BG
Balaji G
Fri, Aug 6, 2021 4:23 PM
Hello Everyone,
I have been working on the addition of a new feature, DataFrame >>
dataTypes, which briefs us about the data type of columns in dataframes we
work on.
Summarising a dataset is really important during the initial stage of any
Data Science and Machine Learning tasks. Knowing the data type of the
attribute is one major thing to begin with.
I have tried to work with some sample datasets for a clear understanding of
this new feature.
Please go through the following blog post. Any kind of suggestion or
feedback is welcome.
Link to the Post :
https://balaji612141526.wordpress.com/2021/08/06/introducingnewfeatureindataframeprojectdatatypes/
Previous discussions can be found here :
https://lists.pharo.org/empathy/thread/BFOHPRUU72MDYVTJP3YV2DQ5LAZHXELE and
here :
https://lists.pharo.org/empathy/thread/JZXKXGHSURC3DCDA2NXA7KDWZ2EINAZ5
Cheers
Balaji G
Hello Everyone,
I have been working on the addition of a new feature, DataFrame >>
dataTypes, which briefs us about the data type of columns in dataframes we
work on.
Summarising a dataset is really important during the initial stage of any
Data Science and Machine Learning tasks. Knowing the data type of the
attribute is one major thing to begin with.
I have tried to work with some sample datasets for a clear understanding of
this new feature.
Please go through the following blog post. Any kind of suggestion or
feedback is welcome.
Link to the Post :
https://balaji612141526.wordpress.com/2021/08/06/introducingnewfeatureindataframeprojectdatatypes/
Previous discussions can be found here :
https://lists.pharo.org/empathy/thread/BFOHPRUU72MDYVTJP3YV2DQ5LAZHXELE and
here :
https://lists.pharo.org/empathy/thread/JZXKXGHSURC3DCDA2NXA7KDWZ2EINAZ5
Cheers
Balaji G
KH
Konrad Hinsen
Sat, Aug 7, 2021 4:28 PM
Please go through the following blog post. Any kind of suggestion or
feedback is welcome.
That's a nice description, easy to follow. But there is a missing piece:
how do you actually find the dataType for a series of values? My first
guess was that you are using the same method as Collection >>
commonSuperclass, but then a nil value should yield UndefinedObject, not
Object as in your example.
Cheers
Konrad.
Hi Balaji,
> Please go through the following blog post. Any kind of suggestion or
> feedback is welcome.
That's a nice description, easy to follow. But there is a missing piece:
how do you actually find the dataType for a series of values? My first
guess was that you are using the same method as Collection >>
commonSuperclass, but then a nil value should yield UndefinedObject, not
Object as in your example.
Cheers
Konrad.
BG
Balaji G
Sat, Aug 7, 2021 5:57 PM
Hi Konrad
Thanks for pointing this out. You are right with how the data types are
calculated, similar to collection >> commonSuperClass. But this time, it is
calculated only during the creation of DataFrame once and for all.
Now coming to the next part, Kindly have a look at the below code.
series := #( nil nil nil ) asDataSeries. series calculateDataType.
"UndefinedObject" .
A nil value, or a Series of nil values yields UndefinedObject as its super
class. There was an error with the dataset used on that example blog post.
I have corrected it now. The problem was that the dataset was not clean,
and had some garbage values of different types in that column. This
resulted in a column, with type Object .
This was the dataset and specifically the last column resulted in this
mistake.
On Sat, Aug 7, 2021 at 9:58 PM Konrad Hinsen konrad.hinsen@fastmail.net
wrote:
Please go through the following blog post. Any kind of suggestion or
feedback is welcome.
That's a nice description, easy to follow. But there is a missing piece:
how do you actually find the dataType for a series of values? My first
guess was that you are using the same method as Collection >>
commonSuperclass, but then a nil value should yield UndefinedObject, not
Object as in your example.
Cheers
Konrad.
Hi Konrad
Thanks for pointing this out. You are right with how the data types are
calculated, similar to collection >> commonSuperClass. But this time, it is
calculated only during the creation of DataFrame once and for all.
Now coming to the next part, Kindly have a look at the below code.

series := #( nil nil nil ) asDataSeries. series calculateDataType.
"UndefinedObject" .
A nil value, or a Series of nil values yields UndefinedObject as its super
class. There was an error with the dataset used on that example blog post.
I have corrected it now. The problem was that the dataset was not clean,
and had some garbage values of different types in that column. This
resulted in a column, with type Object .
