Hello,
Team
Discovery.
I'm
Jim
Wisnowski,
along
with
Andrew
Karl
from
Adsurgo,
and
we're
here
to
talk
a
little
bit
about
operationalization
and
how
you
can
effectively
employ
some
JMP
capabilities
to
do
so.
The
genesis
of
this
presentation
came
from
engagement
with
a
customer
last
year
around
this
time
who
said,
"Our
company
just
loves
JMP
as
a
sandbox
tool
and
maybe
in
the
lab,
but
it
really
doesn't
do
anything
for
operationalization
for
us."
At
which
point,
it
struck
me
as,
"I'm
not
really
sure
I
understand
what
the
word
operationalization
nor
really
know
how
to
say
it
necessarily."
S
omehow
there's
a
disconnect
because
all
of
our
projects,
we
seem
like
we
deliver
a
product,
and
it
is
finished.
What
we
did
is
we
figured
out,
"What
exactly
is
operationalization
and
then
how
can
we
do
some
neat
things
in
JMP
with
it?"
What
we're
going
to
do
is
I'm
going
to
talk
about
what
it
is
and
then
give
you
some
quick
wins
in
the
form
of
the
depot.
Then
Andrew,
as
always,
will
do
the
work
where
it
takes
the
high
intellect,
and
he'll
also
show
you
the
beautiful
part
of
operationalization.
Key
here
is
we
all
know
all
models
are
wrong
and
some
are
useful,
but
the
other
side
of
that
is
your
boss
or
your
boss's
boss,
she
is
thinking
all
models
are
useless,
whether
she
says
it
explicit
or
not.
They're
useless
unless
somehow
they're
supporting
a
decision
of
mine
or
we
get
them
into
the
production
environment.
We
need
to
make
sure
that
even
though
we
have
a
good
model,
that
it
can
go
to
the
next
step
and
be
implemented.
By
the
way,
I
do
want
to
show
George
Box
over
here
on
the
right,
and
then
Stu
Hunter
on
the
left,
who
just
celebrated
his
100th
birthday
on
the
3rd
of
June.
By
definition,
what
is
operationalization?
When
we
think
of
things,
we
can
think
of
ideas
such
as
the
leadership
saying
the
usual,
What
is
the
problem?
What
do
I
do
about
it?
How
does
it
impact
me?
And
then
the
now
what?
What
is
the
solution?
The
solution
isn't
necessarily
all
the
things
that
we
do
in
the
data
analytics,
data
science,
world of
the
discovery,
data
prep,
and
all
that.
It
really
lies
in
the
operationalization
piece
for
that
senior leader
to
understand
how
you're
going
to
solve
the
problem.
In
other
words,
it's
really
how
do
we
get
from
that
lab
environment
to
the
production
line
where
we
have
a
solution
that's
going
to
be
useful
to
us.
As
we
do
that,
let's
not
forget
SAS's
recommendation
here
that
we
better
make
sure
that
we
have
a
good
disciplined
approach
as
well
as
automated
in
that
world.
Next
up,
we
can
dig
a
little
bit
deeper
into
what
operationalization
is
on
a
micro
level
kind of thing.
I
asked
Chat
GPT-4,
I
said,
"Can
you
give
me
an
image
of
what
operationalization
looks
like
in
data
science?"
This
is
essentially
what
they
did.
I
had
to
do
the
graphics,
but
they
gave
me
all
the
words.
If
we
think
about
our
usual
data,
do
our
data
development,
and
then
we
figure
out
what
model
we
like,
and
then
we
have
to
figure
out
how
we're
going
to
employ
or
deploy
that,
what
language?
Is
it
going
to
be
JavaScript,
C,
or
Python?
Then
we
do
the
deployment,
and
then
we
do
maybe
perhaps
an
API
integration.
Good
news
is
JMP
has
a
lot
of
tools
for
us
to
do
that.
We're
not
left
in
just
that
lab
environment
as
suggested.
Then
on
the
bottom
here,
we
got
the
idea
that
once
we
have
that
model
out
there,
it's
not
a
launch
and
lead
kind of
thing.
