dataframe-1.0.0.0: src/DataFrame/Operations/Core.hs
{-# LANGUAGE ExplicitNamespaces #-}
{-# LANGUAGE FlexibleContexts #-}
{-# LANGUAGE GADTs #-}
{-# LANGUAGE OverloadedStrings #-}
{-# LANGUAGE RankNTypes #-}
{-# LANGUAGE ScopedTypeVariables #-}
{-# LANGUAGE TypeApplications #-}
module DataFrame.Operations.Core where
import qualified Data.List as L
import qualified Data.Map as M
import qualified Data.Map.Strict as MS
import qualified Data.Text as T
import qualified Data.Vector as V
import qualified Data.Vector.Generic as VG
import qualified Data.Vector.Unboxed as VU
import Control.Exception (throw)
import Data.Either
import qualified Data.Foldable as Fold
import Data.Function (on, (&))
import Data.Maybe
import Data.Type.Equality (TestEquality (..))
import DataFrame.Errors
import DataFrame.Internal.Column (
Column (..),
Columnable,
TypedColumn (..),
columnLength,
columnTypeString,
expandColumn,
fromList,
fromVector,
toDoubleVector,
toFloatVector,
toIntVector,
toUnboxedVector,
toVector,
)
import DataFrame.Internal.DataFrame (
DataFrame (..),
columnIndices,
derivingExpressions,
empty,
getColumn,
)
import DataFrame.Internal.Expression
import DataFrame.Internal.Interpreter
import DataFrame.Internal.Parsing (isNullish)
import DataFrame.Internal.Row (Any, mkColumnFromRow)
import Type.Reflection
import Prelude hiding (null)
{- | O(1) Get DataFrame dimensions i.e. (rows, columns)
==== __Example__
@
>>> :set -XOverloadedStrings
>>> import qualified DataFrame as D
>>> df = D.fromNamedColumns [("a", D.fromList [1..100]), ("b", D.fromList [1..100]), ("c", D.fromList [1..100])]
>>> D.dimensions df
(100, 3)
@
-}
dimensions :: DataFrame -> (Int, Int)
dimensions = dataframeDimensions
{-# INLINE dimensions #-}
{- | O(1) Get number of rows in a dataframe.
==== __Example__
@
>>> :set -XOverloadedStrings
>>> import qualified DataFrame as D
>>> df = D.fromNamedColumns [("a", D.fromList [1..100]), ("b", D.fromList [1..100]), ("c", D.fromList [1..100])]
>>> D.nRows df
100
@
-}
nRows :: DataFrame -> Int
nRows = fst . dataframeDimensions
{- | O(1) Get number of columns in a dataframe.
==== __Example__
@
>>> :set -XOverloadedStrings
>>> import qualified DataFrame as D
>>> df = D.fromNamedColumns [("a", D.fromList [1..100]), ("b", D.fromList [1..100]), ("c", D.fromList [1..100])]
>>> D.nColumns df
3
@
-}
nColumns :: DataFrame -> Int
nColumns = snd . dataframeDimensions
{- | O(k) Get column names of the DataFrame in order of insertion.
==== __Example__
@
>>> :set -XOverloadedStrings
>>> import qualified DataFrame as D
>>> df = D.fromNamedColumns [("a", D.fromList [1..100]), ("b", D.fromList [1..100]), ("c", D.fromList [1..100])]
>>> D.columnNames df
["a", "b", "c"]
@
-}
columnNames :: DataFrame -> [T.Text]
columnNames = map fst . L.sortBy (compare `on` snd) . M.toList . columnIndices
{-# INLINE columnNames #-}
{- | Adds a vector to the dataframe. If the vector has less elements than the dataframe and the dataframe is not empty
the vector is converted to type `Maybe a` filled with `Nothing` to match the size of the dataframe. Similarly,
if the vector has more elements than what's currently in the dataframe, the other columns in the dataframe are
change to `Maybe <Type>` and filled with `Nothing`.
