Talk given to the Philly Python Users Group (PUG) on October 1, 2015: http://www.meetup.com/phillypug/ Thanks SIG (http://www.sig.com) for hosting!

1. Continuum Analytics and Python
Travis Oliphant
CEO, Co-Founder
Continuum Analytics

2. ABOUT CONTINUUM
2

3. Travis Oliphant - CEO
3
• PhD 2001 from Mayo Clinic in Biomedical
Engineering
• MS/BS degrees in Elec. Comp. Engineering
• Creator of SciPy (1999-2009)
• Professor at BYU (2001-2007)
• Author of NumPy (2005-2012)
• Started Numba (2012)
• Founding Chair of Numfocus / PyData
• Previous PSF Director
SciPy

4. Started as a Scientist / Engineer
4
Images from BYU CERS Lab

5. Science led to Python
5
Raja Muthupillai
Armando Manduca
Richard Ehman
Jim Greenleaf
1997
⇢0 (2⇡f)
2
Ui (a, f) = [Cijkl (a, f) Uk,l (a, f)],j
⌅ = r ⇥ U

6. “Distractions” led to my calling
6

7. 7
Data Science

8. 8
• Data volume is growing exponentially within companies. Most don't know
how to harvest its value or how to really compute on it.
• Growing mess of tools, databases, and products. New products
increase integration headaches, instead of simplifying.
• New hardware & architectures are tempting, but are avoided or sit idle
because of software challenges.
• Promises of the "Big Red Solve" button continue to disappoint.
(If someone can give you this button, they are your competitor.)
Data Scientist Challenges

9. Our Solution
9
• A language-based platform is needed. No simple point-and-click
app is enough to solve these business problems, which require
advanced modeling and data exploration.
• That language must be powerful, yet still be accessible to domain
experts and subject matter experts.
• That language must leverage the growing capability and rapid
innovation in open-source.
Anaconda Platform: Enterprise platform for Data Exploration, Advanced
Analytics, and Rapid Application Development and Deployment (RADD)
Harnesses the exploding popularity of the Python ecosystem that our
principals helped create.

10. Why Python?
10
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

11. Python Is Sweeping Education
11

12. Tools used for Data
12
Source: O’Reilly Strata attendee survey 2012 and 2013

13. Python for Data Science
13
http://readwrite.com/2013/11/25/python-displacing-r-as-the-programming-language-for-data-science

14. Python is the top language in schools!
14

15. OUR CUSTOMERS
& OUR MARKET
15

16. Some Users
16

17. Anaconda: Game-changing Python distribution
17
"Hands down, the best Scientific Python distribution products
for analytics and machine learning."
"Thanks for such a great product, been dabbling in python for years
(2002), always knew it was going to be massive as it hit the sweet
spot in so many ways, with llvm-numba its magic!!!"
"It was quick and easy and had everything included, even a decent
IDE. I'm so grateful it brings tears to my eyes."
"I love Anaconda more than the sun, moon, and stars…"

18. Anaconda: Game-changing Python distribution
18
• 2 million downloads in last 2 years
• 200k / month and growing
• conda package manager serves up 5 million packages
per month
• Recommended installer for IPython/Jupyter, Pandas,
SciPy, Scikit-learn, etc.

19. Conferences & Community
19
• PyData: London, Berlin, New York, Bay Area, Seattle
• Strata: Tutorials, PyData Track
• PyCon, Scipy, EuroScipy, EuroPython, PyCon Brasil...
• Spark Summit
• JSM, SIAM, IEEE Vis, ICML, ODSC, SuperComputing

20. Observations & Off-the-record
20
• Hype is out of whack with reality
• Dashboards are "old school" BI, but still important for
narrative/confirmatory analysis
• Agility of data engineering and exploration is critical
• Get POCs out faster; iterate faster on existing things
• Need cutting-edge tools, but production is hard
• Notebooks, reproducibility, provenance - all matter

21. 21http://tuulos.github.io/sf-python-meetup-sep-2013/#/

22. Data Science Platforms
22
• All-in-one new "platform" startups are walled gardens
• Cloud vendor native capabilities are all about lock-in:
"Warehouse all your data here!"
• Machine Learning and Advanced Analytics is too early,
and disrupting too fast, to place bets on any single
walled garden.
• Especially since most have no experience with
"exotic" regulatory and security requirements

23. Good News
23
You can have a modern, advanced analytics system that
integrates well with your infrastructure
Bad News
It's not available as a SKU from any vendor.
META-PLATFORM CONSTRUCTION KIT

24. Great News
24
• If done well, it adds deep, fundamental business capability
• Many wall street banks and firms using this.
• All major Silicon Valley companies know this
• Facebook, LinkedIn, Uber, Tesla, SpaceX, Netflix, ...

