[Haskell-cafe] Loading a csv file with ~200 columns into Haskell Record

[Guru Devanla]

Did not mean to complain. For example, being able to use Data Frame library
in Pandas, did not involve a big learning curve to understand the syntax of
Pandas. With the basic knowledge of Python is was easy to learn and start
using it.  Trying, to replicate that kind of program in Haskell seems to be
a lot difficult for me. For example,  the leap from dynamic typing to
static typing does involve this kind of boiler plate an I am fine with it.
Now, when I try to reach out to the libraries in use, it involves a lot of
learning of the library syntax/special operators etc to get stuff done.
I understand that is the philosophy eschewed by Haskell community, but it
takes up a lot of the spare time I have to both learn and also build my toy
projects. I love coding in Haskell. But, that love takes a lot of time
before it translates to any good code I  can show. It could be just me.

Again, I am happy to do this out of my love for Haskell. But, I am hesitant
to recommend that to other team members since it is difficult for me to
quantify the gains. And I say this with limited experience building real
world Haskell applications and therefore my train of thought is totally
mis-guided.

On Sun, Oct 1, 2017 at 7:22 PM, Saurabh Nanda <saurabhnanda at gmail.com>
wrote:

> > Having to not have something which I can quickly start off on
>
> What do you mean by that? And what precisely is the  discomfort between
> Haskell vs python for your use-case?
>
> On 02-Oct-2017 7:29 AM, "Guru Devanla" <gurudev.devanla at gmail.com> wrote:
>
>> Thank you all for your helpful suggestions. As I wrote the original
>> question, even I was trying to decide between the approach of using Records
>> to represent each row or  define a vector for each column and each vector
>> becomes an attribute of the record.  Even, I was leaning towards the latter
>> given the performance needs.
>>
>> Since, the file is currently available as a CSV adding Persistent and any
>> ORM library would be an added dependency.
>>
>> I was trying to solve this problem without too many dependencies of other
>> libraries and wanting to learn new DSLs. Its a tempting time killer as
>> everyone here would understand.
>>
>> @Anthony Thank your for your answer as well. I have explored Frames
>> library in the past as I tried to look for Pandas like features in Haskell
>> The library is useful and I have played around with it. But, I was never
>> confident in adopting it for a serious project. Part of my reluctance,
>> would be the learning curve plus I also need to familiarize myself with
>> `lens` as well. But, looks like this project I have in hand is a good
>> motivation to do both. I will try to use Frames and then report back. Also,
>> apologies for not being able to share the data I am working on.
>>
>> With the original question, what I was trying to get to is, how are these
>> kinds of problems solved in real-world projects. Like when Haskell is used
>> in data mining, or in financial applications. I believe these applications
>> deal with this kind of data where the tables are wide. Having to not have
>> something which I can quickly start off on troubles me and makes me wonder
>> if the reason is my lack of understanding or just the pain of using static
>> typing.
>>
>> Regards
>>
>>
>> On Sun, Oct 1, 2017 at 1:58 PM, Anthony Cowley <acowley at seas.upenn.edu>
>> wrote:
>>
>>>
>>>
>>> > On Sep 30, 2017, at 9:30 PM, Guru Devanla <gurudev.devanla at gmail.com>
>>> wrote:
>>> >
>>> > Hello All,
>>> >
>>> > I am in the process of replicating some code in Python in Haskell.
>>> >
>>> > In Python, I load a couple of csv files, each file having more than
>>> 100 columns into a Pandas' data frame. Panda's data-frame, in short is a
>>> tabular structure which lets me performs on bunch of joins, and filter out
>>> data. I generated different shapes of reports using these operations. Of
>>> course, I would love some type checking to help me with these merge, join
>>> operations as I create different reports.
>>> >
>>> > I am not looking to replicate the Pandas data-frame functionality in
>>> Haskell. First thing I want to do is reach out to the 'record' data
>>> structure. Here are some ideas I have:
>>> >
>>> > 1.  I need to declare all these 100+ columns into multiple record
>>> structures.
>>> > 2.  Some of the columns can have NULL/NaN values. Therefore, some of
>>> the attributes of the record structure would be 'MayBe' values. Now, I
>>> could drop some columns during load and cut down the number of attributes i
>>> created per record structure.
>>> > 3.  Create a dictionary of each record structure which will help me
>>> index into into them.'
>>> >
>>> > I would like some feedback on the first 2 points. Seems like there is
>>> a lot of boiler plate code I have to generate for creating 100s of record
>>> attributes. Is this the only sane way to do this?  What other patterns
>>> should I consider while solving such a problem.
>>> >
>>> > Also, I do not want to add too many dependencies into the project, but
>>> open to suggestions.
>>> >
>>> > Any input/advice on this would be very helpful.
>>> >
>>> > Thank you for the time!
>>> > Guru
>>>
>>> The Frames package generates a vinyl record based on your data (like
>>> hlist; with a functor parameter that can be Maybe to support missing data),
>>> storing each column in a vector for very good runtime performance. As you
>>> get past 100 columns, you may encounter compile-time performance issues. If
>>> you have a sample data file you can make available, I can help diagnose
>>> performance troubles.
>>>
>>> Anthony
>>>
>>>
>>>
>>
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