==== __Example__
@
>>> :set -XOverloadedStrings
>>> import qualified DataFrame as D
>>> import qualified Data.Vector as V
>>> D.insertVector "numbers" (V.fromList [(1 :: Int)..10]) D.empty
--------
numbers
--------
Int
--------
1
2
3
4
5
6
7
8
9
10
@
-}
insertVector ::
forall a.
(Columnable a) =>
-- | Column Name
T.Text ->
-- | Vector to add to column
V.Vector a ->
-- | DataFrame to add column to
DataFrame ->
DataFrame
insertVector name xs = insertColumn name (fromVector xs)
{-# INLINE insertVector #-}
{- | Adds a foldable collection to the dataframe. If the collection has less elements than the
dataframe and the dataframe is not empty
the collection is converted to type `Maybe a` filled with `Nothing` to match the size of the dataframe. Similarly,
if the collection has more elements than what's currently in the dataframe, the other columns in the dataframe are
change to `Maybe <Type>` and filled with `Nothing`.
Be careful not to insert infinite collections with this function as that will crash the program.
==== __Example__
@
>>> :set -XOverloadedStrings
>>> import qualified DataFrame as D
>>> D.insert "numbers" [(1 :: Int)..10] D.empty
--------
numbers
--------
Int
--------
1
2
3
4
5
6
7
8
9
10
@
-}
insert ::
forall a t.
(Columnable a, Foldable t) =>
-- | Column Name
T.Text ->
-- | Sequence to add to dataframe
t a ->
-- | DataFrame to add column to
DataFrame ->
DataFrame
insert name xs = insertColumn name (fromList (Fold.foldr' (:) [] xs)) -- TODO: Do reflection on container type so we can sometimes avoid the list construction.
{-# INLINE insert #-}
{- | Adds a vector to the dataframe and pads it with a default value if it has less elements than the number of rows.
==== __Example__
@
>>> :set -XOverloadedStrings
>>> import qualified Data.Vector as V
>>> import qualified DataFrame as D
>>> df = D.fromNamedColumns [("x", D.fromList [(1 :: Int)..10])]
>>> D.insertVectorWithDefault 0 "numbers" (V.fromList [(1 :: Int),2,3]) df
-------------
x | numbers
----|--------
Int | Int
----|--------
1 | 1
2 | 2
3 | 3
4 | 0
5 | 0
6 | 0
7 | 0
8 | 0
9 | 0
10 | 0
@
-}
insertVectorWithDefault ::
forall a.
(Columnable a) =>
-- | Default Value
a ->
-- | Column name
T.Text ->
-- | Data to add to column
V.Vector a ->
-- | DataFrame to add the column to
DataFrame ->
DataFrame
insertVectorWithDefault defaultValue name xs d =
let (rows, _) = dataframeDimensions d
values = xs V.++ V.replicate (rows - V.length xs) defaultValue
in insertColumn name (fromVector values) d
{- | Adds a list to the dataframe and pads it with a default value if it has less elements than the number of rows.
==== __Example__
@
>>> :set -XOverloadedStrings
>>> import qualified DataFrame as D
>>> df = D.fromNamedColumns [("x", D.fromList [(1 :: Int)..10])]
>>> D.insertWithDefault 0 "numbers" [(1 :: Int),2,3] df
-------------
x | numbers
----|--------
Int | Int
----|--------
1 | 1
2 | 2
3 | 3
4 | 0
5 | 0
6 | 0
7 | 0
8 | 0
9 | 0
10 | 0
@
-}
insertWithDefault ::
forall a t.
(Columnable a, Foldable t) =>
-- | Default Value
a ->
-- | Column name
T.Text ->
-- | Data to add to column
t a ->
-- | DataFrame to add the column to
DataFrame ->
DataFrame
insertWithDefault defaultValue name xs d =
let (rows, _) = dataframeDimensions d
xs' = Fold.foldr' (:) [] xs
values = xs' ++ replicate (rows - length xs') defaultValue
in insertColumn name (fromList values) d
{- | /O(n)/ Adds an unboxed vector to the dataframe.
