and explore some interesting data structures that come with it
  • Explore with various visualization choices how Python can be very useful in computation in the field of finance and statistics
  • Get to know why Python is the second choice after Java, and is used frequently in the field of machine learning
  • Compare Python with other visualization approaches using Julia and a JavaScript-based framework such as D3.js
  • Discover how Python can be used in conjunction with NoSQL such as Hive to produce results efficiently in a distributed environment

Authors

Table of Contents

Chapter 1: A Conceptual Framework for Data Visualization
Data, information, knowledge, and insight
The transformation of data
Data visualization history
How does visualization help decision-making?
Visualization plots
Summary
Chapter 2: Data Analysis and Visualization
Why does visualization require planning?
The Ebola example
A sports example
Creating interesting stories with data
Perception and presentation methods
Some best practices for visualization
Visualization tools in Python
Interactive visualization
Summary
Chapter 3: Getting Started with the Python IDE
The IDE tools in Python
Visualization plots with Anaconda
Interactive visualization packages
Summary
Chapter 4: Numerical Computing and Interactive Plotting
NumPy, SciPy, and MKL functions
Scalar selection
Slicing
Array indexing
Other data structures
Visualization using matplotlib
The visualization example in sports
Summary
Chapter 5: Financial and Statistical Models
The deterministic model
The stochastic model
The threshold model
An overview of statistical and machine learning
Creating animated and interactive plots
Summary
Chapter 6: Statistical and Machine Learning
Classification methods
Understanding linear regression
Linear regression
Decision tree
The Bayes theorem
The Naïve Bayes classifier
The Naïve Bayes classifier using TextBlob
Viewing positive sentiments using word clouds
k-nearest neighbors
Logistic regression
Support vector machines
Principal component analysis
k-means clustering
Summary
Chapter 7: Bioinformatics, Genetics, and Network Models
Directed graphs and multigraphs
The clustering coefficient of graphs
Analysis of social networks
The planar graph test
The directed acyclic graph test
Maximum flow and minimum cut
A genetic programming example
Stochastic block models
Summary
Chapter 8: Advanced Visualization
Computer simulation
Summary

Book Details

ISBN 139781783988327
Paperback372 pages
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