# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022-2026)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import annotations

import json
from dataclasses import dataclass
from typing import (
    TYPE_CHECKING,
    Any,
    Final,
    Literal,
    TypeAlias,
    TypedDict,
    cast,
    overload,
)

from streamlit import dataframe_util
from streamlit.dataframe import lazy_df_source as dataframe_source
from streamlit.deprecation_util import (
    make_deprecated_name_warning,
    show_deprecation_warning,
)
from streamlit.elements.lib.column_config_utils import (
    INDEX_IDENTIFIER,
    ColumnConfigMappingInput,
    apply_data_specific_configs,
    extract_button_column_configs,
    marshall_column_config,
    process_config_mapping,
    register_button_column_widgets,
    update_column_config,
)
from streamlit.elements.lib.form_utils import current_form_id
from streamlit.elements.lib.layout_utils import (
    Height,
    LayoutConfig,
    Width,
    validate_height,
    validate_width,
)
from streamlit.elements.lib.pandas_styler_utils import marshall_styler
from streamlit.elements.lib.policies import check_widget_policies
from streamlit.elements.lib.utils import Key, compute_and_register_element_id, to_key
from streamlit.errors import StreamlitAPIException, StreamlitValueError
from streamlit.proto.Dataframe_pb2 import (
    Dataframe as DataframeProto,
)
from streamlit.proto.Dataframe_pb2 import (
    LazyDataframe as LazyDataframeProto,
)
from streamlit.runtime.metrics_util import gather_metrics
from streamlit.runtime.scriptrunner_utils.script_run_context import (
    get_script_run_ctx,
)
from streamlit.runtime.state import WidgetCallback, register_widget
from streamlit.util import ReadOnlyAttributeDictionary

if TYPE_CHECKING:
    from collections.abc import Iterable

    from streamlit.dataframe_util import Data
    from streamlit.delta_generator import DeltaGenerator
    from streamlit.proto.ArrowData_pb2 import ArrowData as ArrowDataProto
    from streamlit.runtime.dataframe_source_manager import DataframeSourceManager


SelectionMode: TypeAlias = Literal[
    "single-row",
    "single-row-required",
    "multi-row",
    "single-column",
    "multi-column",
    "single-cell",
    "multi-cell",
]
_SELECTION_MODES: Final[set[SelectionMode]] = {
    "single-row",
    "single-row-required",
    "multi-row",
    "single-column",
    "multi-column",
    "single-cell",
    "multi-cell",
}
_ROW_SELECTION_MODES: Final[set[SelectionMode]] = {
    "single-row",
    "single-row-required",
    "multi-row",
}


class DataframeSelectionStateInput(TypedDict, total=False):
    """The accepted dictionary schema for a dataframe selection."""

    rows: list[int]
    columns: list[str]
    cells: list[tuple[int, str]]


class DataframeStateInput(TypedDict):
    """The accepted dictionary schema for a dataframe event state."""

    selection: DataframeSelectionStateInput


class DataframeSelectionState(ReadOnlyAttributeDictionary):
    """
    The schema for the dataframe selection state.

    The selection state is stored in a dictionary-like object that supports both
    key and attribute notation. Selection states can be programmatically set
    through Session State by assigning a dictionary matching
    ``DataframeSelectionStateInput`` to the ``"selection"`` key of a
    ``DataframeStateInput`` dictionary.

    Programmatic selection is supported for all selection modes
    except ``"multi-cell"``. If ``"single-cell"`` isn't included in the
    selection modes of the dataframe, programmatic cell selections are ignored.

    .. note::
        Row selections are preserved when a user sorts the dataframe in their
        browser. The selected rows continue to reference the same underlying
        data rows by their original integer position.

    Attributes
    ----------
    rows : list[int]
        The selected rows, identified by their integer position. The integer
        positions match the original dataframe, even if the user sorts the
        dataframe in their browser. For a ``pandas.DataFrame``, you can
        retrieve data from its integer position using methods like ``.iloc[]``
        or ``.iat[]``.
    columns : list[str]
        The selected columns, identified by their names.
    cells : list[tuple[int, str]]
        The selected cells, provided as a tuple of row integer position
        and column name. For example, the first cell in a column named "col 1"
        is represented as ``(0, "col 1")``. Cells within index columns are not
        returned.

    Examples
    --------
    **Example 1: Enable dataframe selections**

    The following example has multi-row and multi-column selections enabled.
    Try selecting some rows. To select multiple columns, hold ``CMD`` (macOS)
    or ``Ctrl`` (Windows) while selecting columns. Hold ``Shift`` to select a
    range of columns.

    .. code-block:: python
        :filename: streamlit_app.py

        import pandas as pd
        import streamlit as st
        from numpy.random import default_rng as rng

        df = pd.DataFrame(
            rng(0).standard_normal((12, 5)), columns=["a", "b", "c", "d", "e"]
        )

        event = st.dataframe(
            df,
            key="data",
            on_select="rerun",
            selection_mode=["multi-row", "multi-column", "multi-cell"],
        )

        event.selection

    .. output::
        https://doc-dataframe-events-selection-state.streamlit.app
        height: 600px

    **Example 2: Programmatically set selections**

    To programmatically set dataframe selections, assign a key to your
    dataframe and set the selection through Session State.

