this it?

Author: MarcoGorelli

demo.py

@@ -6,25 +6,24 @@
 
 
 def func(df_raw: Any) -> Any:
-    df = nw.NarwhalsFrame(df_raw)
-
-    print(df)
+    df = nw.DataFrame(df_raw)
     res = df.with_columns(
         d=nw.col("a") + 1,
         e=nw.col("a") + nw.col("b"),
     )
-
-    res = res.group_by("a").agg(nw.col("b").sum())
-    print(res)
-
+    res = res.group_by(["a"]).agg(
+        nw.col("b").sum(),
+        d=nw.col("c").sum(),
+        # e=nw.len(),
+    )
     return nw.to_native(res)
 
 
 import pandas as pd
 
-# df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
-# print(func(df))
-df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
+df = pd.DataFrame({"a": [1, 1, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
+print(func(df))
+df = pl.DataFrame({"a": [1, 1, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
 print(func(df))
-df = pl.LazyFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
+df = pl.LazyFrame({"a": [1, 1, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
 print(func(df).collect())

narwhals/__init__.py

@@ -3,7 +3,7 @@
 from narwhals.containers import is_pandas
 from narwhals.containers import is_polars
 from narwhals.containers import is_series
-from narwhals.dataframe import NarwhalsFrame
+from narwhals.dataframe import DataFrame
 from narwhals.expression import col
 from narwhals.expression import len
 from narwhals.translate import get_namespace
@@ -27,5 +27,5 @@
     "to_native",
     "col",
     "len",
-    "NarwhalsFrame",
+    "DataFrame",
 ]

narwhals/dataframe.py

@@ -1,12 +1,14 @@
 from __future__ import annotations
 
-from narwhals.pandas_like.utils import evaluate_into_exprs
+from narwhals.pandas_like.dataframe import PandasDataFrame
+from narwhals.polars import PolarsDataFrame
 from narwhals.translate import get_pandas
 from narwhals.translate import get_polars
 
 
 def extract_native(obj: Any, implementation) -> Any:
     from narwhals.expression import NarwhalsExpr
+    from narwhals.series import Series
 
     # if isinstance(obj, NarwhalsExpr):
     #     return obj._call(pl.col)
@@ -17,14 +19,16 @@ def extract_native(obj: Any, implementation) -> Any:
             return obj._call(pl.col)
     # if isinstance(obj, DType):
     #     return obj._dtype
-    if isinstance(obj, NarwhalsFrame):
+    if isinstance(obj, DataFrame):
         return obj._dataframe
+    if isinstance(obj, Series):
+        return obj._series
     # if isinstance(obj, PolarsSeries):
     #     return obj._series
     return obj
 
 
-class NarwhalsFrame:
+class DataFrame:
     def __init__(
         self, df, *, is_eager=False, is_lazy=False, implementation: str | None = None
     ):
@@ -35,24 +39,14 @@ def __init__(
             self._implementation = implementation
             return
         if (pl := get_polars()) is not None:
-            if isinstance(df, pl.DataFrame):
-                if is_lazy:
-                    raise ValueError(
-                        "can't instantiate with `is_lazy` if you pass a polars DataFrame"
-                    )
-                self._dataframe = df
-                self._implementation = "polars"
-                return
-            elif isinstance(df, pl.LazyFrame):
-                if is_eager:
-                    raise ValueError(
-                        "can't instantiate with `is_eager` if you pass a polars LazyFrame"
-                    )
-                self._dataframe = df
+            if isinstance(df, (pl.DataFrame, pl.LazyFrame)):
+                self._dataframe = PolarsDataFrame(df, is_eager=is_eager, is_lazy=is_lazy)
                 self._implementation = "polars"
                 return
         if (pd := get_pandas()) is not None and isinstance(df, pd.DataFrame):
-            self._dataframe = df
+            self._dataframe = PandasDataFrame(
+                df, is_eager=is_eager, is_lazy=is_lazy, implementation="pandas"
+            )
             self._implementation = "pandas"
             return
         raise TypeError(
@@ -68,73 +62,57 @@ def _from_dataframe(self, df: Any) -> Self:
             implementation=self._implementation,
         )
 
