Mastering Python Data Visualization电子书

Mastering Python Data Visualization

Table of Contents

Mastering Python Data Visualization

Credits

About the Author

About the Reviewers

www.PacktPub.com

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Why subscribe?

Free access for Packt account holders

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

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

Index