This was the dataset and specifically the last column resulted in this
mistake.
On Sat, Aug 7, 2021 at 9:58 PM Konrad Hinsen <konrad.hinsen@fastmail.net>
wrote:
> Hi Balaji,
>
> > Please go through the following blog post. Any kind of suggestion or
> > feedback is welcome.
>
> That's a nice description, easy to follow. But there is a missing piece:
> how do you actually find the dataType for a series of values? My first
> guess was that you are using the same method as Collection >>
> commonSuperclass, but then a nil value should yield UndefinedObject, not
> Object as in your example.
>
> Cheers
> Konrad.
>
RO
Richard O'Keefe
Sun, Aug 8, 2021 10:51 AM
I am not quite sure what the point of the datatypes feature is.
x := nil.
aSequence do: [:each 
each ifNotNil: [
x := x ifNil: [each class] ifNotNil: [x commonSuperclassWith: each class]]].
doesn't seem terribly complicated.
My difficulty is that from a statistics/data science perspective,
it doesn't seem terribly useful.
I'm currently reading a book about geostatistics with R (based on a
survey of the Kola
peninsula). For that task, it is ESSENTIAL to know the units in which
the items are
recorded. If Calcium is measured in mg/kg and Caesium is measured in µg/kg,
you really really need to know that. This is not information you can
derive by looking
at the representation of the data in Pharo. Consider for example
 mass of animals in kg
 maximum speed of cars in km/h
 volume of rain in successive dates, in mL (for fixed area)
 directions taken by sandhoppers released at different times of
day, in degrees
 region of space illuminated by light bulbs in steradians.
These might all have the same representation in Pharo, but they are
semantically
very different. 1 and 2 are linear, but cannot be negative. 3 also
cannot be negative,
but the variable is a time series, which 1 and 2 are not. 4 is a
circular measure,
and taking the usual arithmetic mean or median would be an elementary blunder
producing meaningless answers. 5 is perhaps best viewed as a proportion.
(These are all actual examples, by the way.)
THIS kind of information IS valuable for analysis. The difference
between SmallInteger
and Float64 is nowhere near as interesting.
There's a bunch of weather data that i'm very interested in which has
things like
air temperature, soil temperature, relative humidity, wind speed and
direction (last
5 minutes), gust speed and direction (maximum in last 5 minutes), illumination
in W/m^2 (visible, UVB, UVA), rainfall, and of course date+time.
Temperatures are measured on an interval scale, so dividing them makes no sense.
Nor does adding them. If it's 10C today and 10C tomorrow, nothing is 20C. But
oddly enough arithmetic means DO make sense.
Humidity is bounded between 0 and 100; adding two relative humidities makes no
sense at all. Medians make sense but means do not.
Wind speed and direction are reported as separate variables,
but they are arguably one 2D vector quantity.
Illumination is on a ratio scale. Dividing one illumination by another makes
sense, or would if there were no moonless nights...
The total illumination over a day makes sense.
Rainfall is also on a ratio scale. Dividing the rainfall on one day by that
on another would make sense if only the usual measurement were not 0.
Total rainfall over a day makes sense.
The whole problem a statistician/data scientist faces is that there is important
information you need to know even which basic operations make sense
that has already disappeared by the time Pharo stores it, and cannot be
inferred from the DataFrame. I remember one time I was given a CSV file
with about 50 variables and it took me about 2 weeks to recover this missing
metainformation.
On Sat, 7 Aug 2021 at 04:23, Balaji G gbalaji20002000@gmail.com wrote:
I am not quite sure what the point of the datatypes feature is.
x := nil.
aSequence do: [:each 
each ifNotNil: [
x := x ifNil: [each class] ifNotNil: [x commonSuperclassWith: each class]]].
doesn't seem terribly complicated.
My difficulty is that from a statistics/data science perspective,
it doesn't seem terribly *useful*.
I'm currently reading a book about geostatistics with R (based on a
survey of the Kola
peninsula). For that task, it is ESSENTIAL to know the units in which
the items are
recorded. If Calcium is measured in mg/kg and Caesium is measured in µg/kg,
you really really need to know that. This is not information you can
derive by looking
at the representation of the data in Pharo. Consider for example
1. mass of animals in kg
2. maximum speed of cars in km/h
3. volume of rain in successive dates, in mL (for fixed area)
4. directions taken by sandhoppers released at different times of
day, in degrees
5. region of space illuminated by light bulbs in steradians.
