aRegius

group_1, group_2 = [], []

for index, value in enumerate(data):
	 if index in indices:
		   group_1.append(value)
	 else:
		   group_2.append(value)

for item, count in itogo.items():
	  print(item, count, sep=": ")

SELECT * 
FROM my_table 
WHERE msg LIKE '%123%';

result = pd.DataFrame(result)
pd_result = result.groupby(1, as_index=False).max().reindex(columns=[0, 1])
np_result = pd_result.to_numpy()

from collections import defaultdict

data = defaultdict(list)
for value, key in result:
	  data[key].append(value)

result = [[max(value), key] for key, value in data.items()]

keys = ('id', 'personal_number' ...)
data = [dict(zip(keys, values)) for values in query]

any(len(list(group)) > num for _, group in groupby(word.lower()))