The document discusses different data frame interfaces, including their strengths and weaknesses. It describes R data frames as having a thin layer on top of R lists with simple column/row selection. Key R packages like dplyr and data.table add functionality. Spark DataFrames provide a pandas-inspired API for tabular data manipulation across languages. While progressing towards decoupling, interfaces still bind users to their specific systems. The author advocates for quality tools forged through real-world usage.

1. 1
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Inc.
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rights
reserved.
DataFrames:
The
Good,
Bad,
and
Ugly
Wes
McKinney
@wesmckinn
NY
R
Conference,
2015-­‐04-­‐25

2. 2
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rights
reserved.
Disclaimer:
the
views
presented
in
this
talk
are
my
personal
opinions
and
not
necessarily
those
of
Cloudera

3. 3
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rights
reserved.
This
talk
• Some
commentary
on
all
the
data
frame
interfaces
out
there
• Biased
observaTons
and
cursory
judgments
• Thoughts
on
craVing
high
quality
data
tools

4. 4
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rights
reserved.
Disclaimer
#2:
This
is
a
nuanced
discussion

5. 5
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reserved.
Who
am
I?
• Father
of
pandas
(2008
-­‐
)
• Financial
analyTcs
in
R
/
Python
starTng
2007
• 2010-­‐2012
• Hiatus
from
gainful
employment
• Make
pandas
ready
for
primeTme
• Write
"Python
for
Data
Analysis”
• 2013-­‐2014:
DataPad
with
Chang
She
&
co
• 2014
-­‐
:
Cloudera

6. 6
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Inc.
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rights
reserved.
What’s
in
a
DataFrame?

7. 7
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reserved.
What’s
in
a
DataFrame?
A
table
with
some
rows
By
any
other
name
would
analyze
as
sweet.

8. 8
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Got
a
table?

9. 9
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rights
reserved.
Got
a
table?
Put
a
DataFrame
(interface)
on
it!

10. 10
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rights
reserved.
What
is
this
“data
frame”
that
you
speak
of
• A
table-­‐like
data
structure
• An
API
/
user
interface
for
the
table
• SelecTng
data
• Math
and
relaTonal
algebra
(join,
filter,
etc.)
• File
/
database
IO
• ad
infinitum

11. 11
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rights
reserved.
Some
axes
of
comparison
• Data
structure
internals
(types,
in-­‐memory
representaTon,
etc.)
• Basic
table
API
• RelaTonal
algebra
support
• Group-­‐by
/
split-­‐apply-­‐combine
API
• Performance,
memory
use,
evaluaTon
semanTcs
• Missing
data
• Data
Tdying
/
ETL
tools
• IO
uTliTes
• Domain
specific
tools
(e.g.
Tme
series)
• …

12. 12
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rights
reserved.
The
Great
Data
Tool
Decoupling™
• Thesis:
over
Tme,
user
interfaces,
data
storage,
and
execuTon
engines
will
decouple
and
specialize
• In
fact,
you
should
really
want
this
to
happen
• Share
systems
among
languages
• Reduce
fragmentaTon
and
“lock-­‐in”
• ShiV
developer
focus
to
usability
• PredicTon:
we’ll
be
there
by
2025;
sooner
if
we
all
get
our
act
together

13. 13
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rights
reserved.
CraVing
quality
data
tools
• Quality
/
usefulness
is
usually
forged
by
the
fire
of
basle
• Real
world
use
cases
and
social
proof
trump
theory
• Eat
that
dog
food
• When
in
doubt?
Look
at
the
test
suite.

14. 14
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R
data
frames
• Thin
layer
on
top
of
R
list
type
• Sequence
of
named
vectors
• Can
have
row
names
(any
R
vector)
• Simple
column
and
row
selecTon
API
• AnalyTcs,
data
transformaTon,
etc.
leV
to
base
package
and
add-­‐on
libraries
• Richness
/
usability
comes
largely
from
libraries

15. 15
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Some
awesome
R
data
frame
stuff
• “Hadley
Stack”
• dplyr,
Tdyr
• legacy:
plyr,
reshape2
• ggplot2
• data.table
(data.frame
+
indices,
fast
algorithms)
• xts
:
Tme
series

16. 16
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rights
reserved.
R
data
frames:
rough
edges
• Copy-­‐on-­‐write
semanTcs
• API
fragmentaTon
/
inconsistency
• Use
the
“Hadley
stack”
for
improved
sanity
• Factor
/
String
dichotomy
• stringsAsFactors=FALSE
a
blessing
and
curse
• Somewhat
limited
type
system

17. 17
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dplyr
• Composable
table
API
• Good
example
of
what
the
“decoupled”
future
might
look
like
• New
in-­‐memory
R/RCpp
execuTon
engine
• SQL
backends
for
large
subset
of
API

18. 18
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rights
reserved.
Spark
DataFrames
• R/pandas-­‐inspired
API
for
tabular
data
manipulaTon
in
Scala,
Python,
etc.
• Logical
operaTon
graphs
rewrisen
internally
in
more
efficient
form
• Good
interop
with
Spark
SQL
• Some
interoperability
with
pandas
• ParTal
API
Decoupling!
(it
sTll
binds
you
to
Spark)

19. 19
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pandas
• Several
key
data
structures,
data
frame
among
them
• Considerably
more
complex
internals
than
other
data
frame
libraries
• Some
good
things
• Born
of
need
• A
“baseries
included”
approach
• Hierarchical
axis
labeling:
addresses
some
hard
use
cases
at
expense
of
semanTc
complexity
• Strong
Tme
series
support

20. 20
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reserved.
pandas:
rough
edges
• Axis
labelling
can
get
in
the
way
for
folks
needing
“just
a
table”
• Ceded
control
of
its
type
system
/
data
rep’n
from
day
1
to
NumPy
• Inefficient
string
handling
(uses
NumPy
object
arrays)
• Missing
data
handling
less
precise
than
other
tools

21. 21
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Julia:
DataFrames.jl
• Started
by
Harlan
Harris
&
co
• Part
of
broader
JuliaStats
iniTaTve
• More
R-­‐like
than
pandas-­‐like
• Very
acTve:
>
50
contributors!
• STll
comparaTvely
early
• Less
comprehensive
API
• More
limited
IO
capabiliTes

22. 22
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rights
reserved.
Other
data
frames
• Saddle
(Scala)
• Dev’d
by
Adam
Klein
(ex-­‐AQR)
at
Novus
Partners
(fintech
startup)
• Designed
and
used
for
financial
use
cases
• Deedle
(F#
/
.NET)
• Dev’d
by
AK’s
colleagues
at
BlueMountain
(hedge
fund)
• GraphLab
/
Dato
• Really
good
C++
data
frame
with
Python
interface
• Dual-­‐licensed:
AGPL
+
Commercial
• That’s
not
all!
Haskell,
Go,
usw…

23. 23
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rights
reserved.
We’re
not
done
yet
• The
future
is
JSON-­‐like
• Support
for
nested
types
/
semi-­‐structured
data
is
sTll
weak
• Wanted:
Apache-­‐licensed,
community
standard
C/C++
data
frame
that
we
all
use
(R,
Python,
Julia)
• Bring
on
the
Great
Decoupling

24. 24
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Cloudera,
Inc.
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rights
reserved.
Thank
you
@wesmckinn