Repository: narwhals-dev/narwhals (python)

Issue:
bug: `select` and `with_columns` don't always preserve column index name for pandas-like
```python
import pandas as pd
# import narwhals.stable.v1 as nw
import narwhals as nw

df = pd.DataFrame({'a': [1,2,3], 'b': [4,5,6]})
df.columns.name = 'foo'

print(nw.from_native(df).select(c=nw.col('a')+1).to_native())
print(nw.from_native(df).with_columns(c=nw.col('a')+1).to_native())
print(nw.from_native(df).to_native())
print(nw.from_native(df).to_native().assign(c=lambda df: df['a']+1))
```

outputs
```python
   c
0  2
1  3
2  4
   a  b  c
0  1  4  2
1  2  5  3
2  3  6  4
foo  a  b
0    1  4
1    2  5
2    3  6
foo  a  b  c
0    1  4  2
1    2  5  3
2    3  6  4
```

expected:
```python
foo  c
0    2
1    3
2    4
foo  a  b  c
0    1  4  2
1    2  5  3
2    3  6  4
foo  a  b
0    1  4
1    2  5
2    3  6
foo  a  b  c
0    1  4  2
1    2  5  3
2    3  6  4
```

Pull request:
fix: Preserve pandas column name attribute
## What type of PR is this? (check all applicable)

- [ ] 💾 Refactor
- [ ] ✨ Feature
- [x] 🐛 Bug Fix
- [ ] 🔧 Optimization
- [ ] 📝 Documentation
- [ ] ✅ Test
- [ ] 🐳 Other

## Related issues

- Closes #1483 

## Checklist

- [x] Code follows style guide (ruff)
- [x] Tests added
- [x] Documented the changes


Patch:
diff --git a/docs/api-reference/expr.md b/docs/api-reference/expr.md
index e0f7b657..02e4e02a 100644
--- a/docs/api-reference/expr.md
+++ b/docs/api-reference/expr.md
@@ -36,6 +36,7 @@
         - is_null
         - is_unique
         - len
+        - log
         - map_batches
         - max
         - mean
diff --git a/docs/api-reference/series.md b/docs/api-reference/series.md
index 95a6539a..3a49856e 100644
--- a/docs/api-reference/series.md
+++ b/docs/api-reference/series.md
@@ -45,6 +45,7 @@
         - is_unique
         - item
         - len
+        - log
         - max
         - mean
         - median
diff --git a/docs/ecosystem.md b/docs/ecosystem.md
index 0bc7d0df..45a667b9 100644
--- a/docs/ecosystem.md
+++ b/docs/ecosystem.md
@@ -55,7 +55,7 @@ to various SQL (as well as Polars LazyFrame) engines. Some differences with Narw
 
 * Narwhals allows you to write "Dataframe X in, Dataframe X out" functions.
   Ibis allows materialising to pandas, Polars (eager), and PyArrow, but has
-  no way to get back to the input type exactly (e.g. `Enum` don't round-trip in Ibis)
+  no way to get back to the input type exactly (e.g. `Enum`s don't round-trip in Ibis)
 * Narwhals respects input data types as much as possible, Ibis doesn't
   support Categorical / Enum.
 * Narwhals separates between lazy and eager APIs, with the eager API
@@ -70,8 +70,6 @@ to various SQL (as well as Polars LazyFrame) engines. Some differences with Narw
 * Narwhals supports pandas and Dask, which Ibis has deprecated support for.
 
 Although people often ask about the two tools, we consider them to be
-very different and not in competition. Further efforts to clarify the
-distinction are welcome 🙏!
-
-Furthermore, [there is work is in progress](https://github.com/narwhals-dev/narwhals/pull/2536)
-to support `ibis.Table` in Narwhals!
+very different and not in competition. In particular,
+**Narwhals supports ibis Tables**, meaning that dataframe-agnostic code
+written using Narwhals' lazy API also supports Ibis.
diff --git a/narwhals/_arrow/expr.py b/narwhals/_arrow/expr.py
index 2f0134ea..0f494d10 100644
--- a/narwhals/_arrow/expr.py
+++ b/narwhals/_arrow/expr.py
@@ -210,4 +210,7 @@ class ArrowExpr(EagerExpr["ArrowDataFrame", ArrowSeries]):
     def rank(self, method: RankMethod, *, descending: bool) -> Self:
         return self._reuse_series("rank", method=method, descending=descending)
 