Same as insertVector but takes an unboxed vector. If you insert a vector of numbers through insertVector it will either way be converted
into an unboxed vector so this function saves that extra work/conversion.
-}
insertUnboxedVector ::
forall a.
(Columnable a, VU.Unbox a) =>
-- | Column Name
T.Text ->
-- | Unboxed vector to add to column
VU.Vector a ->
-- | DataFrame to add the column to
DataFrame ->
DataFrame
insertUnboxedVector name xs = insertColumn name (UnboxedColumn xs)
{- | /O(n)/ Add a column to the dataframe.
==== __Example__
@
>>> :set -XOverloadedStrings
>>> import qualified DataFrame as D
>>> D.insertColumn "numbers" (D.fromList [(1 :: Int)..10]) D.empty
--------
numbers
--------
Int
--------
1
2
3
4
5
6
7
8
9
10
@
-}
insertColumn ::
-- | Column Name
T.Text ->
-- | Column to add
Column ->
-- | DataFrame to add the column to
DataFrame ->
DataFrame
insertColumn name column d =
let
(r, c) = dataframeDimensions d
n = max (columnLength column) r
exprs = M.delete name (derivingExpressions d)
in
case M.lookup name (columnIndices d) of
Just i ->
DataFrame
(V.map (expandColumn n) (columns d V.// [(i, column)]))
(columnIndices d)
(n, c)
exprs
Nothing ->
DataFrame
(V.map (expandColumn n) (columns d `V.snoc` column))
(M.insert name c (columnIndices d))
(n, c + 1)
exprs
{- | /O(n)/ Clones a column and places it under a new name in the dataframe.
==== __Example__
@
>>> :set -XOverloadedStrings
>>> import qualified Data.Vector as V
>>> df = insertVector "numbers" (V.fromList [1..10]) D.empty
>>> D.cloneColumn "numbers" "others" df
-----------------
numbers | others
---------|-------
Int | Int
---------|-------
1 | 1
2 | 2
3 | 3
4 | 4
5 | 5
6 | 6
7 | 7
8 | 8
9 | 9
10 | 10
@
-}
cloneColumn :: T.Text -> T.Text -> DataFrame -> DataFrame
cloneColumn original new df = fromMaybe
( throw $
ColumnNotFoundException original "cloneColumn" (M.keys $ columnIndices df)
)
$ do
column <- getColumn original df
return $ insertColumn new column df
{- | /O(n)/ Renames a single column.
==== __Example__
@
>>> :set -XOverloadedStrings
>>> import qualified DataFrame as D
>>> import qualified Data.Vector as V
>>> df = insertVector "numbers" (V.fromList [1..10]) D.empty
>>> D.rename "numbers" "others" df
-------
others
-------
Int
-------
1
2
3
4
5
6
7
8
9
10
@
-}
rename :: T.Text -> T.Text -> DataFrame -> DataFrame
rename orig new df = either throw id (renameSafe orig new df)
{- | /O(n)/ Renames many columns.
==== __Example__
@
>>> :set -XOverloadedStrings
>>> import qualified DataFrame as D
>>> import qualified Data.Vector as V
>>> df = D.insertVector "others" (V.fromList [11..20]) (D.insertVector "numbers" (V.fromList [1..10]) D.empty)
>>> df
-----------------
numbers | others
---------|-------
Int | Int
---------|-------
1 | 11
2 | 12
3 | 13
4 | 14
5 | 15
6 | 16
7 | 17
8 | 18
9 | 19
10 | 20
>>> D.renameMany [("numbers", "first_10"), ("others", "next_10")] df
-------------------
first_10 | next_10
----------|--------
Int | Int
----------|--------
1 | 11
2 | 12
3 | 13
4 | 14
5 | 15
6 | 16
7 | 17
8 | 18
9 | 19
10 | 20
@
-}
renameMany :: [(T.Text, T.Text)] -> DataFrame -> DataFrame
renameMany = fold (uncurry rename)
renameSafe ::
T.Text -> T.Text -> DataFrame -> Either DataFrameException DataFrame
renameSafe orig new df = fromMaybe
(Left $ ColumnNotFoundException orig "rename" (M.keys $ columnIndices df))
$ do
columnIndex <- M.lookup orig (columnIndices df)
let origRemoved = M.delete orig (columnIndices df)
let newAdded = M.insert new columnIndex origRemoved
return (Right df{columnIndices = newAdded})
data ColumnInfo = ColumnInfo
{ nameOfColumn :: !T.Text
, nonNullValues :: !Int
, nullValues :: !Int
, typeOfColumn :: !T.Text
}
{- | O(n * k ^ 2) Returns the number of non-null columns in the dataframe and the type associated with each column.
