from __future__ import annotations

from typing import TYPE_CHECKING, Callable

from polars import functions as F
from polars._utils.convert import parse_as_duration_string
from polars._utils.deprecation import deprecated

if TYPE_CHECKING:
    import sys
    from collections.abc import Iterable
    from datetime import timedelta

    from polars import DataFrame
    from polars._typing import (
        ClosedInterval,
        IntoExpr,
        Label,
        QuantileMethod,
        SchemaDict,
        StartBy,
    )

    if sys.version_info >= (3, 11):
        from typing import Self
    else:
        from typing_extensions import Self

    if sys.version_info >= (3, 13):
        from warnings import deprecated
    else:
        from typing_extensions import deprecated  # noqa: TC004


class GroupBy:
    """Starts a new GroupBy operation."""

    def __init__(
        self,
        df: DataFrame,
        *by: IntoExpr | Iterable[IntoExpr],
        maintain_order: bool,
        **named_by: IntoExpr,
    ) -> None:
        """
        Utility class for performing a group by operation over the given DataFrame.

        Generated by calling `df.group_by(...)`.

        Parameters
        ----------
        df
            DataFrame to perform the group by operation over.
        *by
            Column or columns to group by. Accepts expression input. Strings are parsed
            as column names.
        maintain_order
            Ensure that the order of the groups is consistent with the input data.
            This is slower than a default group by.
        **named_by
            Additional column(s) to group by, specified as keyword arguments.
            The columns will be named as the keyword used.
        """
        self.df = df
        self.by = by
        self.named_by = named_by
        self.maintain_order = maintain_order

    def __iter__(self) -> Self:
        """
        Allows iteration over the groups of the group by operation.

        Each group is represented by a tuple of `(name, data)`. The group names are
        tuples of the distinct group values that identify each group.

        Examples
        --------
        >>> df = pl.DataFrame({"foo": ["a", "a", "b"], "bar": [1, 2, 3]})
        >>> for name, data in df.group_by("foo"):  # doctest: +SKIP
        ...     print(name)
        ...     print(data)
        (a,)
        shape: (2, 2)
        ┌─────┬─────┐
        │ foo ┆ bar │
        │ --- ┆ --- │
        │ str ┆ i64 │
        ╞═════╪═════╡
        │ a   ┆ 1   │
        │ a   ┆ 2   │
        └─────┴─────┘
        (b,)
        shape: (1, 2)
        ┌─────┬─────┐
        │ foo ┆ bar │
        │ --- ┆ --- │
        │ str ┆ i64 │
        ╞═════╪═════╡
        │ b   ┆ 3   │
        └─────┴─────┘
        """
        # Every group gather can trigger a rechunk, so do early.
        from polars.lazyframe.opt_flags import QueryOptFlags

        self.df = self.df.rechunk()
        temp_col = "__POLARS_GB_GROUP_INDICES"
        groups_df = (
            self.df.lazy()
            .group_by(*self.by, **self.named_by, maintain_order=self.maintain_order)
            .agg(F.first().agg_groups().alias(temp_col))
            .collect(optimizations=QueryOptFlags.none())
        )

        self._group_names = groups_df.select(F.all().exclude(temp_col)).iter_rows()
        self._group_indices = groups_df.select(temp_col).to_series()
        self._current_index = 0

        return self

    def __next__(self) -> tuple[tuple[object, ...], DataFrame]:
        if self._current_index >= len(self._group_indices):
            raise StopIteration

        group_name = next(self._group_names)
        group_data = self.df[self._group_indices[self._current_index], :]
        self._current_index += 1

        return group_name, group_data

    def agg(
        self,
        *aggs: IntoExpr | Iterable[IntoExpr],
        **named_aggs: IntoExpr,
    ) -> DataFrame:
        """
        Compute aggregations for each group of a group by operation.

        Parameters
        ----------
        *aggs
            Aggregations to compute for each group of the group by operation,
            specified as positional arguments.
            Accepts expression input. Strings are parsed as column names.
        **named_aggs
            Additional aggregations, specified as keyword arguments.
            The resulting columns will be renamed to the keyword used.

        Examples
        --------
        Compute the aggregation of the columns for each group.

