PyGotham 2016 talk on data, analysis, and Python!

1. Push Data, Pull Data, Present Data

2. HELLO!
My name is Adrian
Software Engineer, Search & Discovery
Teachers Pay Teachers

3. Let’s look at data

8. (output is only a snippet of the top results)

9. Let’s explore more
What do those labels tell us?

10. ('fast', 1),
('increase', 1),
('columnar', 1),
('store', 1),
('website', 1),
('product', 1),
('way', 1),
('business', 1),
('workflow', 1),
('framework', 1),
('task', 1),
('traffic', 1),
('bit', 1),
('day', 1),
('pull', 1),
('pipeline', 1),
[('web', 2),
('story', 2),
('lot', 2),
('site', 2),
('data', 2),
('cycle', 2),
('order', 2),
('load', 1),
('development', 1),
('code', 1),
('number', 1),
('hand-in-hand', 1),
('design', 1),
('morning', 1),
('click', 1),
('sum', 1),
NN: noun, common, singular or mass
('tracking', 1),
('anything', 1),
('database', 1),
('light', 1),
('analysis', 1),
('server', 1),
('thing', 1),
('amount', 1),
('architecture', 1),
('behavior',
1),('team', 1),
('piece', 1)]

12. [('of', 7),
('in', 6),
('on', 4),
('about', 4),
('that', 4),
('like', 3),
('for', 3),
('through', 3),
('if', 2),
('from', 1),
('whether', 1),
('into', 1),
('between',
1),
('per', 1),
('so', 1),
('with', 1),
('by', 1)]
IN: preposition or conjunction, subordinating

13. [('data', 16),
('icons', 3),
('users', 2),
('dashboards', 2),
('needs', 1),
('referrals', 1),
('things', 1),
('downloads', 1),
NNS: noun, common, plural
('sessions', 1),
('decisions', 1),
('scenarios', 1),
('tasks', 1),
('examples', 1),
('events', 1),
('issues', 1),
('logs', 1)]

14. What about the verbs?

16. [('have', 4),
('presenting', 4),
('be', 3),
('pulling', 3),
('want', 2),
('know', 2),
('tracking', 2),
('seeing', 1),
('shed', 1),
('using', 1),
('need', 1),
('check', 1),
('visualize', 1),
('thinking', 1),
('fix', 1),
VB, VBG
('build', 1),
('hopefully', 1),
('tell', 1),
('do', 1),
('processing', 1),
('deploy', 1),
('pushing', 1),
('coming', 1),
('aggregate', 1),
('changing', 1),
('present', 1),
('making', 1),
('make', 1)]

17. What do we know thus far?
NN, NNS are nouns.
The most common nouns
together sound like it’s a
text related to something
technical: 'web', 'story',
'lot', 'site', ‘data', 'cycle',
'order', 'data', 'icons',
'users', 'dashboards'
The verbs that we pulled
(VB, VBG) sound typical by
themselves but are useful.
Most common verbs:
'have', 'presenting', 'be',
'pulling', 'want', 'know',
'tracking'

18. Let’s try and make phrases
verb + noun combinations

20. verb + noun phrases
'have data',
'have icons',
'have users',
'have dashboards',
'have needs',
'presenting data',
'presenting icons',
'presenting users',
'presenting dashboards',
'presenting needs',
'be data',
'be icons',
'be users',
'be dashboards',
'be needs',
'pulling data',
'pulling icons',
'pulling users',
'pulling dashboards',
'pulling needs',
'want data',
'want icons',
'want users',
'want dashboards',
'want needs'

21. So what is the text?

23. Push, Pull, Present
Abstract text was pushed
to PyGotham and stored
More realistic example
would be collecting click
tracking data from a web
frontend.
Abstract text was pulled
once, but constantly
pulled from to manipulate,
extract, and sift through.
Typical jobs like data
cleansing and sorting
would be in the pull
process.
Matplotlib and standard
Python library utils were
used to present
Whether presenting slides
or a Jupyter Notebook, the
medium to present does
not matter.

24. Before we continue
Some background

25. Teachers Pay Teachers
Open marketplace for a community of millions
of educators

27. ◉ Searches are all education-focused
◉ Sellers want to rank higher to sell better
◉ We can’t make search changes to please everyone,
but we listen carefully to our community
◉ We A/B test a lot
TpT Search Notes

28. Our data flow is simple...

29. Event
tracking
Event
data
Dashboard Analysis

30. An example of exclusion search
◉ “apples bananas -pumpkins”
◉ Searches are education focused; suffixes are searched for (-
ed, -ing, -ly, etc.)
◉ Looking through historical searches of 519,512,359 we
found only a small fraction of searches that included a dash

31. 0.0003905085153132998
Only 202, 874 of the 519M historical searches had a dash

33. A/B tests
Decisions driven by data

34. Problem
Users are looking to click on the large file icon for a link, but it is just an
image and not a link.
Hypothesis
If we change out the large file icon,
users will click more on the Preview
button instead and have a better
user experience.

35. A/B Variants
A Variant: small icon B Variant: no icon

38. Clicks per A/B variant
Off Small Icon No icon
clicks 430,656 26,790 0

39. A/B tests in a nutshell
hypothesis test analyze

40. Simple Running Example
Strava

44. Fetch all weekday run activities that are less than 8km

46. References
◉ Bird, Steven, Edward Loper and Ewan Klein
(2009), Natural Language Processing with Python.
O’Reilly Media Inc.
◉ stravalib: Python Strava API client
https://github.com/hozn/stravalib
◉ Examples seen in this talk http://bit.ly/29zQDBb
(https://github.com/drincruz/PyGotham-2016)

48. We’re hiring!
https://www.teacherspayteachers.com/Careers

49. THANKS!
Any questions?
You can find me at
{github, twitter, irc, etc.} @drincruz