docs[python] more expr docstrings (#4992)

py-polars/polars/internals/expr/datetime.py

@@ -685,6 +685,39 @@ def second(self, fractional: bool = False) -> pli.Expr:
         │ 5.765431 │
         └──────────┘
 
+        >>> from datetime import timedelta, datetime
+        >>> start = datetime(2001, 1, 1)
+        >>> stop = datetime(2001, 1, 1, 0, 0, 4)
+        >>> df = pl.DataFrame(
+        ...     {"date": pl.date_range(start, stop, timedelta(seconds=2))}
+        ... )
+        >>> df
+        shape: (3, 1)
+        ┌─────────────────────┐
+        │ date                │
+        │ ---                 │
+        │ datetime[μs]        │
+        ╞═════════════════════╡
+        │ 2001-01-01 00:00:00 │
+        ├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+        │ 2001-01-01 00:00:02 │
+        ├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+        │ 2001-01-01 00:00:04 │
+        └─────────────────────┘
+        >>> df.select(pl.col("date").dt.second())
+        shape: (3, 1)
+        ┌──────┐
+        │ date │
+        │ ---  │
+        │ u32  │
+        ╞══════╡
+        │ 0    │
+        ├╌╌╌╌╌╌┤
+        │ 2    │
+        ├╌╌╌╌╌╌┤
+        │ 4    │
+        └──────┘
+
         """
         sec = pli.wrap_expr(self._pyexpr.second())
         return (

py-polars/polars/internals/expr/expr.py

@@ -2074,7 +2074,7 @@ def fill_null(
         """
         Fill null values using the specified value or strategy.
 
-        To interpolate over null values see interpolate
+        To interpolate over null values see interpolate.
 
         Parameters
         ----------
@@ -3630,7 +3630,7 @@ def interpolate(self) -> Expr:
         """
         Fill nulls with linear interpolation over missing values.
 
-        Can also be used to regrid data to a new grid - see examples below
+        Can also be used to regrid data to a new grid - see examples below.
 
         Examples
         --------
@@ -5939,18 +5939,14 @@ def set_sorted(self, reverse: bool = False) -> Expr:
 
         Examples
         --------
-        >>> df = pl.DataFrame({"values": [1, 3, 2]})
-        >>> df.select(pl.col("values").sort().set_sorted())
-        shape: (3, 1)
+        >>> df = pl.DataFrame({"values": [1, 2, 3]})
+        >>> df.select(pl.col("values").set_sorted().max())
+        shape: (1, 1)
         ┌────────┐
         │ values │
         │ ---    │
         │ i64    │
         ╞════════╡
-        │ 1      │
-        ├╌╌╌╌╌╌╌╌┤
-        │ 2      │
-        ├╌╌╌╌╌╌╌╌┤
         │ 3      │
         └────────┘