Mastering Python Data Visualization 1st edition by Kirthi Raman 9781783988327 1783988320

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ISBN 10:1783988320
ISBN 13:9781783988327
Author:Kirthi Raman
Generate effective results in a variety of visually appealing charts using the plotting packages in Python About This Book • Explore various tools and their strengths while building meaningful representations that can make it easier to understand data • Packed with computational methods and algorithms in diverse fields of science • Written in an easy-to-follow categorical style, this book discusses some niche techniques that will make your code easier to work with and reuse Who This Book Is For If you are a Python developer who performs data visualization and wants to develop existing knowledge about Python to build analytical results and produce some amazing visual display, then this book is for you. A basic knowledge level and understanding of Python libraries is assumed. 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 In Detail 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. Style and approach The approach of this book is not step by step, but rather categorical. The categories are based on fields such as bioinformatics, statistical and machine learning, financial computation, and linear algebra. This approach is beneficial for the community in many different fields of work and also helps you learn how one approach can make sense across many fields

Mastering Python Data Visualization 1st Table of contents

1. A Conceptual Framework for Data Visualization

Data, information, knowledge, and insight

Data

Information

Knowledge

Data analysis and insight

The transformation of data

Transforming data into information

Data collection

Data preprocessing

Data processing

Organizing data

Getting datasets

Transforming information into knowledge

Transforming knowledge into insight

Data visualization history

Visualization before computers

Minard’s Russian campaign (1812)

The Cholera epidemics in London (1831-1855)

Statistical graphics (1850-1915)

Later developments in data visualization

How does visualization help decision-making?

Where does visualization fit in?

Data visualization today

What is a good visualization?

Visualization plots

Bar graphs and pie charts

Bar graphs

Pie charts

Box plots

Scatter plots and bubble charts

Scatter plots

Bubble charts

KDE plots

Summary

2. Data Analysis and Visualization

Why does visualization require planning?

The Ebola example

A sports example

Visually representing the results

Creating interesting stories with data

Why are stories so important?

Reader-driven narratives

Gapminder

The State of the Union address

Mortality rate in the USA

A few other example narratives

Author-driven narratives

Perception and presentation methods

The Gestalt principles of perception

Some best practices for visualization

Comparison and ranking

Correlation

Distribution

Location-specific or geodata

Part-to-whole relationships

Trends over time

Visualization tools in Python

Development tools

Canopy from Enthought

Anaconda from Continuum Analytics

Interactive visualization

Event listeners

Layouts

Circular layout

Radial layout

Balloon layout

Summary

3. Getting Started with the Python IDE

The IDE tools in Python

Python 3.x versus Python 2.7

Types of interactive tools

IPython

Plotly

Types of Python IDE

PyCharm

PyDev

Interactive Editor for Python (IEP)

Canopy from Enthought

Anaconda from Continuum Analytics

An overview of Spyder

An overview of conda

Visualization plots with Anaconda

The surface-3D plot

The square map plot

Interactive visualization packages

Bokeh

VisPy

Summary

4. Numerical Computing and Interactive Plotting

NumPy, SciPy, and MKL functions

NumPy

NumPy universal functions

Shape and reshape manipulation

An example of interpolation

Vectorizing functions

Summary of NumPy linear algebra

SciPy

An example of linear equations

The vectorized numerical derivative

MKL functions

The performance of Python

Scalar selection

Slicing

Slice using flat

Array indexing

Numerical indexing

Logical indexing

Other data structures

Stacks

Tuples

Sets

Queues

Dictionaries

Dictionaries for matrix representation

Sparse matrices

Visualizing sparseness

Dictionaries for memoization

Tries

Visualization using matplotlib

Word clouds

Installing word clouds

Input for word clouds

Web feeds

The Twitter text

Plotting the stock price chart

Obtaining data

The visualization example in sports

Summary

5. Financial and Statistical Models

The deterministic model

Gross returns

The stochastic model

Monte Carlo simulation

What exactly is Monte Carlo simulation?

An inventory problem in Monte Carlo simulation

Monte Carlo simulation in basketball

The volatility plot

Implied volatilities

The portfolio valuation

The simulation model

Geometric Brownian simulation

The diffusion-based simulation

The threshold model

Schelling’s Segregation Model

An overview of statistical and machine learning

K-nearest neighbors

Generalized linear models

Bayesian linear regression

Creating animated and interactive plots

Summary

6. Statistical and Machine Learning

Classification methods

Understanding linear regression

Linear regression

Decision tree

An example

The Bayes theorem

The Naïve Bayes classifier

The Naïve Bayes classifier using TextBlob

Installing TextBlob

Downloading corpora

The Naïve Bayes classifier using TextBlob

Viewing positive sentiments using word clouds

k-nearest neighbors

Logistic regression

Support vector machines

Principal component analysis

Installing scikit-learn

k-means clustering

Summary

7. Bioinformatics, Genetics, and Network Models

Directed graphs and multigraphs

Storing graph data

Displaying graphs

igraph

NetworkX

Graph-tool

PageRank

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

8. Advanced Visualization

Computer simulation

Python’s random package

SciPy’s random functions

Simulation examples

Signal processing

Animation

Visualization methods using HTML5

How is Julia different from Python?

D3.js for visualization

Dashboards

Summary

A. Go Forth and Explore Visualization

An overview of conda

Packages installed with Anaconda

Packages websites

About matplotlib

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