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

def correlation_analysis(
    data: Union[str, pd.DataFrame, pl.DataFrame, io.BytesIO],
    columns: List[str],
    method: str = "pearson"
) -> pl.DataFrame:
    """
    Correlation Analysis Between Indicators
    ======================================

    What
    ----
    Measure how indicators move together (e.g., needs vs. funding).

    When
    ----
    Exploring relationships before modeling or dashboards.

    Why
    ---
    Detect strong links to focus analysis or actions.

    Parameters
    ----------
    data : str | pd.DataFrame | pl.DataFrame | io.BytesIO
        Input dataset.
    columns : list[str]
        Numeric columns to compute pairwise correlation.
    method : str, default "pearson"
        Correlation method (pearson/spearman).

    Returns
    -------
    pl.DataFrame
        Correlation matrix (placeholder, to be implemented).

    Example
    -------
    >>> correlation_analysis(df, columns=["in_need","funded"]) 
    """
    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")

    # Placeholder: return column names only
    return pl.DataFrame({"column": columns, "method": [method]*len(columns)})