25. EXAMPLE PROJECTS
25

26. 26
Bitcoin is a digital currency invented in 2008 and operates on a peer-to-peer system for transaction validation. This
decentralized currency is an attempt to mimic physical currencies in that there is limited supply of Bitcoins in the world,
each Bitcoin must be “mined”, and each transaction can be verified for authenticity. Bitcoins are used to exchange
every day goods and services, but it also has known ties to black markets, illicit drugs, and illegal gambling transactions.
The dataset is also very inclined towards anonymization of behavior, though true anonymization is rarely achieved.
The Bitcoin Dataset
The Bitcoin dataset was obtained from http://compbio.cs.uic.edu/data/bitcoin/ and captures transaction-level
information. For each transaction, there can be multiple senders and multiple receivers as detailed here:
https://en.bitcoin.it/wiki/Transactions. This dataset provides a challenge in that multiple addresses are usually
associated with a single entity or person. However, some initial work has been done to associated keys with a single
user by looking at transactions that are associated with each other (for example, if a transaction has multiple public keys
as input on a single transaction, then a single user owns both private keys). The dataset provided provides these known
associations by grouping these addresses together under a single UserId (which then maps to a set of all associated
addresses).
Key Challenge Questions
# Transactions: 15.8 Million+
# Edges: 37.4 Million +
# Senders: 5.4 Million+
# Receivers: 6.3 Million+
# Bitcoins Transacted: 1.4 Million +
Bitcoin Data Set Overview (May 15, 2013)
Figure 1: Bitcoin Transactions Over Time
Bitcoin Blockchain

27. Microcap Stock Fraud
27

28. Memex Dark/Deep Web Analytics
28

29. TECH & DEMOS
29

30. Data Science @ NYT
30
@jakevdp
eSciences Institute, Univ. Washington

31. 31
conda cross-platform, multi-language package & container tool
bokeh interactive web plotting for Python, R; no JS/HTML required
numba JIT compiler for Python & NumPy, using LLVM, supports GPU
blaze deconvolve data, expression, and computation; data-web
dask lightweight, fast, Pythonic scheduler for medium data
xray easily handle heterogeneously-shaped dense arrays
holoviews slice & view dense cubes of data in the browser
seaborn easy, beautiful, powerful statistical plotting
beaker polyglot alternative Notebook-like project

32. 32
• Databricks Canvas
• Graphlab Create
• Zeppelin
• Beaker
• Microsoft AzureML
• Domino
• Rodeo? Sense?
• H2O, DataRobot, ...
Notebooks Becoming Table Stakes

33. 33

34. 34
"With
more
than
200,000
Jupyter
notebooks
already
on
GitHub
we're
excited
to
level-­‐up
the
GitHub-­‐Jupyter
experience."

35. Anaconda
35
✦ Centralized analytics environment
• browser-based interface
• deploys on existing infrastructure
✦ Collaboration
• cross-functional teams using same data and software
✦ Publishing
• code
• data
• visualizations

36. Bokeh
36
http://bokeh.pydata.org
• Interactive visualization
• Novel graphics
• Streaming, dynamic, large data
• For the browser, with or without a server
• No need to write Javascript

37. Versatile Plots
37

38. Novel Graphics
38

39. Previous: Javascript code generation
39
server.py Browser
js_str = """ <d3.js>
<highchart.js>
<etc.js>
"""
plot.js.template
App Model
D3
highcharts
flot
crossfilter
etc. ...
One-shot; no MVC interaction; no data streaming
HTML

40. bokeh.py & bokeh.js
40
server.py BrowserApp Model
BokehJS
object graph
bokeh-server
bokeh.py
object graph
JSON

41. 41

42. 42
4GB Interactive Web Viz

43. rBokeh
43
http://hafen.github.io/rbokeh

44. 44

45. 45

46. 46

47. 47
hBp://nbviewer.ipython.org/github/bokeh/bokeh-­‐notebooks/blob/master/tutorial/00
-­‐
intro.ipynb#InteracHon