These might all have the *same* representation in Pharo, but they are
*semantically*
very different. 1 and 2 are linear, but cannot be negative. 3 also
cannot be negative,
but the variable is a *time series*, which 1 and 2 are not. 4 is a
circular measure,
and taking the usual arithmetic mean or median would be an elementary blunder
producing meaningless answers. 5 is perhaps best viewed as a proportion.
(These are all actual examples, by the way.)
THIS kind of information IS valuable for analysis. The difference
between SmallInteger
and Float64 is nowhere near as interesting.
There's a bunch of weather data that i'm very interested in which has
things like
air temperature, soil temperature, relative humidity, wind speed and
direction (last
5 minutes), gust speed and direction (maximum in last 5 minutes), illumination
in W/m^2 (visible, UVB, UVA), rainfall, and of course date+time.
Temperatures are measured on an interval scale, so dividing them makes no sense.
Nor does adding them. If it's 10C today and 10C tomorrow, nothing is 20C. But
oddly enough arithmetic means DO make sense.
Humidity is bounded between 0 and 100; adding two relative humidities makes no
sense at all. Medians make sense but means do not.
Wind speed and direction are reported as separate variables,
but they are arguably one 2D vector quantity.
Illumination is on a ratio scale. Dividing one illumination by another makes
sense, or would if there were no moonless nights...
The total illumination over a day makes sense.
Rainfall is also on a ratio scale. Dividing the rainfall on one day by that
on another would make sense if only the usual measurement were not 0.
Total rainfall over a day makes sense.
The whole problem a statistician/data scientist faces is that there is important
information you need to know even which *basic* operations make sense
that has already disappeared by the time Pharo stores it, and cannot be
inferred from the DataFrame. I remember one time I was given a CSV file
with about 50 variables and it took me about 2 weeks to recover this missing
metainformation.
On Sat, 7 Aug 2021 at 04:23, Balaji G <gbalaji20002000@gmail.com> wrote:
>
> Hello Everyone,
>
> I have been working on the addition of a new feature, DataFrame >> dataTypes, which briefs us about the data type of columns in dataframes we work on.
> Summarising a dataset is really important during the initial stage of any Data Science and Machine Learning tasks. Knowing the data type of the attribute is one major thing to begin with.
> I have tried to work with some sample datasets for a clear understanding of this new feature.
> Please go through the following blog post. Any kind of suggestion or feedback is welcome.
>
> Link to the Post : https://balaji612141526.wordpress.com/2021/08/06/introducingnewfeatureindataframeprojectdatatypes/
>
> Previous discussions can be found here : https://lists.pharo.org/empathy/thread/BFOHPRUU72MDYVTJP3YV2DQ5LAZHXELE and here : https://lists.pharo.org/empathy/thread/JZXKXGHSURC3DCDA2NXA7KDWZ2EINAZ5
>
>
>
> Cheers
> Balaji G
KH
Konrad Hinsen
Sun, Aug 8, 2021 2:58 PM
Thanks for pointing this out. You are right with how the data types are
calculated, similar to collection >> commonSuperClass. But this time, it is
calculated only during the creation of DataFrame once and for all.
A nil value, or a Series of nil values yields UndefinedObject as its super
class. There was an error with the dataset used on that example blog post.
I have corrected it now.
OK, that explains my confusion.
Cheers,
Konrad.
Hi Balaji,
> Thanks for pointing this out. You are right with how the data types are
> calculated, similar to collection >> commonSuperClass. But this time, it is
> calculated only during the creation of DataFrame once and for all.
That sounds good.
> A nil value, or a Series of nil values yields UndefinedObject as its super
> class. There was an error with the dataset used on that example blog post.
> I have corrected it now.
OK, that explains my confusion.
Cheers,
Konrad.
KH
Konrad Hinsen
Sun, Aug 8, 2021 3:03 PM
My difficulty is that from a statistics/data science perspective,
it doesn't seem terribly useful.
There are two common use cases in my experience:

Error checking, most frequently right after reading in a dataset.
A quick look at the data types of all columns shows if it is coherent
with your expectations. If you have a column called "data" of data
type "Object", then most probably something went wrong with parsing
some date format.

Type checking for specific operations. For example, you might want to
compute an average over all rows for each numerical column in your
dataset. That's easiest to do by selecting columns of the right data
type.
You are completely right that data type information is not sufficient
for checking for all possible problems, such as unit mismatch. But it
remains a useful tool.