-    def _extract_native(self, obj):
-        return extract_native(obj, implementation=self._implementation)
-
     def with_columns(
         self, *exprs: IntoExpr | Iterable[IntoExpr], **named_exprs: IntoExpr
     ) -> Self:
-        if self._implementation == "polars":
-            return self._from_dataframe(
-                self._dataframe.with_columns(
-                    *[self._extract_native(v) for v in exprs],
-                    **{
-                        key: self._extract_native(value)
-                        for key, value in named_exprs.items()
-                    },
-                )
-            )
-        elif self._implementation == "pandas":
-            new_series = evaluate_into_exprs(self, *exprs, **named_exprs)
-            df = self._dataframe.assign(
-                **{series.name: series._series for series in new_series}
-            )
-            return self._from_dataframe(df)
+        return self._from_dataframe(
+            self._dataframe.with_columns(*exprs, **named_exprs),
+        )
 
     def filter(self, *predicates: IntoExpr | Iterable[IntoExpr]) -> Self:
         return self._from_dataframe(
-            self._dataframe.filter(*[self._extract_native(v) for v in predicates])
+            self._dataframe.filter(*predicates),
         )
 
     def group_by(self, *keys: str | Iterable[str]) -> GroupBy:
         from narwhals.group_by import NarwhalsGroupBy
 
-        return NarwhalsGroupBy(
-            self,
-            *keys,
-            is_eager=self._is_eager,
-            is_lazy=self._is_lazy,
-        )
+        return NarwhalsGroupBy(self, *keys)
 
     def sort(
         self,
         by: str | Iterable[str],
         *more_by: str,
         descending: bool | Sequence[bool] = False,
     ) -> Self:
-        if self._implementation == "polars":
-            return self._from_dataframe(
-                self._dataframe.sort(by, *more_by, descending=descending)
-            )
+        return self._from_dataframe(
+            self._dataframe.sort(by, *more_by, descending=descending)
+        )
 
     def collect(self) -> Self:
-        if not self._is_lazy:
-            raise RuntimeError(
-                "DataFrame.collect can only be called if frame was instantiated with `is_lazy=True`"
-            )
-        if self._implementation == "polars":
-            import polars as pl
-
-            assert isinstance(self._dataframe, pl.LazyFrame)
-            return self.__class__(self._dataframe.collect(), is_eager=True, is_lazy=False)
+        return self.__class__(
+            self._dataframe.collect(),
+            is_eager=True,
+            is_lazy=False,
+            implementation=self._implementation,
+        )
 
     def to_dict(self, *, as_series: bool = True) -> dict[str, Any]:
-        if not self._is_eager:
-            raise RuntimeError(
-                "DataFrame.to_dict can only be called if frame was instantiated with `is_eager=True`"
-            )
-        if self._implementation == "polars":
-            import polars as pl
+        return self._dataframe.to_dict(as_series=as_series)
 
-            assert isinstance(self._dataframe, pl.DataFrame)
-            return self._dataframe.to_dict(as_series=as_series)
+    def join(
+        self,
+        other: Self,
+        *,
+        how: Literal[inner] = "inner",
+        left_on: str | list[str],
+        right_on: str | list[str],
+    ) -> Self:
+        return self._from_dataframe(
+            self._dataframe.join(
+                other._dataframe,
+                how=how,
+                left_on=left_on,
+                right_on=right_on,
+            )
+        )

narwhals/expression.py

@@ -2,8 +2,6 @@
 
 from typing import Any
 
-from narwhals.translate import get_polars
-
 
 def extract_native(expr, other: Any) -> Any:
     if isinstance(other, NarwhalsExpr):
@@ -97,46 +95,46 @@ def sum(self) -> Expr:
         return self.__class__(lambda expr: self._call(expr).sum())
 
     def min(self) -> Expr:
-        return self.__class__(self._expr.min())
+        return self.__class__(lambda expr: self._call(expr).min())
 
     def max(self) -> Expr:
-        return self.__class__(self._expr.max())
+        return self.__class__(lambda expr: self._call(expr).max())
 
     def n_unique(self) -> Expr:
-        return self.__class__(self._expr.n_unique())
+        return self.__class__(lambda expr: self._call(expr).n_unique())
 
     def unique(self) -> Expr:
-        return self.__class__(self._expr.unique())
+        return self.__class__(lambda expr: self._call(expr).unique())
 
