Python 在数据框中查找句子中的多个单词并转换为分数总和

我有以下数据帧：

    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，查看上面句子中的单词，并根据以下不同的数据帧计算每个单词的分数总和

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

例如，对于第0行，分数将为1（猫）+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

---

首先，您可以尝试基于

拆分

和

映射

的方法，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'))

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化

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

ps = PorterStemmer()

创建一本方便的词典

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

并生成分数

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

---

尝试将

findall

与

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

---

我终于可以继续了。

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