48. Additional Demos & Topics
48
• Airline flights
• Pandas table
• Streaming / Animation
• Large data rendering

49. 49
Latest Cosmological Theory

50. 50
Dark Data: CSV, hdf5, npz, logs, emails, and other files
in your company outside a traditional data store

51. 51
Dark Data: CSV, hdf5, npz, logs, emails, and other files
in your company outside a traditional data store

52. 52
Database Approach
Data
Sources
Data Store
Data
Sources
Clients

53. 53
Bring the Database to the Data
Data
Sources
Data
Sources
Clients
Blaze
(datashape,dask)
NumPy,Pandas,SciPy,
sklearn,etc.
(for analytics)

54. Anaconda — portable environments
54
PYTHON'&'R'OPEN'SOURCE'ANALYTICS
NumPy, SciPy, Pandas, Scikit=learn, Jupyter / IPython,
Numba, Matplotlib, Spyder, Numexpr, Cython, Theano,
Scikit=image, NLTK, NetworkX, IRKernel, dplyr, shiny,
ggplot2, tidyr, caret, nnet and 330+ packages
conda
Easy to install
Quick & agile data
exploration
Powerful data analysis
Simple to collaborate
Accessible to all

55. 55
• Infrastructure for meta-data, meta-compute, and expression graphs/dataflow
• Data glue for scale-up or scale-out
• Generic remote computation & query system
• (NumPy+Pandas+LINQ+OLAP+PADL).mashup()
Blaze is an extensible interface for data analytics. It
feels like NumPy/Pandas. It drives other data systems.
Blaze expressions enable high-level reasoning
http://blaze.pydata.org
Blaze

56. 56
Blaze
?

57. 57
Expressions
Metadata
Runtime
Blaze

58. 58
Blaze
+ - / * ^ []
join, groupby, filter
map, sort, take
where, topk
datashape,dtype,
shape,stride
hdf5,json,csv,xls
protobuf,avro,...
NumPy,Pandas,R,
Julia,K,SQL,Spark,
Mongo,Cassandra,...

59. 59
numpy
pandas
sql DB
Data Runtime Expressions
spark
datashape
metadata storage
odo
paralleloptimized
dask
numbaDyND
blaze
castra
bcolz

60. 60
Data Runtime
Expressions
metadata
storage/containers
compute
APIs, syntax, language
datashape
blaze
dask
odo
parallelize optimize, JIT

61. 61

62. 62
Blaze Server
Provide
RESTful
web
API
over
any
data
supported
by
Blaze.
Server
side:
>>> my_spark_rdd = …
>>> from blaze import Server
>>> Server(my_spark_rdd).run()
Hosting computation on localhost:6363
Client
Side:
$ curl -H "Content-Type: application/json"
-d ’{"expr": {"op": "sum", "args": [ ... ] }’
my.domain.com:6363/compute.json
• Quickly share local data to collaborators
on the web.
• Expose any system (Mongo, SQL, Spark,
in-memory) simply
• Share local computation as well, sending
computations to server to run remotely.
• Conveniently drive remote server with
interactive Blaze client

63. 63
Dask: Out-of-Core Scheduler
• A parallel computing framework
• That leverages the excellent Python ecosystem
• Using blocked algorithms and task scheduling
• Written in pure Python
Core Ideas
• Dynamic task scheduling yields sane parallelism
• Simple library to enable parallelism
• Dask.array/dataframe to encapsulate the functionality
• Distributed scheduler coming

64. Example: Ocean Temp Data
64
• http://www.esrl.noaa.gov/psd/data/gridded/
data.noaa.oisst.v2.highres.html
• Every 1/4 degree, 720x1440 array each day

65. Bigger data...
65
36 years: 720 x 1440 x 12341 x 4 = 51 GB uncompressed
If you don't have this much RAM...
... better start chunking.

66. DAG of Computation
66

67. Simple Architecture
67

68. Core Concepts
68

69. dask.array: OOC, parallel, ND array
69
Arithmetic: +, *, ...
Reductions: mean, max, ...
Slicing: x[10:, 100:50:-2]
Fancy indexing: x[:, [3, 1, 2]]
Some linear algebra: tensordot, qr, svd
Parallel algorithms (approximate quantiles, topk, ...)
Slightly overlapping arrays
Integration with HDF5

70. dask.dataframe: OOC, parallel, ND array
70
Elementwise operations: df.x + df.y
Row-wise selections: df[df.x > 0]
Aggregations: df.x.max()
groupby-aggregate: df.groupby(df.x).y.max()
Value counts: df.x.value_counts()
Drop duplicates: df.x.drop_duplicates()
Join on index: dd.merge(df1, df2, left_index=True,
right_index=True)