+    def log(self, base: float) -> Self:
+        return self._reuse_series("log", base=base)
+
     ewm_mean = not_implemented()
diff --git a/narwhals/_arrow/series.py b/narwhals/_arrow/series.py
index 83a478b5..44d5d13e 100644
--- a/narwhals/_arrow/series.py
+++ b/narwhals/_arrow/series.py
@@ -1148,6 +1148,9 @@ class ArrowSeries(EagerSeries["ChunkedArrayAny"]):
             msg = f"Unable to compare other of type {type(other)} with series of type {self.dtype}."
             raise InvalidOperationError(msg) from exc
 
+    def log(self, base: float) -> Self:
+        return self._with_native(pc.logb(self.native, lit(base)))
+
     @property
     def dt(self) -> ArrowSeriesDateTimeNamespace:
         return ArrowSeriesDateTimeNamespace(self)
diff --git a/narwhals/_compliant/expr.py b/narwhals/_compliant/expr.py
index 922a69c1..afe91d08 100644
--- a/narwhals/_compliant/expr.py
+++ b/narwhals/_compliant/expr.py
@@ -143,6 +143,7 @@ class CompliantExpr(Protocol38[CompliantFrameT, CompliantSeriesOrNativeExprT_co]
     def diff(self) -> Self: ...
     def unique(self) -> Self: ...
     def len(self) -> Self: ...
+    def log(self, base: float) -> Self: ...
     def round(self, decimals: int) -> Self: ...
     def mode(self) -> Self: ...
     def head(self, n: int) -> Self: ...
diff --git a/narwhals/_compliant/series.py b/narwhals/_compliant/series.py
index f1c94513..aebcfae8 100644
--- a/narwhals/_compliant/series.py
+++ b/narwhals/_compliant/series.py
@@ -200,6 +200,7 @@ class CompliantSeries(
     def is_unique(self) -> Self: ...
     def item(self, index: int | None) -> Any: ...
     def len(self) -> int: ...
+    def log(self, base: float) -> Self: ...
     def max(self) -> Any: ...
     def mean(self) -> float: ...
     def median(self) -> float: ...
diff --git a/narwhals/_dask/expr.py b/narwhals/_dask/expr.py
index 5b5cc1e2..8af4a1b5 100644
--- a/narwhals/_dask/expr.py
+++ b/narwhals/_dask/expr.py
@@ -663,6 +663,14 @@ class DaskExpr(
 
         return self._with_callable(da.isfinite, "is_finite")
 
+    def log(self, base: float) -> Self:
+        import dask.array as da
+
+        def _log(_input: dx.Series) -> dx.Series:
+            return da.log(_input) / da.log(base)
+
+        return self._with_callable(_log, "log")
+
     @property
     def str(self) -> DaskExprStringNamespace:
         return DaskExprStringNamespace(self)
diff --git a/narwhals/_duckdb/expr.py b/narwhals/_duckdb/expr.py
index ed0dabaa..40e40857 100644
--- a/narwhals/_duckdb/expr.py
+++ b/narwhals/_duckdb/expr.py
@@ -753,6 +753,17 @@ class DuckDBExpr(LazyExpr["DuckDBLazyFrame", "duckdb.Expression"]):
 
         return self._with_callable(_rank)
 
+    def log(self, base: float) -> Self:
+        def _log(_input: duckdb.Expression) -> duckdb.Expression:
+            log = FunctionExpression("log", _input)
+            return (
+                when(_input < lit(0), lit(float("nan")))
+                .when(_input == lit(0), lit(float("-inf")))
+                .otherwise(log / FunctionExpression("log", lit(base)))
+            )
+
+        return self._with_callable(_log)
+
     @property
     def str(self) -> DuckDBExprStringNamespace:
         return DuckDBExprStringNamespace(self)
diff --git a/narwhals/_ibis/expr.py b/narwhals/_ibis/expr.py
index 146ae68d..cfa45328 100644
--- a/narwhals/_ibis/expr.py
+++ b/narwhals/_ibis/expr.py
@@ -648,6 +648,17 @@ class IbisExpr(LazyExpr["IbisLazyFrame", "ir.Column"]):
 
         return self._with_callable(_rank)
 