        >>> df = pl.DataFrame(
        ...     {
        ...         "a": ["a", "b", "a", "b", "c"],
        ...         "b": [1, 2, 1, 3, 3],
        ...         "c": [5, 4, 3, 2, 1],
        ...     }
        ... )
        >>> df.group_by("a").agg(pl.col("b"), pl.col("c"))  # doctest: +IGNORE_RESULT
        shape: (3, 3)
        ┌─────┬───────────┬───────────┐
        │ a   ┆ b         ┆ c         │
        │ --- ┆ ---       ┆ ---       │
        │ str ┆ list[i64] ┆ list[i64] │
        ╞═════╪═══════════╪═══════════╡
        │ a   ┆ [1, 1]    ┆ [5, 3]    │
        ├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
        │ b   ┆ [2, 3]    ┆ [4, 2]    │
        ├╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
        │ c   ┆ [3]       ┆ [1]       │
        └─────┴───────────┴───────────┘

        Compute the sum of a column for each group.

        >>> df.group_by("a").agg(pl.col("b").sum())  # doctest: +IGNORE_RESULT
        shape: (3, 2)
        ┌─────┬─────┐
        │ a   ┆ b   │
        │ --- ┆ --- │
        │ str ┆ i64 │
        ╞═════╪═════╡
        │ a   ┆ 2   │
        │ b   ┆ 5   │
        │ c   ┆ 3   │
        └─────┴─────┘

        Compute multiple aggregates at once by passing a list of expressions.

        >>> df.group_by("a").agg([pl.sum("b"), pl.mean("c")])  # doctest: +IGNORE_RESULT
        shape: (3, 3)
        ┌─────┬─────┬─────┐
        │ a   ┆ b   ┆ c   │
        │ --- ┆ --- ┆ --- │
        │ str ┆ i64 ┆ f64 │
        ╞═════╪═════╪═════╡
        │ c   ┆ 3   ┆ 1.0 │
        │ a   ┆ 2   ┆ 4.0 │
        │ b   ┆ 5   ┆ 3.0 │
        └─────┴─────┴─────┘

        Or use positional arguments to compute multiple aggregations in the same way.

        >>> df.group_by("a").agg(
        ...     pl.sum("b").name.suffix("_sum"),
        ...     (pl.col("c") ** 2).mean().name.suffix("_mean_squared"),
        ... )  # doctest: +IGNORE_RESULT
        shape: (3, 3)
        ┌─────┬───────┬────────────────┐
        │ a   ┆ b_sum ┆ c_mean_squared │
        │ --- ┆ ---   ┆ ---            │
        │ str ┆ i64   ┆ f64            │
        ╞═════╪═══════╪════════════════╡
        │ a   ┆ 2     ┆ 17.0           │
        │ c   ┆ 3     ┆ 1.0            │
        │ b   ┆ 5     ┆ 10.0           │
        └─────┴───────┴────────────────┘

        Use keyword arguments to easily name your expression inputs.

        >>> df.group_by("a").agg(
        ...     b_sum=pl.sum("b"),
        ...     c_mean_squared=(pl.col("c") ** 2).mean(),
        ... )  # doctest: +IGNORE_RESULT
        shape: (3, 3)
        ┌─────┬───────┬────────────────┐
        │ a   ┆ b_sum ┆ c_mean_squared │
        │ --- ┆ ---   ┆ ---            │
        │ str ┆ i64   ┆ f64            │
        ╞═════╪═══════╪════════════════╡
        │ a   ┆ 2     ┆ 17.0           │
        │ c   ┆ 3     ┆ 1.0            │
        │ b   ┆ 5     ┆ 10.0           │
        └─────┴───────┴────────────────┘
        """
        from polars.lazyframe.opt_flags import QueryOptFlags

        return (
            self.df.lazy()
            .group_by(*self.by, **self.named_by, maintain_order=self.maintain_order)
            .agg(*aggs, **named_aggs)
            .collect(optimizations=QueryOptFlags.none())
        )

    def map_groups(self, function: Callable[[DataFrame], DataFrame]) -> DataFrame:
        """
        Apply a custom/user-defined function (UDF) over the groups as a sub-DataFrame.

        .. warning::
            This method is much slower than the native expressions API.
            Only use it if you cannot implement your logic otherwise.

        Implementing logic using a Python function is almost always *significantly*
        slower and more memory intensive than implementing the same logic using
        the native expression API because:

        - The native expression engine runs in Rust; UDFs run in Python.
        - Use of Python UDFs forces the DataFrame to be materialized in memory.
        - Polars-native expressions can be parallelised (UDFs cannot).
        - Polars-native expressions can be logically optimised (UDFs cannot).

        Wherever possible you should strongly prefer the native expression API
        to achieve the best performance.