    .. code-block:: python
        :filename: streamlit_app.py

        import pandas as pd
        import streamlit as st
        from numpy.random import default_rng as rng

        df = pd.DataFrame(
            rng(0).standard_normal((12, 5)), columns=["a", "b", "c", "d", "e"]
        )

        if st.button("Select the first row"):
            st.session_state.data = {"selection": {"rows": [0]}}
        if st.button("Select column a"):
            st.session_state.data = {"selection": {"columns": ["a"]}}
        if st.button("Select the first cell of column a"):
            st.session_state.data = {"selection": {"cells": [[0, "a"]]}}

        event = st.dataframe(
            df,
            key="data",
            on_select="rerun",
            selection_mode=["single-cell", "single-row", "single-column"],
        )

        event.selection

    .. output::
        https://doc-dataframe-programmatic-selections.streamlit.app
        height: 600px

    """

    rows: list[int]
    columns: list[str]
    cells: list[tuple[int, str]]

    @overload
    def __getitem__(self, key: Literal["rows"]) -> list[int]: ...

    @overload
    def __getitem__(self, key: Literal["columns"]) -> list[str]: ...

    @overload
    def __getitem__(self, key: Literal["cells"]) -> list[tuple[int, str]]: ...

    @overload
    def __getitem__(self, key: Any) -> Any: ...

    def __getitem__(self, key: Any) -> Any:
        return super().__getitem__(key)


class DataframeState(ReadOnlyAttributeDictionary):
    """
    The schema for the dataframe event state.

    To use this type in an annotation, import it from ``streamlit.typing``.

    The event state is stored in a dictionary-like object that supports both
    key and attribute notation. Event states can be programmatically set
    through session state by assigning a dictionary with the same schema to the
    widget's key, e.g.,
    ``st.session_state["my_key"] = {"selection": {"rows": [0, 2]}}``.

    Only selection events are supported at this time.

    Attributes
    ----------
    selection : dict
        The state of the ``on_select`` event. This attribute returns a
        dictionary-like object that supports both key and attribute notation.
        The attributes are described by ``DataframeSelectionState``.

    """

    selection: DataframeSelectionState

    # ReadOnlyAttributeDictionary routes attribute access through __getitem__,
    # so the override below is enough to return DataframeSelectionState. Use
    # dict.__getitem__ for the selection key so the read-only base class does
    # not re-wrap the already-typed nested instance.
    @overload
    def __getitem__(self, key: Literal["selection"]) -> DataframeSelectionState: ...

    @overload
    def __getitem__(self, key: Any) -> Any: ...

    def __getitem__(self, key: Any) -> Any:
        if key == "selection":
            item = dict.__getitem__(self, key)
            if not isinstance(item, DataframeSelectionState):
                item = DataframeSelectionState(item)
                # Cache via dict.__setitem__ — ReadOnlyAttributeDictionary
                # blocks normal mutation, but storing the wrapped instance
                # keeps identity stable across accesses.
                dict.__setitem__(self, key, item)
            return item
        return super().__getitem__(key)


@dataclass
class DataframeSelectionSerde:
    """DataframeSelectionSerde is used to serialize and deserialize the dataframe selection state."""

    selection_default: DataframeStateInput | DataframeState | None = None
    is_required_row_mode: bool = False
    num_rows: int = 0

    def deserialize(self, ui_value: str | None) -> DataframeState:
        # Keep the empty selection as a plain dict until the end so required-row
        # and missing-key mutations below can still run before we wrap.
        empty_selection_state: dict[str, Any] = {
            "selection": {
                "rows": [],
                "columns": [],
                "cells": [],
            },
        }

        if ui_value is not None:
            selection_state: Any = json.loads(ui_value)
        elif self.selection_default is not None:
            # When a selection_default is provided, use it as the initial
            # deserialized value so the first-render Python return matches
            # the default selection the frontend will display.
            # Shallow copy to avoid mutating the caller's default.
            selection_state = {"selection": dict(self.selection_default["selection"])}
        else:
            selection_state = empty_selection_state

        if "selection" not in selection_state:
            selection_state = empty_selection_state

        if "rows" not in selection_state["selection"]:
            selection_state["selection"]["rows"] = []

        if "columns" not in selection_state["selection"]:
            selection_state["selection"]["columns"] = []

        if "cells" not in selection_state["selection"]:
            selection_state["selection"]["cells"] = []
        else:
            # Explicitly convert all cells to a tuple (from list).
            # This is necessary since there isn't a concept of tuples in JSON
            # The format that the data is transferred to the backend.
            selection_state["selection"]["cells"] = [
                tuple(cell)
                for cell in cast(
                    "list[list[Any]]", selection_state["selection"]["cells"]
                )
            ]

        # In single-row-required mode, auto-select the first row if no rows
        # are selected (and the dataframe has at least one row).
        if (
            self.is_required_row_mode
            and self.num_rows > 0
            and not selection_state["selection"]["rows"]
        ):
            selection_state["selection"]["rows"] = [0]

        # Eagerly wrap selection so bracket access returns a stable typed
        # instance instead of creating a shallow copy on every access.
        selection_state["selection"] = DataframeSelectionState(
            selection_state["selection"]
        )
        return DataframeState(selection_state)

    def serialize(self, state: DataframeState | DataframeStateInput) -> str:
        return json.dumps(state)


def parse_selection_mode(
    selection_mode: SelectionMode | Iterable[SelectionMode],
) -> set[DataframeProto.SelectionMode.ValueType]:
    """Parse and check the user provided selection modes."""
    selection_mode_set = _normalize_selection_mode(selection_mode)
    return _selection_mode_set_to_proto_values(selection_mode_set)


def _normalize_selection_mode(
    selection_mode: SelectionMode | Iterable[SelectionMode],
) -> set[SelectionMode]:
    """Normalize and validate the user provided selection modes."""
    if isinstance(selection_mode, str):
        # Only a single selection mode was passed
        raw_selection_mode_set = {selection_mode}
    else:
        # Multiple selection modes were passed
        raw_selection_mode_set = set(selection_mode)

    if not raw_selection_mode_set <= _SELECTION_MODES:
        raise StreamlitValueError(
            "selection_mode",
            [f"'{mode}'" for mode in sorted(_SELECTION_MODES)],
        )