We
have
to
babysit
it
and
perhaps
update
hyperparameters
or
add
new
data
and
see
if
it's
still
valid.
Then
we
have
this
idea
here
that
you
know
what?
Not only are
our
users
liking
it,
they
want
to
add
more
capabilities,
so
we
start
scaling
up.
We
have
to
make
sure
that
we
continue
our
good
configuration
management
and
data
compliance
and
documentation,
ultimately
resulting
in
business
value.
The
bottom
line
is
how
do
I
go
from
this
lab
and
sandbox
environment
to
having
business
value?
That's
what
we're
looking
for
in
our
operationalization.
Forbes
gives
the
five
steps
here.
Important
for
us
is
to
think
about,
first,
you
have
to
have
a
good
story
so
that
management
believes
that
you
have
a
credible
way
to
approach
this
and
solve
the
problem.
Then
the
last
part
here
is,
once
you
do
have
a
model,
and
deployed,
and
you
have it
operational
ized
make
sure
that
you
have
some
metrics
to
make
sure
that
it
is
in
fact
performing.
But
this
is
like
that
last
mile
idea
is
we
take
all
of
this
work
that
we
do
to
create
the
models,
but
getting
it
to
that
operationalization
piece
is
the
tough
part.
In
fact,
we
can
see
that
the
data
out
there
doesn't
suggest
that
we're
doing
all
that
great.
Overall,
we're
maybe
even
fewer
than
half
of
these
models
make
it.
Then
if
they
do,
it
takes
upwards
of
three
months
or
so
to
do
so.
DevOps,
we're
all
familiar
with
in
terms
of
a
good
disciplined
approach
for
software
development.
When
we
take
that
step
into
our
model
deployment
world,
we'll
call
it
ModelOps,
where
we
want
to
have
some
a
culture
or
process
and
technologies
to
effectively
employ
them.
If
we
look
at
these
three
circles
here,
it's
really
this
intersection
between
two
and
three
that
we're
focused
on
to
make
sure
that
the
deployment
is influencing
those
business
decisions.
I'd
like
to
go
and
do
a
demonstration
here
in
JMP.
Before
I
do
so,
I
do
want
to
point
out
two
fantastic
presentations
from
previous
discoveries
that
do
talk
about
this
idea
of
deployment
and
operationalization
by
Nascif
and
Dan
Valente
in
2016
as
well
as
2017.
You
can
see
over
here,
they
have
this
whole
idea
that
if
you
give
a
man
a
fish,
he
eats
for
a
day,
and
if
you
teach
him to fish, he'll
eat
for
a
life,
that's
this
operationalization
piece,
which
they
also
call
the
dark
side
of
analytics.
That's
what
we're
going
to
get
into.
M
eijian
I
also
wrote
a
decent
paper
as
well
on
how
you
can
do
that.
But
for
us,
what
I
want
to
show
you
is
using
the
Formula
Depot,
and
I
got
a
little
plus
there
because
it's
going
to
go
a
little
bit
beyond
just
using
the
Formula
Depot
because
that
is
not
enough.
We'll
use
our
well-traveled
Boston
housing
data.
We'll
look
at
what's
the
price
of
it
as
a
function
of
rooms,
distance,
and
Lstat,
which
is
an
economic
indicator.
We'll
create
a
main
effects
model
as
well
as
a
Bootstrap.
Then
we'll
look
at
the
profiler,
and
I'll
show
you
a
quick
trick
that
could
be
helpful
for
you.
Then
we'll
look
at
how
do
I
convert
this
to
an
operational
solution
and
being
able
to
deploy
it
in
a
Python
environment.
Certainly,
this
is
a
very
small
data
set,
but
we
could
easily
have
done
this
plugging
into
some
data
source
and
using
query
builder
and
things
like
that.
But
just
want
to
show
you
some
quick
wins
that
you
can
go
to
the
next
step.
Because
often
we
hear
that
it's
great
that
you
do
all
this
work,
but
the
actual
implementation
has
to
be
on
this
server,
and
this
server
can
only
use
JavaScript
or
C++,
whatever
it
happens
to
be,
Python,
maybe.
How
can
we
take
our
good
work
and
transport
it
into
use
in
operation?
