Mastering Python data visualization generate effective results in a variety of visually appealing charts using the plotting packages in Python
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 i...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing
2015.
|
| Edición: | 1st edition |
| Colección: | Community experience distilled.
|
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629574106719 |
Tabla de Contenidos:
- 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.
