Add DataFrame.explode/2

lib/explorer/backend/data_frame.ex

@@ -195,6 +195,7 @@ defmodule Explorer.Backend.DataFrame do
   @callback put(df, out_df :: df(), column_name(), series()) :: df
   @callback describe(df, percentiles :: option(list(float()))) :: df()
   @callback nil_count(df) :: df()
+  @callback explode(df, out_df :: df(), columns :: [column_name()]) :: df()
 
   # Two or more table verbs
 

lib/explorer/data_frame.ex

@@ -5283,8 +5283,8 @@ defmodule Explorer.DataFrame do
           %Series{data: %LazySeries{aggregation: true}} ->
             value
 
-          %Series{data: %LazySeries{op: :column}} ->
-            value
+          %Series{data: %LazySeries{op: :column}} = value ->
+            %{value | dtype: {:list, value.dtype}}
 
           %Series{data: %LazySeries{}} = series ->
             raise "expecting summarise with an aggregation operation or plain column, " <>
@@ -5315,6 +5315,79 @@ defmodule Explorer.DataFrame do
     groups ++ names_with_dtypes
   end
 
+  @doc """
+  Explodes one or multiple list column into multiple rows.
+
+  When exploding multiple columns, the number of list elements in each row for
+  the exploded columns must be the same.
+
+  ## Examples
+
+      iex> df = Explorer.DataFrame.new(a: [[1, 2], [3, 4]], b: [[5, 6], [7, 8]], c: ["a", "b"])
+      iex> Explorer.DataFrame.explode(df, :a)
+      #Explorer.DataFrame<
+        Polars[4 x 3]
+        a integer [1, 2, 3, 4]
+        b list[integer] [[5, 6], [5, 6], [7, 8], [7, ...]]
+        c string ["a", "a", "b", "b"]
+      >
+      iex> Explorer.DataFrame.explode(df, [:a, :b])
+      #Explorer.DataFrame<
+        Polars[4 x 3]
+        a integer [1, 2, 3, 4]
+        b integer [5, 6, 7, 8]
+        c string ["a", "a", "b", "b"]
+      >
+
+  You can think of exploding multiple list columns as being the inverse of
+  aggregating the elements the exploded columns into lists:
+
+      iex> df = Explorer.DataFrame.new(a: [1, 2, 3, 4], b: [5, 6, 7, 8], c: ["a", "a", "b", "b"])
+      iex> df = df |> Explorer.DataFrame.group_by(:c) |> Explorer.DataFrame.summarise(a: a, b: b)
+      #Explorer.DataFrame<
+        Polars[2 x 3]
+        c string ["a", "b"]
+        a list[integer] [[1, 2], [3, 4]]
+        b list[integer] [[5, 6], [7, 8]]
+      >
+      iex> Explorer.DataFrame.explode(df, [:a, :b]) # we are back where we started
+      #Explorer.DataFrame<
+        Polars[4 x 3]
+        c string ["a", "a", "b", "b"]
+        a integer [1, 2, 3, 4]
+        b integer [5, 6, 7, 8]
+      >
+
+  If you want to perform the cartesian product of two list columns, you must
+  call `explode/2` once for each column:
+
+      iex> df = Explorer.DataFrame.new(a: [[1, 2], [3, 4]], b: [[5, 6], [7, 8]], c: ["a", "b"])
+      iex> df |> Explorer.DataFrame.explode(:a) |> Explorer.DataFrame.explode(:b)
+      #Explorer.DataFrame<
+        Polars[8 x 3]
+        a integer [1, 1, 2, 2, 3, ...]
+        b integer [5, 6, 5, 6, 7, ...]
+        c string ["a", "a", "a", "a", "b", ...]
+      >
+  """
+  @doc type: :single
+  @spec explode(df :: DataFrame.t(), column :: column_name() | [column_name()]) :: DataFrame.t()
+  def explode(%DataFrame{} = df, column_or_columns) do
+    columns = to_existing_columns(df, List.wrap(column_or_columns))
+
+    out_dtypes =
+      Enum.reduce(columns, df.dtypes, fn column, dtypes ->
+        Map.update!(dtypes, column, fn
+          {:list, inner_dtype} -> inner_dtype
+          dtype -> dtype
+        end)
+      end)
+
+    out_df = %{df | dtypes: out_dtypes}
+
+    Shared.apply_impl(df, :explode, [out_df, columns])
+  end
+
   @doc """
   Prints the DataFrame in a tabular fashion.
 

lib/explorer/polars_backend/data_frame.ex

@@ -748,6 +748,11 @@ defmodule Explorer.PolarsBackend.DataFrame do
     |> Shared.create_dataframe()
   end
 
+  @impl true
+  def explode(df, out_df, columns) do
+    Shared.apply_dataframe(df, out_df, :df_explode, [columns])
+  end
+
   # Two or more table verbs
 
   @impl true

lib/explorer/polars_backend/lazy_frame.ex

@@ -413,6 +413,10 @@ defmodule Explorer.PolarsBackend.LazyFrame do
         values_to
       ])
 
+  @impl true
+  def explode(%DF{} = df, %DF{} = out_df, columns),
+    do: Shared.apply_dataframe(df, out_df, :lf_explode, [columns])
+
   # Groups
 
