Introduction to plotting in Python
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An introduction to plotting in Python landscape. Mostly matplotlib, but also peek at other packages. Jupyter notebook with examples: https://gist.github.com/bzamecnik/b58579e319287abcb3ca Talk held on 2015-01-07 at the Python for Data Science workshop organized by Jumpshot & AVAST.
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14th Edition of International conference on computer vision
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Introduction to plotting in Python
- 1. Bohumír Zámečník @bzamecnik Introduction to plotting in Python for Data Science Workshop 2016-01-07
- 2. Agenda ● mostly how to plot with ● practical examples ● tips for other packages http://matplotlib.org/basemap/users/examples.html
- 3. Why plotting in Python? ● GUI is not practical for complex plots ● "no more Excel headache" ● code can be automatized easily ● nice integrates with data processing ● Python is full language (sorry MATLAB) ● coding in Python is fun
- 4. ● Python package ● for 2D plotting ● publication quality ● & interactive plots ● very powerful ● very popular ● many extensions http://stanford.edu/~mwaskom/software/seaborn/examples/hexbin_marginals.html What is ?
- 5. History & motivation ● since 2002 (~14 years) ● needed to plot brain activity (EEG) ● MATLAB was not powerful enough John Hunter (1968-2012) http://matplotlib.org/users/screenshots.html
- 6. The basic workflow 1. specify plot as Python code 2. output to PNG, PDF, GUI, Jupyter, etc.
- 7. Hello, world! in matplotlib Motto: "Make easy things easy and hard things possible."
- 8. Architecture ● scripting layer ● artist layer ● backend layer from book: Mastering matplotlib by Duncan M. McGreggor Packt Publishing, 2015
- 9. Scripting layer ● pyplot ○ syntax sugar ○ stateful API ○ high-level object API ○ you typically use this ● pylab ○ compatibility with MATLAB ○ deprecated
- 10. Artist layer ● what should be rendered? ● parts of the plot ● object-oriented API ● primitives - Line2D, Rectangle, Text, Image ● containers ○ Figure - full plot ○ Axes - single subplot ○ Axis - one axis
- 11. Backend layer ● how it should be rendered? ● interactive ○ Tk, GTK, Qt, OS X, WX ● hardcopy ○ raster – AGG, GDK, NBAGG, Cairo ○ vector – PS, PDF, SVG, Cairo ● FigureCanvas - area where Figure is drawn ● Renderer - knows how to draw
- 13. Practical examples in a Jupyter notebook https://gist.github.com/bzamecnik/b58579e319287abcb3ca
- 14. Extensions & other packages seaborn ggplot plot.ly bokeh
- 15. matplotlib drawbacks ● quite old ● too focused on server-side ● too cross-platform ● not awesomely beautiful by default
- 16. Seaborn: statistical data visualization ● matplotlib extension ● beautiful theme ● statistical plots ○ distribution ■ hexbin, violin plot, etc. ○ scatterplot ○ pairwise correlations http://stanford.edu/~mwaskom/software/seaborn/examples/grouped_violinplots.html
- 17. ggplot from ŷhat ● based on ○ Grammar of Graphics ○ ggplot2 from R http://ggplot.yhathq.com/
- 18. Bokeh ● based on D3.js ● client-side ● interactive ● beautiful ● high-performance http://bokeh.pydata.org/
- 19. Plot.ly ● cloud service ● can publish plots ● free for public plots ● API & Python library ● interactive, beautiful ● good for dashboards https://plot.ly/python/choropleth-maps/
- 20. Summary ● plotting in Python is nice & powerful ● sometimes a bit overwhelming ● it is worth learning it! Wish you nice plots!
- 22. Thanks! Q&A Bohumír Zámečník @bzamecnik Python Workshop Intro to Plotting in Python