        Parameters
        ----------
        function
            Custom function that receives a DataFrame and returns a DataFrame.

        Returns
        -------
        DataFrame

        Examples
        --------
        For each color group sample two rows:

        >>> df = pl.DataFrame(
        ...     {
        ...         "id": [0, 1, 2, 3, 4],
        ...         "color": ["red", "green", "green", "red", "red"],
        ...         "shape": ["square", "triangle", "square", "triangle", "square"],
        ...     }
        ... )
        >>> df.group_by("color").map_groups(
        ...     lambda group_df: group_df.sample(2)
        ... )  # doctest: +IGNORE_RESULT
        shape: (4, 3)
        ┌─────┬───────┬──────────┐
        │ id  ┆ color ┆ shape    │
        │ --- ┆ ---   ┆ ---      │
        │ i64 ┆ str   ┆ str      │
        ╞═════╪═══════╪══════════╡
        │ 1   ┆ green ┆ triangle │
        │ 2   ┆ green ┆ square   │
        │ 4   ┆ red   ┆ square   │
        │ 3   ┆ red   ┆ triangle │
        └─────┴───────┴──────────┘

        It is better to implement this with an expression:

        >>> df.filter(
        ...     pl.int_range(pl.len()).shuffle().over("color") < 2
        ... )  # doctest: +IGNORE_RESULT
        """
        if self.named_by:
            msg = "cannot call `map_groups` when grouping by named expressions"
            raise TypeError(msg)
        if not all(isinstance(c, str) for c in self.by):
            msg = "cannot call `map_groups` when grouping by an expression"
            raise TypeError(msg)

        return self.df.__class__._from_pydf(
            self.df._df.group_by_map_groups(
                list(self.by), function, self.maintain_order
            )
        )

    def head(self, n: int = 5) -> DataFrame:
        """
        Get the first `n` rows of each group.

        Parameters
        ----------
        n
            Number of rows to return.

        Examples
        --------
        >>> df = pl.DataFrame(
        ...     {
        ...         "letters": ["c", "c", "a", "c", "a", "b"],
        ...         "nrs": [1, 2, 3, 4, 5, 6],
        ...     }
        ... )
        >>> df
        shape: (6, 2)
        ┌─────────┬─────┐
        │ letters ┆ nrs │
        │ ---     ┆ --- │
        │ str     ┆ i64 │
        ╞═════════╪═════╡
        │ c       ┆ 1   │
        │ c       ┆ 2   │
        │ a       ┆ 3   │
        │ c       ┆ 4   │
        │ a       ┆ 5   │
        │ b       ┆ 6   │
        └─────────┴─────┘
        >>> df.group_by("letters").head(2).sort("letters")
        shape: (5, 2)
        ┌─────────┬─────┐
        │ letters ┆ nrs │
        │ ---     ┆ --- │
        │ str     ┆ i64 │
        ╞═════════╪═════╡
        │ a       ┆ 3   │
        │ a       ┆ 5   │
        │ b       ┆ 6   │
        │ c       ┆ 1   │
        │ c       ┆ 2   │
        └─────────┴─────┘
        """
        from polars.lazyframe.opt_flags import QueryOptFlags

        return (
            self.df.lazy()
            .group_by(*self.by, **self.named_by, maintain_order=self.maintain_order)
            .head(n)
            .collect(optimizations=QueryOptFlags.none())
        )

    def tail(self, n: int = 5) -> DataFrame:
        """
        Get the last `n` rows of each group.

        Parameters
        ----------
        n
            Number of rows to return.

        Examples
        --------
        >>> df = pl.DataFrame(
        ...     {
        ...         "letters": ["c", "c", "a", "c", "a", "b"],
        ...         "nrs": [1, 2, 3, 4, 5, 6],
        ...     }
        ... )
        >>> df
        shape: (6, 2)
        ┌─────────┬─────┐
        │ letters ┆ nrs │
        │ ---     ┆ --- │
        │ str     ┆ i64 │
        ╞═════════╪═════╡
        │ c       ┆ 1   │
        │ c       ┆ 2   │
        │ a       ┆ 3   │
        │ c       ┆ 4   │
        │ a       ┆ 5   │
        │ b       ┆ 6   │
        └─────────┴─────┘
        >>> df.group_by("letters").tail(2).sort("letters")
        shape: (5, 2)
        ┌─────────┬─────┐
        │ letters ┆ nrs │
        │ ---     ┆ --- │
        │ str     ┆ i64 │
        ╞═════════╪═════╡
        │ a       ┆ 3   │
        │ a       ┆ 5   │
        │ b       ┆ 6   │
        │ c       ┆ 2   │
        │ c       ┆ 4   │
        └─────────┴─────┘
        """
        from polars.lazyframe.opt_flags import QueryOptFlags

        return (
            self.df.lazy()
            .group_by(*self.by, **self.named_by, maintain_order=self.maintain_order)
            .tail(n)
            .collect(optimizations=QueryOptFlags.none())
        )

    def all(self) -> DataFrame:
        """
        Aggregate the groups into Series.