    # Intersection preserves the SelectionMode literal type for ty/mypy.
    selection_mode_set = _SELECTION_MODES & raw_selection_mode_set

    # Ensure at most one row selection mode is specified.
    row_modes = selection_mode_set & _ROW_SELECTION_MODES
    if len(row_modes) > 1:
        raise StreamlitAPIException(
            "Only one row selection mode can be specified. "
            f"Found: {', '.join(f'`{m}`' for m in sorted(row_modes))}."
        )

    if selection_mode_set.issuperset({"single-column", "multi-column"}):
        raise StreamlitAPIException(
            "Only one of `single-column` or `multi-column` can be selected as selection mode."
        )

    if selection_mode_set.issuperset({"single-cell", "multi-cell"}):
        raise StreamlitAPIException(
            "Only one of `single-cell` or `multi-cell` can be selected as selection mode."
        )

    return selection_mode_set


_SELECTION_MODE_TO_PROTO: Final[
    dict[SelectionMode, DataframeProto.SelectionMode.ValueType]
] = {
    "single-row": DataframeProto.SelectionMode.SINGLE_ROW,
    "single-row-required": DataframeProto.SelectionMode.SINGLE_ROW_REQUIRED,
    "multi-row": DataframeProto.SelectionMode.MULTI_ROW,
    "single-column": DataframeProto.SelectionMode.SINGLE_COLUMN,
    "multi-column": DataframeProto.SelectionMode.MULTI_COLUMN,
    "single-cell": DataframeProto.SelectionMode.SINGLE_CELL,
    "multi-cell": DataframeProto.SelectionMode.MULTI_CELL,
}


_ACCESS_MODE_TO_PROTO: Final[
    dict[dataframe_source.AccessMode, LazyDataframeProto.AccessMode.ValueType]
] = {
    dataframe_source.AccessMode.RANDOM_ACCESS: (
        LazyDataframeProto.AccessMode.RANDOM_ACCESS
    ),
    dataframe_source.AccessMode.SEQUENTIAL: LazyDataframeProto.AccessMode.SEQUENTIAL,
}


def _selection_mode_set_to_proto_values(
    selection_mode_set: set[SelectionMode],
) -> set[DataframeProto.SelectionMode.ValueType]:
    """Convert normalized selection mode strings to protobuf enum values."""
    return {_SELECTION_MODE_TO_PROTO[mode] for mode in selection_mode_set}


def _validate_selection_state(
    value: Any,
    num_rows: int,
    column_names: list[str],
    selection_mode_set: set[SelectionMode],
) -> DataframeStateInput:
    """Validate a programmatically set selection state.

    Parameters
    ----------
    value
        The untrusted selection state to validate. Typed as ``Any`` because
        users can assign arbitrary values via ``st.session_state``.
    num_rows
        The number of rows in the dataframe.
    column_names
        The list of column names in the dataframe.
    selection_mode_set
        The set of allowed selection modes.

    Returns
    -------
    DataframeState
        The validated selection state (with invalid entries filtered out).

    Raises
    ------
    StreamlitAPIException
        If the selection state structure is invalid.
    """
    if not isinstance(value, dict) or not isinstance(value.get("selection"), dict):
        raise StreamlitAPIException(
            "Selection state must be a dictionary with a 'selection' key "
            "containing 'rows', 'columns', and 'cells' arrays."
        )

    selection = value["selection"]

    validated_selection: DataframeSelectionStateInput = {
        "rows": [],
        "columns": [],
        "cells": [],
    }

    column_name_set = set(column_names)

    # Validate and filter rows.
    # Non-list values are silently ignored to be defensive against bad input.
    raw_rows = selection.get("rows")
    is_row_selection_mode = bool(selection_mode_set & _ROW_SELECTION_MODES)
    if isinstance(raw_rows, list) and is_row_selection_mode:
        valid_rows = list(
            dict.fromkeys(
                row_idx
                for row_idx in raw_rows
                if isinstance(row_idx, int) and 0 <= row_idx < num_rows
            )
        )
        validated_selection["rows"] = (
            valid_rows if "multi-row" in selection_mode_set else valid_rows[:1]
        )

    # In single-row-required mode, auto-select first row if no rows are selected
    # (and the dataframe has at least one row).
    if (
        "single-row-required" in selection_mode_set
        and num_rows > 0
        and not validated_selection["rows"]
    ):
        validated_selection["rows"] = [0]

    # Validate and filter columns.
    # Non-list values are silently ignored to be defensive against bad input.
    raw_columns = selection.get("columns")
    if isinstance(raw_columns, list) and selection_mode_set & {
        "single-column",
        "multi-column",
    }:
        valid_columns = list(
            dict.fromkeys(
                col_name
                for col_name in raw_columns
                if isinstance(col_name, str) and col_name in column_name_set
            )
        )
        validated_selection["columns"] = (
            valid_columns if "multi-column" in selection_mode_set else valid_columns[:1]
        )

    # Validate and filter cells (single-cell mode only).
    # Multi-cell selections use rectangular ranges that cannot be reconstructed
    # from individual cell positions, so programmatic cell setting is only
    # supported for single-cell mode. Non-list values are silently ignored.
    raw_cells = selection.get("cells")
    if isinstance(raw_cells, list) and "single-cell" in selection_mode_set:
        valid_cells: list[tuple[int, str]] = list(
            dict.fromkeys(
                (cell[0], cell[1])
                for cell in raw_cells
                if isinstance(cell, (list, tuple))
                and len(cell) == 2
                and isinstance(cell[0], int)
                and 0 <= cell[0] < num_rows
                and isinstance(cell[1], str)
                and cell[1] in column_name_set
            )
        )
        validated_selection["cells"] = valid_cells[:1]

    return {"selection": validated_selection}


def _get_dataframe_source_mgr() -> DataframeSourceManager | None:
    """Return the runtime's dataframe source manager, or ``None`` if no runtime.

