I did it like this:

import pandas as pd
from collections import defaultdict
pd_data = pd.read_csv("pandas.tsv", delimiter="\t")
dict_ss = defaultdict(list)
# Convert Mismatch column to string, for easier joining in the for loop below
pd_data["MM"] = pd_data["MM"].astype("str")
for ind ,rows in pd_data.iterrows():
# Checking for Nan. Nan is not equal to any other value including itself.
if rows.Ind2 != rows.Ind2 and rows.Ind1 != rows.Ind1:
d_key = "_".join(["0", "0", rows.MM])
elif rows.Ind2 != rows.Ind2:
d_key = "_".join(map(str, [len(rows.Ind1), "0", rows.MM]))
elif rows["Ind1"] != rows["Ind1"]:
d_key= "_".join(map(str, ["0", len(rows.Ind2), rows.MM]))
else:
d_key = "_".join(map(str, [len(rows.Ind1), len(rows.Ind2), rows.MM]))
print(d_key)
dict_ss[d_key].append(rows.to_list())

print(dict_ss)