Cheers,
Konrad.
"Richard O'Keefe" <raoknz@gmail.com> writes:
> My difficulty is that from a statistics/data science perspective,
> it doesn't seem terribly *useful*.
There are two common use cases in my experience:
1) Error checking, most frequently right after reading in a dataset.
A quick look at the data types of all columns shows if it is coherent
with your expectations. If you have a column called "data" of data
type "Object", then most probably something went wrong with parsing
some date format.
2) Type checking for specific operations. For example, you might want to
compute an average over all rows for each numerical column in your
dataset. That's easiest to do by selecting columns of the right data
type.
You are completely right that data type information is not sufficient
for checking for all possible problems, such as unit mismatch. But it
remains a useful tool.
Cheers,
Konrad.
RO
Richard O'Keefe
Sun, Aug 8, 2021 11:58 PM
Neither of those use cases actually works.
Consider the following partial class hierarchy from my Smalltalk system:
Object
VectorSpace
Complex
Quaternion
Magnitude
MagnitudeWithAddition
DateAndTime
QuasiArithmetic
Duration
Number
AbstractRationalNumber
Integer
SmallInteger
There is a whole fleet of "numeric" things like Matrix3x3 which have
some arithmetic properties
but which cannot be given a total order consistent with those
properties. Complex is one of them.
It makes less than no sense to make Complex inherit from Magnitude, so
it cannot inherit from
Number, This means that the common superclass of 1 and 1  2 i is
Object. Yet it makes perfect
sense to have a column of Gaussian integers some of which have zero
imaginary part.
So "the dataType is Object means there's an error" fails at the first
hurdle. Conversely, the
common superclass of 1 and DateAndTime now is MagnitudeWithAddition,
which is not Object,
but the combination is probably wrong, and the dataType test fails at
the second hurdle.
"You might want to compute an average..." But dataType is no use for
that either, as I was at
pains to explain. If you have a bunch of angles expressed as Numbers,
you can compute an
arithmetic mean of them, but you shouldn't, because that's not how
you compute the
average of circular measures. The obvious algorithm (self sum / self
size) does not work at
all for a collection of DateAndTimes, but the notion of average makes
perfect sense and a
subtly different algorithm works well. (I wrote a technical report
about this, if anyone is interested.)
dataType will tell you you CAN take an average when you cannot or should not.
dataType will tell you you CAN'T take an average when you really honestly can.
The distinctions we need to make are not the distinctions that the
class hierarchy makes.
For example, how about the distinction between ordered factors and
unordered factors?
On Mon, 9 Aug 2021 at 03:03, Konrad Hinsen konrad.hinsen@fastmail.net wrote:
"Richard O'Keefe" raoknz@gmail.com writes:
My difficulty is that from a statistics/data science perspective,
it doesn't seem terribly useful.
There are two common use cases in my experience:

Error checking, most frequently right after reading in a dataset.
A quick look at the data types of all columns shows if it is coherent
with your expectations. If you have a column called "data" of data
type "Object", then most probably something went wrong with parsing
some date format.

Type checking for specific operations. For example, you might want to
compute an average over all rows for each numerical column in your
dataset. That's easiest to do by selecting columns of the right data
type.
You are completely right that data type information is not sufficient
for checking for all possible problems, such as unit mismatch. But it
remains a useful tool.
Cheers,
Konrad.
Neither of those use cases actually works.
Consider the following partial class hierarchy from my Smalltalk system:
Object
VectorSpace
Complex
Quaternion
Magnitude
MagnitudeWithAddition
DateAndTime
QuasiArithmetic
Duration
Number
AbstractRationalNumber
Integer
SmallInteger
There is a whole fleet of "numeric" things like Matrix3x3 which have
some arithmetic properties
but which cannot be given a total order consistent with those
properties. Complex is one of them.
It makes less than no sense to make Complex inherit from Magnitude, so
it cannot inherit from
Number, This means that the common superclass of 1 and 1  2 i is
Object. Yet it makes perfect
sense to have a column of Gaussian integers some of which have zero
imaginary part.
So "the dataType is Object means there's an error" fails at the first
hurdle. Conversely, the
common superclass of 1 and DateAndTime now is MagnitudeWithAddition,
which is not Object,
but the combination is probably wrong, and the dataType test fails at
the second hurdle.