I'm
going
to
transition
over
to
JMP.
Here's
a
journal
that
we
have
for
you,
and
it
goes
through
a
few
of
our
different
options
and
what
we're
doing
here.
But
here's
Boston
Housing
that
we
know
and
love.
Here
is
my
Least
Squares
model.
What
you
do
in
the
Least
Squares
model,
or
any
model
for
that
matter,
is
under
Save
Columns,
you're
going
to
publish
that
prediction
formula.
Then
the
Formula
Depot
comes
up
for
you.
Let's
go
ahead
and
do
the
Bootstrap Forest
as
well.
Now we
have
the
Bootstrap Forest,
which
we
can
also,
under
Save
Columns,
publish
that
prediction
formula.
If
we
come
back
up
here
and
we
check
out
that
Formula
Depot
report,
we
can
see
that
it
is
something
that
we
are
familiar
with
in
this
fashion.
That
if
I
come
down
here
off
of
the
red
triangle
next
to
Formula
Depot,
I
can
look
at
the
profiler
.
I
want
to
look
at
the
profiler
of
both
these
models.
I
do
OK,
and
there
it
is.
The
main
effects
model,
of
course,
doesn't
have
any
interaction,
but
maybe
you
want
to
make
the
point
somehow
that
when
you
have
very
short
distance,
you have a real steep
economic
impact kind of
a thing.
What
we
could
do
is
we
could
show
this
dynamically
in
PowerPoint,
because
often,
that
is
the
endpoint
of
some
of
our
work,
and
an
analysis
is
a
PowerPoint
presentation
that
we'd
like
to
have
some
sort of a
dynamic
display.
There
are
certainly
many
screen
capture
capabilities
that
you're
probably
all
familiar
with.
But
if
we
just
go
back
to
PowerPoint,
we're
able
to
do
this
inherently
in
the
software,
and
it's
pretty
quick.
Here
is
a
blank
slide.
What
I
want
to
do
is
I
want
to
show
that
dynamic
display.
I
just
come
under
Insert,
and
I'll
do
this
screen
recording
right
here.
What
I'll
do
is
I
will
get
out
of
that
and
I'll
come
back
to
here,
and
then
I'll
come
back
to
JMP,
go
to
PowerPoint.
Now I do
the
screen
recording,
and
I
want
to
be
at
the
right
spot
if
it's
a
problem.
I'll
just
go
ahead
and
take
a
snapshot
of
what
I
want.
I
will
go
ahead
and
hit
the
Record
button,
and
it'll
give
me
the
3-second
countdown.
Then
now
I
can
narrate
and
say
that
we
don't
see
any
interaction
in
our
linear
your
model,
but
you
can
see
a
Boosted Tree
does have
quite
a
bit
of
interaction.
You're
happy
with
that.
You
go
ahead
and
stop
the
recording.
Then
now
you
have
this
embedded
in
your
PowerPoint
that
easily.
You
can
go
ahead
and
run
it
here,
and
you
can
hear
me
in
the
background
that
I
was
explaining
it,
so
the
audio
is
in
there.
Then
clearly,
as
you're
in
presentation
mode,
it
will
run
automatically
as
well.
Now back
to
the
task
at
hand
is
what
we
want
to
do
is
deploy
this
solution
in
a
model
that
is
Python.
What
we
can
do
under
the
Formula
Depot
options
here
is
we
can
go
ahead
and
generate
the
Python
code,
and
we'll
do
it
for
the
sake
of
argument
for
both
of
these.
We
can
see
that
the
Boosted
Forest
here
has
Python
code
that
consists
of
55,000
lines
of
code.
Good
to
know.
But
we'll
put
that
aside
for
now.
What
we're
going
to
ultimately
decide
on,
we'll
say,
is
t
his
main
effects
model
only
is
what
we're
going
to
use
that
we
want
to
deploy
to
our
system.
I'll
go
ahead
and
hit...
By
the
way,
before
I
do
that,
there
is
this
one
particular
line
of
code
here
that
says
import
JMP
score.
That
is
a
Python
file
that
is
inherent,
and
all
of
you
have
it
on
your
install
directory,
and
you're
going
to
need
that
to
use
it.
