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Mastering Python Data Visualization.

Mastering Python Data Visualization.
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ISBN 9781783988334
-- 9781783988327
Author Raman, Kirthi
Title Mastering Python Data Visualization.
Description 1 online resource (372 pages)
Contents Cover -- Copyright -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 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 -- Chapter 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 -- Chapter 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 -- Visualization plots with Anaconda -- The surface-3D plot -- The square map plot -- Interactive visualization packages -- Bokeh -- VisPy -- Summary -- Chapter 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 -- 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 -- Chapter 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 -- Chapter 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 -- Chapter 7: Bioinformatics, Genetics, and Network Models -- Directed graphs and multigraphs -- Storing graph data -- Displaying graphs -- igraph -- NetworkX -- Graph-tool -- 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 -- 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 -- Appendix: Go Forth and Explore Visualization -- An overview of conda -- Packages installed with Anaconda -- Packages websites -- About matplotlib -- Index.
Subject Computer security
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