"You might want to compute an average..." But dataType is no use for
that either, as I was at
pains to explain. If you have a bunch of angles expressed as Numbers,
you *can* compute an
arithmetic mean of them, but you *shouldn't*, because that's not how
you compute the
average of circular measures. The obvious algorithm (self sum / self
size) does not work at
all for a collection of DateAndTimes, but the notion of average makes
perfect sense and a
subtly different algorithm works well. (I wrote a technical report
about this, if anyone is interested.)
dataType will tell you you CAN take an average when you cannot or should not.
dataType will tell you you CAN'T take an average when you really honestly can.
The distinctions we need to make are not the distinctions that the
class hierarchy makes.
For example, how about the distinction between *ordered* factors and
*unordered* factors?
On Mon, 9 Aug 2021 at 03:03, Konrad Hinsen <konrad.hinsen@fastmail.net> wrote:
>
> "Richard O'Keefe" <raoknz@gmail.com> writes:
>
> > My difficulty is that from a statistics/data science perspective,
> > it doesn't seem terribly *useful*.
>
> There are two common use cases in my experience:
>
> 1) Error checking, most frequently right after reading in a dataset.
> A quick look at the data types of all columns shows if it is coherent
> with your expectations. If you have a column called "data" of data
> type "Object", then most probably something went wrong with parsing
> some date format.
>
> 2) Type checking for specific operations. For example, you might want to
> compute an average over all rows for each numerical column in your
> dataset. That's easiest to do by selecting columns of the right data
> type.
>
> You are completely right that data type information is not sufficient
> for checking for all possible problems, such as unit mismatch. But it
> remains a useful tool.
>
> Cheers,
> Konrad.
KH
Konrad Hinsen
Mon, Aug 9, 2021 7:02 AM
Neither of those use cases actually works.
Consider the following partial class hierarchy from my Smalltalk system:
That's not what I have to deal with in practice. My data comes in as a
CSV file or a JSON file, i.e. formats with very shallow data
classification. All I can expect to distinguish are strings vs. numbers,
plus a few welldefined subsets of each, such as integers, specific
constants (nil, true, false), or data recognizable by their format, such
as dates.
For internal processing, I might then want to redefine a column's data
type to be something more specific. In particular, I might want to
distinguish between "categorical" (a predefined finite set of string
values) vs. "generic string". But most of the classes in a Smalltalk
hierarchy just never occur in data frames. It's a simple data structure
for simply structured data.
"You might want to compute an average..." But dataType is no use for
that either, as I was at pains to explain. If you have a bunch of
angles expressed as Numbers, you can compute an arithmetic mean of
them, but you shouldn't, because that's not how you compute the
average of circular measures.
I agree. There are many things you shouldn't do for any given dataset
but which no formal structure will prevent you from doing. Data science
is very much about shallow computations, with automation limited to
simple stuff that is then applied to large datasets. Tools like data
type analysis are no more than a help for the data scientist, who is the
ultimate arbiter of what is or is not OK to do.
Think of this as the data equivalent of a spell checker. A spell checker
won't recognize bad grammar, but it's still a useful tool to have.
Cheers,
Konrad.
"Richard O'Keefe" <raoknz@gmail.com> writes:
> Neither of those use cases actually works.
In practice, they do.
> Consider the following partial class hierarchy from my Smalltalk system:
That's not what I have to deal with in practice. My data comes in as a
CSV file or a JSON file, i.e. formats with very shallow data
classification. All I can expect to distinguish are strings vs. numbers,
plus a few welldefined subsets of each, such as integers, specific
constants (nil, true, false), or data recognizable by their format, such
as dates.
For internal processing, I might then want to redefine a column's data
type to be something more specific. In particular, I might want to
distinguish between "categorical" (a predefined finite set of string
values) vs. "generic string". But most of the classes in a Smalltalk
hierarchy just never occur in data frames. It's a simple data structure
for simply structured data.
> "You might want to compute an average..." But dataType is no use for
> that either, as I was at pains to explain. If you have a bunch of
> angles expressed as Numbers, you *can* compute an arithmetic mean of
> them, but you *shouldn't*, because that's not how you compute the
> average of circular measures.
I agree. There are many things you shouldn't do for any given dataset
but which no formal structure will prevent you from doing. Data science
is very much about shallow computations, with automation limited to
simple stuff that is then applied to large datasets. Tools like data
type analysis are no more than a help for the data scientist, who is the
ultimate arbiter of what is or is not OK to do.
Think of this as the data equivalent of a spell checker. A spell checker
won't recognize bad grammar, but it's still a useful tool to have.
Cheers,
Konrad.