What
it
is,
it's
really
just
a
helper
menu.
It
tells
you
here's
how
you
do
vectorization,
here's
how
you
do
power
and
things
like
that,
but
important
that
it's
there.
I'll
go
ahead
and
do
File,
Save.
What
that
creates
is
this
Least
Squares,
and
then
we'll
call
it
BH
for
Boston
House.
Now I've
got
that
Python
code.
What
would
be
nice
is
if
I
could
just
go
to
Spyder
off
of
Anaconda
and
just
run
it
and
then
score
all
my
new
data.
The
problem
is
it's
not
that
easy.
It
doesn't
run
you
yet.
It
takes
you
a
good
way
there,
but
you
still
have
to
put
some
wrappers
and
things
around
it.
You
have
to
import
your
Excel
file
or
whatever
it
is.
I'm
not
a
Python
coder.
In
fact,
people
say,
"What
do
you
do?"
"I'm
a
data
scientist."
"Oh,
you
must
be
good
at
Python."
"No,
I've
never
actually
used
it."
I'm
an
impostor
in
that
fashion.
But
maybe
there's
a
way
that
we
could
use
this
latest
technology,
ChatGPT-4,
and
have
it
create
a
Jupyter
Notebook
for
me.
If
I
come
under
my
ChatGPT-
4
here,
I
have
this
question
I
ask
it.
"I
have
Python
code
generated
from
a
model
in
the
JMP
Formula
Depot.
Go
ahead
and
create
a
Jupyter
Notebook
for
me
that's
going
to
import
data
from
Excel."
I
say,
"Can
it
do
it?"
And
it
says,
"Absolutely."
Then
what
it
does
is
it
gives
me
the
code
right
here.
I
copy
the
code,
and
I
put
it
in
my
instantiation
of Jupyter from
Anaconda,
all
three.
Then
I
run
it,
and
lo
and
behold,
it
doesn't
work.
Nothing
works
for
me
the
first
time.
But
I
say,
"It
didn't
work,
and
here
was
the
error
message
I
got."
It
says,
"Oh,
well,
try
this."
Then
I
tried
that,
and
then
it
worked.
What
did
it
work?
If
I
come
back
over
here,
this
is
my
Jupyter
Notebook
that
was
given
to
me
by
ChatGPT-4.
Again,
I
know
nothing
about
Python,
but I do
know
that
it
gave
me
these
lines.
I
just
go
ahead
and
say,
I'm
going
to
import
Pandas
because
I
need
that
to
get
my
Excel
file
in.
Then
here
is
that
Fit
Least
Squares.
That's
what
I
got
from
the
Formula
Depot.
It
does
that.
I'm
running
each
one
of
these,
by
the
way.
Now it
says
go
ahead
and
import.
I'm
going
to
import
an
Excel
file
that
has
what
I
want
to
score,
and
that's
going
to
be
under
Boston
Housing
data.
It's
new
data
that
I
want
to
score.
Then
here's
this
outdata
thing
that
it
told
me
was
my
error.
I
said,
"
I'll
do
that."
Then
this
says,
"Hey,
just
let
me
know
that
I
brought
in
some
data,"
and
it
does.
Then
now
I'm
going
to
go
ahead
and
score
it.
I
go
ahead
and
score
it.
Then
did
it
score?
Sure
enough,
it
did.
There's
the
first
five
values
of
it.
Then
I
can
go
ahead
and
save
that,
and
we'll
just
call
this
For
Demo.
Maybe
I'll
even
add
100
in
there.
Since
200
is
100,
I'll
go
ahead
and
put
that
out.
Then
I'll
say,
"W
here
does
that
live?"
Maybe
I'll
see
it
right
here.
Here
it
is.
Here's
the
scored
data
Excel
file.
There
are
all
800
or
so
predicted
values
from
that
linear
model.
This
is
easy
as
that.
Next
up,
what
we
want
to
do
is
set
Andrew
up
to
why
everything
is
beautiful
in
this
world.
Coming
back
to
PowerPoint
here,
the
scenario
is
this.
