"""Utils required for dataframes."""

from typing import Dict, List, Optional

import pandas as pd


def dict_to_dataframe(records: List[Dict[str, int | bool]], dtypes=None) -> pd.DataFrame:
    """Convert a list of dictionaries to a pandas DataFrame.

    Args:
        records (List[Dict[str, Any]]): List of dictionaries to convert.
        dtypes (Dict[str, str] | None): dictionary specifying column data types.
    Returns:
        pd.DataFrame: DataFrame created from the records.
    """
    if not records:
        return pd.DataFrame()
    df = pd.DataFrame(records)
    if dtypes:
        df = df.astype(dtypes)
    return df


def merge_dataframes(
    dataframes: List[pd.DataFrame],
    columns: Optional[List[str]] = None,
    drop_duplicates: bool = False,
    ignore_index: bool = True,
) -> pd.DataFrame:
    """Merge multiple pandas DataFrames into one, optionally selecting columns and dropping duplicates.

    Args:
        dataframes (List[pd.DataFrame]): List of DataFrames to concatenate.
        columns (Optional[List[str]]): Columns to select after concatenation.
            Defaults to None (all columns).
        drop_duplicates (bool): Whether to drop duplicate rows after concatenation.
            Defaults to False.
        ignore_index (bool): Whether to reset the index after concatenation.
            Defaults to True.

    Returns:
        pd.DataFrame: The merged DataFrame.
    """
    non_empty_dfs = [dataframe for dataframe in dataframes if not dataframe.empty]

    if not non_empty_dfs:
        return pd.DataFrame(columns=columns if columns else [])

    merged_df = pd.concat(non_empty_dfs, ignore_index=ignore_index)

    if columns:
        merged_df = merged_df.loc[:, columns]

    if drop_duplicates:
        merged_df = merged_df.drop_duplicates()

    return merged_df