在Dataframe中查找句子中的多个单词并转换为分数之和

Question

我有以下数据：

    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

我需要创建一个python代码，它查看上面句子中的单词，并根据以下不同的数据框架计算每个单词的得分之和。

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

例如，对于第0行，得分为1 (cat) +3(狮子)=4。

我希望创建一个如下所示的输出。

    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

Answer 1

首先，您可以尝试split和map-based方法，然后使用groupby计算分数。

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

Answer 2

nltk

你可能需要下载一些东西

import nltk

nltk.download('punkt')

然后设置词干和标记。

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

ps = PorterStemmer()

创建一本方便的字典

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

产生分数

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

Answer 3

尝试将findall与re re.I结合使用

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