    Lazy delivery requires a running Streamlit server to serve chunk requests.
    In bare "python myscript.py" mode there is no frontend, so callers fall back
    to eager rendering.
    """
    from streamlit import runtime

    if not runtime.exists():
        return None
    return runtime.get_instance().dataframe_source_mgr


def _marshall_lazy_dataframe(
    proto: DataframeProto,
    source: dataframe_source.DataframeSource,
    dg: DeltaGenerator,
) -> bool:
    """Register ``source`` and populate ``proto.lazy_data``.

    Returns ``True`` if the source was registered and lazy metadata was set, or
    ``False`` if lazy delivery is unavailable (no active runtime), in which case
    the caller should fall back to eager rendering.
    """
    source_mgr = _get_dataframe_source_mgr()
    if source_mgr is None:
        return False

    # Resolve every source-dependent value and serialize the first page before
    # registration. If loading or serialization fails, no coordinate reference
    # is left behind retaining a potentially large in-memory or remote source.
    page_size = dataframe_source.DEFAULT_PAGE_SIZE
    row_count = source.row_count
    access_mode = source.access_mode
    sortable = source.sortable
    initial_table = source.load_rows(0, page_size)
    initial_bytes = dataframe_util.convert_arrow_table_to_arrow_bytes(initial_table)

    coordinates = dg._get_delta_path_str()
    registered = source_mgr.register_source(source, coordinates, page_size=page_size)

    lazy_data = proto.lazy_data
    lazy_data.source_id = registered.source_id
    lazy_data.page_size = registered.page_size
    lazy_data.access_mode = _ACCESS_MODE_TO_PROTO[access_mode]
    lazy_data.sortable = sortable
    # ``row_count`` is optional in the proto: unknown-size (future sequential)
    # sources report ``None`` and leave the field unset.
    if row_count is not None:
        lazy_data.row_count = row_count

    # Serve the first page as the initial chunk. It carries the Arrow schema and
    # the first visible rows so the frontend can render columns immediately.
    lazy_data.initial_chunk.data = initial_bytes
    return True


class ArrowMixin:
    @overload
    def dataframe(
        self,
        data: Data = None,
        width: Width = "stretch",
        height: Height | Literal["auto"] = "auto",
        *,
        use_container_width: bool | None = None,
        hide_index: bool | None = None,
        column_order: Iterable[str] | None = None,
        column_config: ColumnConfigMappingInput | None = None,
        key: Key | None = None,
        on_select: Literal["ignore"] = "ignore",
        selection_mode: SelectionMode | Iterable[SelectionMode] = "multi-row",
        selection_default: DataframeStateInput | DataframeState | None = None,
        row_height: int | None = None,
        placeholder: str | None = None,
        lazy: bool | None = None,
    ) -> DeltaGenerator: ...

    @overload
    def dataframe(
        self,
        data: Data = None,
        width: Width = "stretch",
        height: Height | Literal["auto"] = "auto",
        *,
        use_container_width: bool | None = None,
        hide_index: bool | None = None,
        column_order: Iterable[str] | None = None,
        column_config: ColumnConfigMappingInput | None = None,
        key: Key | None = None,
        on_select: Literal["rerun"] | WidgetCallback,
        selection_mode: SelectionMode | Iterable[SelectionMode] = "multi-row",
        selection_default: DataframeStateInput | DataframeState | None = None,
        row_height: int | None = None,
        placeholder: str | None = None,
        lazy: bool | None = None,
    ) -> DataframeState: ...

    @gather_metrics("dataframe")
    def dataframe(
        self,
        data: Data = None,
        width: Width = "stretch",
        height: Height | Literal["auto"] = "auto",
        *,
        use_container_width: bool | None = None,
        hide_index: bool | None = None,
        column_order: Iterable[str] | None = None,
        column_config: ColumnConfigMappingInput | None = None,
        key: Key | None = None,
        on_select: Literal["ignore", "rerun"] | WidgetCallback = "ignore",
        selection_mode: SelectionMode | Iterable[SelectionMode] = "multi-row",
        selection_default: DataframeStateInput | DataframeState | None = None,
        row_height: int | None = None,
        placeholder: str | None = None,
        lazy: bool | None = None,
    ) -> DeltaGenerator | DataframeState:
        """Display a dataframe as an interactive table.

        This command works with a wide variety of collection-like and
        dataframe-like object types.

        To disable data export (CSV download and clipboard copy), set the
        ``client.disableDataExport`` configuration option to ``True``.

        Parameters
        ----------
        data : dataframe-like, collection-like, or None
            The data to display.

            Dataframe-like objects include dataframe and series objects from
            popular libraries like Dask, Modin, Numpy, pandas, Polars, PyArrow,
            Snowpark, Xarray, and more. You can use database cursors and
            clients that comply with the
            `Python Database API Specification v2.0 (PEP 249)
            <https://peps.python.org/pep-0249/>`_. Additionally, you can use
            anything that supports the `Python dataframe interchange protocol
            <https://data-apis.org/dataframe-protocol/latest/>`_.

            For example, you can use the following:

            - ``pandas.DataFrame``, ``pandas.Series``, ``pandas.Index``,
              ``pandas.Styler``, and ``pandas.Array``
            - ``polars.DataFrame``, ``polars.LazyFrame``, and ``polars.Series``
            - ``snowflake.snowpark.dataframe.DataFrame``,
              ``snowflake.snowpark.table.Table``

            If a data type is not recognized, Streamlit will convert the object
            to a ``pandas.DataFrame`` or ``pyarrow.Table`` using a
            ``.to_pandas()`` or ``.to_arrow()`` method, respectively, if
            available.