+    def log(self, base: float) -> Self:
+        def _log(_input: ir.NumericColumn) -> ir.Value:
+            otherwise = _input.log(cast("ir.NumericValue", lit(base)))
+            return ibis.cases(
+                (_input < lit(0), lit(float("nan"))),
+                (_input == lit(0), lit(float("-inf"))),
+                else_=otherwise,
+            )
+
+        return self._with_callable(_log)
+
     @property
     def str(self) -> IbisExprStringNamespace:
         return IbisExprStringNamespace(self)
diff --git a/narwhals/_pandas_like/dataframe.py b/narwhals/_pandas_like/dataframe.py
index 37298d5d..3dd662ba 100644
--- a/narwhals/_pandas_like/dataframe.py
+++ b/narwhals/_pandas_like/dataframe.py
@@ -422,6 +422,8 @@ class PandasLikeDataFrame(
         new_series = align_series_full_broadcast(*new_series)
         namespace = self.__narwhals_namespace__()
         df = namespace._concat_horizontal([s.native for s in new_series])
+        # `concat` creates a new object, so fine to modify `.columns.name` inplace.
+        df.columns.name = self.native.columns.name
         return self._with_native(df, validate_column_names=True)
 
     def drop_nulls(
@@ -480,6 +482,8 @@ class PandasLikeDataFrame(
         to_concat.extend(self._extract_comparand(s) for s in name_columns.values())
         namespace = self.__narwhals_namespace__()
         df = namespace._concat_horizontal(to_concat)
+        # `concat` creates a new object, so fine to modify `.columns.name` inplace.
+        df.columns.name = self.native.columns.name
         return self._with_native(df, validate_column_names=False)
 
     def rename(self, mapping: Mapping[str, str]) -> Self:
diff --git a/narwhals/_pandas_like/expr.py b/narwhals/_pandas_like/expr.py
index 73bf1f2d..d862ffec 100644
--- a/narwhals/_pandas_like/expr.py
+++ b/narwhals/_pandas_like/expr.py
@@ -393,3 +393,6 @@ class PandasLikeExpr(EagerExpr["PandasLikeDataFrame", PandasLikeSeries]):
         return self._reuse_series(
             "rank", call_kwargs={"method": method, "descending": descending}
         )
+
+    def log(self, base: float) -> Self:
+        return self._reuse_series("log", call_kwargs={"base": base})
diff --git a/narwhals/_pandas_like/series.py b/narwhals/_pandas_like/series.py
index 005c1cfb..ea6b60a9 100644
--- a/narwhals/_pandas_like/series.py
+++ b/narwhals/_pandas_like/series.py
@@ -42,6 +42,7 @@ if TYPE_CHECKING:
     from typing_extensions import Self
     from typing_extensions import TypeIs
 
+    from narwhals._arrow.typing import ChunkedArrayAny
     from narwhals._pandas_like.dataframe import PandasLikeDataFrame
     from narwhals._pandas_like.namespace import PandasLikeNamespace
     from narwhals.dtypes import DType
@@ -1015,6 +1016,39 @@ class PandasLikeSeries(EagerSeries[Any]):
         data["count"] = result.reset_index(drop=True)
         return PandasLikeDataFrame.from_native(ns.DataFrame(data), context=self)
 