==== __Example__
@
>>> import qualified Data.Vector as V
>>> df = D.insertVector "others" (V.fromList [11..20]) (D.insertVector "numbers" (V.fromList [1..10]) D.empty)
>>> D.describeColumns df
--------------------------------------------------------
Column Name | # Non-null Values | # Null Values | Type
-------------|-------------------|---------------|-----
Text | Int | Int | Text
-------------|-------------------|---------------|-----
others | 10 | 0 | Int
numbers | 10 | 0 | Int
@
-}
describeColumns :: DataFrame -> DataFrame
describeColumns df =
empty
& insertColumn "Column Name" (fromList (map nameOfColumn infos))
& insertColumn "# Non-null Values" (fromList (map nonNullValues infos))
& insertColumn "# Null Values" (fromList (map nullValues infos))
& insertColumn "Type" (fromList (map typeOfColumn infos))
where
infos =
L.sortBy (compare `on` nonNullValues) (V.ifoldl' go [] (columns df)) ::
[ColumnInfo]
indexMap = M.fromList (map (\(a, b) -> (b, a)) $ M.toList (columnIndices df))
columnName i = M.lookup i indexMap
go acc i col@(OptionalColumn (c :: V.Vector a)) =
let
cname = columnName i
countNulls = nulls col
columnType = T.pack $ show $ typeRep @a
in
if isNothing cname
then acc
else
ColumnInfo
(fromMaybe "" cname)
(columnLength col - countNulls)
countNulls
columnType
: acc
go acc i col@(BoxedColumn (c :: V.Vector a)) =
let
cname = columnName i
columnType = T.pack $ show $ typeRep @a
in
if isNothing cname
then acc
else
ColumnInfo
(fromMaybe "" cname)
(columnLength col)
0
columnType
: acc
go acc i col@(UnboxedColumn c) =
let
cname = columnName i
columnType = T.pack $ columnTypeString col
in
-- Unboxed columns cannot have nulls since Maybe
-- is not an instance of Unbox a
if isNothing cname
then acc
else
ColumnInfo (fromMaybe "" cname) (columnLength col) 0 columnType : acc
nulls :: Column -> Int
nulls (OptionalColumn xs) = VG.length $ VG.filter isNothing xs
nulls (BoxedColumn (xs :: V.Vector a)) = case testEquality (typeRep @a) (typeRep @T.Text) of
Just Refl -> VG.length $ VG.filter isNullish xs
Nothing -> case testEquality (typeRep @a) (typeRep @String) of
Just Refl -> VG.length $ VG.filter (isNullish . T.pack) xs
Nothing -> case typeRep @a of
App t1 t2 -> case eqTypeRep t1 (typeRep @Maybe) of
Just HRefl -> VG.length $ VG.filter isNothing xs
Nothing -> 0
_ -> 0
nulls _ = 0
partiallyParsed :: Column -> Int
partiallyParsed (BoxedColumn (xs :: V.Vector a)) =
case typeRep @a of
App (App tycon t1) t2 -> case eqTypeRep tycon (typeRep @Either) of
Just HRefl -> VG.length $ VG.filter isLeft xs
Nothing -> 0
_ -> 0
partiallyParsed _ = 0
{- | Creates a dataframe from a list of tuples with name and column.