            If ``data`` is a ``pandas.Styler``, it will be used to style its
            underlying ``pandas.DataFrame``. Streamlit supports custom cell
            values, colors, and font weights. It does not support some of the
            more exotic styling options, like bar charts, hovering, and
            captions. For these styling options, use column configuration
            instead. Text and number formatting from ``column_config`` always
            takes precedence over text and number formatting from ``pandas.Styler``.

            Collection-like objects include all Python-native ``Collection``
            types, such as ``dict``, ``list``, and ``set``.

            If ``data`` is ``None``, Streamlit renders an empty table.

        width : "stretch", "content", or int
            The width of the dataframe element. This can be one of the following:

            - ``"stretch"`` (default): The width of the element matches the
              width of the parent container.
            - ``"content"``: The width of the element matches the width of its
              content, but doesn't exceed the width of the parent container.
            - An integer specifying the width in pixels: The element has a
              fixed width. If the specified width is greater than the width of
              the parent container, the width of the element matches the width
              of the parent container.

        height : "auto", "content", "stretch", or int
            The height of the dataframe element. This can be one of the following:

            - ``"auto"`` (default): Streamlit sets the height to show at most
              ten rows.
            - ``"content"``: The height of the element matches the height of
              its content. The height is capped at 10,000 pixels to prevent
              performance issues with very large dataframes.
            - ``"stretch"``: The height of the element expands to fill the
              available vertical space in its parent container. When multiple
              elements with stretch height are in the same container, they
              share the available vertical space evenly. The dataframe will
              maintain a minimum height to display up to three rows, but
              otherwise won't exceed the available height in its parent
              container.
            - An integer specifying the height in pixels: The element has a
              fixed height.

            Vertical scrolling within the dataframe element is enabled when the
            height does not accommodate all rows.

        use_container_width : bool
            Whether to override ``width`` with the width of the parent
            container. If this is ``True`` (default), Streamlit sets the width
            of the dataframe to match the width of the parent container. If
            this is ``False``, Streamlit sets the dataframe's width according
            to ``width``.

            .. deprecated::
                ``use_container_width`` is deprecated and will be removed in a
                future release. For ``use_container_width=True``, use
                ``width="stretch"``.

        hide_index : bool or None
            Whether to hide the index column(s). If ``hide_index`` is ``None``
            (default), the visibility of index columns is automatically
            determined based on the data and other configurations.

        column_order : Iterable[str] or None
            The ordered list of columns to display. If this is ``None``
            (default), Streamlit displays all columns in the order inherited
            from the underlying data structure. If this is a list, the
            indicated columns will display in the order they appear within the
            list. Columns may be omitted or repeated within the list.

            For example, ``column_order=("col2", "col1")`` will display
            ``"col2"`` first, followed by ``"col1"``, and will hide all other
            non-index columns.

            ``column_order`` does not accept positional column indices and
            can't move the index column(s).

            .. note::
                Columns omitted from ``column_order`` are hidden by default
                but can still be shown by the user via the column visibility
                menu in the table toolbar. If a column contains sensitive data
                that should not be exposed to the user, remove it from the
                data before passing it to the function.

        column_config : dict or None
            Configuration to customize how columns are displayed. If this is
            ``None`` (default), columns are styled based on the underlying data
            type of each column.

            Column configuration can modify column names, visibility, type,
            width, format, and more. If this is a dictionary, the keys are
            column names (strings) and/or positional column indices (integers),
            and the values are one of the following:

            - ``None`` to hide the column. Hidden columns can still be shown
              by the user via the table toolbar.
            - A string to set the display label of the column.
            - One of the column types defined under ``st.column_config``. For
              example, to show a column as dollar amounts, use
              ``st.column_config.NumberColumn("Dollar values", format="$ %d")``.
              See more info on the available column types and config options
              `here <https://docs.streamlit.io/develop/api-reference/data/st.column_config>`_.

            To configure the index column(s), use ``"_index"`` as the column
            name, or use a positional column index where ``0`` refers to the
            first index column.

        key : str, int, or None
            An optional string to use for giving this element a stable
            identity. If this is ``None`` (default), the element's identity
            will be determined based on the values of the other parameters.

            If selections are activated, a key lets you read or update the
            selection state via ``st.session_state[key]``. The value in
            Session State must be a dictionary consistent with the
            ``DataframeState`` schema. For more details, see `Widget behavior
            <https://docs.streamlit.io/develop/concepts/architecture/widget-behavior>`_.

            Additionally, if ``key`` is provided, it will be used as a
            CSS class name prefixed with ``st-key-``.

        on_select : "ignore" or "rerun" or callable
            How the dataframe should respond to user selection events. This
            controls whether or not the dataframe behaves like an input widget.
            ``on_select`` can be one of the following:

            - ``"ignore"`` (default): Streamlit will not react to any selection
              events in the dataframe. The dataframe will not behave like an
              input widget.

            - ``"rerun"``: Streamlit will rerun the app when the user selects
              rows, columns, or cells in the dataframe. In this case,
              ``st.dataframe`` will return the selection data as a dictionary.