It's
that
we
were
working
with
a
group
of
doctors
across
the
US,
and
they
wanted
reports
for
every
one
of
them
how
productive
they
were
in
2022.
They
thought
that
perhaps
they
could
be
more
productive
by
changing
some
of
the
codes
that they use for
certain
procedures.
They
gave
us
millions
of
rows
of
data,
and
we
came
up
with
exactly
what
they
asked
for.
We
created
a
template
of
10
pages
or
so
of
the
report
with
nice
JMP
graphics
in
there,
and
it
was
virtually
perfect,
except
for
one
thing.
The
one
thing
is
that
this
data
table
at
the
very
end
that
gave
the
results,
we
couldn't
get
it
sized
properly
or
put
the
borders
on
it.
It's
as
simple
as
just
selecting
it
and
then
right-clicking
and
do
AutoFit
and
then
hitting
the
border
symbol
next
to
it.
That's
what
I
told
Dr.
Jay
right
here.
This
is
for
Dr.
No.
You
can
see
his
annual
report.
Essentially,
Andrew
swam
the
English
channel
and
gave
them
the
99.8%
solution
to
their
problem,
but
they
weren't
quite
happy.
It
wasn't
until
we
went
and
took
the
step
to
make
this
automated.
Again,
this
is
a
two-second
process,
but
because
it
took
that
by
100s
of
things,
they
weren't
happy.
But
then
we
ended
up
fixing
that,
and
that's
when
the
customer
said,
"This
is
absolutely
beautiful,"
hence
we
have
beautiful
operationalization.
With
that,
I'm
going
to
turn
it
over
to
Andrew
to
let
you
in
on
a
few
secrets
of
how
you
can
get
some
massively
increased
productivity.
Thanks,
Jim.
I'm
Andrew,
and
I'm
going
to
show
you
how
we
put
together
this
report.
The
assumption
is
we
have
some
suppliers,
and
here
are
our
suppliers.
We've
got
four
different
suppliers,
and
we've
got
some
metrics
that
we
track
in
this
data
set
that
we
update
frequently.
We
want
to
be
able
to
report
this
to
the
suppliers
so
we
can
track
how
they're
doing
and
have
a
record
of
long-term
performance.
What
we'd
like
to
do
is
to
get
something
like
this
is we
have
this
template
we
like
where
we
have
our
header
up
top,
our
logo.
We
have
in
bold,
we've
got
the
company
name.
Then
each
of
these,
we
substitute
in
these
calculated
values
from
the
data
set.
The
standard
JMP
functions
of,
we
export
a
report
to
Word,
we
get
everything,
all
the
outline
boxes
are
open,
but
it
doesn't
have
our
template
function,
and we have to
fill
in
the
blanks.
We
can
manually
copy
out
the
pictures,
right-click
those
little
gray
triangles,
and
say,
edit
copy
picture
and
get
a
nice
version
of
the
pictures
out.
But
it's
still
a
manual
process
that
has
to
be
done.
This
little
application
here,
I've
got
four
suppliers.
What
if
you
have
400,
and
they're
doing
that
once
a
month?
That
becomes
unwieldy.
How
can
we
do
this?
Not
natively
within
JMP
or
JSL.
You
can
get
close
sometimes
to
being
on
your
application,
but
a
more
flexible
example
is
to
take
something
like
this
where
we
have
a
template
file,
we
write
the
report
once
the
way
we
want
it.
Every
time
we
have
something
we
want
substituted
in
by
JMP,
we
have
these
double
brackets,
and
we're
going
to
put
a
little
keyword
in
there.
In
this
case,
team_p
is going
to
be
the
team
name.
Then
down
here,
I've
got
mill_time_
p
with
an
underscore,
and
now,
I've
got
that
bold
and
highlighted.
We
put
whatever
format
we
want,
and
anything
we
want
substituted
in,
we
just
put
in
brackets.
It's
a
natural
solution,
so
it'd
be
great
if
we
get
this
to
work.
Then
also,
in
addition
to
text,
we
can
also
do
that
with
pictures.
We've
got
the
q_pic11.