        Examples
        --------
        >>> df = pl.DataFrame({"a": ["one", "two", "one", "two"], "b": [1, 2, 3, 4]})
        >>> df.group_by("a", maintain_order=True).all()
        shape: (2, 2)
        ┌─────┬───────────┐
        │ a   ┆ b         │
        │ --- ┆ ---       │
        │ str ┆ list[i64] │
        ╞═════╪═══════════╡
        │ one ┆ [1, 3]    │
        │ two ┆ [2, 4]    │
        └─────┴───────────┘
        """
        return self.agg(F.all())

    def len(self, name: str | None = None) -> DataFrame:
        """
        Return the number of rows in each group.

        Parameters
        ----------
        name
            Assign a name to the resulting column; if unset, defaults to "len".

        Examples
        --------
        >>> df = pl.DataFrame({"a": ["Apple", "Apple", "Orange"], "b": [1, None, 2]})
        >>> df.group_by("a").len()  # doctest: +IGNORE_RESULT
        shape: (2, 2)
        ┌────────┬─────┐
        │ a      ┆ len │
        │ ---    ┆ --- │
        │ str    ┆ u32 │
        ╞════════╪═════╡
        │ Apple  ┆ 2   │
        │ Orange ┆ 1   │
        └────────┴─────┘
        >>> df.group_by("a").len(name="n")  # doctest: +IGNORE_RESULT
        shape: (2, 2)
        ┌────────┬─────┐
        │ a      ┆ n   │
        │ ---    ┆ --- │
        │ str    ┆ u32 │
        ╞════════╪═════╡
        │ Apple  ┆ 2   │
        │ Orange ┆ 1   │
        └────────┴─────┘
        """
        len_expr = F.len()
        if name is not None:
            len_expr = len_expr.alias(name)
        return self.agg(len_expr)

    @deprecated("`GroupBy.count` was renamed; use `GroupBy.len` instead")
    def count(self) -> DataFrame:
        """
        Return the number of rows in each group.

        .. deprecated:: 0.20.5
            This method has been renamed to :func:`GroupBy.len`.

        Rows containing null values count towards the total.

        Examples
        --------
        >>> df = pl.DataFrame(
        ...     {
        ...         "a": ["Apple", "Apple", "Orange"],
        ...         "b": [1, None, 2],
        ...     }
        ... )
        >>> df.group_by("a").count()  # doctest: +SKIP
        shape: (2, 2)
        ┌────────┬───────┐
        │ a      ┆ count │
        │ ---    ┆ ---   │
        │ str    ┆ u32   │
        ╞════════╪═══════╡
        │ Apple  ┆ 2     │
        │ Orange ┆ 1     │
        └────────┴───────┘
        """
        return self.agg(F.len().alias("count"))

    def first(self) -> DataFrame:
        """
        Aggregate the first values in the group.

        Examples
        --------
        >>> df = pl.DataFrame(
        ...     {
        ...         "a": [1, 2, 2, 3, 4, 5],
        ...         "b": [0.5, 0.5, 4, 10, 13, 14],
        ...         "c": [True, True, True, False, False, True],
        ...         "d": ["Apple", "Orange", "Apple", "Apple", "Banana", "Banana"],
        ...     }
        ... )
        >>> df.group_by("d", maintain_order=True).first()
        shape: (3, 4)
        ┌────────┬─────┬──────┬───────┐
        │ d      ┆ a   ┆ b    ┆ c     │
        │ ---    ┆ --- ┆ ---  ┆ ---   │
        │ str    ┆ i64 ┆ f64  ┆ bool  │
        ╞════════╪═════╪══════╪═══════╡
        │ Apple  ┆ 1   ┆ 0.5  ┆ true  │
        │ Orange ┆ 2   ┆ 0.5  ┆ true  │
        │ Banana ┆ 4   ┆ 13.0 ┆ false │
        └────────┴─────┴──────┴───────┘
        """
        return self.agg(F.all().first())

    def last(self) -> DataFrame:
        """
        Aggregate the last values in the group.

