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# Data Visualization Tools in Python

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Overview of tools available in python for performing data visualization (statistical, geographical, reporting, etc). Prepared for Minsk DataViz Day (October 4, 2017)

Publié dans : Données & analyses
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### Data Visualization Tools in Python

1. 1. Data visualization tools in Python Roman Merkulov Data Scientist at InData Labs r_merkulov@indatalabs.com merkylovecom@mail.ru
2. 2. Content - why dataviz is important - dataviz libraries in python - facets tool - interactive maps - Apache Superset
3. 3. data visualization - EDA & understanding the data - fix data - show insights - models validation - analytics & reporting
4. 4. Plots vs descriptive statistics Anscombe's quartet *https://en.wikipedia.org/wiki/Anscombe%27s_quartet
5. 5. Plots vs descriptive statistics Anscombe's quartet *https://en.wikipedia.org/wiki/Anscombe%27s_quartet Property Value Accuracy Mean of X 9 exact Sample variance of X 11 exact Mean of y 7.5 2 decimal places Sample variance of y 4.125 +- 0.003 Correlation coef. 0.816 3 decimal places Linear regression y = 3.00 + 0.5x 2 decimal places Determ. coef. 0.67 2 decimal places
6. 6. *http://blog.revolutionanalytics.com/2017/05/the-datasaurus-dozen.html
7. 7. *https://matplotlib.org/gallery.html
8. 8. Pros: - very powerful - large community, long history
9. 9. Doesn’t look simple enough...
10. 10. Cons: - imperative API - poor support for interactivity Just to add a popup...
11. 11. matplotlib based solutions *https://speakerdeck.com/jakevdp/pythons-visualization-landscape-pycon-2017
12. 12. matplotlib based solutions http://yhat.github.io/ggpy/ http://scitools.org.uk/cartopy/docs/latest/gallery.html https://seaborn.pydata.org/examples/index.html https://networkx.github.io/documentation/networkx-1.9.1/examples/drawing/random_geometric_graph.html
13. 13. javascript based solutions *https://speakerdeck.com/jakevdp/pythons-visualization-landscape-pycon-2017 folium bqplot
14. 14. *https://plot.ly/python/ Pros: - interactivity - lots of visualization types - both declarative and imperative capabilities Cons: - paid features
15. 15. bokeh Pros: - interactivity - lots of visualization types - both declarative and imperative capabilities Cons: - limited vector graphic export
16. 16. Datashader when you have millions and billions of points NYC Taxi US Census 2010 *https://datashader.readthedocs.io/en/latest/
17. 17. Altair (based on Vega-Lite) Fully declarative paradigm *https://altair-viz.github.io/#
18. 18. Facets Overview Dive Quick Draw Dataset https://pair-code.github.io/facets/quickdraw.html *https://pair-code.github.io/facets/ https://github.com/PAIR-code/facets
19. 19. *https://pair-code.github.io/facets/quickdraw.html