If
we
want
a
JSL
line
of
box
equivalent,
then
we
can
get
this
table
structure
within
Word,
and
we
just
put
in
our
keywords
where
we
want
to
substitute
things
in.
Also,
we're
going
to
have
a
table
at
the
end
that
doesn't
have
a
key,
that
doesn't
have
a
holder
here.
I'll
get
to
that
in
a
second.
Come
back
to
my
journal.
At
the
end,
what
we
want
it
to
look
like,
the
intended
document
result,
is
we
also
have
this
table
at
the
end
that
goes
through
Tabulate.
We
have
this
nice
color
gradient
we
can
put
it
into
JMP,
and
we
want
to
get
this
into
Word.
But
also,
as
Jim
mentioned,
we
want
to
AutoF
it
this
table
and
we
want
it
to
look
nice,
because
a
lot
of
times,
the
tables
don't
fit
automatically.
We
can
go
through
all
the
work
and
create
the
tables,
but
if
we
don't
make
them
fit,
then
we're
going
to
have
a
lot
of
work
ahead
of
us
to
go
through
and
do
that
manually.
It's
not
something
we
can
program
natively
within
JMP.
What
can
we
do
is
we
will
have
a
script
that,
and
this
is
shared
on
the
Discovery
website,
where
we
can
open
up
the...
We
have
a
reference
for
the
data
table,
the
container
data,
and
this
gets
updated.
Every
time
we
run
this
to
generate
the
reports,
it
pulls
in
the
new
data.
We
have
the
output
path,
which
is
a
folder
where
all
the
temporary
files
get
written
to
that
we
normally
clean
up
plus
the
output
reports.
Then
also
template
file,
that
Word
file
that
contains
those
keywords.
A
ll
the
rest
of
the
script
is
going
to
be
going
down
and
calculating
the
individual
values
that
gets
substituted
in.
A
t
the
end,
we
have
this
Python
script
that
does
the
actual
substitution
and
then
also,
auto
fits
and
pulls
in
this
table.
If
you're
saying,
"I
don't
know
Python,
I
don't
know
how
applicable
this
is
going
to
be
to
me,"
we
don't
know
Python
either,
but
we
got
this
to
work
pretty
quick
because
GPT-4
wrote
the
Python
code
for
us.
I'm
going
to
show
you
an
example
of
how
we
did
that.
What
this
script
will
do
is
we'll
write
the
temporary
files
to
the
output
folder.
For
example,
here's
our
q12
graph,
and
the
temporary
files
get
written
here.
The
images
get
written
to
the
output
folder.
Then
with
the
static
code
in
Python,
it
knows
to
pull
these
files
in.
The
individual
calculations
will
be
run
within
JMP,
and
then
saved
within
the
script,
and
then
passed
along
to
Python
at
the
end.
Here
we
run
all
of
our
JMP
code.
We
load
any
of
our
values
into
variables.
An
example
of
that
is,
here
we
have
a
tabulate
function.
We're
going
to
make
into
data
table.
From
that
data
table,
we're
going
to
take
the
mean
mill
time
column
and
take
the
first
entry
in
that
column,
and
then
that's
going
to
become
our
mill_
time_
p
variable.
That
is
what
will
get
sent
down
in
the
Python
code.
When
we
initiate
Python,
we
tell
it
whatever
variables
we
wanted
to
send
over
from
JMP
to
Python.
Here's
that
mill_
time_
p.
That
will
hold
any
of
the
mill time
calculations,
and
that
is
what
gets
substituted
in
to
the
mill_time_
p
underscore
area
within
the
template.
I'm
going
to
go
back
to
my
template
file.
Here's
my
mill_time_
p
area.
That's
what
gets
substituted
in.
The
intermediate
output
from
the
Python
code
for
doing
this
is
the
temp
report.
We
can
see
these
values
get
substituted
in.
The
graphs
get
placed
in.
We
get
our
nice
grid
of
graphs.
At
the
end,
we
don't
have
our
table
yet.
The
reason
we
don't
have
our
table
yet
is
because
we
like
the
way
that
if
we
move
a
table
from
JMP
using
the
get
as
a
report
to
a
journal
and
then
export
that
journal
to
Word
with
Save
MS
Word,
we
like
the
way
it
keeps
the
table
structure,
but
we
still
need
the
AutoF
it.