71. More Complex Graphs
71
cross validation

72. 72
http://continuum.io/blog/xray-dask

73. 73
from dask import dataframe as dd
columns = ["name", "amenity", "Longitude", "Latitude"]
data = dd.read_csv('POIWorld.csv', usecols=columns)
with_name = data[data.name.notnull()]
with_amenity = data[data.amenity.notnull()]
is_starbucks = with_name.name.str.contains('[Ss]tarbucks')
is_dunkin = with_name.name.str.contains('[Dd]unkin')
starbucks = with_name[is_starbucks]
dunkin = with_name[is_dunkin]
locs = dd.compute(starbucks.Longitude,
starbucks.Latitude,
dunkin.Longitude,
dunkin.Latitude)
# extract arrays of values fro the series:
lon_s, lat_s, lon_d, lat_d = [loc.values for loc in locs]
%matplotlib inline
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
def draw_USA():
"""initialize a basemap centered on the continental USA"""
plt.figure(figsize=(14, 10))
return Basemap(projection='lcc', resolution='l',
llcrnrlon=-119, urcrnrlon=-64,
llcrnrlat=22, urcrnrlat=49,
lat_1=33, lat_2=45, lon_0=-95,
area_thresh=10000)
m = draw_USA()
# Draw map background
m.fillcontinents(color='white', lake_color='#eeeeee')
m.drawstates(color='lightgray')
m.drawcoastlines(color='lightgray')
m.drawcountries(color='lightgray')
m.drawmapboundary(fill_color='#eeeeee')
# Plot the values in Starbucks Green and Dunkin Donuts Orange
style = dict(s=5, marker='o', alpha=0.5, zorder=2)
m.scatter(lon_s, lat_s, latlon=True,
label="Starbucks", color='#00592D', **style)
m.scatter(lon_d, lat_d, latlon=True,
label="Dunkin' Donuts", color='#FC772A', **style)
plt.legend(loc='lower left', frameon=False);

74. 74
• Dynamic, just-in-time compiler for Python & NumPy
• Uses LLVM
• Outputs x86 and GPU (CUDA, HSA)
• (Premium version is in Accelerate product)
http://numba.pydata.org
Numba

75. Python Compilation Space
75
Ahead Of Time Just In Time
Relies on
CPython /
libpython
Cython
Shedskin
Nuitka (today)
Pythran
Numba
HOPE
Theano
Replaces
CPython /
libpython
Nuitka (future) Pyston
PyPy

76. Example
76
Numba

77. 77
@jit('void(f8[:,:],f8[:,:],f8[:,:])')
def filter(image, filt, output):
M, N = image.shape
m, n = filt.shape
for i in range(m//2, M-m//2):
for j in range(n//2, N-n//2):
result = 0.0
for k in range(m):
for l in range(n):
result += image[i+k-m//2,j+l-n//2]*filt[k, l]
output[i,j] = result
~1500x speed-up

78. Features
78
• Windows, OS X, and Linux
• 32 and 64-bit x86 CPUs and NVIDIA GPUs
• Python 2 and 3
• NumPy versions 1.6 through 1.9
• Does not require a C/C++ compiler on the user’s system.
• < 70 MB to install.
• Does not replace the standard Python interpreter
(all of your existing Python libraries are still available)

79. How Numba Works
79
Bytecode
Analysis
Python Function
(bytecode)
Function
Arguments
Type
Inference
Numba IR
Rewrite IR
Lowering
LLVM IRLLVM JIT
Machine
Code
@jit
def do_math(a,b):
…
>>> do_math(x, y)
Cache
Execute!

80. THE ANACONDA PLATFORM
80

81. Anaconda — portable environments
81
PYTHON'&'R'OPEN'SOURCE'ANALYTICS
NumPy, SciPy, Pandas, Scikit=learn, Jupyter / IPython,
Numba, Matplotlib, Spyder, Numexpr, Cython, Theano,
Scikit=image, NLTK, NetworkX, IRKernel, dplyr, shiny,
ggplot2, tidyr, caret, nnet and 330+ packages
conda
Easy to install
Quick & agile data
exploration
Powerful data analysis
Simple to collaborate
Accessible to all

82. 82
• cross platform package manager
• can create sandboxes ("environments"), akin to
Windows Portable Applications or WinSxS
• "un-container" for deploying data science/data
processing workflows
http://conda.pydata.org
Conda

