pydata: past, present future (pydata sv 2014 keynote)
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PyData: Past, Present, Future
Peter Wang @pwang
!
Continuum Analytics !
PyData SV 2014
How did we get here?
“Python Data Workshop” March 3, 2012, Google HQ
“Guido, please help us convince core dev to
work with us to solve the packaging problem!”
“Guido, please help us convince core dev to
work with us to solve the packaging problem!”
“Meh. Feel free to solve it
yourselves.”
“Guido, please help us convince core dev to
work with us to solve the packaging problem!”
“Meh. Feel free to solve it
yourselves.”
“What Packaging Problem?”
“What Packaging Problem?”“I just use….”
“What Packaging Problem?”“I just use….”
• pip & virtualenv
“What Packaging Problem?”“I just use….”
• pip & virtualenv• homebrew
“What Packaging Problem?”“I just use….”
• pip & virtualenv• homebrew• rpm
“What Packaging Problem?”“I just use….”
• pip & virtualenv• homebrew• rpm• apt-get
“What Packaging Problem?”“I just use….”
• pip & virtualenv• homebrew• rpm• apt-get• emerge
“What Packaging Problem?”“I just use….”
• pip & virtualenv• homebrew• rpm• apt-get• emerge• tar -zxf
“What Packaging Problem?”“I just use….”
• pip & virtualenv• homebrew• rpm• apt-get• emerge• tar -zxf• double-click MSI
“What Packaging Problem?”“I just use….”
• pip & virtualenv• homebrew• rpm• apt-get• emerge• tar -zxf• double-click MSI• configure ; make ; make install
“What Packaging Problem?”“I just use….”
• pip & virtualenv• homebrew• rpm• apt-get• emerge• tar -zxf• double-click MSI• configure ; make ; make install• export PYTHONPATH=…
“What Packaging Problem?”“I just use….”
• pip & virtualenv• homebrew• rpm• apt-get• emerge• tar -zxf• double-click MSI• configure ; make ; make install• export PYTHONPATH=…
“What Packaging Problem?”“I just use….”
• pip & virtualenv• homebrew• rpm• apt-get• emerge• tar -zxf• double-click MSI• configure ; make ; make install• export PYTHONPATH=…
from python import \! technical_debt
This Packaging Problem
This Packaging Problem
This Packaging Problem
This Packaging Problem
This Packaging Problem
PyData: The First 2 Years• Oct 2012: First PyData Conf, NYC
!
• March 2013: PyData SV (PyCon) • July 2013: PyData Boston (Microsoft) • Oct 2013: PyData NYC (JP Morgan)
!
• Feb 2014: PyData UK (Level39) • May 2014: PyData SV (Facebook) • July 2014: PyData Berlin (EuroPython) • October 2014: NYC (Strata NYC)
!
• October 2014: NYC (YOUR COMPANY HERE)
PyData: The First 10 years
PyData: The First 10 years
• IPython Notebook: 2005-2011 • pandas: 2008-2009 • scikit-learn: 2007 • NumPy: 2006
PyData: The First 15 Years
• IPython Notebook: 2005-2011 • pandas: 2008-2009 • scikit-learn: 2007 • NumPy: 2006 • SciPy: 1999 • IPython: 2001 • matplotlib: 2002
PyData: The First 15 Years
• IPython Notebook: 2005-2011 • pandas: 2008-2009 • scikit-learn: 2007 • NumPy: 2006 • SciPy: 1999 • IPython: 2001 • matplotlib: 2002
http://numfocus.org/johnhunter.html
PyData: The First 20 Years
• Numarray: 2001 • Numeric: 1995
• Matrix Obj: 1994
• IPython Notebook: 2005-2011 • pandas: 2008-2009 • scikit-learn: 2007 • NumPy: 2006 • IPython: 2001 • matplotlib: 2002
Way Way Back
Way Way Back
• python: 1989-1991
Way Way Back
• python: 1989-1991• v1.0: 1994
Way Way Back
• python: 1989-1991• v1.0: 1994• “ABC, SETL…
Way Way Back
• python: 1989-1991• v1.0: 1994• “ABC, SETL… …That would appeal to UNIX/C hackers”
Way Way Back
• python: 1989-1991• v1.0: 1994• “ABC, SETL… …That would appeal to UNIX/C hackers”
$ conda create -n py10 python=1.0
Way Way Back
• python: 1989-1991• v1.0: 1994• “ABC, SETL… …That would appeal to UNIX/C hackers”
http://continuum.io/blog/python-1.0$ conda create -n py10 python=1.0
Way Way Back
It is interactive, structured, high-level, and intended to be used instead of BASIC, Pascal, or AWK. !