What
we
do
is,
in
addition
to
the
report
that
gets
written
out
from
the
template,
is
we
also
write
out
this
other
temporary
table
file.
We
get
the
table
import.
Here
it
is,
and
what
we
need
to
do
when
we
want
to
automate
is
this
AutoF
it t
o
Window,
and
then
also
reducing
the
font
size
where
it
actually
fits.
What
we
need
to
do
is,
after
the
Python
code
that
substitutes
out
into
the
template,
what
we
found
is
we
have
to
convert
that
DOC
file
created
by
JMP,
convert
it
to
a
DOC
X
file,
and
then
we
have
Python
code
that
will
open
up
that
DOC
X
file,
take
the
first
table,
it
will
apply
AutoF
it,
change
the
font
size
to
7,
and
then
it
will
append
it
to
the
original
report
file.
How
did
we
know
to
create
this?
Again,
you
can
copy
what
we've
done.
We've
got
this
file
available.
But
how
can
you
reproduce
this
for
your
own
and
create
your
own
Python
to
do
this?
I recorded
a
video
of
going
through
on
GPT-4,
how
I
did
this,
and
I'm
going
to
show
that
now
and
narrate
that.
The
prompt
I
give
is
that
I've
got
a
Word
document
that
I
want
to
use.
I'm
so
bad
at
Python.
I'm
going
to
go
ahead
and
give
GPT-4
my
actual
path
here
because
I
don't
know
how
to
substitute
out
the
backslashes,
and
the
spaces,
and
everything.
I
say,
"G
o
to
this
path,
open
it
up,
take
the
first
table
you
see
in
here,
and
then
give
me
back
that
table
AutoFit."
It's
going
to
tell
us,
"You
need
to
install
this
other
package."
I've
just
got
a
base
Ana
conda
installation.
You
can
ask
it
for
instructions
and
help.
You
make
sure
you
install
that
package
and
then
you'll
be
able
to
run
this
code.
Whenever
it
gives
me
this
thing,
the
first
thing
I
noticed
is
it
tells
me,
"Hey,
I'm
going
to
overwrite
your
report
file,
be
careful."
I
say,
"No,
I
don't
want
that."
It's
interactive
the
way
it
gives
you
the
code.
I
say,
"F
ix
this
code.
I
want
to
write
to
a
different
file
in
the
same
directory."
It's
going
to
modify
the
code
to
give
me
a
different
output
file
in
the
same
directory.
What
I'm
going
to
do
is
I
just
copy-paste
this
over
to
Spyder,
which
is
basically
the
JSL
editor
for
Python.
It's
the
Python
editor.
I
hit
Run,
I
hit
the
green
arrow,
and
I
get
this
note
about,
"There's
an
error."
I
don't
know
what
the
error
means.
I
don't
want
to
spend
time
on
Stack
Exchange
or
Stack
Overflow
or
anything
looking
that
up.
I
paste
the
error
back
to
Python,
and
it's
nice
enough
that
it
apologizes
and
explains
what
it
did
wrong.
I'm
not
that
worried
about
what
it
did
wrong.
I
just
want
something
that
works
and
gives
me
my
report.
I'm
going
to
copy-
paste
the
code
it
gives
me
and
then
go
back
to
the
Spyder,
run
that.
I
get
one
more
error.
It
says,
"Table
is
not
subscriptable."
Not
really
sure
what
that
means.
I
tell
GPT-4
about
that.
It
apologizes
again.
It
thought
that
the
tables
were
zero-
based
index.
It
turns
out
it's
one-
based.
It
fixes
that.
I'm
going
to
copy
this
code
over.
This
time,
it
runs
without
error.
I
go
to
the
folder
I
specified
to
it,
and
here's
the
modified
folder.
Now you
can
see
AutoF
it's
been
applied
to
this
table.
We
just
made
a
Python
code
without
really
knowing
Python.
You
don't
have
to
stop
there.
If
you
have
any
version
of
modification
that
you
want
to
give
to
it,
you
can
change
the
font
size.