+    def log(self, base: float) -> Self:
+        native = self.native
+        implementation = self._implementation
+
+        dtype_backend = get_dtype_backend(native.dtype, implementation=implementation)
+
+        if implementation.is_cudf():
+            import cupy as cp
+
+            return self._with_native(cp.log(native) / cp.log(base))
+
+        if dtype_backend == "pyarrow":
+            import pyarrow.compute as pc
+
+            from narwhals._arrow.utils import native_to_narwhals_dtype
+
+            ca = native.array._pa_array
+            result_arr = cast("ChunkedArrayAny", pc.logb(ca, base))
+            nw_dtype = native_to_narwhals_dtype(result_arr.type, self._version)
+            out_dtype = narwhals_to_native_dtype(
+                nw_dtype,
+                "pyarrow",
+                self._implementation,
+                self._backend_version,
+                self._version,
+            )
+            result_native = native.__class__(
+                result_arr, dtype=out_dtype, index=native.index, name=native.name
+            )
+        else:
+            result_native = np.log(native) / np.log(base)
+        return self._with_native(result_native)
+
     @property
     def str(self) -> PandasLikeSeriesStringNamespace:
         return PandasLikeSeriesStringNamespace(self)
diff --git a/narwhals/_polars/expr.py b/narwhals/_polars/expr.py
index c46aede6..52a563de 100644
--- a/narwhals/_polars/expr.py
+++ b/narwhals/_polars/expr.py
@@ -290,6 +290,7 @@ class PolarsExpr:
     is_null: Method[Self]
     is_unique: Method[Self]
     len: Method[Self]
+    log: Method[Self]
     max: Method[Self]
     mean: Method[Self]
     median: Method[Self]
diff --git a/narwhals/_polars/series.py b/narwhals/_polars/series.py
index b75b2381..fd891240 100644
--- a/narwhals/_polars/series.py
+++ b/narwhals/_polars/series.py
@@ -95,6 +95,7 @@ INHERITED_METHODS = frozenset(
         "is_unique",
         "item",
         "len",
+        "log",
         "max",
         "mean",
         "min",
@@ -674,6 +675,7 @@ class PolarsSeries:
     is_unique: Method[Self]
     item: Method[Any]
     len: Method[int]
+    log: Method[Self]
     max: Method[Any]
     mean: Method[float]
     min: Method[Any]
diff --git a/narwhals/_spark_like/expr.py b/narwhals/_spark_like/expr.py
index c52bdd12..6d2d4a39 100644
--- a/narwhals/_spark_like/expr.py
+++ b/narwhals/_spark_like/expr.py
@@ -811,6 +811,16 @@ class SparkLikeExpr(LazyExpr["SparkLikeLazyFrame", "Column"]):
 
         return self._with_callable(_rank)
 
+    def log(self, base: float) -> Self:
+        def _log(_input: Column) -> Column:
+            return (
+                self._F.when(_input < 0, self._F.lit(float("nan")))
+                .when(_input == 0, self._F.lit(float("-inf")))
+                .otherwise(self._F.log(base, _input))
+            )
+
+        return self._with_callable(_log)
+
     @property
     def str(self) -> SparkLikeExprStringNamespace:
         return SparkLikeExprStringNamespace(self)
diff --git a/narwhals/expr.py b/narwhals/expr.py
index faee62dc..3db1d65e 100644
--- a/narwhals/expr.py
+++ b/narwhals/expr.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import math
 from typing import TYPE_CHECKING
 from typing import Any
 from typing import Callable
@@ -2482,6 +2483,41 @@ class Expr:
             )
         )
 
+    def log(self, base: float = math.e) -> Self:
+        r"""Compute the logarithm to a given base.
+
+        Arguments:
+            base: Given base, defaults to `e`
+
+        Returns:
+            A new expression log values data.
+
+        Examples:
+            >>> import pyarrow as pa
+            >>> import narwhals as nw
+            >>> df_native = pa.table({"values": [1, 2, 4]})
+            >>> df = nw.from_native(df_native)
+            >>> result = df.with_columns(
+            ...     log=nw.col("values").log(), log_2=nw.col("values").log(base=2)
+            ... )
+            >>> result
+            ┌────────────────────────────────────────────────┐
+            |               Narwhals DataFrame               |
+            |------------------------------------------------|
+            |pyarrow.Table                                   |
+            |values: int64                                   |
+            |log: double                                     |
+            |log_2: double                                   |
+            |----                                            |
+            |values: [[1,2,4]]                               |
+            |log: [[0,0.6931471805599453,1.3862943611198906]]|
+            |log_2: [[0,1,2]]                                |
+            └────────────────────────────────────────────────┘
+        """
+        return self._with_elementwise_op(
+            lambda plx: self._to_compliant_expr(plx).log(base=base)
+        )
+
     @property
     def str(self) -> ExprStringNamespace[Self]:
         return ExprStringNamespace(self)
diff --git a/narwhals/series.py b/narwhals/series.py
index ca85f2ba..a83afbb7 100644
--- a/narwhals/series.py
+++ b/narwhals/series.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import math
 from typing import TYPE_CHECKING
 from typing import Any
 from typing import Callable
@@ -2601,6 +2602,32 @@ class Series(Generic[IntoSeriesT]):
             level=self._level,
         )
 
