Please refer to the package description for an overview of cassava.

Here's the two second crash course in using the library. Given a CSV file with this content:

John Doe,50000
Jane Doe,60000

here's how you'd process it record-by-record:

{-# LANGUAGE ScopedTypeVariables #-}

import qualified Data.ByteString.Lazy as BL
import Data.Csv
import qualified Data.Vector as V

main :: IO ()
main = do
    csvData <- BL.readFile "salaries.csv"
    case decode NoHeader csvData of
        Left err -> putStrLn err
        Right v -> V.forM_ v $ \ (name, salary :: Int) ->
            putStrLn $ name ++ " earns " ++ show salary ++ " dollars"

If you want to parse a file that includes a header, like this one

name,salary
John Doe,50000
Jane Doe,60000

use decodeByName:

{-# LANGUAGE OverloadedStrings #-}

import Control.Applicative
import qualified Data.ByteString.Lazy as BL
import Data.Csv
import qualified Data.Vector as V

data Person = Person
    { name   :: !String
    , salary :: !Int
    }

instance FromNamedRecord Person where
    parseNamedRecord r = Person <$> r .: "name" <*> r .: "salary"

main :: IO ()
main = do
    csvData <- BL.readFile "salaries.csv"
    case decodeByName csvData of
        Left err -> putStrLn err
        Right (_, v) -> V.forM_ v $ \ p ->
            putStrLn $ name p ++ " earns " ++ show (salary p) ++ " dollars"

You can find more code examples in the examples/ folder as well as smaller usage examples in the Data.Csv module documentation.

There's no end to what people consider CSV data. Most programs don't follow RFC4180 so one has to make a judgment call which contributions to accept. Consequently, not everything gets accepted, because then we'd end up with a (slow) general purpose parsing library. There are plenty of those. The goal is to roughly accept what the Python csv module accepts.

The Python csv module (which is implemented in C) is also considered the base-line for performance. Adding options (e.g. the above mentioned parsing "flexibility") will have to be a trade off against performance. There's been complaints about performance in the past, therefore, if in doubt performance wins over features.

Last but not least, it's important to keep the dependency footprint light, as each additional dependency incurs costs and risks in terms of additional maintenance overhead and loss of flexibility. So adding a new package dependency should only be done if that dependency is known to be a reliable package and there's a clear benefit which outweights the cost.

The primary API documentation for cassava is its Haddock documentation which can be found at http://hackage.haskell.org/package/cassava/docs/Data-Csv.html

Below are listed additional recommended third-party blogposts and tutorials

• CSV encoding and decoding in Haskell with Cassava