     # --- transform ---
     def is_between(
         self, lower_bound: Any, upper_bound: Any, closed: str = "both"
     ) -> Expr:
-        return self.__class__(self._expr.is_between(lower_bound, upper_bound, closed))  # type: ignore[arg-type]
+        return self.__class__(
+            lambda expr: self._call(expr).is_between(lower_bound, upper_bound, closed)
+        )  # type: ignore[arg-type]
 
     def is_in(self, other: Any) -> Expr:
-        return self.__class__(self._expr.is_in(other))
+        return self.__class__(lambda expr: self._call(expr).is_in(other))
 
     def is_null(self) -> Expr:
-        return self.__class__(self._expr.is_null())
+        return self.__class__(lambda expr: self._call(expr).is_null())
 
     # --- partial reduction ---
     def drop_nulls(self) -> Expr:
-        return self.__class__(self._expr.drop_nulls())
+        return self.__class__(lambda expr: self._call(expr).drop_nulls())
 
     def sample(self, n: int, fraction: float, *, with_replacement: bool) -> Expr:
         return self.__class__(
-            self._expr.sample(n, fraction=fraction, with_replacement=with_replacement)
+            lambda expr: self._call(expr).sample(
+                n, fraction=fraction, with_replacement=with_replacement
+            )
         )
 
 
 def col(col_name: str):
-    return NarwhalsExpr(lambda expr: expr(col_name))
+    return NarwhalsExpr(lambda plx: plx.col(col_name))
 
 
 def len():
-    def func(expr):
-        if (pl := get_polars()) is not None and issubclass(expr, pl.col):
-            return pl.len()
-
-    return NarwhalsExpr(func)
+    return NarwhalsExpr(lambda plx: plx.len())

narwhals/pandas_like/utils.py

@@ -105,7 +105,7 @@ def parse_into_expr(implementation: str, into_expr: IntoExpr) -> Expr:
     plx = Namespace(implementation=implementation)
 
     if isinstance(into_expr, NarwhalsExpr):
-        return into_expr._call(plx.col)
+        return into_expr._call(plx)
     if isinstance(into_expr, str):
         return plx.col(into_expr)
     if isinstance(into_expr, Expr):

narwhals/polars.py

@@ -29,7 +29,7 @@ def extract_native(obj: Any) -> Any:
     from narwhals.expression import NarwhalsExpr
 
     if isinstance(obj, NarwhalsExpr):
-        return obj._call(pl.col)
+        return obj._call(pl)
     if isinstance(obj, Expr):
         return obj._expr
     if isinstance(obj, DType):

narwhals/translate.py

@@ -175,14 +175,14 @@ def get_namespace(obj: Any) -> Namespace:
 
 
 def to_native(obj: Any) -> Any:
-    from narwhals.dataframe import NarwhalsFrame
+    from narwhals.dataframe import DataFrame
     from narwhals.pandas_like.dataframe import PandasDataFrame
     from narwhals.pandas_like.series import PandasSeries
     from narwhals.polars import PolarsDataFrame
     from narwhals.polars import PolarsSeries
 
-    if isinstance(obj, NarwhalsFrame):
-        return obj._dataframe
+    if isinstance(obj, DataFrame):
+        return obj._dataframe._dataframe
     if isinstance(obj, PandasDataFrame):
         return obj._dataframe
     if isinstance(obj, PandasSeries):

tests/tpch_q1_test.py

@@ -13,13 +13,13 @@
 @pytest.mark.parametrize(
     "df_raw",
     [
-        # (polars.read_parquet("tests/data/lineitem.parquet").to_pandas()),
+        (polars.read_parquet("tests/data/lineitem.parquet").to_pandas()),
         polars.scan_parquet("tests/data/lineitem.parquet"),
     ],
 )
 def test_q1(df_raw: Any) -> None:
     var_1 = datetime(1998, 9, 2)
-    df = nw.NarwhalsFrame(df_raw, is_lazy=True)
+    df = nw.DataFrame(df_raw, is_lazy=True)
     query_result = (
         df.filter(nw.col("l_shipdate") <= var_1)
         .group_by(["l_returnflag", "l_linestatus"])