import pandas as pd


class TimestampParsingError(Exception):
    """Custom exception for timestamp parsing errors"""
    pass


class DataLoadingError(Exception):
    """Custom exception for data loading errors"""
    pass


class DataSavingError(Exception):
    """Custom exception for data saving errors"""
    pass


def _parse_timestamp_column(data: pd.DataFrame, column_name: str) -> pd.DataFrame:
    """Parse timestamp column handling both Unix timestamps and datetime strings
    
    Args:
        data: DataFrame containing the timestamp column
        column_name: Name of the timestamp column
        
    Returns:
        DataFrame with parsed timestamp column
        
    Raises:
        TimestampParsingError: If timestamp parsing fails
    """
    try:
        sample_timestamp = str(data[column_name].iloc[0])
        try:
            # Check if it's a Unix timestamp (numeric)
            float(sample_timestamp)
            # It's a Unix timestamp, convert using unit='s'
            data[column_name] = pd.to_datetime(data[column_name], unit='s')
        except ValueError:
            # It's already in datetime string format, convert without unit
            data[column_name] = pd.to_datetime(data[column_name])
        return data
    except Exception as e:
        raise TimestampParsingError(f"Failed to parse timestamp column '{column_name}': {e}")


def _filter_by_date_range(data: pd.DataFrame, timestamp_col: str, 
                         start_date: pd.Timestamp, stop_date: pd.Timestamp) -> pd.DataFrame:
    """Filter DataFrame by date range
    
    Args:
        data: DataFrame to filter
        timestamp_col: Name of timestamp column
        start_date: Start date for filtering
        stop_date: Stop date for filtering
        
    Returns:
        Filtered DataFrame
    """
    return data[(data[timestamp_col] >= start_date) & (data[timestamp_col] <= stop_date)]


def _normalize_column_names(data: pd.DataFrame) -> pd.DataFrame:
    """Convert all column names to lowercase
    
    Args:
        data: DataFrame to normalize
        
    Returns:
        DataFrame with lowercase column names
    """
    data.columns = data.columns.str.lower()
    return data