==== __Example__
@
>>> df = D.fromNamedColumns [("numbers", D.fromList [1..10]), ("others", D.fromList [11..20])]
>>> df
-----------------
numbers | others
---------|-------
Int | Int
---------|-------
1 | 11
2 | 12
3 | 13
4 | 14
5 | 15
6 | 16
7 | 17
8 | 18
9 | 19
10 | 20
@
-}
fromNamedColumns :: [(T.Text, Column)] -> DataFrame
fromNamedColumns = L.foldl' (\df (name, column) -> insertColumn name column df) empty
{- | Create a dataframe from a list of columns. The column names are "0", "1"... etc.
Useful for quick exploration but you should probably always rename the columns after
or drop the ones you don't want.
==== __Example__
@
>>> df = D.fromUnnamedColumns [D.fromList [1..10], D.fromList [11..20]]
>>> df
-----------------
0 | 1
-----|----
Int | Int
-----|----
1 | 11
2 | 12
3 | 13
4 | 14
5 | 15
6 | 16
7 | 17
8 | 18
9 | 19
10 | 20
@
-}
fromUnnamedColumns :: [Column] -> DataFrame
fromUnnamedColumns = fromNamedColumns . zip (map (T.pack . show) [0 ..])
{- | Create a dataframe from a list of column names and rows.
==== __Example__
@
>>> df = D.fromRows ["A", "B"] [[D.toAny 1, D.toAny 11], [D.toAny 2, D.toAny 12], [D.toAny 3, D.toAny 13]]
>>> df
----------
A | B
-----|----
Int | Int
-----|----
1 | 11
2 | 12
3 | 13
@
-}
fromRows :: [T.Text] -> [[Any]] -> DataFrame
fromRows names rows =
L.foldl'
(\df i -> insertColumn (names !! i) (mkColumnFromRow i rows) df)
empty
[0 .. length names - 1]
{- | O (k * n) Counts the occurences of each value in a given column.
==== __Example__
@
>>> df = D.fromUnnamedColumns [D.fromList [1..10], D.fromList [11..20]]
>>> D.valueCounts @Int "0" df
[(1,1),(2,1),(3,1),(4,1),(5,1),(6,1),(7,1),(8,1),(9,1),(10,1)]
@
-}
valueCounts ::
forall a. (Ord a, Columnable a) => Expr a -> DataFrame -> [(a, Int)]
valueCounts expr df = case columnAsVector expr df of
Left e -> throw e
Right column' ->
let
column = V.foldl' (\m v -> MS.insertWith (+) v (1 :: Int) m) M.empty column'
in
M.toAscList column
{- | O (k * n) Shows the proportions of each value in a given column.
==== __Example__
@
>>> df = D.fromUnnamedColumns [D.fromList [1..10], D.fromList [11..20]]
>>> D.valueCounts @Int "0" df
[(1,0.1),(2,0.1),(3,0.1),(4,0.1),(5,0.1),(6,0.1),(7,0.1),(8,0.1),(9,0.1),(10,0.1)]
@
-}
valueProportions ::
forall a. (Ord a, Columnable a) => Expr a -> DataFrame -> [(a, Double)]
valueProportions expr df = case columnAsVector expr df of
Left e -> throw e
Right column' ->
let
counts =
M.toAscList
(V.foldl' (\m v -> MS.insertWith (+) v (1 :: Int) m) M.empty column')
total = fromIntegral (sum (map snd counts))
in
map (fmap ((/ total) . fromIntegral)) counts
{- | A left fold for dataframes that takes the dataframe as the last object.
This makes it easier to chain operations.
