apply porters stemmer to a Pandas column for each word

Question

i have a pandas dataframe called 'data_stem' and there is a column named 'TWEET_SENT_1' which have strings like below (50 rows)

TWEET_SENT_1

the mack daddy of kiss cross

i liked that video body party

i want to apply porters stemmer in to 'TWEET_SENT_1' column (for all words of a row) i tried below code and it gives an error . could you please help me to overcome this

from nltk.stem import PorterStemmer, WordNetLemmatizer
porter_stemmer = PorterStemmer()
data_stem[' TWEET_SENT_1 '] = data_stem[' TWEET_SENT_1 '].apply(lambda x: [porter_stemmer.stem(y) for y in x])

below is the error

    ---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-412-c16b1beddfb5> in <module>()
      1 from nltk.stem import PorterStemmer, WordNetLemmatizer
      2 porter_stemmer = PorterStemmer()
----> 3 data_stem[' TWEET_SENT_1 '] = data_stem[' TWEET_SENT_1 '].apply(lambda x: [porter_stemmer.stem(y) for y in x])

C:\Users\SampathR\Anaconda2\envs\dato-env\lib\site-packages\pandas\core\series.pyc in apply(self, func, convert_dtype, args, **kwds)
   2058             values = lib.map_infer(values, lib.Timestamp)
   2059 
-> 2060         mapped = lib.map_infer(values, f, convert=convert_dtype)
   2061         if len(mapped) and isinstance(mapped[0], Series):
   2062             from pandas.core.frame import DataFrame

pandas\src\inference.pyx in pandas.lib.map_infer (pandas\lib.c:58435)()

<ipython-input-412-c16b1beddfb5> in <lambda>(x)
      1 from nltk.stem import PorterStemmer, WordNetLemmatizer
      2 porter_stemmer = PorterStemmer()
----> 3 data_stem[' TWEET_SENT_1 '] = data_stem[' TWEET_SENT_1 '].apply(lambda x: [porter_stemmer.stem(y) for y in x])

TypeError: 'NoneType' object is not iterable

Answer 1

Applying three different operations to the series with millions of rows is very expensive operation. Instead, apply all at once:

def stem_sentences(sentence):
    tokens = sentence.split()
    stemmed_tokens = [porter_stemmer.stem(token) for token in tokens]
    return ' '.join(stemmed_tokens)

data_stem['TWEET_SENT_1'] = data_stem['TWEET_SENT_1'].apply(stem_sentences)

(Note: This is just a modified version of the accepted answer)