README.md - 2016-07-28_manchester_datavis

This repository contains files for the Python hands-on session of the Software Sustainability Institute Research Data Visualisation Workshop (RDVW) at the University of Manchester, 28th July 2016.

• Course website: http://software.ac.uk/rdvw
• Course registration: Eventbrite
• Workshop report: SSI blog post
• Videos of workshop keynotes: YouTube
• Speaker slides: Google Drive
• Storify of tweets: Storify

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Acknowledgements

These exercises borrow heavily and adapt or use material from several excellent sources, including:

• Data Carpentry: http://www.datacarpentry.org/
• Software Carpentry: http://software-carpentry.org/ - especially the R novice Gapfinder material
• ggplot2: Elegant Graphics for Data Analysis: http://www.springer.com/us/book/9780387981413
• bokeh documentation: http://bokeh.pydata.org/en/latest/docs/gallery.html#gallery
• “39 studies about human data perception in 30 minues”, Kennedy Elliott: link
• The Data Visualisation Catalogue: http://www.datavizcatalogue.com

If you recognise some of this content and are not acknowledged here, please let me know and I will correct that! I don’t think I’m in breach of any copyright or licensing, but if you notice any such breaches please let me know and I will rectify as soon as is possible.

Exercises

One-variable, continuous data

• one_variable_continuous.ipynb

Learning Outcomes

• Generating randomly-distributed example data
• Representing one-dimensional continuous-valued data with histograms, KDE plots, and rug plots
• Using matplotlib and seaborn libraries
• Presenting arrays of images
• Use of figure() and subplots()

Two-variable, continuous x, y data

• continuous_x_continuous_y.ipynb

Learning Outcomes

• Representing two-dimensional continuous x and y data using matplotlib and seaborn libraries
• Use of figure() and subplots
• Annotating plots with text
• Working with long and wide form DataFrames in pandas
• Using statistical overlays and seaborn’s statistical plots

Grammar of Graphics

• grammar_of_graphics.ipynb

Learning Outcomes

• Understand the Grammar of Graphics
• Use the Grammar of Graphics with the ggplot module to produce plots from aesthetics and geometric representations.
• Use layers to produce new visualisations specifically to suit your data, showing data and statistical summaries
• Use multi-panel figures to display complex datasets

Imaging 2D arrays, colormaps, and 3D surface plots

• colormaps_surfaces_netcdf.ipynb

Learning Outcomes

• Read and use NetCDF format data using Python
• Visualise array data as a heatmap/image
• Explore array data visualisation with a range of colour palettes, and understand how selection and normalisation of colour palettes can influence the interpretation of data
• Visualise 2D array data as a 3D surface plot
• Explore the application and influence of colour palettes and lighting effects on the interpretation of 3D surface plots

Interactive map with bokeh

• interactive_bokeh_map.ipynb

Learning Outcomes

• Import and process shapefile GIS data using pyshp
• Import public .csv (electoral) data using pandas
• Render GIS boundary data in an interactive bokeh plot
• Colour geospatial data by results in an interactive bokeh plot

Making movies with matplotlib and seaborn

• making_movies.ipynb

Learning Outcomes

• Understand matplotlib’s animation functionality
• Generate animations using FuncAnimation
• Generate line graph and bubble plot animations