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

def anomaly_detection(
    data: Union[str, pd.DataFrame, pl.DataFrame, io.BytesIO],
    value_column: str,
    date_column: str = "date",
    group_by: Optional[List[str]] = None,
    method: str = "zscore",
    threshold: float = 3.0
) -> pl.DataFrame:
    """
    Anomaly Detection (Sudden Spikes)
    =================================

    What
    ----
    Flag unusual spikes or drops in a time series.

    When
    ----
    Monitoring dashboards to catch data issues or real events.

    Why
    ---
    Early warning and quality control.

    Parameters
    ----------
    data : str | pd.DataFrame | pl.DataFrame | io.BytesIO
        Input dataset.
    value_column : str
        Numeric column to monitor.
    date_column : str, default "date"
        Date column.
    group_by : list[str] | None
        Optional grouping.
    method : str, default "zscore"
        Detection method.
    threshold : float, default 3.0
        Z-score threshold for anomalies.

    Returns
    -------
    pl.DataFrame
        Original data plus placeholder anomaly flag column.
    """
    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.with_columns(pl.lit(False).alias("is_anomaly_placeholder"))
