Любой человек, ежедневно работающий с данными, рано или поздно сталкивается с необходимостью их визуализации. В своём рассказе я приведу примеры использования простых, но в то же время мощных инструментов визуализации данных, которые могут сэкономить время data scientist'у на всех фазах проекта. А в дополнение к этому рассмотрим современный open-source business intelligence инструмент Apache Superset.

2. Data visualization tools in
Python
Roman Merkulov
Data Scientist at InData Labs
r_merkulov@indatalabs.com
merkylovecom@mail.ru

3. Content
- why dataviz is important
- dataviz libraries in python
- facets tool
- interactive maps
- Apache Superset

4. data visualization
- EDA & understanding the data
- fix data
- show insights
- models validation
- analytics & reporting

5. Plots vs descriptive statistics
Anscombe's quartet
*https://en.wikipedia.org/wiki/Anscombe%27s_quartet

6. 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

7. *http://blog.revolutionanalytics.com/2017/05/the-datasaurus-dozen.html

8. *https://matplotlib.org/gallery.html

9. Pros:
- very powerful
- large community, long history

10. Doesn’t look simple enough...

11. Cons:
- imperative API
- poor support for interactivity
Just to add a popup...

12. matplotlib based solutions
*https://speakerdeck.com/jakevdp/pythons-visualization-landscape-pycon-2017

13. 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

14. javascript based solutions
*https://speakerdeck.com/jakevdp/pythons-visualization-landscape-pycon-2017
folium
bqplot

15. *https://plot.ly/python/
Pros:
- interactivity
- lots of visualization
types
- both declarative and
imperative capabilities
Cons:
- paid features

16. bokeh
Pros:
- interactivity
- lots of visualization
types
- both declarative and
imperative capabilities
Cons:
- limited vector graphic
export

17. Datashader
when you have millions and billions of points
NYC Taxi
US Census 2010
*https://datashader.readthedocs.io/en/latest/

18. Altair
(based on Vega-Lite)
Fully declarative paradigm
*https://altair-viz.github.io/#

19. 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

20. *https://pair-code.github.io/facets/quickdraw.html

21. https://research.googleblog.com/2017/07/facets-open-source-visualization-tool.html

22. Folium
*https://github.com/python-visualization/folium

23. https://indatalabs.com/discover-hong-kong-through-the-lense-of-instagram/
https://indatalabs.com/brands-on-london-instagram
Visualization of the week according to InsideBigData
https://insidebigdata.com/2016/02/03/visualization-of-the-week-hong-kong-social-media-data-map/

24. Apache Superset
*https://superset.incubator.apache.org/

25. Apache Superset
Whatever!
if SQLAlchemy dialect is available for your DB
*https://github.com/apache/incubator-superset

26. Apache Superset
Who uses:
Airbnb Amino Brilliant.org Clark.de Digit Game Studios Douban
Endress+Hauser FBK - ICT center Faasos GfK Data Lab InData Labs
Maieutical Labs Qunar Shopkick Tails.com Tobii Tooploox Udemy Yahoo!
Zalando
Panoramix Caravel Superset
*https://github.com/apache/incubator-superset
Article on Superset benefits
and limitations
https://indatalabs.com/blog/data-strategy/open-
source-data-visualization-tool-superset
Roaring Elephant podcast
Episode 41
https://roaringelephant.org/2017/04/25/episode-41-
news-news-and-some-more-news/

27. Thanks for your attention!
some examples shown are available here
https://github.com/merkylove/data_visualisations_for_datathon_2017
https://www.slideshare.net/RomanMerkulov/data-visualization-tools-in-python/1
Contacts:
r_merkulov@indatalabs.com
merkylovecom@mail.ru
https://www.linkedin.com/in/roman-merkulov-a61804a4/