More python documentation (#1414)

py-polars/polars/eager/frame.py

@@ -790,7 +790,7 @@ def to_csv(
         >>>     "bar": [6, 7, 8, 9, 10],
         >>>     "ham": ['a', 'b', 'c', 'd','e']
         >>>     })
-        >>> df.to_csv('new_file.csv', sep=',')
+        >>> df.to_csv('new_file.csv', delimiter=',')
 
         """
         if file is None:
@@ -1837,6 +1837,40 @@ def downsample(self, by: Union[str, tp.List[str]], rule: str, n: int) -> "GroupB
 
         n
             Number of units (e.g. 5 "day", 15 "minute".
+
+        Examples
+        --------
+
+        >>> df = pl.DataFrame(
+        >>>     {
+        >>>         "A": ["2020-01-01", "2020-01-02", "2020-01-03","2020-01-04","2020-01-05"],
+        >>>         "B": [1.0, 8.0, 6.0, 2.0, 16.0, 10.0],
+        >>>         "C": [3.0, 6.0, 9.0, 2.0, 13.0, 8.0],
+        >>>         "D": [12.0, 5.0, 9.0, 2.0, 11.0, 2.0],
+        >>>     }
+        >>> )
+        >>> df['A'] = df['A'].str.strptime(pl.datatypes.Date32, "%Y-%m-%d")
+        >>>
+        >>> df.downsample("A", rule="day", n=3).agg(
+        >>>     {
+        >>>         "B": "max",
+        >>>         "C": "min",
+        >>>         "D": "last"
+        >>>     }
+        >>> )
+        shape: (3, 4)
+        ┌──────────────┬───────┬───────┬────────┐
+        │ A            ┆ B_max ┆ C_min ┆ D_last │
+        │ ---          ┆ ---   ┆ ---   ┆ ---    │
+        │ date32(days) ┆ f64   ┆ f64   ┆ f64    │
+        ╞══════════════╪═══════╪═══════╪════════╡
+        │ 2019-12-31   ┆ 8     ┆ 3     ┆ 5      │
+        ├╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
+        │ 2020-01-03   ┆ 16    ┆ 2     ┆ 11     │
+        ├╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤
+        │ 2020-01-06   ┆ 10    ┆ 8     ┆ 2      │
+        └──────────────┴───────┴───────┴────────┘
+
         """
         return GroupBy(
             self._df,

py-polars/polars/eager/series.py

@@ -1567,6 +1567,26 @@ def cast(self, dtype: Type[DataType], strict: bool = True) -> "Series":
             DataType to cast to
         strict
             Throw an error if a cast could not be done for instance due to an overflow
+
+        Examples
+        --------
+        >>> s = pl.Series("a", ["2020-01-01", "2020-01-02", "2020-01-03"])
+        shape: (3,)
+        Series: 'a' [str]
+        [
+            "2020-01-01"
+            "2020-01-02"
+            "2020-01-03"
+        ]
+        >>> s.cast(pl.datatypes.Date32)git
+        shape: (3,)
+        Series: 'a' [date32]
+        [
+            2020-01-01
+            2020-01-02
+            2020-01-03
+        ]
+
         """
         if dtype == int:
             dtype = Int64

py-polars/polars/lazy/expr.py

@@ -1305,6 +1305,8 @@ def rolling_mean(
         will (optionally) be multiplied with the weights given by the `weight` vector. The resultingParameters
         values will be aggregated to their sum.
 
+        Parameters
+        ----------
         window_size
             The length of the window.
         weight
@@ -1317,6 +1319,37 @@ def rolling_mean(
         min_periods
             The number of values in the window that should be non-null before computing a result.
             If None, it will be set equal to window size.
+
+        Examples
+        --------
+
+        >>> df = pl.DataFrame(
+        >>>     {
+        >>>         "A": [1.0, 8.0, 6.0, 2.0, 16.0, 10.0]
+        >>>     }
+        >>> )
+        >>> df.select([
+        >>>     col("A").rolling_mean(window_size=2)
+        >>> ])
+        shape: (6, 1)
+        ┌──────┐
+        │ A    │
+        │ ---  │
+        │ f64  │
+        ╞══════╡
+        │ null │
+        ├╌╌╌╌╌╌┤
+        │ 4.5  │
+        ├╌╌╌╌╌╌┤
+        │ 7    │
+        ├╌╌╌╌╌╌┤
+        │ 4    │
+        ├╌╌╌╌╌╌┤
+        │ 9    │
+        ├╌╌╌╌╌╌┤
+        │ 13   │
+        └──────┘
+
         """
         if min_periods is None:
             min_periods = window_size