        Examples
        --------
        >>> df = pl.DataFrame(
        ...     {
        ...         "a": [1, 2, 2, 3, 4, 5],
        ...         "b": [0.5, 0.5, 4, 10, 14, 13],
        ...         "c": [True, True, True, False, False, True],
        ...         "d": ["Apple", "Orange", "Apple", "Apple", "Banana", "Banana"],
        ...     }
        ... )
        >>> df.group_by("d", maintain_order=True).last()
        shape: (3, 4)
        ┌────────┬─────┬──────┬───────┐
        │ d      ┆ a   ┆ b    ┆ c     │
        │ ---    ┆ --- ┆ ---  ┆ ---   │
        │ str    ┆ i64 ┆ f64  ┆ bool  │
        ╞════════╪═════╪══════╪═══════╡
        │ Apple  ┆ 3   ┆ 10.0 ┆ false │
        │ Orange ┆ 2   ┆ 0.5  ┆ true  │
        │ Banana ┆ 5   ┆ 13.0 ┆ true  │
        └────────┴─────┴──────┴───────┘
        """
        return self.agg(F.all().last())

    def max(self) -> DataFrame:
        """
        Reduce the groups to the maximal value.

        Examples
        --------
        >>> df = pl.DataFrame(
        ...     {
        ...         "a": [1, 2, 2, 3, 4, 5],
        ...         "b": [0.5, 0.5, 4, 10, 13, 14],
        ...         "c": [True, True, True, False, False, True],
        ...         "d": ["Apple", "Orange", "Apple", "Apple", "Banana", "Banana"],
        ...     }
        ... )
        >>> df.group_by("d", maintain_order=True).max()
        shape: (3, 4)
        ┌────────┬─────┬──────┬──────┐
        │ d      ┆ a   ┆ b    ┆ c    │
        │ ---    ┆ --- ┆ ---  ┆ ---  │
        │ str    ┆ i64 ┆ f64  ┆ bool │
        ╞════════╪═════╪══════╪══════╡
        │ Apple  ┆ 3   ┆ 10.0 ┆ true │
        │ Orange ┆ 2   ┆ 0.5  ┆ true │
        │ Banana ┆ 5   ┆ 14.0 ┆ true │
        └────────┴─────┴──────┴──────┘
        """
        return self.agg(F.all().max())

    def mean(self) -> DataFrame:
        """
        Reduce the groups to the mean values.

        Examples
        --------
        >>> df = pl.DataFrame(
        ...     {
        ...         "a": [1, 2, 2, 3, 4, 5],
        ...         "b": [0.5, 0.5, 4, 10, 13, 14],
        ...         "c": [True, True, True, False, False, True],
        ...         "d": ["Apple", "Orange", "Apple", "Apple", "Banana", "Banana"],
        ...     }
        ... )
        >>> df.group_by("d", maintain_order=True).mean()
        shape: (3, 4)
        ┌────────┬─────┬──────────┬──────────┐
        │ d      ┆ a   ┆ b        ┆ c        │
        │ ---    ┆ --- ┆ ---      ┆ ---      │
        │ str    ┆ f64 ┆ f64      ┆ f64      │
        ╞════════╪═════╪══════════╪══════════╡
        │ Apple  ┆ 2.0 ┆ 4.833333 ┆ 0.666667 │
        │ Orange ┆ 2.0 ┆ 0.5      ┆ 1.0      │
        │ Banana ┆ 4.5 ┆ 13.5     ┆ 0.5      │
        └────────┴─────┴──────────┴──────────┘
        """
        return self.agg(F.all().mean())

    def median(self) -> DataFrame:
        """
        Return the median per group.

        Examples
        --------
        >>> df = pl.DataFrame(
        ...     {
        ...         "a": [1, 2, 2, 3, 4, 5],
        ...         "b": [0.5, 0.5, 4, 10, 13, 14],
        ...         "d": ["Apple", "Banana", "Apple", "Apple", "Banana", "Banana"],
        ...     }
        ... )
        >>> df.group_by("d", maintain_order=True).median()
        shape: (2, 3)
        ┌────────┬─────┬──────┐
        │ d      ┆ a   ┆ b    │
        │ ---    ┆ --- ┆ ---  │
        │ str    ┆ f64 ┆ f64  │
        ╞════════╪═════╪══════╡
        │ Apple  ┆ 2.0 ┆ 4.0  │
        │ Banana ┆ 4.0 ┆ 13.0 │
        └────────┴─────┴──────┘
        """
        return self.agg(F.all().median())

    def min(self) -> DataFrame:
        """
        Reduce the groups to the minimal value.

