Python，Pandas：如何检查一行是否包含另一行中找到的值？

Question

I want to get an output that identifies id 1 and 2 as duplicates. Because id:2 has value 1 which is also found in id 2 which contains both values 1 and 2. Ie id 2 is a subset of one

I tried using the duplicate function but it does not identify ids 1 and 2 as duplicates.

#check by id if value is a duplicate
test_df = pd.DataFrame({'id':['1', '2', '3', '4'],
                   'value':['1, 2', '1', '18', '19']}) 

print(test_df)
duplicateRowsDF = test_df['value'].duplicated() #returns boolean values
duplicateRowsDF

This should be the reflected boolean values

duplicateRowsDF 0 True 1 True 2 False 3 False Name: value, dtype: bool

Expected output table as shown below

expected_output = pd.DataFrame({'id':['1', '2', '3', '4'],
                   'value':['1, 2', '1', '18', '19'], 'duplicate':['Yes', 'Yes', 'No', 'No']}) 
expected_output

Answer 1

Use for pandas 0.25+:

#split by , and create Series with index by id column
s = test_df.set_index('id')['value'].str.split(', ').explode()

#check duplicates and get Trues per id if exist at least one, last convert to dict
d = s.duplicated(keep=False).groupby(level=0).transform('any').to_dict()
print (d)
{'1': True, '2': True, '3': False, '4': False}

#map id by dictionary and set values by mask
test_df['duplicate'] = np.where(test_df['id'].map(d), 'yes','no')
print (test_df)
  id value duplicate
0  1  1, 2       yes
1  2     1       yes
2  3    18        no
3  4    19        no