It is not meant to be a systems-programming language but is intended for teaching or prototyping.
“In June [1960] we were introduced to this tall college kid that always signed his name with lowercase letters. He was don knuth … don claimed that he could write the [Algol] compiler and a language manual all by himself during his three and a half month summer vacation.”
PyData NYC 2013 Keynote
PyData NYC 2013 Keynote
PyData NYC 2013 Keynote
http://tuulos.github.io/sf-python-meetup-sep-2013/#/
“One of the most exciting features in development is the Numba-based UDF
compiler. Building UDFs for Impala currently requires writing C++ or Java
code and registering them manually with the cluster. Writing C++/Java code is
more difficult, time-consuming, and error-prone for many data analysts.”
http://blog.cloudera.com/blog/2014/04/a-new-python-client-for-impala/
http://grokbase.com/t/python/python-list/01az9hmtf1/python-development-practices
http://grokbase.com/t/python/python-list/01az9hmtf1/python-development-practices
Glue 2.0Python’s legacy as a powerful glue language
• manipulate files • call fast libraries
!
Next-gen Glue: • Link data silos • Link disjoint memory & compute • Unify disparate runtime models • Transcend legacy models of
computers
Hard Problems in Data ScienceLots of data Messy data Noisy data
Hard Problems in Data ScienceLots of data Messy data Noisy data
Lots of computers Lots of tools
Lots of hacking
Hard Problems in Data ScienceLots of data Messy data Noisy data
Lots of computers Lots of tools
Lots of hacking
More questions More data
More people
The Hype & The Opportunity
“Internet Revolution” True Believer, 1996: Businesses that build network capability into their core will outcompete and destroy their competition.
The Hype & The Opportunity
“Internet Revolution” True Believer, 1996: Businesses that build network capability into their core will outcompete and destroy their competition.
“Data Revolution” True Believer, 2014: Businesses that build data comprehension into their core will destroy their competition over the next 5-15 years.
The Hype & The Opportunity
“Internet Revolution” True Believer, 1996: Businesses that build network capability into their core will outcompete and destroy their competition.
“Data Revolution” True Believer, 2014: Businesses that build data comprehension into their core will destroy their competition over the next 5-15 years.
(1993 == 2011?)
Soft Problems in Data Science
Soft Problems in Data Science
Computers
EE
Soft Problems in Data Science
Computers
EE
Applications
CS
Soft Problems in Data Science
Computers
EE
Applications
CS
DATAInsights
Math, Stats
Computers
Applications
Data
Insights
Computers
Applications
Data
Insights
Computers
DATA
Applications
Data Scientist
2013 Data Science Salary Survey!http://www.oreilly.com/data/free/stratasurvey.csp
“Python is the second best language…”
...Because it blurs the lines between “user” and “maker”. !
We stand on the shoulders of Users who became Makers. !
Some people say: “R has a very strong user community.” !
I want people to say that “Python has a strong maker community.”
Standing Tall
Standing Tall
• Science: Standing on the shoulders of giants
Standing Tall
• Science: Standing on the shoulders of giants
• Programming: Standing on each others toes
Standing Tall
• Science: Standing on the shoulders of giants
• Programming: Standing on each others toes
• But in Python, we stand on each others’
shoulders - community that bootstraps itself
“For there is but one veritable problem - the problem of human relations…”
— Antoine de Saint-Exupéry
https://archive.org/details/Scipy2010-PeterWang-PythonEvangelism101
Participate
• Submit issues and pull requests • Represent for the tools you love in social
media conversations • Start PyData meetups • Come to PyData conferences and present • Encourage diversity!!
How did we get here?
• Hard Work • By a community of people • Who cared • About code and people
Where do we go from here?
• More hard work • More community • More caring • More code • More people
Python is not just glue. Python and PyData are communities!
Where do we go from here?
• More hard work • More community • More caring • More code • More people
Python is not just glue. Python and PyData are communities!
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