UNFCCC_non-AnnexI_data/code/UNFCCC_reader/Mexico/config_MEX_BUR3.py

import pandas as pd

def fix_rows(data: pd.DataFrame, rows_to_fix: list, col_to_use: str,
             n_rows: int) -> pd.DataFrame:
    for row in rows_to_fix:
        # print(row)
        # find the row number and collect the row and the next two rows
        index = data.loc[data[col_to_use] == row].index
        if not list(index):
            print(f"Can't merge split row {row}")
            print(data[col_to_use])
        print(f"Merging split row {row}")
        indices_to_drop = []
        ####print(index)
        for item in index:
            loc = data.index.get_loc(item)
            ####print(data[col_to_use].loc[loc + 1])
            if n_rows == -2:
                locs_to_merge = list(range(loc - 1, loc + 1))
                loc_to_check = loc - 1
            if n_rows == -6:
                locs_to_merge = list(range(loc - 3, loc + 3))
                loc_to_check = loc - 3
            elif n_rows == -3:
                locs_to_merge = list(range(loc - 1, loc + 2))
                loc_to_check = loc - 1
            else:
                locs_to_merge = list(range(loc, loc + n_rows))
                loc_to_check = loc + 1

            if (data[col_to_use].loc[loc_to_check] == '') or n_rows == 2:
                rows_to_merge = data.iloc[locs_to_merge]
                indices_to_merge = rows_to_merge.index
                # replace numerical NaN values
                ####print(rows_to_merge)
                rows_to_merge = rows_to_merge.fillna('')
                ####print("fillna")
                ####print(rows_to_merge)
                # join the three rows
                new_row = rows_to_merge.agg(' '.join)
                # replace the double spaces that are created
                # must be done here and not at the end as splits are not always
                # the same and join would produce different col values
                new_row = new_row.str.replace("  ", " ")
                new_row = new_row.str.strip()
                # new_row = new_row.str.replace("N O", "NO")
                # new_row = new_row.str.replace(", N", ",N")
                # new_row = new_row.str.replace("- ", "-")
                data.loc[indices_to_merge[0]] = new_row
                indices_to_drop = indices_to_drop + list(indices_to_merge[1:])

        data = data.drop(indices_to_drop)
        data = data.reset_index(drop=True)
    return data

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            3: ["Todas las emisiones y las absorciones",
                "Todas las emisiones (sin [3B] Tierra ni",
                "[1A] Actividades de quema del",
                "[1A2] Industrias manufactura y de la",
                "[1B] Emisiones fugitivas provenientes de",
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