1. © 2016 Continuum Analytics - Confidential & Proprietary
Python for Data:
Past, Present, and Future
Peter Wang
CTO, Co-founder
Anaconda / Continuum Analytics

2. © 2017 Anaconda, Inc.
• Our Journey with Anaconda
• Why Python for Data?
• The Future
Agenda
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3. 3
My Journey with Anaconda

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• Degree in Physics (Cornell Univ.)
• Computer graphics developer (C, C++)
• Scientific Python developer and consultant (Chaco, Traits, …)
• Founded Continuum Analytics in 2012 with Travis Oliphant
• Launched / Created: PyData conferences and community, Anaconda
distribution, conda package manager, Bokeh web visualization, Blaze
data library
• Think a lot about future of Python for data+science, machine learning
About Peter
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5. When we started 5 years ago…

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The birth of conda…
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“Guido, please help
convince core dev to
work with us to solve
the packaging problem!”
“Meh. Feel free to
solve it yourselves.”

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• 500+ Popular Python Packages
• Optimized & Compiled
• Free for Everyone
• Extensible via Conda Package Manager
• Sandbox Packages & Libraries
• Cross-Platform – Windows, Linux, Mac
• Not just Python - over 230 R packages

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Thousands
Anaconda& Miniconda Downloads
Anaconda Miniconda
Over 20 Million Downloads

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The Growth of Data Science - Python Leading the Way
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https://stackoverflow.blog/2017/09/06/incredible-growth-python/

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Other Problems in 2012…
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• Performance: You had to choose between vectorized system like NumPy,
or going to Cython or wrapping C code. No nice JIT like Julia.
• We created Numba
• No system for building simple data-driven web apps, like Shiny for R.
• We created Bokeh, to serve as both Shiny and D3 for Python
• No easy parallelism, or intrinsic parallel primitives like Spark.
• We created Dask, which has parallel arrays and dataframes.
• Also solves “data doesn't fit in RAM” problem.

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• Everyone is learning it, major universities are teaching it
• Proven in production at Serious Places, not merely hip startups
• Vastly outstrips scripting language rivals like Ruby, Perl
• Growing faster than pure analysis langs like R, SAS, Matlab
• Data science, machine learning application is taking off like a rocket
• Python is most popular language for Deep Learning, the most
rapidly-innovating area of machine learning
• Python 2 vs 3 rift is less of an issue for most people
Python in 2017

12. https://www.youtube.com/watch?v=nU09j2gGHYg

13. Why Python for Data?
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1973 19811968 1974
SQL
Numeric
19962005 1993 1991

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Python & ABC
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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.

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Analyst
• Uses graphical tools
• Can call functions,
cut & paste code
• Can change some
variables
Gets paid for:
Insight
Excel, VB, Tableau,
Analyst / Data
Developer
• Builds simple apps & workflows
• Used to be "just an analyst"
• Likes coding to solve problems
• Doesn't want to be a "full-time
programmer"
Gets paid (like a rock star) for:
Code that produces insight
SAS, R, Matlab,
Programmer
• Creates frameworks
& compilers
• Uses IDEs
• Degree in CompSci
• Knows multiple
languages
Gets paid for:
Code
C, C++, Java, JS,
Python Python Python

17. © 2017 Anaconda, Inc.
• VERY common misconception
• Python is probably the most misunderstood
language
• There are “tribes” and ecosystems in Python:
web dev, scipy, pydata, embedded, scripting,
3D graphics, etc.
• But businesses tend to pigeonhole it:
• IT/software/data engineering view: competes
with Java, C#, Ruby…
• Analytics, stats, data science view: competes
with R, SAS, Matlab, SPSS, BI systems
Data science != Software Development
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• Data exploration and analysis are going to be a new kind of literacy that
will be required to do great work in any field.
• Language is a human instinct and is a natural path to insight. We see
this in our interaction with Python/PyData users, whose passion chiefly
stems from this expressiveness and agility.
• An analytical language is “thoughtware”, not “software”.
Era of Data Literacy
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20. What’s Next?
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• Python will become a preferred way to develop cognitive applications:
online model learning and training
• There will be a steady income stream for people who want to maintain
Python 2.x codebases
• Multi-language interoperability will be greatly improved once people
adopt the Apache Arrow format for storing data. This means Python code
running alongside Java/Scala/JVM will not be a second-class citizen.
• Constant improvements in memory and storage, as well as GPUs, mean
that people will continue doing lots of Python locally on big workstations.
A Few Predictions
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23. © 2017 Anaconda, Inc.
• Not about licenses
• Empowering people &
communities to innovate
• Aligns us with users, customers,
innovators
• “Software is eating the world”
• Open source is eating software
Open Source and Developers
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25. © 2017 Anaconda, Inc.
• Not about cost of software (“capital
expense”)
• Not even about maintenance of
software (“operational expense”)
• Core business goals:
• Avoid lock-in
• Harness innovation
Open Source and Businesses
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27. 5 Years
25+ Conferences
100s of talks

28. © 2017 Anaconda, Inc.
Questions?
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