        Examples
        --------
        >>> df = pl.DataFrame(
        ...     {
        ...         "a": [1, 2, 2, 3, 4, 5],
        ...         "b": [0.5, 0.5, 4, 10, 13, 14],
        ...         "c": [True, True, True, False, False, True],
        ...         "d": ["Apple", "Orange", "Apple", "Apple", "Banana", "Banana"],
        ...     }
        ... )
        >>> df.group_by("d", maintain_order=True).min()
        shape: (3, 4)
        ┌────────┬─────┬──────┬───────┐
        │ d      ┆ a   ┆ b    ┆ c     │
        │ ---    ┆ --- ┆ ---  ┆ ---   │
        │ str    ┆ i64 ┆ f64  ┆ bool  │
        ╞════════╪═════╪══════╪═══════╡
        │ Apple  ┆ 1   ┆ 0.5  ┆ false │
        │ Orange ┆ 2   ┆ 0.5  ┆ true  │
        │ Banana ┆ 4   ┆ 13.0 ┆ false │
        └────────┴─────┴──────┴───────┘
        """
        return self.agg(F.all().min())

    def n_unique(self) -> DataFrame:
        """
        Count the unique values per group.

        Examples
        --------
        >>> df = pl.DataFrame(
        ...     {
        ...         "a": [1, 2, 1, 3, 4, 5],
        ...         "b": [0.5, 0.5, 0.5, 10, 13, 14],
        ...         "d": ["Apple", "Banana", "Apple", "Apple", "Banana", "Banana"],
        ...     }
        ... )
        >>> df.group_by("d", maintain_order=True).n_unique()
        shape: (2, 3)
        ┌────────┬─────┬─────┐
        │ d      ┆ a   ┆ b   │
        │ ---    ┆ --- ┆ --- │
        │ str    ┆ u32 ┆ u32 │
        ╞════════╪═════╪═════╡
        │ Apple  ┆ 2   ┆ 2   │
        │ Banana ┆ 3   ┆ 3   │
        └────────┴─────┴─────┘
        """
        return self.agg(F.all().n_unique())

    def quantile(
        self, quantile: float, interpolation: QuantileMethod = "nearest"
    ) -> DataFrame:
        """
        Compute the quantile per group.

        Parameters
        ----------
        quantile
            Quantile between 0.0 and 1.0.
        interpolation : {'nearest', 'higher', 'lower', 'midpoint', 'linear', 'equiprobable'}
            Interpolation method.

        Examples
        --------
        >>> df = pl.DataFrame(
        ...     {
        ...         "a": [1, 2, 2, 3, 4, 5],
        ...         "b": [0.5, 0.5, 4, 10, 13, 14],
        ...         "d": ["Apple", "Orange", "Apple", "Apple", "Banana", "Banana"],
        ...     }
        ... )
        >>> df.group_by("d", maintain_order=True).quantile(1)
        shape: (3, 3)
        ┌────────┬─────┬──────┐
        │ d      ┆ a   ┆ b    │
        │ ---    ┆ --- ┆ ---  │
        │ str    ┆ f64 ┆ f64  │
        ╞════════╪═════╪══════╡
        │ Apple  ┆ 3.0 ┆ 10.0 │
        │ Orange ┆ 2.0 ┆ 0.5  │
        │ Banana ┆ 5.0 ┆ 14.0 │
        └────────┴─────┴──────┘
        """  # noqa: W505
        return self.agg(F.all().quantile(quantile, interpolation=interpolation))

    def sum(self) -> DataFrame:
        """
        Reduce the groups to the sum.

