NLTK TypeError: unhashable type: 'list'

Question

I am currently working on the lemmantization of a word from a csv file, where afterwards I passed all words in lowercase letters, removed all punctuation and split the column.

I use only two CSV columns: analyze.info():

<class 'pandas.core.frame.DataFrame'> RangeIndex: 4637 entries, 0 to 4636. Data columns (total 2 columns):
#   Column          Non-Null Count  Dtype
0   Comments        4637 non-null   object
1   Classification  4637 non-null   object

import string
import pandas as pd
from nltk.corpus import stopwords
from nltk.stem import 

analyze = pd.read_csv('C:/Users/(..)/Talk London/ALL_dataset.csv', delimiter=';', low_memory=False, encoding='cp1252', usecols=['Comments', 'Classification'])

lower_case = analyze['Comments'].str.lower()

cleaned_text = lower_case.str.translate(str.maketrans('', '', string.punctuation))

tokenized_words = cleaned_text.str.split()

final_words = []
for word in tokenized_words:
    if word not in stopwords.words('english'):
       final_words.append(word)

wnl = WordNetLemmatizer()
lemma_words = []
lem = ' '.join([wnl.lemmatize(word) for word in tokenized_words])
lemma_words.append(lem)

When I run the code return this error:

Traceback (most recent call last):
File "C:/Users/suiso/PycharmProjects/SA_working/SA_Main.py", line 52, in lem = ' '.join([wnl.lemmatize(word) for word in tokenized_words])
File "C:/Users/suiso/PycharmProjects/SA_working/SA_Main.py", line 52, in lem = ' '.join([wnl.lemmatize(word) for word in tokenized_words])
File "C:\Users\suiso\PycharmProjects\SA_working\venv\lib\site-packages\nltk\stem\wordnet.py", line 38, in lemmatize lemmas = wordnet._morphy(word, pos)
File "C:\Users\suiso\PycharmProjects\SA_working\venv\lib\site-packages\nltk\corpus\reader\wordnet.py", line 1897, in _morphy
if form in exceptions:
TypeError: unhashable type: 'list'

Answer 1

tokenized_words is a column of lists. The reason it's not a column of strings is because you used the split method. So you need to use a double for loop like so

lem = ' '.join([wnl.lemmatize(word) for word_list in tokenized_words for word in word_list])