            - A ``callable``: Streamlit will rerun the app and execute the
              ``callable`` as a callback function before the rest of the app.
              In this case, ``st.dataframe`` will return the selection data
              as a dictionary.

        selection_mode : "single-row", "single-row-required", "multi-row", \
            "single-column", "multi-column", "single-cell", "multi-cell", \
            or Iterable of these
            The types of selections Streamlit should allow when selections are
            enabled with ``on_select``. This can be one of the following:

            - "multi-row" (default): Multiple rows can be selected at a time.
            - "single-row": Only one row can be selected at a time.
            - "single-row-required": Exactly one row must always be selected
              (radio-like behavior). Auto-selects the first row if no default
              is given.
            - "multi-column": Multiple columns can be selected at a time.
            - "single-column": Only one column can be selected at a time.
            - "multi-cell": A rectangular range of cells can be selected.
            - "single-cell": Only one cell can be selected at a time.
            - An ``Iterable`` of the above options: The table will allow
              selection based on the modes specified. For example, to allow the
              user to select multiple rows and multiple cells, use
              ``["multi-row", "multi-cell"]``.

            When column selections are enabled, column sorting is disabled.

        selection_default : dict or None
            The default selection state to apply on first render when selections
            are activated and no prior selection is stored. This uses the same
            schema as ``st.session_state[key]`` for selections, for example:
            ``{"selection": {"rows": [0, 2]}}``.

            ``selection_default`` is only applied when ``on_select`` is not
            ``"ignore"``. It does not override user selections on subsequent
            runs, and multi-cell selections cannot be set programmatically.

        row_height : int or None
            The height of each row in the dataframe in pixels. If ``row_height``
            is ``None`` (default), Streamlit will use a default row height,
            which fits one line of text.

        placeholder : str or None
            The text that should be shown for missing values. If this is
            ``None`` (default), missing values are displayed as "None". To
            leave a cell empty, use an empty string (``""``). Other common
            values are ``"null"``, ``"NaN"`` and ``"-"``.

        lazy : bool or None
            Whether to load rows lazily from the app server instead of sending
            the full dataset to the browser. When rows are loaded lazily,
            Streamlit sends only the rows the user is viewing and fetches more
            as they scroll or sort. This is useful for datasets that are too
            large to send to the browser all at once. It can also make your app
            faster on reruns, since far less data needs to be transferred from
            the app server to the client (browser). This can be one of the
            following:

            - ``None`` (default): Streamlit chooses automatically. In-memory
              ``pandas`` and ``polars`` dataframes and ``pyarrow.Table`` objects
              are loaded lazily when they have more than 150,000 rows.
              Unevaluated objects, such as a Polars ``LazyFrame``, are loaded
              lazily when they have more than 10,000 rows. Everything else is
              loaded eagerly, including the capped preview for unsupported
              unevaluated objects.
            - ``True``: Always load rows lazily. For inputs that support lazy
              access natively (for example, a Polars ``LazyFrame``), Streamlit
              reads rows directly from the source. Other supported inputs are
              held in the app server's memory and served in chunks. Small
              datasets (1,000 rows or fewer) are still loaded eagerly as an
              optimization. If lazy loading is incompatible with the data or the
              other parameters (for example, dataframes with multi-level,
              ``MultiIndex``, column headers), Streamlit raises a
              ``StreamlitAPIException``.
            - ``False``: Never load rows lazily. Streamlit loads data eagerly
              and uses the capped preview for unevaluated data objects.

            .. note::
                When rows are loaded lazily, search, CSV download, selections
                (``on_select``), and ``pandas.Styler`` styling are not
                supported. Server-side sorting is supported. To use these
                features, set ``lazy=False``.

        Returns
        -------
        element or DataframeState
            If ``on_select`` is ``"ignore"`` (default), this command returns an
            internal placeholder for the dataframe element. Otherwise, this
            command returns a ``DataframeState`` object. This object is
            dictionary-like and supports both key and attribute notation. To
            use this type in an annotation, import it from
            ``streamlit.typing``.

        Examples
        --------
        **Example 1: Display a dataframe**

        >>> import pandas as pd
        >>> import streamlit as st
        >>> from numpy.random import default_rng as rng
        >>>
        >>> df = pd.DataFrame(
        ...     rng(0).standard_normal((50, 20)), columns=("col %d" % i for i in range(20))
        ... )
        >>>
        >>> st.dataframe(df)

        .. output::
           https://doc-dataframe.streamlit.app/
           height: 500px

        **Example 2: Use Pandas Styler**

        You can also pass a Pandas Styler object to change the style of
        the rendered DataFrame:

        >>> import pandas as pd
        >>> import streamlit as st
        >>> from numpy.random import default_rng as rng
        >>>
        >>> df = pd.DataFrame(
        ...     rng(0).standard_normal((10, 20)), columns=("col %d" % i for i in range(20))
        ... )
        >>>
        >>> st.dataframe(df.style.highlight_max(axis=0))

        .. output::
           https://doc-dataframe1.streamlit.app/
           height: 500px

        **Example 3: Use column configuration**

        You can customize a dataframe via ``column_config``, ``hide_index``, or ``column_order``.

        >>> import pandas as pd
        >>> import streamlit as st
        >>> from numpy.random import default_rng as rng
        >>>
        >>> df = pd.DataFrame(
        ...     {
        ...         "name": ["Roadmap", "Extras", "Issues"],
        ...         "url": [
        ...             "https://roadmap.streamlit.app",
        ...             "https://extras.streamlit.app",
        ...             "https://issues.streamlit.app",
        ...         ],
        ...         "stars": rng(0).integers(0, 1000, size=3),
        ...         "views_history": rng(0).integers(0, 5000, size=(3, 30)).tolist(),
        ...     }
        ... )
        >>>
        >>> st.dataframe(
        ...     df,
        ...     column_config={
        ...         "name": "App name",
        ...         "stars": st.column_config.NumberColumn(
        ...             "Github Stars",
        ...             help="Number of stars on GitHub",
        ...             format="%d ⭐",
        ...         ),
        ...         "url": st.column_config.LinkColumn("App URL"),
        ...         "views_history": st.column_config.LineChartColumn(
        ...             "Views (past 30 days)", y_min=0, y_max=5000
        ...         ),
        ...     },
        ...     hide_index=True,
        ... )

        .. output::
           https://doc-dataframe-config.streamlit.app/
           height: 350px

        **Example 4: Customize your index**

        You can use column configuration to format your index.