==== __Example__
@
>>> df = D.fromNamedColumns [("x", D.fromList [1..100]), ("y", D.fromList [11..110])]
>>> D.fold D.dropLast [1..5] df
---------
x | y
----|----
Int | Int
----|----
1 | 11
2 | 12
3 | 13
4 | 14
5 | 15
6 | 16
7 | 17
8 | 18
9 | 19
10 | 20
11 | 21
12 | 22
13 | 23
14 | 24
15 | 25
16 | 26
17 | 27
18 | 28
19 | 29
20 | 30
Showing 20 rows out of 85
@
-}
fold :: (a -> DataFrame -> DataFrame) -> [a] -> DataFrame -> DataFrame
fold f xs acc = L.foldl' (flip f) acc xs
{- | Returns a dataframe as a two dimensional vector of floats.
Converts all columns in the dataframe to float vectors and transposes them
into a row-major matrix representation.
This is useful for handing data over into ML systems.
Returns 'Left' with an error if any column cannot be converted to floats.
-}
toFloatMatrix ::
DataFrame -> Either DataFrameException (V.Vector (VU.Vector Float))
toFloatMatrix df = case V.foldl'
(\acc c -> V.snoc <$> acc <*> toFloatVector c)
(Right V.empty :: Either DataFrameException (V.Vector (VU.Vector Float)))
(columns df) of
Left e -> Left e
Right m ->
pure $
V.generate
(fst (dataframeDimensions df))
( \i ->
foldl
(\acc j -> acc `VU.snoc` ((m VG.! j) VG.! i))
VU.empty
[0 .. (V.length m - 1)]
)
{- | Returns a dataframe as a two dimensional vector of doubles.
Converts all columns in the dataframe to double vectors and transposes them
into a row-major matrix representation.
This is useful for handing data over into ML systems.
Returns 'Left' with an error if any column cannot be converted to doubles.
-}
toDoubleMatrix ::
DataFrame -> Either DataFrameException (V.Vector (VU.Vector Double))
toDoubleMatrix df = case V.foldl'
(\acc c -> V.snoc <$> acc <*> toDoubleVector c)
(Right V.empty :: Either DataFrameException (V.Vector (VU.Vector Double)))
(columns df) of
Left e -> Left e
Right m ->
pure $
V.generate
(fst (dataframeDimensions df))
( \i ->
foldl
(\acc j -> acc `VU.snoc` ((m VG.! j) VG.! i))
VU.empty
[0 .. (V.length m - 1)]
)
{- | Returns a dataframe as a two dimensional vector of ints.
Converts all columns in the dataframe to int vectors and transposes them
into a row-major matrix representation.
This is useful for handing data over into ML systems.
Returns 'Left' with an error if any column cannot be converted to ints.
-}
toIntMatrix :: DataFrame -> Either DataFrameException (V.Vector (VU.Vector Int))
toIntMatrix df = case V.foldl'
(\acc c -> V.snoc <$> acc <*> toIntVector c)
(Right V.empty :: Either DataFrameException (V.Vector (VU.Vector Int)))
(columns df) of
Left e -> Left e
Right m ->
pure $
V.generate
(fst (dataframeDimensions df))
( \i ->
foldl
(\acc j -> acc `VU.snoc` ((m VG.! j) VG.! i))
VU.empty
[0 .. (V.length m - 1)]
)
{- | Get a specific column as a vector.
You must specify the type via type applications.
==== __Examples__
>>> columnAsVector (F.col @Int "age") df
Right [25, 30, 35, ...]
>>> columnAsVector (F.col @Text "name") df
Right ["Alice", "Bob", "Charlie", ...]
-}
columnAsVector ::
forall a.
(Columnable a) => Expr a -> DataFrame -> Either DataFrameException (V.Vector a)
columnAsVector (Col name) df = case getColumn name df of
Just col -> toVector col
Nothing ->
Left $ ColumnNotFoundException name "columnAsVector" (M.keys $ columnIndices df)
columnAsVector expr df = case interpret df expr of
Left e -> throw e
Right (TColumn col) -> toVector col
{- | Retrieves a column as an unboxed vector of 'Int' values.
Returns 'Left' with a 'DataFrameException' if the column cannot be converted to ints.
This may occur if the column contains non-numeric data or values outside the 'Int' range.
