import polars as pl
import pandas as pd
from typing import Union, Optional, List
import io

def gender_disaggregated_analysis(
    data: Union[str, pd.DataFrame, pl.DataFrame, io.BytesIO],
    gender_col: str = "gender",
    value_columns: Optional[List[str]] = None,
    group_by: Optional[List[str]] = None
) -> pl.DataFrame:
    """
    Gender-disaggregated Analysis
    =============================

    What
    ----
    Compare indicators by gender (and optionally by other groups).

    When
    ----
    Gender mainstreaming and reporting.

    Why
    ---
    Ensure equitable assistance and highlight gaps.

    Parameters
    ----------
    data : str | pd.DataFrame | pl.DataFrame | io.BytesIO
        Input dataset.
    gender_col : str, default "gender"
        Column with gender values.
    value_columns : list[str] | None
        Numeric columns to summarize by gender.
    group_by : list[str] | None
        Additional grouping columns (e.g., province).

    Returns
    -------
    pl.DataFrame
        Placeholder gender breakdown.
    """
    if isinstance(data, str):
        df = pl.read_csv(data)
    elif isinstance(data, io.BytesIO):
        df = pl.read_csv(data)
    elif isinstance(data, pd.DataFrame):
        df = pl.from_pandas(data)
    elif isinstance(data, pl.DataFrame):
        df = data
    else:
        raise TypeError("Unsupported data type")
    return df
