Python Hype?
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The document analyzes the popularity and growth trends of Python over the past decade, highlighting factors such as user demographics, retention rates, and perceptions of the language. It discusses survey results indicating generally positive impressions among users and identifies areas for improvement such as documentation and performance. Ultimately, it suggests that Python is experiencing steady growth despite some challenges and emphasizes the importance of community involvement and continued learning.
Python Hype?
- 2. Hi, I’m Brian Ray • Directive Years 1998-2003 • Engineering Years 2003-2006 • Leadership Years 2009-2010 • Indy Consulting Years 2010-2013 Taken in China May 20th, 2016 • Big Four Consulting 2013-current
- 3. Why “Python Hype”? In the last 10 years, we are seeing Python having (select one): A. Slow and steady growth. B. Spiked and now on decline. C. Spiked + Declined now stabilized. D. Lives in independent domain. E. We (Python fans) live in a bubble.
- 4. What measure? • Hype Cycle • TIOBE Index • On Github • PYPL • Some other
- 5. Don’t tell me there aren’t trends
- 6. Programming language “popularity” is hard to measure. Lenses to help measure: 1. Learned: was taught Python in course 2. Migrated: from language to language 3. Addressed: problem class to solve 4. Platform-ed: ecosystem of tools 5. Retained: sticking with Python 6. Promoted: Promoted
- 7. OUR SURVEY 236 respondents broken up into 3 groups
- 8. Who 1 2 3 Groups The missing group 4: Those who didn’t take the survey
- 10. Treatment of groups • Curve Questions • Retained • Promoted • Addressed • Platform-ed • Learned • Migrated 1 2 all3
- 11. Group 1: Learned/Migrated • 60% heard of python Word of mouth • 56% had very positive first impression, 31% had positive, less than 13% neutral or less. • Net-promoter to recommend
- 13. Group 1: Learned/Migrated “Python is Now the Most Popular Introductory Teaching Language at Top U.S. Universities” By Philip Guo July 7, 2014
- 14. Group 2: addressed / platformed • 63% very positive 1st impression (3% higher than Group 1) • 77.5% very positive 2nd impression (after months) • 71 % very positive 3rd impression Hype curve-esk?
- 15. Group 2: addressed / platformed Dislikes: • Poor documentation • Don’t like whitespace • Slow • Prefer statically typed • Threading • Runtime not as ubiquitous as Java • GIL • Models not pip installable • Inheritance can be confusing • Lack of Mobile dev support • That it’s not Lisp • Python 2 or 3 choice • Package support for Python 3 • Python 2 vs 3 • Dependency Management • Installation Issues • Smarmy attitude Likes: • Flexibility, simplicity, transparency • Legibility • Easy to learn • Approachable • Community • “Batteries included” • Correct or “pythonic” way • Standard library • Online resources • Scientific libraries • Versatile • Third party libraries • Online communities • Concise • Easy to get started • Not Java 8 • Garbage collection • Great depth • Complex times included
- 16. Group 3: Retainer / Promoter Group 1 Group 3 • 53% think Python Very high quality, 39% High, less then 9% Natural or below
- 17. Group 3: Retainer / Promoter Group 1
- 18. Group 3: Retainer / Promoter Small drawbacks: • 45% Speed • 44% GIL • 30% easy to duck type / monkey patch Big Drawbacks: • 9% GIL • 15% Unicode Support Critical: • 5% Unicode Support 25%– 50%– 100%–– Python 2 (before 2.7) 10.64% 12.77% 2.13% Python 2.7 - 2.x 16.67% 30.30% 43.94% Python 3+ 22.03% 32.20% 23.73% PyPy 14.89% 0.00% 2.13% Jython 4.44% 4.44% 0.00%
- 19. Group 3: third-party • Surveyed 58 most downloaded pypi packages • 53% marked “Used” • 24% marked “long time user” • 14% marked “plan on long time” • 7% marked “stop”
- 20. Group 3: third-party Top Plan on long time: pip kid virtualenv ipython pep8 requests pandas django celery reportlab Top Stopped: plone pylons pycurl twissted zope nose pyramid