        Examples
        --------
        >>> df = pl.DataFrame(
        ...     {
        ...         "a": [1, 2, 2, 3, 4, 5],
        ...         "b": [0.5, 0.5, 4, 10, 13, 14],
        ...         "c": [True, True, True, False, False, True],
        ...         "d": ["Apple", "Orange", "Apple", "Apple", "Banana", "Banana"],
        ...     }
        ... )
        >>> df.group_by("d", maintain_order=True).sum()
        shape: (3, 4)
        ┌────────┬─────┬──────┬─────┐
        │ d      ┆ a   ┆ b    ┆ c   │
        │ ---    ┆ --- ┆ ---  ┆ --- │
        │ str    ┆ i64 ┆ f64  ┆ u32 │
        ╞════════╪═════╪══════╪═════╡
        │ Apple  ┆ 6   ┆ 14.5 ┆ 2   │
        │ Orange ┆ 2   ┆ 0.5  ┆ 1   │
        │ Banana ┆ 9   ┆ 27.0 ┆ 1   │
        └────────┴─────┴──────┴─────┘
        """
        return self.agg(F.all().sum())


class RollingGroupBy:
    """
    A rolling grouper.

    This has an `.agg` method which will allow you to run all polars expressions in a
    group by context.
    """

    def __init__(
        self,
        df: DataFrame,
        index_column: IntoExpr,
        *,
        period: str | timedelta,
        offset: str | timedelta | None,
        closed: ClosedInterval,
        group_by: IntoExpr | Iterable[IntoExpr] | None,
    ) -> None:
        period = parse_as_duration_string(period)
        offset = parse_as_duration_string(offset)

        self.df = df
        self.time_column = index_column
        self.period = period
        self.offset = offset
        self.closed = closed
        self.group_by = group_by

    def __iter__(self) -> Self:
        from polars.lazyframe.opt_flags import QueryOptFlags

        temp_col = "__POLARS_GB_GROUP_INDICES"
        groups_df = (
            self.df.lazy()
            .rolling(
                index_column=self.time_column,
                period=self.period,
                offset=self.offset,
                closed=self.closed,
                group_by=self.group_by,
            )
            .agg(F.first().agg_groups().alias(temp_col))
            .collect(optimizations=QueryOptFlags.none())
        )

        self._group_names = groups_df.select(F.all().exclude(temp_col)).iter_rows()
        self._group_indices = groups_df.select(temp_col).to_series()
        self._current_index = 0

        return self

    def __next__(self) -> tuple[tuple[object, ...], DataFrame]:
        if self._current_index >= len(self._group_indices):
            raise StopIteration

        group_name = next(self._group_names)
        group_data = self.df[self._group_indices[self._current_index], :]
        self._current_index += 1

        return group_name, group_data

    def agg(
        self,
        *aggs: IntoExpr | Iterable[IntoExpr],
        **named_aggs: IntoExpr,
    ) -> DataFrame:
        """
        Compute aggregations for each group of a group by operation.

        Parameters
        ----------
        *aggs
            Aggregations to compute for each group of the group by operation,
            specified as positional arguments.
            Accepts expression input. Strings are parsed as column names.
        **named_aggs
            Additional aggregations, specified as keyword arguments.
            The resulting columns will be renamed to the keyword used.
        """
        from polars.lazyframe.opt_flags import QueryOptFlags

        return (
            self.df.lazy()
            .rolling(
                index_column=self.time_column,
                period=self.period,
                offset=self.offset,
                closed=self.closed,
                group_by=self.group_by,
            )
            .agg(*aggs, **named_aggs)
            .collect(optimizations=QueryOptFlags.none())
        )

    def map_groups(
        self,
        function: Callable[[DataFrame], DataFrame],
        schema: SchemaDict | None,
    ) -> DataFrame:
        """
        Apply a custom/user-defined function (UDF) over the groups as a new DataFrame.

        Using this is considered an anti-pattern as it will be very slow because:

        - it forces the engine to materialize the whole `DataFrames` for the groups.
        - it is not parallelized.
        - it blocks optimizations as the passed python function is opaque to the
          optimizer.

        The idiomatic way to apply custom functions over multiple columns is using:

        `pl.struct([my_columns]).map_elements(lambda struct_series: ..)`

        Parameters
        ----------
        function
            Function to apply over each group of the `LazyFrame`; it receives
            a DataFrame and should return a DataFrame.
        schema
            Schema of the output function. This has to be known statically. If the
            given schema is incorrect, this is a bug in the caller's query and may
            lead to errors. If set to None, polars assumes the schema is unchanged.
        """
        from polars.lazyframe.opt_flags import QueryOptFlags

        return (
            self.df.lazy()
            .rolling(
                index_column=self.time_column,
                period=self.period,
                offset=self.offset,
                closed=self.closed,
                group_by=self.group_by,
            )
            .map_groups(function, schema)
            .collect(optimizations=QueryOptFlags.none())
        )


class DynamicGroupBy:
    """
    A dynamic grouper.