-}
columnAsIntVector ::
(Columnable a, Num a) =>
Expr a -> DataFrame -> Either DataFrameException (VU.Vector Int)
columnAsIntVector (Col name) df = case getColumn name df of
Just col -> toIntVector col
Nothing ->
Left $
ColumnNotFoundException name "columnAsIntVector" (M.keys $ columnIndices df)
columnAsIntVector expr df = case interpret df expr of
Left e -> throw e
Right (TColumn col) -> toIntVector col
{- | Retrieves a column as an unboxed vector of 'Double' values.
Returns 'Left' with a 'DataFrameException' if the column cannot be converted to doubles.
This may occur if the column contains non-numeric data.
-}
columnAsDoubleVector ::
(Columnable a, Num a) =>
Expr a -> DataFrame -> Either DataFrameException (VU.Vector Double)
columnAsDoubleVector (Col name) df = case getColumn name df of
Just col -> toDoubleVector col
Nothing ->
Left $
ColumnNotFoundException name "columnAsDoubleVector" (M.keys $ columnIndices df)
columnAsDoubleVector expr df = case interpret df expr of
Left e -> throw e
Right (TColumn col) -> toDoubleVector col
{- | Retrieves a column as an unboxed vector of 'Float' values.
Returns 'Left' with a 'DataFrameException' if the column cannot be converted to floats.
This may occur if the column contains non-numeric data.
-}
columnAsFloatVector ::
(Columnable a, Num a) =>
Expr a -> DataFrame -> Either DataFrameException (VU.Vector Float)
columnAsFloatVector (Col name) df = case getColumn name df of
Just col -> toFloatVector col
Nothing ->
Left $
ColumnNotFoundException name "columnAsFloatVector" (M.keys $ columnIndices df)
columnAsFloatVector expr df = case interpret df expr of
Left e -> throw e
Right (TColumn col) -> toFloatVector col
columnAsUnboxedVector ::
forall a.
(Columnable a, VU.Unbox a) =>
Expr a -> DataFrame -> Either DataFrameException (VU.Vector a)
columnAsUnboxedVector (Col name) df = case getColumn name df of
Just col -> toUnboxedVector col
Nothing ->
Left $
ColumnNotFoundException name "columnAsFloatVector" (M.keys $ columnIndices df)
columnAsUnboxedVector expr df = case interpret df expr of
Left e -> throw e
Right (TColumn col) -> toUnboxedVector col
{-# SPECIALIZE columnAsUnboxedVector ::
Expr Double -> DataFrame -> Either DataFrameException (VU.Vector Double)
#-}
{-# INLINE columnAsUnboxedVector #-}
{- | Get a specific column as a list.
You must specify the type via type applications.
==== __Examples__
>>> columnAsList @Int "age" df
[25, 30, 35, ...]
>>> columnAsList @Text "name" df
["Alice", "Bob", "Charlie", ...]
==== __Throws__
* 'error' - if the column type doesn't match the requested type
-}
columnAsList :: forall a. (Columnable a) => Expr a -> DataFrame -> [a]
columnAsList expr df = either throw V.toList (columnAsVector expr df)
{- | Returns the provenance of all columns in the DataFrame as a list of
@(name, expression)@ pairs. Derived columns show their expression;
raw columns show an identity @col \@type name@ expression.
-}
showDerivedExpressions :: DataFrame -> [NamedExpr]
showDerivedExpressions df =
let exprs = derivingExpressions df
names = columnNames df
toNamedExpr name = case M.lookup name exprs of
Just uexpr -> (name, uexpr)
Nothing -> (name, identityUExpr name)
in map toNamedExpr names
where
identityUExpr name = case getColumn name df of
Just (BoxedColumn (_ :: V.Vector a)) -> UExpr (Col @a name)
Just (UnboxedColumn (_ :: VU.Vector a)) -> UExpr (Col @a name)
Just (OptionalColumn (_ :: V.Vector (Maybe a))) -> UExpr (Col @(Maybe a) name)
Nothing -> error $ "showDerivedExpressions: column not found: " ++ T.unpack name