- 22. All Groups • TIOBE When did Python Peak: • 2007: 1% • 2010: 28% • Never: 46% • Other: 23% Other: • 2011: when google recruited for • Science/web lead to second wave • 2011-2012 • 2014 • Peak is still coming • Big Data will lead to future peak
- 23. All Groups • Github 2008-2009 2014+2010-2013
- 24. Github Top Python Projects How forked 0.0 10.0 20.0 30.0 40.0 50.0 60.0 before 2010 2013-2014 after 2014 JavaScript Ruby PHP Python Objective-C C C++ Java Shell CoffeeScript 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 PHP Python Objective-C C C++ Java Shell Activity, based on count: watched + forked
- 25. All Groups • PYPL Why seeing Steady upward Line? • Has broader range of uses, unlike Ruby (rails and devops) • Mirrors Data Science Usage • Mirrors Big Data Usage • Steady growth
- 26. OTHER FACTORS Some other “Popularity” Metrics
- 27. A local approach • Jobs • Meetups
- 28. Corporate Suite • Python (and R) compatibility with Commercial vendors: Datameer, IBM, Microsoft Azure, Oracle, Platfora, SAP, Tableau, Teradata and Tibco Software.
- 29. Adoption in Data Science • KDNuggets reporting that 49% of analytics and data mining developers have used R, and 35% have used Python
- 30. Google hiring Python It all got started, I believe, because the very earliest Googlers (Sergey, Larry, Craig, ...) made a good engineering decision: "Python where we can, C++ where we must” - Alex Martelli Python's growth and acceptance in its many roles just hasn't followed any ups-and-downs curve as models would predict -- it's been pretty steadily, gradually upwards instead.
- 31. HIGHLIGHTS Some interpretation of results…
- 32. Revisiting our question A. Slow and steady growth. B. Spiked and now on decline. C. Spiked + Declined now stabilized. D. Lives in independent domain. E. We (Python fans) live in a bubble.
- 33. Slow and steady growth. Supports: • Strong first impressions from Group 1, 2, 3 • Strong retention in group 2 • Spikes not measures as large • 30% of hardcore users have switched to Python 3+ 50% of the time or more • Because Alex Martelli says so Negates: • 20% no disruptive • 5% increase in watchers+forkers on github • We did measure some spikes
- 34. Spiked and now on decline. Supports: • Some domain-specific languages, push down? • Lack of mobile support • Small amount of degative: 2/3 support, swarmy Negates: • Lack of significant data showing decline in Python popularity • Very low activity scores confirming decline • Not much negative press • 46% say never peaked
- 35. Spiked + Declined now stabilized Supports: • 30% Data Science market uses Python • ¼ surveyed see as in reached Productivity/maturity • TOIBE shows some spike-ish around 2010- 28% surveyed agree Negates: • Hard to measure market penetration, is it 20% • Of third party packages, only 14% plan on using what they use now for a long time
- 36. Lives in independent domain Supports: • Python remained someone on effected on the PYPL Index where clearly other languages ebbed and flowed Negates: • Google and others site using Python with other languages • Considered good-glue • Commercial software vendors adding Python support
- 37. We (Python fans) live in a bubble Supports: • Nearly 90% approval rating is insane, and that’s who took the survey • 45% of users found Python from Word of Mouth Negates: • Python lives in two many different independent domains to be blind sided
- 38. SOME CLOSING THOUGHTS In my own words
- 39. The Future of Python • A good choice to learn • Not going away (anytime soon) • Get involved with your local community • Contribute in your area of interest • Python Addition Helpline • Openness allows self fulfilling prophecy • Still, don’t live in a vacuum, learn other languages!
- 40. THANK YOU! Brian Ray Email: brianhray@gmail.com or brray@deloitte.com https://www.linkedin.com/in/brianray https://twitter.com/brianray https://github.com/brianray http://chipy.org