   @impl true

lib/explorer/polars_backend/native.ex

@@ -166,6 +166,7 @@ defmodule Explorer.PolarsBackend.Native do
   def df_width(_df), do: err()
   def df_describe(_df, _percentiles), do: err()
   def df_nil_count(_df), do: err()
+  def df_explode(_df, _columns), do: err()
 
   # Expressions (for lazy queries)
   @multi_arity_expressions [slice: 2, slice: 3, log: 1, log: 2]
@@ -207,6 +208,7 @@ defmodule Explorer.PolarsBackend.Native do
   def lf_select(_df, _columns), do: err()
   def lf_tail(_df, _n_rows), do: err()
   def lf_slice(_df, _offset, _length), do: err()
+  def lf_explode(_df, _columns), do: err()
   def lf_from_ipc(_filename), do: err()
   def lf_from_ndjson(_filename, _infer_schema_length, _batch_size), do: err()
   def lf_from_parquet(_filename, _stop_after_n_rows, _maybe_columns), do: err()

native/explorer/src/dataframe.rs

@@ -652,6 +652,12 @@ pub fn df_pivot_wider(
     Ok(ExDataFrame::new(new_df))
 }
 
+#[rustler::nif(schedule = "DirtyCpu")]
+pub fn df_explode(df: ExDataFrame, columns: Vec<&str>) -> Result<ExDataFrame, ExplorerError> {
+    let new_df = df.explode(columns)?;
+    Ok(ExDataFrame::new(new_df))
+}
+
 #[rustler::nif(schedule = "DirtyCpu")]
 pub fn df_lazy(df: ExDataFrame) -> Result<ExLazyFrame, ExplorerError> {
     let new_lf = df.clone_inner().lazy();

native/explorer/src/lazyframe.rs

@@ -84,6 +84,12 @@ pub fn lf_slice(data: ExLazyFrame, offset: i64, length: u32) -> Result<ExLazyFra
     Ok(ExLazyFrame::new(lf.slice(offset, length)))
 }
 
+#[rustler::nif]
+pub fn lf_explode(data: ExLazyFrame, columns: Vec<&str>) -> Result<ExLazyFrame, ExplorerError> {
+    let lf = data.clone_inner().explode(columns);
+    Ok(ExLazyFrame::new(lf))
+}
+
 #[rustler::nif]
 pub fn lf_filter_with(data: ExLazyFrame, ex_expr: ExExpr) -> Result<ExLazyFrame, ExplorerError> {
     let ldf = data.clone_inner();

native/explorer/src/lib.rs

@@ -90,6 +90,7 @@ rustler::init!(
         df_dump_parquet,
         df_dump_ipc,
         df_dump_ipc_stream,
+        df_explode,
         df_filter_with,
         df_from_csv,
         df_from_ipc,
@@ -282,6 +283,7 @@ rustler::init!(
         lf_select,
         lf_tail,
         lf_slice,
+        lf_explode,
         lf_from_csv,
         lf_from_ipc,
         lf_from_parquet,

test/explorer/data_frame/lazy_test.exs

@@ -1403,4 +1403,18 @@ defmodule Explorer.DataFrame.LazyTest do
       assert Exception.message(error) =~ "syntax error"
     end
   end
+
+  describe "explode/2" do
+    test "explodes a list column" do
+      ldf = DF.new([letters: [~w(a e), ~w(b c d)], is_vowel: [true, false]], lazy: true)
+
+      ldf1 = DF.explode(ldf, :letters)
+      df1 = DF.collect(ldf1)
+
+      assert DF.to_columns(df1, atom_keys: true) == %{
+               letters: ["a", "e", "b", "c", "d"],
+               is_vowel: [true, true, false, false, false]
+             }
+    end
+  end
 end

test/explorer/data_frame_test.exs

@@ -3608,6 +3608,8 @@ defmodule Explorer.DataFrameTest do
                is_vowel: [true, false],
                letters: [["a", "e", "i"], ["b", "c", "d", "f", "g", "h", "j"]]
              }
+
+      assert DF.dtypes(df) == %{"is_vowel" => :boolean, "letters" => {:list, :string}}
     end
 
     test "mode/1" do
@@ -3702,4 +3704,31 @@ defmodule Explorer.DataFrameTest do
              }
     end
   end
+
+  describe "explode/2" do
+    test "explodes a list column" do
+      df = DF.new(letters: [~w(a e), ~w(b c d)], is_vowel: [true, false])
+
+      df1 = DF.explode(df, :letters)
+
+      assert DF.to_columns(df1, atom_keys: true) == %{
+               letters: ["a", "e", "b", "c", "d"],
+               is_vowel: [true, true, false, false, false]
+             }
+
+      assert DF.dtypes(df1) == %{"is_vowel" => :boolean, "letters" => :string}
+    end
+
+    test "works with multiple columns" do
+      df = DF.new(a: [[1, 2], [3, 4]], b: [[5, 6], [7, 8]], c: ["a", "b"])
+
+      df1 = DF.explode(df, [:a, :b])
+
+      assert DF.to_columns(df1, atom_keys: true) == %{
+               a: [1, 2, 3, 4],
+               b: [5, 6, 7, 8],
+               c: ["a", "a", "b", "b"]
+             }
+    end
+  end
 end