extract_trends.py

def trends_only(source_df: pd.DataFrame, trend_col: str) -> (float, pd.DataFrame):
    """ Extract trends as training features vs total volume """
    df = source_df.copy()
    start_val = df.at[0, trend_col]
    df.sin = df[[trend_col]].diff()
    df.at[0, trend_col] = 0.00
    return start_val, df


def restore_daily(source_df: pd.DataFrame, start_val: float, trend_col: str):
    """ Restore daily cumulative values from trend data """
    df = source_df.copy()
    df.at[0, trend_col] = start_val
    df[trend_col] = df[trend_col].cumsum()
    df[trend_col] = df[trend_col].apply(pd.to_numeric, downcast='float', errors='coerce').round(2)
    df.dropna(inplace=True)
    return df

# Extract trends from timeseries column to create training set
start_val, trends_df = trends_only(train_df, trend_col)
trends_df