+    def log(self, base: float = math.e) -> Self:
+        r"""Compute the logarithm to a given base.
+
+        Arguments:
+            base: Given base, defaults to `e`
+
+        Returns:
+            A new expression log values data.
+
+        Examples:
+            >>> import pandas as pd
+            >>> import narwhals as nw
+            >>> s_native = pd.Series([1, 2, 4], name="a")
+            >>> s = nw.from_native(s_native, series_only=True)
+            >>> s.log(base=2)
+            ┌───────────────────────┐
+            |    Narwhals Series    |
+            |-----------------------|
+            |0    0.0               |
+            |1    1.0               |
+            |2    2.0               |
+            |Name: a, dtype: float64|
+            └───────────────────────┘
+        """
+        return self._with_compliant(self._compliant_series.log(base=base))
+
     @property
     def str(self) -> SeriesStringNamespace[Self]:
         return SeriesStringNamespace(self)
diff --git a/pyproject.toml b/pyproject.toml
index 40d1e22a..c18f9c03 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -290,6 +290,7 @@ strict = true
 [[tool.mypy.overrides]]
 module = [
   "cudf.*",
+  "cupy.*",
   "dask.*",
   "dask_expr.*",
   "duckdb.*",


Test patch:
diff --git a/tests/expr_and_series/log_test.py b/tests/expr_and_series/log_test.py
new file mode 100644
index 00000000..bd6580b3
--- /dev/null
+++ b/tests/expr_and_series/log_test.py
@@ -0,0 +1,69 @@
+from __future__ import annotations
+
+import math
+
+import pandas as pd
+import pytest
+
+import narwhals.stable.v1 as nw
+from tests.utils import PANDAS_VERSION
+from tests.utils import Constructor
+from tests.utils import ConstructorEager
+from tests.utils import assert_equal_data
+
+data = {"a": [-1, 0, 1, 2, 4]}
+
+expected = {  # base: expected values
+    2: [float("nan"), float("-inf"), 0, 1, 2],
+    10: [float("nan"), float("-inf"), 0, 0.30103, 0.60206],
+    math.e: [float("nan"), float("-inf"), 0, 0.693147, 1.386294],
+}
+
+
+@pytest.mark.filterwarnings("ignore::RuntimeWarning")
+@pytest.mark.parametrize("base", [2, 10, math.e])
+def test_log_expr(constructor: Constructor, base: float) -> None:
+    df = nw.from_native(constructor(data))
+    result = df.select(nw.col("a").log(base=base))
+    assert_equal_data(result, {"a": expected[base]})
+
+
+@pytest.mark.filterwarnings("ignore::RuntimeWarning")
+@pytest.mark.parametrize("base", [2, 10, math.e])
+def test_log_series(constructor_eager: ConstructorEager, base: float) -> None:
+    series = nw.from_native(constructor_eager(data), eager_only=True)["a"]
+    result = series.log(base=base)
+    assert_equal_data({"a": result}, {"a": expected[base]})
+
+
+def test_log_dtype_pandas() -> None:
+    s = pd.Series([1.0, 2.0], name="a", dtype="float32", index=[8, 7])
+    result = nw.from_native(s, series_only=True).log().to_native()
+    expected = pd.Series([0.0, 0.693147], name="a", dtype="float32", index=[8, 7])
+    pd.testing.assert_series_equal(result, expected)
+
+
+@pytest.mark.skipif(
+    PANDAS_VERSION < (2, 0, 0),
+    reason="nullable types require pandas2+",
+)
+def test_log_dtype_pandas_nullabe() -> None:
+    s = pd.Series([1.0, None, 2.0], name="a", dtype="Float32", index=[8, 7, 6])
+    result = nw.from_native(s, series_only=True).log().to_native()
+    expected = pd.Series(
+        [0.0, None, 0.693147], name="a", dtype="Float32", index=[8, 7, 6]
+    )
+    pd.testing.assert_series_equal(result, expected)
+
+
+@pytest.mark.skipif(
+    PANDAS_VERSION < (2, 1, 0),
+    reason="nullable types require pandas2+",
+)
+def test_log_dtype_pandas_pyarrow() -> None:
+    s = pd.Series([1.0, None, 2.0], name="a", dtype="Float32[pyarrow]", index=[8, 7, 6])
+    result = nw.from_native(s, series_only=True).log().to_native()
+    expected = pd.Series(
+        [0.0, None, 0.693147], name="a", dtype="Float64[pyarrow]", index=[8, 7, 6]
+    )
+    pd.testing.assert_series_equal(result, expected)
diff --git a/tests/frame/join_test.py b/tests/frame/join_test.py
index 43b5e1c4..98c4a750 100644
--- a/tests/frame/join_test.py
+++ b/tests/frame/join_test.py
@@ -9,7 +9,6 @@ import pandas as pd
 import pytest
 
