Python data visualization cookbook over 70 recipes, based on the principal concepts of data visualization, to get you started with popular Python libraries
Over 70 recipes to get you started with popular Python libraries based on the principal concepts of data visualization About This Book Learn how to set up an optimal Python environment for data visualization Understand how to import, clean and organize your data Determine different approaches to dat...
| Otros Autores: | , , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing
2015.
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| Edición: | Second edition |
| Colección: | Quick answers to common problems.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629686106719 |
Tabla de Contenidos:
- Cover
- Copyright
- Credits
- About the Authors
- About the Reviewer
- www.PacktPub.com
- Table of Contents
- Preface
- Chapter 1: Preparing Your Working Environment
- Introduction
- Installing matplotlib, NumPy, and SciPy
- Installing virtualenv and virtualenvwrapper
- Installing matplotlib on Mac OS X
- Installing matplotlib on Windows
- Installing Python Imaging Library (PIL) for image processing
- Installing a requests module
- Customizing matplotlib's parameters in code
- Customizing matplotlib's parameters per project
- Chapter 2: Knowing Your Data
- Introduction
- Importing data from CSV
- Importing data from Microsoft Excel files
- Importing data from fixed-width data files
- Importing data from tab-delimited files
- Importing data from a JSON resource
- Exporting data to JSON, CSV, and Excel
- Importing and manipulating data with Pandas
- Importing data from a database
- Cleaning up data from outliers
- Reading files in chunks
- Reading streaming data sources
- Importing image data into NumPy arrays
- Generating controlled random datasets
- Smoothing the noise in real-world data
- Chapter 3: Drawing Your First Plots and Customizing Them
- Introduction
- Defining plot types - bar, line, and stacked charts
- Drawing simple sine and cosine plots
- Defining axis lengths and limits
- Defining plot line styles, properties, and format strings
- Setting ticks, labels, and grids
- Adding legends and annotations
- Moving spines to the center
- Making histograms
- Making bar charts with error bars
- Making pie charts count
- Plotting with filled areas
- Making stacked plots
- Drawing scatter plots with colored markers
- Chapter 4: More Plots and Customizations
- Introduction
- Setting the transparency and size of axis labels
- Adding a shadow to the chart line.
- Adding a data table to the figure
- Using subplots
- Customizing grids
- Creating contour plots
- Filling an under-plot area
- Drawing polar plots
- Visualizing the filesystem tree using a polar bar
- Customizing matplotlib with style
- Chapter 5: Making 3D Visualizations
- Introduction
- Creating 3D bars
- Creating 3D histograms
- Animating in matplotlib
- Animating with OpenGL
- Chapter 6: Plotting Charts with Images and Maps
- Introduction
- Processing images with PIL
- Plotting with images
- Displaying images with other plots in the figure
- Plotting data on a map using Basemap
- Plotting data on a map using the Google Map API
- Generating CAPTCHA images
- Chapter 7: Using the Right Plots to Understand Data
- Introduction
- Understanding logarithmic plots
- Understanding spectrograms
- Creating stem plot
- Drawing streamlines of vector flow
- Using colormaps
- Using scatter plots and histograms
- Plotting the cross correlation between two variables
- Importance of autocorrelation
- Chapter 8: More on matplotlib Gems
- Introduction
- Drawing barbs
- Making a box and a whisker plot
- Making Gantt charts
- Making error bars
- Making use of text and font properties
- Rendering text with LaTeX
- Understanding the difference between pyplot and OO API
- Chapter 9: Visualizations in the Clouds with Plot.ly
- Introduction
- Creating line charts
- Creating bar charts
- Plotting a 3D trefoil knot
- Visualizing maps and bubbles
- Index.
