Syntax error when lemmatizing column in pandas

Question

I am trying to lemmatize words in a particular column ('body') using pandas.

I have tried the following code, that I found here

import nltk
from nltk.tokenize import word_tokenize
from nltk.stem.wordnet import WordNetLemmatizer 
lemmatizer = nltk.stem.WordNetLemmatizer()
wordnet_lemmatizer = WordNetLemmatizer()

df['body'] = df['body'].apply(lambda x: "".join([Word(word).lemmatize() for word in 
df['body'].head()

When I attempt to run the code, I get an error message that simply says

File "<ipython-input-41-c002479904b0>", line 33
  df['body'] = df['body'].apply(lambda x: "".join([Word(word).lemmatize() for word in x)
   ^
SyntaxError: invalid syntax

I have also tried the solution presented in this post but didn't have any luck.

UPDATE: this is the full code so far

import pandas as pd
import re
import string


df1 = pd.read_csv('RP_text_posts.csv')
df2 = pd.read_csv('RP_text_comments.csv')
# Renaming columns so the post part - currently 'selftext' matches the post variable in the comments - 'body'
df1.columns = ['author','subreddit','score','num_comments','retrieved_on','id','created_utc','body']
# Dropping columns that aren't subreddit or the post content
df1 = df1.drop(columns=['author','score','num_comments','retrieved_on','id','created_utc'])
df2 = df2.drop(labels=None, columns=['author', 'score', 'created_utc'])
# Combining data
df = pd.concat([df1, df2])

import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem.wordnet import WordNetLemmatizer
lemmatizer = nltk.stem.WordNetLemmatizer()
wordnet_lemmatizer = WordNetLemmatizer()
stop = stopwords.words('english')

# Lemmatizing
df['body'] = df['body'].apply(lambda x: "".join([Word(word).lemmatize() for word in x) 
df['body'].head()`

Answer 1

It miss the end of the lambda function:

df['words'] = df['words'].apply(lambda x: "".join([Word(word).lemmatize() for word in x])) 

Update The line should be more like that but you can only lemmatize by one pos(adjective, or verb, or ...):

df['words'] = df['body'].apply(lambda x: " ".join([wordnet_lemmatizer.lemmatize(word) for word in word_tokenize(x)]))
print(df.head()))

If you want more, you can try the following code:

import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem.wordnet import WordNetLemmatizer
from nltk.corpus import wordnet
lemmatizer = nltk.stem.WordNetLemmatizer()
wordnet_lemmatizer = WordNetLemmatizer()
stop = stopwords.words('english')


def nltk_tag_to_wordnet_tag(nltk_tag):
    if nltk_tag.startswith('J'):
        return wordnet.ADJ
    elif nltk_tag.startswith('V'):
        return wordnet.VERB
    elif nltk_tag.startswith('N'):
        return wordnet.NOUN
    elif nltk_tag.startswith('R'):
        return wordnet.ADV
    else:
        return None

def lemmatize_sentence(sentence):
    #tokenize the sentence and find the POS tag for each token
    nltk_tagged = nltk.pos_tag(nltk.word_tokenize(sentence))
    #tuple of (token, wordnet_tag)
    wordnet_tagged = map(lambda x: (x[0], nltk_tag_to_wordnet_tag(x[1])), nltk_tagged)
    lemmatized_sentence = []
    for word, tag in wordnet_tagged:
        if tag is None:
            #if there is no available tag, append the token as is
            lemmatized_sentence.append(word)
        else:
            #else use the tag to lemmatize the token
            lemmatized_sentence.append(lemmatizer.lemmatize(word, tag))
    return " ".join(lemmatized_sentence)



# Lemmatizing
df['words'] = df['body'].apply(lambda x: lemmatize_sentence(x))
print(df.head())

df result:

            body                    |        words

0  Best scores, good cats, it rocks | Best score , good cat , it rock

1          You received best scores |          You receive best score

2                         Good news |                       Good news

3                          Bad news |                        Bad news

4                    I am loving it |                    I be love it

5                    it rocks a lot |                   it rock a lot

6     it is still good to do better |     it be still good to do good