Fill blank row with nearest populated row with data using a chainage from/to location

find_nearest.py

for group_index, group in df.groupby(["road_number","cway"]):

    blank_rows = group[group["cluster"].isna()]
    filled_rows = group[group["cluster"].notna()]

    for blank_row_index, blank_row in blank_rows.iterrows():

        # find distance by looing for minimum "signed overlap"
        overlap_min = np.maximum(filled_rows["slk_from"], blank_row["slk_from"])
        overlap_max = np.minimum(filled_rows["slk_to"], blank_row["slk_to"])
        overlap = overlap_max - overlap_min

        # when overlap is positive the nearest filled row is overlapping the blank row
        # when overlap is negative, the nearest filled row is some distance away
        distance_to_filled_rows = np.minimum(overlap, 0).abs()

        # finally select the ID of the row with the minimum value
        nearest_filled_row_id = distance_to_filled_rows.idxmin()

        # fill in the blank row with the value from the nearest filled row
        blank_row["cluster"] = filled_rows.loc[nearest_filled_row_id, "cluster"]