Update references

py-polars/tests/unit/dataframe/test_df.py

@@ -2047,7 +2047,7 @@ def test_shift_and_fill() -> None:
             "ham": ["a", "b", "c"],
         }
     )
-    result = df.shift_and_fill(fill_value=0, periods=1)
+    result = df.shift_and_fill(fill_value=0, n=1)
     expected = pl.DataFrame(
         {
             "foo": [0, 1, 2],

py-polars/tests/unit/datatypes/test_categorical.py

@@ -146,7 +146,7 @@ def test_shift_and_fill() -> None:
         [pl.col("a").cast(pl.Categorical)]
     )
 
-    s = df.with_columns(pl.col("a").shift_and_fill("c", periods=1))["a"]
+    s = df.with_columns(pl.col("a").shift_and_fill("c", n=1))["a"]
     assert s.dtype == pl.Categorical
     assert s.to_list() == ["c", "a"]
 

py-polars/tests/unit/datatypes/test_temporal.py

@@ -1421,7 +1421,7 @@ def test_supertype_timezones_4174() -> None:
 
     # test if this runs without error
     date_to_fill = df["dt_London"][0]
-    df.with_columns(df["dt_London"].shift_and_fill(date_to_fill, periods=1))
+    df.with_columns(df["dt_London"].shift_and_fill(date_to_fill, n=1))
 
 
 @pytest.mark.skip(reason="from_dicts cannot yet infer timezones")
@@ -1446,7 +1446,7 @@ def test_shift_and_fill_group_logicals() -> None:
         schema=["d", "s"],
     )
     assert df.select(
-        pl.col("d").shift_and_fill(pl.col("d").max(), periods=-1).over("s")
+        pl.col("d").shift_and_fill(pl.col("d").max(), n=-1).over("s")
     ).dtypes == [pl.Date]
 
 

py-polars/tests/unit/operations/map/test_map_batches.py

@@ -22,7 +22,7 @@ def test_map_no_dtype_set_8531() -> None:
     df = pl.DataFrame({"a": [1]})
 
     result = df.with_columns(
-        pl.col("a").map_batches(lambda x: x * 2).shift_and_fill(fill_value=0, periods=0)
+        pl.col("a").map_batches(lambda x: x * 2).shift_and_fill(fill_value=0, n=0)
     )
 
     expected = pl.DataFrame({"a": [2]})

py-polars/tests/unit/operations/rolling/test_rolling.py

@@ -386,7 +386,7 @@ def test_rolling_slice_pushdown() -> None:
         )
         .agg(
             [
-                (pl.col("c") - pl.col("c").shift_and_fill(fill_value=0, periods=1))
+                (pl.col("c") - pl.col("c").shift_and_fill(fill_value=0, n=1))
                 .sum()
                 .alias("c")
             ]

py-polars/tests/unit/operations/test_group_by_dynamic.py

@@ -320,7 +320,7 @@ def test_group_by_dynamic_slice_pushdown() -> None:
         df.sort("a")
         .group_by_dynamic("a", by="b", every="2i")
         .agg(
-            (pl.col("c") - pl.col("c").shift_and_fill(fill_value=0, periods=1))
+            (pl.col("c") - pl.col("c").shift_and_fill(fill_value=0, n=1))
             .sum()
             .alias("c")
         )

py-polars/tests/unit/operations/test_window.py

@@ -243,15 +243,11 @@ def test_window_functions_list_types() -> None:
     # as it is the same as shift.
     # that's why we don't add it to the allowed types.
     assert (
-        df.select(
-            pl.col("col_list").shift_and_fill(None, periods=1).alias("list_shifted")
-        )
+        df.select(pl.col("col_list").shift_and_fill(None, n=1).alias("list_shifted"))
     )["list_shifted"].to_list() == [None, [1], [1], [2]]
 
     assert (
-        df.select(
-            pl.col("col_list").shift_and_fill([], periods=1).alias("list_shifted")
-        )
+        df.select(pl.col("col_list").shift_and_fill([], n=1).alias("list_shifted"))
     )["list_shifted"].to_list() == [[], [1], [1], [2]]
 
 

py-polars/tests/unit/series/test_series.py

@@ -1085,7 +1085,7 @@ def test_shift() -> None:
     assert_series_equal(a.shift(1), pl.Series("a", [None, 1, 2]))
     assert_series_equal(a.shift(-1), pl.Series("a", [2, 3, None]))
     assert_series_equal(a.shift(-2), pl.Series("a", [3, None, None]))
-    assert_series_equal(a.shift_and_fill(10, periods=-1), pl.Series("a", [2, 3, 10]))
+    assert_series_equal(a.shift_and_fill(10, n=-1), pl.Series("a", [2, 3, 10]))
 
 
 def test_object() -> None:

py-polars/tests/unit/test_lazy.py

@@ -289,12 +289,12 @@ def test_shift_and_fill() -> None:
 
     # use exprs
     out = ldf.with_columns(
-        pl.col("a").shift_and_fill(pl.col("b").mean(), periods=-2)
+        pl.col("a").shift_and_fill(pl.col("b").mean(), n=-2)
     ).collect()
     assert out["a"].null_count() == 0
 
     # use df method
-    out = ldf.shift_and_fill(pl.col("b").std(), periods=2).collect()
+    out = ldf.shift_and_fill(pl.col("b").std(), n=2).collect()
     assert out["a"].null_count() == 0