    This has an `.agg` method which allows you to run all polars expressions in a
    group by context.
    """

    def __init__(
        self,
        df: DataFrame,
        index_column: IntoExpr,
        *,
        every: str | timedelta,
        period: str | timedelta | None,
        offset: str | timedelta | None,
        include_boundaries: bool,
        closed: ClosedInterval,
        label: Label,
        group_by: IntoExpr | Iterable[IntoExpr] | None,
        start_by: StartBy,
    ) -> None:
        every = parse_as_duration_string(every)
        period = parse_as_duration_string(period)
        offset = parse_as_duration_string(offset)

        self.df = df
        self.time_column = index_column
        self.every = every
        self.period = period
        self.offset = offset
        self.label = label
        self.include_boundaries = include_boundaries
        self.closed = closed
        self.group_by = group_by
        self.start_by = start_by

    def __iter__(self) -> Self:
        from polars.lazyframe.opt_flags import QueryOptFlags

        temp_col = "__POLARS_GB_GROUP_INDICES"
        groups_df = (
            self.df.lazy()
            .group_by_dynamic(
                index_column=self.time_column,
                every=self.every,
                period=self.period,
                offset=self.offset,
                label=self.label,
                include_boundaries=self.include_boundaries,
                closed=self.closed,
                group_by=self.group_by,
                start_by=self.start_by,
            )
            .agg(F.first().agg_groups().alias(temp_col))
            .collect(optimizations=QueryOptFlags.none())
        )

        self._group_names = groups_df.select(F.all().exclude(temp_col)).iter_rows()
        self._group_indices = groups_df.select(temp_col).to_series()
        self._current_index = 0

        return self

    def __next__(self) -> tuple[tuple[object, ...], DataFrame]:
        if self._current_index >= len(self._group_indices):
            raise StopIteration

        group_name = next(self._group_names)
        group_data = self.df[self._group_indices[self._current_index], :]
        self._current_index += 1

        return group_name, group_data

    def agg(
        self,
        *aggs: IntoExpr | Iterable[IntoExpr],
        **named_aggs: IntoExpr,
    ) -> DataFrame:
        """
        Compute aggregations for each group of a group by operation.

        Parameters
        ----------
        *aggs
            Aggregations to compute for each group of the group by operation,
            specified as positional arguments.
            Accepts expression input. Strings are parsed as column names.
        **named_aggs
            Additional aggregations, specified as keyword arguments.
            The resulting columns will be renamed to the keyword used.
        """
        from polars.lazyframe.opt_flags import QueryOptFlags

        return (
            self.df.lazy()
            .group_by_dynamic(
                index_column=self.time_column,
                every=self.every,
                period=self.period,
                offset=self.offset,
                label=self.label,
                include_boundaries=self.include_boundaries,
                closed=self.closed,
                group_by=self.group_by,
                start_by=self.start_by,
            )
            .agg(*aggs, **named_aggs)
            .collect(optimizations=QueryOptFlags.none())
        )

    def map_groups(
        self,
        function: Callable[[DataFrame], DataFrame],
        schema: SchemaDict | None,
    ) -> DataFrame:
        """
        Apply a custom/user-defined function (UDF) over the groups as a new DataFrame.

        Using this is considered an anti-pattern as it will be very slow because:

        - it forces the engine to materialize the whole `DataFrames` for the groups.
        - it is not parallelized.
        - it blocks optimizations as the passed python function is opaque to the
          optimizer.

        The idiomatic way to apply custom functions over multiple columns is using:

        `pl.struct([my_columns]).map_elements(lambda struct_series: ..)`

        Parameters
        ----------
        function
            Function to apply over each group of the `LazyFrame`; it receives
            a DataFrame and should return a DataFrame.
        schema
            Schema of the output function. This has to be known statically. If the
            given schema is incorrect, this is a bug in the caller's query and may
            lead to errors. If set to None, polars assumes the schema is unchanged.
        """
        from polars.lazyframe.opt_flags import QueryOptFlags

        return (
            self.df.lazy()
            .group_by_dynamic(
                index_column=self.time_column,
                every=self.every,
                period=self.period,
                offset=self.offset,
                include_boundaries=self.include_boundaries,
                closed=self.closed,
                group_by=self.group_by,
                start_by=self.start_by,
            )
            .map_groups(function, schema)
            .collect(optimizations=QueryOptFlags.none())
        )