chore: remove some expr._kwargs defaults (#1747)

narwhals-dev · Jan 7, 2025 · a6d76e1 · a6d76e1
1 parent 46a030a
commit a6d76e1
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Showing 5 changed files with 7 additions and 10 deletions.
diff --git a/narwhals/_arrow/group_by.py b/narwhals/_arrow/group_by.py
@@ -171,7 +171,7 @@ def agg_arrow(
         function_name = remove_prefix(expr._function_name, "col->")
 
         if function_name in {"std", "var"}:
-            option = pc.VarianceOptions(ddof=expr._kwargs.get("ddof", 1))
+            option = pc.VarianceOptions(ddof=expr._kwargs["ddof"])
         elif function_name in {"len", "n_unique"}:
             option = pc.CountOptions(mode="all")
         elif function_name == "count":

diff --git a/narwhals/_dask/group_by.py b/narwhals/_dask/group_by.py
@@ -178,9 +178,7 @@ def agg_dask(
 
             function_name = remove_prefix(expr._function_name, "col->")
             kwargs = (
-                {"ddof": expr._kwargs.get("ddof", 1)}
-                if function_name in {"std", "var"}
-                else {}
+                {"ddof": expr._kwargs["ddof"]} if function_name in {"std", "var"} else {}
             )
 
             agg_function = POLARS_TO_DASK_AGGREGATIONS.get(function_name, function_name)

diff --git a/narwhals/_pandas_like/expr.py b/narwhals/_pandas_like/expr.py
@@ -412,12 +412,12 @@ def func(df: PandasLikeDataFrame) -> list[PandasLikeSeries]:
                     df = df.with_columns(~plx.col(*self._root_names).is_null())
 
                 if self._function_name == "col->shift":
-                    kwargs = {"periods": self._kwargs.get("n", 1)}
+                    kwargs = {"periods": self._kwargs["n"]}
                 elif self._function_name == "col->rank":
                     _method = self._kwargs.get("method", "average")
                     kwargs = {
                         "method": "first" if _method == "ordinal" else _method,
-                        "ascending": not self._kwargs.get("descending", False),
+                        "ascending": not self._kwargs["descending"],
                         "na_option": "keep",
                         "pct": False,
                     }

diff --git a/narwhals/_pandas_like/group_by.py b/narwhals/_pandas_like/group_by.py
@@ -209,14 +209,13 @@ def agg_pandas(  # noqa: PLR0915
             is_n_unique = function_name == "nunique"
             is_std = function_name == "std"
             is_var = function_name == "var"
-            ddof = expr._kwargs.get("ddof", 1)
             for root_name, output_name in zip(expr._root_names, expr._output_names):
                 if is_n_unique:
                     nunique_aggs[output_name] = root_name
-                elif is_std and ddof != 1:
+                elif is_std and (ddof := expr._kwargs["ddof"]) != 1:
                     std_aggs[ddof][0].append(root_name)
                     std_aggs[ddof][1].append(output_name)
-                elif is_var and ddof != 1:
+                elif is_var and (ddof := expr._kwargs["ddof"]) != 1:
                     var_aggs[ddof][0].append(root_name)
                     var_aggs[ddof][1].append(output_name)
                 else:

diff --git a/narwhals/_spark_like/group_by.py b/narwhals/_spark_like/group_by.py
@@ -85,7 +85,7 @@ def get_spark_function(function_name: str, **kwargs: Any) -> Column:
 
         return partial(
             _std if function_name == "std" else _var,
-            ddof=kwargs.get("ddof", 1),
+            ddof=kwargs["ddof"],
             np_version=parse_version(np.__version__),
         )
     from pyspark.sql import functions as F  # noqa: N812