 import narwhals as nw
-from narwhals.utils import Implementation
 from tests.utils import DUCKDB_VERSION
 from tests.utils import PANDAS_VERSION
 from tests.utils import POLARS_VERSION
@@ -242,11 +241,14 @@ def test_cross_join_suffix(constructor: Constructor, suffix: str) -> None:
 
 
 def test_cross_join_non_pandas() -> None:
+    _ = pytest.importorskip("modin")
+
+    import modin.pandas as mpd
+
     data = {"antananarivo": [1, 3, 2]}
-    df = nw.from_native(pd.DataFrame(data))
-    # HACK to force testing for a non-pandas codepath
-    df._compliant_frame._implementation = Implementation.MODIN
-    result = df.join(df, how="cross")
+    df1 = nw.from_native(mpd.DataFrame(pd.DataFrame(data)), eager_only=True)
+    df2 = nw.from_native(mpd.DataFrame(pd.DataFrame(data)), eager_only=True)
+    result = df1.join(df2, how="cross")
     expected = {
         "antananarivo": [1, 1, 1, 3, 3, 3, 2, 2, 2],
         "antananarivo_right": [1, 3, 2, 1, 3, 2, 1, 3, 2],
diff --git a/tests/preserve_pandas_like_columns_name_attr_test.py b/tests/preserve_pandas_like_columns_name_attr_test.py
new file mode 100644
index 00000000..507d2fec
--- /dev/null
+++ b/tests/preserve_pandas_like_columns_name_attr_test.py
@@ -0,0 +1,33 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import pytest
+
+import narwhals.stable.v1 as nw
+
+if TYPE_CHECKING:
+    from tests.utils import Constructor
+
+
+def test_ops_preserve_column_index_name(
+    constructor: Constructor, request: pytest.FixtureRequest
+) -> None:
+    if not any(x in str(constructor) for x in ("pandas", "modin", "cudf", "dask")):
+        pytest.skip(
+            reason="Dataframe columns is a list and do not have a `name` like a pandas Index does"
+        )
+    if "dask" in str(constructor):
+        # https://github.com/dask/dask/issues/11874
+        request.applymarker(pytest.mark.xfail)
+
+    data = {"a": [1, 3, 2], "b": [4, 4, 6], "z": [7.0, 8.0, 9.0]}
+    df_native = constructor(data)
+    df_native.columns.name = "foo"  # type: ignore[union-attr]
+
+    df = nw.from_native(df_native)
+
+    result = df.with_columns(b=nw.col("a") + 1, c=nw.col("a") * 2).select("c", "b")
+
+    assert result.to_native().columns.name == "foo"  # type: ignore[union-attr]
+    assert result.lazy().collect(backend="pandas").to_native().columns.name == "foo"