Here
I'm
going
to
ask
it
to
make
it
Wingdings.
Let's
see
if
it
knows
how
to
do
that.
It
gives
me
some
new
code,
and
I
run
it.
Yeah, I
can
get
Wingdings
output.
Just
to
make
sure
it
didn't
just
give
me
gibberishish,
I'm
going
to
copy
it
all
and
make
sure
that
it
translates
back
into
more
easily
readable
text,
and
it
does.
That's
what
we're
looking
for.
Not
only
Python,
but
also
Visual
Basic.
Another
thing
that
we
run
into
is
we
output
things.
You
might
have
a
500-page
report
and
you
want
the
headers
to
look
right
to
header 1
,
header
2
in
words,
so
that
way
you
can
get
a
table
of
contents.
W
e
told
GPT-4
is,
I'm
going
to
use
these
delimiters.
I'm
going
to
script
these
in
to
my
outline
box
titles.
I
get
h1,
close
h1,
h2,
close
h2
for
header
1,
header
2.
I
want
to
substitute
those
in.
When
we
did
that
to
GPT-4,
what
it
gave
us
is
it
gave
us
this
macro
file,
which
I'm
going
to
copy
that.
I'm
not
sure
if
it
was
retained
in
my
Microsoft
Word.
We'll
find
out.
If
not,
I'll
paste
it
in.
You
have
to
enable
this
Developer
tab.
That's
a
Word
option.
When
you
do
that,
we
get
this.
We
just
paste
in
our
macro
here,
which
was
generated
by
GPT-4.
I
don't
like
programming
in
VBA,
but
it's
good
at
it.
When
I
hit
Run
on
this,
it
takes
everything,
all
these
headers
and
fills
them
in.
That
way
I
can
go
up
here,
I
could
say,
References,
Table
of
Contents,
and
put
on
my
table
of
contents.
If
I
generated
500
page
report,
now
I've
got
the
right
structure
for
all
this.
It's
easy
to
go
in
here,
too.
If
you
want
to
add
numbers
to
these
headers
and
stuff,
you
just
right-click
in
here,
and
you
go
to
Modify,
and
you
can
turn
on
that
numbering
structure
if
you
want.
The
last
thing
I'm
going
to
show
for
GPT-4,
specifically
for
JSL,
is
you
might
have
noticed
in
my
script
back
here,
I'm
really
bad
about
commenting
my
script.
I've
got
a
couple
of
comments
in
here.
But
for
the
most
part,
I
don't
have
a
lot
of
comments
describing
it.
If
you
have
code,
you
don't
want
to
give
anything
proprietary,
but
if
you
give
code
to
GPT-4
or
even
JSL,
it'll
add
a
bunch
of
nice
comments
to
you
and
explain
what
each
code
block
is
doing,
and
that
way
other
readers
can
see.
Also
for
Python,
if
you
don't
know
Python
and
you're taking
a
look
at
our
script
that
we've
provided,
and
you're
saying, "
What
does
this
do,"
you
can
provide
this
code
to
GPT-4
and
say,
"Explain
this
Python
code
to
me."
It'll
give
you
a
long
narrative
story
and
say,
"Here
is
exactly
the
workflow
of
what's
happening.
Here
are
the
key
functions
that
are
doing
it."
That's
my
favorite
part.
You
can
say,
"Do
you
have
any suggestions
for
this
code?"
It'll
say,
"Y
ou
don't
have
any
error
handling.
You've
got
some
memory
leak
issues."
It'll
go
through
and
make
a
bunch
of
suggestions.
Then
it's
nice
enough
to
go
ahead
and
give
you
some
new
code
with
all
the
suggestions
implemented.
With
all
this
in
place,
you
can
go
from
doing
all
of
your
calculations
in
JMP
to
actually
getting
this
nice
output
file
that
has
the
right
format
you
want.
Everything
looks
nice.
You're
not
going
through
making
manual
changes.
With
this
in
place,
this
is
what
the
customer
said,
take a
look
at
this
and
said,
"This
is
beautiful."
With
that,
we
hope
that
you
can
take
this
same
idea
and
go
make
some
beautiful
reports
yourself.