        >>> from datetime import datetime, date
        >>> import numpy as np
        >>> import pandas as pd
        >>> import streamlit as st
        >>>
        >>> @st.cache_data
        >>> def load_data():
        >>>     year = datetime.now().year
        >>>     df = pd.DataFrame(
        ...         {
        ...             "Date": [date(year, month, 1) for month in range(1, 4)],
        ...             "Total": np.random.randint(1000, 5000, size=3),
        ...         }
        ...     )
        >>>     df.set_index("Date", inplace=True)
        >>>     return df
        >>>
        >>> df = load_data()
        >>> config = {
        ...     "_index": st.column_config.DateColumn("Month", format="MMM YYYY"),
        ...     "Total": st.column_config.NumberColumn("Total ($)"),
        ... }
        >>>
        >>> st.dataframe(df, column_config=config)

        .. output::
           https://doc-dataframe-config-index.streamlit.app/
           height: 225px

        """
        import pyarrow as pa

        if on_select not in {"ignore", "rerun"} and not callable(on_select):
            raise StreamlitValueError(
                "on_select", ["'rerun'", "'ignore'", "a callback function"]
            )

        key = to_key(key)
        is_selection_activated = on_select != "ignore"
        selection_mode_set: set[SelectionMode] = set()

        if selection_default is not None and not is_selection_activated:
            raise StreamlitAPIException(
                "selection_default can only be used when on_select is not 'ignore'."
            )

        if is_selection_activated:
            # Run some checks that are only relevant when selections are activated
            is_callback = callable(on_select)
            check_widget_policies(
                self.dg,
                key,
                on_change=cast("WidgetCallback", on_select)  # ty: ignore[redundant-cast]
                if is_callback
                else None,
                default_value=None,
                writes_allowed=True,
                enable_check_callback_rules=is_callback,
            )
            selection_mode_set = _normalize_selection_mode(selection_mode)

        if use_container_width is not None:
            show_deprecation_warning(
                make_deprecated_name_warning(
                    "use_container_width",
                    "width",
                    "2025-12-31",
                    "For `use_container_width=True`, use `width='stretch'`. "
                    "For `use_container_width=False`, use `width='content'`.",
                    include_st_prefix=False,
                ),
                show_in_browser=False,
            )
            if use_container_width:
                width = "stretch"
            elif not isinstance(width, int):
                width = "content"

        validate_width(width, allow_content=True)
        validate_height(
            height,
            allow_content=True,
            additional_allowed=["auto"],
        )

        # Resolve whether this dataframe should be delivered lazily. This may
        # raise a StreamlitAPIException for incompatible `lazy=True` requests.
        resolved_dataframe = dataframe_source.resolve_lazy_source(
            data, lazy, is_selection_activated=is_selection_activated
        )
        if isinstance(resolved_dataframe, dataframe_source.EagerDataframeFallback):
            eager_fallback_data = resolved_dataframe.data
            lazy_source = None
        else:
            eager_fallback_data = None
            lazy_source = resolved_dataframe

        processed_column_config, button_columns = extract_button_column_configs(
            column_config
        )

        # Convert the user provided column config into the frontend compatible format:
        column_config_mapping = process_config_mapping(processed_column_config)

        proto = DataframeProto()

        if row_height:
            proto.row_height = row_height

        if column_order:
            proto.column_order[:] = column_order

        if placeholder is not None:
            proto.placeholder = placeholder

        proto.editing_mode = DataframeProto.EditingMode.READ_ONLY

        # Track data dimensions for selection validation
        num_rows: int = 0
        column_names: list[str] = []

        has_range_index: bool = False

        # Try lazy delivery first. If there's no active runtime (e.g. bare
        # "python myscript.py" mode), fall back to eager rendering below.
        lazy_marshalled = False
        if lazy_source is not None:
            lazy_marshalled = _marshall_lazy_dataframe(proto, lazy_source, self.dg)
            if lazy_marshalled:
                apply_data_specific_configs(
                    column_config_mapping, dataframe_util.determine_data_format(data)
                )
                # Unknown-size (future sequential) sources report ``None``; the
                # eager num_rows bookkeeping falls back to 0 for those.
                num_rows = lazy_source.row_count or 0
                column_names = list(lazy_source.schema.names)

        if lazy_marshalled:
            # Lazy data is already marshalled into proto.lazy_data; the eager
            # arrow_data field stays empty.
            pass
        elif isinstance(data, pa.Table):
            # For pyarrow tables, we can just serialize the table directly
            proto.arrow_data.data = dataframe_util.convert_arrow_table_to_arrow_bytes(
                data
            )
            num_rows = data.num_rows
            column_names = data.column_names
        else:
            # For all other data formats, we need to convert them to a pandas.DataFrame
            # thereby, we also apply some data specific configs

            # Determine the input data format
            data_format = dataframe_util.determine_data_format(data)

            if dataframe_util.is_pandas_styler(data):
                # If pandas.Styler uuid is not provided, a hash of the position
                # of the element will be used. This will cause a rerender of the table
                # when the position of the element is changed.
                delta_path = self.dg._get_delta_path_str()
                default_uuid = str(hash(delta_path))
                marshall_styler(proto.arrow_data, data, default_uuid)

