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

Guru Devanla gurudev.devanla at gmail.com
Mon Oct 2 14:56:51 UTC 2017

Yes, Thank you for the encouraging words. I will keep at it.

>> Also, be sure of what exactly is the warm fuzzy feeling that the
compiler is giving you. From whatever you have described, most of your bugs
are going to occur when you change your data transformation pipeline (core
logic) or your CSV format. Compilation and static types will help in only
one of those.

Yes, i am aware of that. I have tests for the core logic, but the
mechanical part of type checking the data that passes through this pipeline
is much desired.

On Sun, Oct 1, 2017 at 10:00 PM, Saurabh Nanda <saurabhnanda at gmail.com>

> I whole heartedly agree with your sentiment. I have felt the same way in
> my initial days, and only my stubborn head prevented me from giving up on
> Haskell [1]
> Haskell is **unnecessarily** hard. It doesn't have to be that way. Stop
> beating yourself up over what is essentially a tooling, API design, and
> documentation problem. Start speaking up instead.
> Wrt the current problem at hand, try thinking of the types as a **spec**
> rather than boilerplate. That spec is necessary to give you your compile
> time guarantees. Without the spec the compiler can't do anything. This spec
> is non-existent in python.
> Also, be sure of what exactly is the warm fuzzy feeling that the compiler
> is giving you. From whatever you have described, most of your bugs are
> going to occur when you change your data transformation pipeline (core
> logic) or your CSV format. Compilation and static types will help in only
> one of those.
> [1] https://medium.com/@saurabhnanda/why-building-web-
> apps-in-haskell-is-harder-than-it-ought-to-be-c9b13de0be4f
> On 02-Oct-2017 8:20 AM, "Guru Devanla" <gurudev.devanla at gmail.com> wrote:
> 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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