[英]Replace words by checking from pandas dataframe
I have a data frame as below.我有一个如下的数据框。
ID Word Synonyms
------------------------
1 drove drive
2 office downtown
3 everyday daily
4 day daily
5 work downtown
I'm reading a sentence and would like to replace words in that sentence with their synonyms as defined above.我正在阅读一个句子,并想用上面定义的同义词替换该句子中的单词。 Here is my code:
这是我的代码:
import nltk
import pandas as pd
import string
sdf = pd.read_excel('C:\synonyms.xlsx')
sd = sdf.apply(lambda x: x.astype(str).str.lower())
words = 'i drove to office everyday in my car'
#######
def tokenize(text):
text = ''.join([ch for ch in text if ch not in string.punctuation])
tokens = nltk.word_tokenize(text)
synonym = synonyms(tokens)
return synonym
def synonyms(words):
for word in words:
if(sd[sd['Word'] == word].index.tolist()):
idx = sd[sd['Word'] == word].index.tolist()
word = sd.loc[idx]['Synonyms'].item()
else:
word
return word
print(tokenize(words))
The code above tokenizes the input sentence.上面的代码标记了输入句子。 I would like to achieve the following output:
我想实现以下输出:
In : i drove to office everyday in my car
在:
i drove to office everyday in my car
Out : i drive to downtown daily in my car
外出:
i drive to downtown daily in my car
But the output I get is但我得到的输出是
Out : car
出:
car
If I skip the synonyms
function, then my output has no issues and is split into individual words.如果我跳过
synonyms
功能,那么我的输出就没有问题并且会被拆分为单个单词。 I am trying to understand what I'm doing wrong in the synonyms
function.我试图了解我在
synonyms
功能中做错了什么。 Also, please advise if there is a better solution to this problem.另外,请告知是否有更好的解决方案来解决此问题。
I would take advantage of Pandas/NumPy indexing.我会利用 Pandas/NumPy 索引。 Since your synonym mapping is many-to-one, you can re-index using the
Word
column.由于您的同义词映射是多对一的,您可以使用
Word
列重新索引。
sd = sd.applymap(str.strip).applymap(str.lower).set_index('Word').Synonyms
print(sd)
Word
drove drive
office downtown
everyday daily
day daily
Name: Synonyms, dtype: object
Then, you can easily align a list of tokens to their respective synonyms.然后,您可以轻松地将标记列表与其各自的同义词对齐。
words = nltk.word_tokenize(u'i drove to office everyday in my car')
sentence = sd[words].reset_index()
print(sentence)
Word Synonyms
0 i NaN
1 drove drive
2 to NaN
3 office downtown
4 everyday daily
5 in NaN
6 my NaN
7 car NaN
Now, it remains to use the tokens from Synonyms
, falling back to Word
.现在,它仍然使用来自
Synonyms
的标记,回退到Word
。 This can be achieved with这可以通过
sentence = sentence.Synonyms.fillna(sentence.Word)
print(sentence.values)
[u'i' 'drive' u'to' 'downtown' 'daily' u'in' u'my' u'car']
import re
import pandas as pd
sdf = pd.read_excel('C:\synonyms.xlsx')
rep = dict(zip(sdf.Word, sdf.Synonyms)) #convert into dictionary
words = "i drove to office everyday in my car"
rep = dict((re.escape(k), v) for k, v in rep.iteritems())
pattern = re.compile("|".join(rep.keys()))
rep = pattern.sub(lambda m: rep[re.escape(m.group(0))], words)
print rep
output输出
i drive to downtown daily in my car
Courtesy : https://stackoverflow.com/a/6117124/6626530礼貌: https : //stackoverflow.com/a/6117124/6626530
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