            # Convert the input data into a pandas.DataFrame
            data_df = dataframe_util.convert_anything_to_pandas_df(
                eager_fallback_data if eager_fallback_data is not None else data,
                ensure_copy=False,
            )
            has_range_index = dataframe_util.has_range_index(data_df)
            apply_data_specific_configs(column_config_mapping, data_format)
            # Serialize the data to bytes:
            proto.arrow_data.data = dataframe_util.convert_pandas_df_to_arrow_bytes(
                data_df
            )
            num_rows = len(data_df)
            column_names = list(data_df.columns)

        if hide_index is not None:
            update_column_config(
                column_config_mapping, INDEX_IDENTIFIER, {"hidden": hide_index}
            )

        elif (
            # Hide index column if row selections are activated and the dataframe has a range index.
            # The range index usually does not add a lot of value.
            is_selection_activated
            and has_range_index
            and selection_mode_set & _ROW_SELECTION_MODES
        ):
            update_column_config(
                column_config_mapping, INDEX_IDENTIFIER, {"hidden": True}
            )

        marshall_column_config(proto, column_config_mapping)

        ctx = get_script_run_ctx()
        register_button_column_widgets(
            dg=self.dg,
            proto=proto,
            button_columns=button_columns,
            ctx=ctx,
        )

        # Preserve the enclosing form ID for dataframe selection state and
        # button-column widgets. Button-column clicks use string triggers, not
        # form-submit semantics, so this only records form association for the
        # frontend.
        if button_columns or is_selection_activated:
            proto.form_id = current_form_id(self.dg)

        # Create layout configuration
        # For height, only include it in LayoutConfig if it's not "auto"
        # "auto" is the default behavior and doesn't need to be sent
        layout_config = LayoutConfig(
            width=width, height=height if height != "auto" else None
        )

        if is_selection_activated:
            # If selection events are activated, we need to register the dataframe
            # element as a widget.
            proto.selection_mode.extend(
                _selection_mode_set_to_proto_values(selection_mode_set)
            )

            normalized_selection_mode = tuple(sorted(selection_mode_set))

            selection_default_json: str | None = None
            validated_default: DataframeStateInput | None = None
            if selection_default is not None:
                validated_default = _validate_selection_state(
                    selection_default,
                    num_rows=num_rows,
                    column_names=column_names,
                    selection_mode_set=selection_mode_set,
                )
                selection_default_json = json.dumps(validated_default)
                proto.selection_default = selection_default_json

            proto.id = compute_and_register_element_id(
                "dataframe",
                user_key=key,
                # There are some edge cases where selections can become orphaned when the data changes
                # - e.g. when rows get removed. The frontend can handle this without errors,
                # but it might be a nice enhancement to automatically reset the backend & frontend
                # selection state in this case.
                key_as_main_identity={"selection_mode", "is_selection_activated"},
                dg=self.dg,
                data=proto.arrow_data.data,
                width=width,
                height=height,
                use_container_width=use_container_width,
                column_order=proto.column_order,
                column_config=proto.columns,
                selection_mode=normalized_selection_mode,
                is_selection_activated=is_selection_activated,
                selection_default=selection_default_json,
                row_height=row_height,
                placeholder=placeholder,
            )

            serde = DataframeSelectionSerde(
                selection_default=validated_default,
                is_required_row_mode="single-row-required" in selection_mode_set,
                num_rows=num_rows,
            )
            widget_state = register_widget(
                proto.id,
                on_change_handler=on_select if callable(on_select) else None,
                deserializer=serde.deserialize,
                serializer=serde.serialize,
                ctx=ctx,
                value_type="string_value",
            )

            # Handle programmatic selection via session state
            if widget_state.value_changed:
                validated_state = _validate_selection_state(
                    widget_state.value,
                    num_rows=num_rows,
                    column_names=column_names,
                    selection_mode_set=selection_mode_set,
                )
                proto.selection_state = json.dumps(validated_state)
                self.dg._enqueue(
                    "dataframe",
                    proto,
                    layout_config=layout_config,
                    has_one_shot_effect=True,
                )
                # Eagerly wrap like deserialize so nested selection identity
                # stays stable on this one-shot programmatic path.
                return DataframeState(
                    {
                        "selection": DataframeSelectionState(
                            validated_state["selection"]
                        ),
                    }
                )

            self.dg._enqueue("dataframe", proto, layout_config=layout_config)
            return DataframeState(widget_state.value)
        return self.dg._enqueue("dataframe", proto, layout_config=layout_config)

    @property
    def dg(self) -> DeltaGenerator:
        """The associated DeltaGenerator."""
        return cast("DeltaGenerator", self)


def marshall(
    proto: ArrowDataProto, data: Data, default_uuid: str | None = None
) -> None:
    """Marshall pandas.DataFrame into an ArrowData proto.

    Parameters
    ----------
    proto : proto.ArrowData
        Output. The protobuf for Streamlit ArrowData proto.

    data : pandas.DataFrame, pandas.Styler, pyarrow.Table, numpy.ndarray, pyspark.sql.DataFrame, snowflake.snowpark.DataFrame, Iterable, dict, or None
        Something that is or can be converted to a dataframe.

    default_uuid : str | None
        If pandas.Styler UUID is not provided, this value will be used.
        This attribute is optional and only used for pandas.Styler, other elements
        (e.g. charts) can ignore it.

    """  # noqa: E501

    if dataframe_util.is_pandas_styler(data):
        # default_uuid is a string only if the data is a `Styler`,
        # and `None` otherwise.
        if not isinstance(default_uuid, str):
            raise StreamlitAPIException(
                "Default UUID must be a string for Styler data."
            )
        marshall_styler(proto, data, default_uuid)

    proto.data = dataframe_util.convert_anything_to_arrow_bytes(data)
