How to store synonyms as column in data frame?

user_12944256 0
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Want to store Getting result of below code in data frame.

Two columns one is the actual name and another is each synonym in the new row.

import nltk
from nltk.corpus import wordnet
import pandas as pd

List = ['protest','riot','conflict']
df=[]
def process_genre(str):
    for genre in str:
        result = []
        for syn in wordnet.synsets(genre):
            for l in syn.lemmas():
                result.append(l.name())
        print(set(result))
process_genre(List)

output:
-------
{'resist', 'objection', 'dissent', 'protestation', 'protest'}
{'bacchanalia', 'riot', 'saturnalia', 'belly_laugh', 'scream', 'wow', 'bacchanal', 'thigh-slapper', 'sidesplitter', 'drunken_revelry', 'carouse', 'rioting', 'roister', 'debauchery', 'orgy', 'public_violence', 'howler', 'debauch'}
{'fight', 'battle', 'difference', 'dispute', 'conflict', 'infringe', 'engagement', 'struggle', 'difference_of_opinion', 'contravene', 'run_afoul'}

Want to store the result in data frame:

# Expected Result:

Col1           Col2
--------------------
protest       resist
protest       objection
protest       dissent
...           ...
riot          scream
riot          carouse
riot          saturnalia
...           ...
conflict      Fight
conflict      battle
...           ...
pandas python
2 Answers

This is a possible solution:

from nltk.corpus import wordnet
import pandas as pd

def process_genres(genres):
    return (pd.DataFrame([(genre, l.name())
                          for genre in genres
                          for syn in wordnet.synsets(genre)
                          for l in syn.lemmas()], columns=['Col1', 'Col2'])
              .drop_duplicates())

Here's how you can use it:

>>> genres = ['protest', 'riot', 'conflict']
>>> df = process_genres(genres)
>>> df
        Col1                   Col2
0    protest                protest
1    protest           protestation
...
11      riot                   riot
12      riot        public_violence
13      riot                rioting
...
34  conflict               conflict
35  conflict               struggle
36  conflict                 battle
...
53  conflict             contravene
user_52961060

Same logic that you have coded, but use list comprehensions to build pandas constructor compatible structure

import nltk
from nltk.corpus import wordnet
import pandas as pd

# same logic as question,  just use list comprehesions to process words and synonyms
# to build DF compatible construction list
df = pd.DataFrame([
    {"col1":word, 
     # use set to take unique values
     "col2":{l.name() 
                  for syn in wordnet.synsets(word)
                  for l in syn.lemmas() 
            }
    }
    for word in ['protest','riot','conflict']
]).explode("col2") # expand embedded list of synonyms

# filter out word as a synonym of itself
df.loc[df.col1!=df.col2].head(10)
col1 col2
0 protest resist
0 protest protestation
0 protest objection
0 protest dissent
1 riot public_violence
1 riot howler
1 riot drunken_revelry
1 riot orgy
1 riot belly_laugh
1 riot scream
user_94414040
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