83. System Package Managers
83
yum (rpm)
apt-get (dpkg)
Linux OSX
macports
homebrew
fink
Windows
chocolatey
npackd
Cross-platform
conda

84. 84
• Excellent support for “system-level” environments — like having mini
VMs but much lighter weight than docker (micro containers)
• Minimizes code-copies (uses hard/soft links if possible)
• Simple format: binary tar-ball + metadata
• Metadata allows static analysis of dependencies
• Easy to create multiple “channels” which are repositories for packages
• User installable (no root privileges needed)
• Integrates very well with pip
• Cross Platform
Conda features

85. Anaconda Cloud: analytics repository
85
• Commercial long-term support
• Licensed for redistribution
• Private, on-premises available
• Proprietary tools for building custom
distribution, like Anaconda
• Enterprise tools for managing custom
packages and environments
• http://anaconda.org

86. Anaconda Cluster: Anaconda + Hadoop + Spark
86
For data scientists:
• Rapidly, easily create clusters on EC2, DigitalOcean, on-prem cloud/provisioner
• Manage Python, R, Java, JS packages across the cluster
For operations & IT:
• Robustly manage runtime state across the cluster
• Outside the scope of rpm, chef, puppet, etc.
• Isolate/sandbox packages & libraries for different jobs or groups of users
• Without introducing complexity of Docker / virtualization
• Cross platform - same tooling for laptops, workstations, servers, clusters

87. Cluster Creation
87
$ conda cluster create mycluster --profile=spark_profile
$ conda cluster submit mycluster mycode.py
$ conda cluster destroy mycluster
spark_profile:
provider: aws_east
num_nodes: 4
node_id: ami-3c994355
node_type: m1.large
aws_east:
secret_id: <aws_access_key_id>
secret_key: <aws_secret_access_key>
keyname: id_rsa.pub
location: us-east-1
private_key: ~/.ssh/id_rsa
cloud_provider: ec2
security_group: all-open
http://continuumio.github.io/conda-cluster/quickstart.html

88. 88
$ conda cluster manage mycluster list
... info -e
... install python=3 pandas flask
... set_env
... push_env <local> <remote>
$ conda cluster ssh mycluster
$ conda cluster run.cmd mycluster "cat /etc/hosts"
Package & environment management:
Easy SSH & remote commands:
http://continuumio.github.io/conda-cluster/manage.html
Cluster Management

89. Anaconda Cluster & Spark
89
# example.py
conf = SparkConf()
conf.setMaster("yarn-client")
conf.setAppName("MY APP")
sc = SparkContext(conf=conf)
# analysis
sc.parallelize(range(1000)).map(lambda x: (x, x % 2)).take(10)
$ conda cluster submit MY_CLUSTER /path/to/example.py

90. Python & Spark in Practice
90
Challenges of real-world usage
• Package management (perennial popular topic in Python)
• Python (& R) are outside the "normal" Java build toolchain
• bash scripts, spark jobs to pip install or conda install <foo>
• Kind of OK for batch; terrible for interactive
• Rapid iteration
• Production vs dev/test clusters
• Data scientist needs vs Ops/IT concerns

91. Fix it twice…
91
PEP 3118: Revising the buffer protocol
Basically the “structure” of NumPy arrays as
a protocol in Python itself to establish a
memory-sharing standard between objects.
It makes it possible for a heterogeneous world of
powerful array-like objects outside of NumPy that
communicate.
Falls short in not defining a general data description language (DDL).
http://python.org/dev/peps/pep-3118/

92. Putting Rest of NumPy in Std Lib
92
• memtype
• dtype system on memory-views
• extensible with Numba and C
• extensible with Python
• gufunc
• generalized low-level function dispatch on memtype
• extensible with Numba and C
• usable by any Python
Working on now with a (small) team — could use funding

93. 93
• Python has had a long and fruitful history in Data Analytics
• It will have a long and bright future with your help!
• Contribute to the PyData community and make the world a
better place!
The Future of Python

94. © 2015 Continuum Analytics- Confidential & Proprietary
Thanks
October1, 2015
•SIG for hosting tonight and inviting me to come
•DARPA XDATA program (Chris White and Wade Shen) which helped fund
Numba, Blaze, Dask and Odo.
•Investors of Continuum.
•Clients and Customers of Continuum who help support these projects.
•Numfocus volunteers
•PyData volunteers