Lookup multiple words in a sentence in a Dataframe and convert to a sum of scores

user_9244542 0
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I have the following dataframe:

    Sentence
0   Cat is a big lion
1   Dogs are descendants of wolf
2   Elephants are pachyderm
3   Pachyderm animals include rhino, Elephants and hippopotamus

I need to create a python code which looks at the words in sentence above and calculates the sum of scores for each based on following distinct data frame.

Name          Score
cat             1
dog             2
wolf            2
lion            3
elephants       5
rhino           4
hippopotamus    5

For example, for row 0, the score will be 1 (cat) + 3 (lion) = 4

I am looking to create an output that looks like following. 

    Sentence                                                      Value
0   Cat is a big lion                                                4
1   Dogs are descendants of wolf                                     4
2   Elephants are pachyderm                                          5
3   Pachyderm animals include rhino, Elephants and hippopotamus      14
pandas data-conversion nltk python-3.x python
3 Answers

As a first effort, you can try a split and map-based approach, and then compute the score using groupby.

v = df1['Sentence'].str.split(r'[\s.!?,]+', expand=True).stack().str.lower()
df1['Value'] = (
    v.map(df2.set_index('Name')['Score'])
     .sum(level=0)
     .fillna(0, downcast='infer'))


df1
                                            Sentence  Value
0                                  Cat is a big lion      4
1                       Dogs are descendants of wolf      4  # s/dog/dogs in df2  
2                            Elephants are pachyderm      5
3  Pachyderm animals include rhino, Elephants and...     14
user_49090870

nltk

You may need to download stuff

import nltk

nltk.download('punkt')

Then set up stemming and tokenizing

from nltk.stem import PorterStemmer
from nltk.tokenize import word_tokenize

ps = PorterStemmer()

Create a handy dictionary

m = dict(zip(map(ps.stem, scores.Name), scores.Score))

And generate scores

def f(s):
  return sum(filter(None, map(m.get, map(ps.stem, word_tokenize(s)))))

df.assign(Score=[*map(f, df.Sentence)])

                                            Sentence  Score
0                                  Cat is a big lion      4
1                       Dogs are descendants of wolf      4
2                            Elephants are pachyderm      5
3  Pachyderm animals include rhino, Elephants and...     14
user_23366540

Trying to using findall with re re.I

df.Sentence.str.findall(df1.Name.str.cat(sep='|'),flags=re.I).\
   map(lambda x : sum([df1.loc[df1.Name==str.lower(y),'Score' ].values for y in x])[0])
Out[49]: 
0     4
1     4
2     5
3    14
Name: Sentence, dtype: int64
user_79645270
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