def prod_nadate_process(prod_df, prod_col_dict, pnadrop=False):
    """
    Processes rows of production data frame for missing time-stamp
    info (NAN).


    Parameters

    ----------
    prod_df: DataFrame
        A data frame corresponding to production data.

    prod_df_col_dict: dict of {str : str}
        A dictionary that contains the column names associated with
        the production data, which consist of at least:

          - **timestamp** (*string*), should be assigned to
            associated time-stamp column name in prod_df

    pnadrop: bool
        Boolean flag that determines what to do with rows where
        time-stamp is missing. A value of `True` will drop these
        rows.  Leaving the default value of `False` will identify
        rows with missing time-stamps for the user, but the function
        will output the same input data frame with no modifications.

    Returns

    -------
    prod_df: DataFrame
        The output data frame.  If pflag = 'drop', an updated version
        of the input data frame is output, but rows with missing
        time-stamps are removed. If default value is maintained, the
        input data frame is output with no modifications.

    addressed: DataFrame
        A data frame showing rows from the input that were addressed
        or identified by this function.
    """

    prod_df = prod_df.copy()

    # creating local dataframes to not modify originals
    prod_df = prod_df.copy()

    prod_ts = prod_col_dict["timestamp"]

    # Dropping rows
    mask = prod_df.loc[:, prod_ts].isna()
    addressed = prod_df[mask]
    if pnadrop:
        prod_df.dropna(subset=[prod_ts], inplace=True)

    return prod_df, addressed