英文:
python pandas: Generate (three) cells from one cell
问题
我有一个简单的数据框，其中包含一些元数据和一个句子列。我想要使用textacy的SVO提取器生成三个新列，分别用于主语、动词和宾语。我尽量以尽可能纯粹的pandas方式来实现这个目标：
metadata sentence
1-0 Thank you so much, Chris.
1-1 And it's truly a great honor to be here.
1-2 I have been blown away by this conference.
1-3 And I say that sincerely.
我尝试过以下代码：
def svo(text):
svotriple = textacy.extract.triples.subject_verb_object_triples(nlp(text))
for item in svotriple:
df['subject'] = str(item[0][-1])
df['verb'] = str(item[1][-1])
df['object'] = str(item[2])
df.apply(svo(df['sentence'].values[0]))
我试图以不同的方式从句子列中提取句子作为字符串。大多数情况下，它们返回的是一个Series。我想要逐行进行操作。我一开始的想法是使用for循环，但我真的想尽量按照pandas的方式来做这个。
英文:
I have a simple dataframe consisting of some metadata in a few columns and then a column with a sentence in it. I would like to use textacy's SVO extractor to generate three new columns, one each for the subject, verb, and object. I am trying to do this in as pandas a way as possible:
metadata sentence
1-0 Thank you so much, Chris.
1-1 And it's truly a great honor to be here.
1-2 I have been blown away by this conference.
1-3 And I say that sincerely.
To which I tried this:
def svo(text):
svotriple = textacy.extract.triples.subject_verb_object_triples(nlp(text))
for item in svotriple:
df['subject'] = str(item[0][-1])
df['verb'] = str(item[1][-1])
df['object'] = str(item[2])
df.apply(svo(df['sentence'].values[0]))
I've tried to get just the sentence as a string out of the sentence column a couple of ways. Most of them returned the fact that I was actually getting a series. I want this to work row-by-row. My impulse was to go with a for loop, but I really want to try to do this the pandas way. (Not that my for loops were working terribly well.)
答案1
得分: 1
你使用apply的方式是不正确的。你应该创建一个空的DataFrame来存储SVO三元组，你正在直接在每次迭代中更新现有DataFrame的列，这将覆盖先前的值。
尝试这种方式
import pandas as pd
import textacy
import spacy
nlp = spacy.load('en_core_web_sm')
def svo(text):
svotriples = textacy.extract.triples.subject_verb_object_triples(nlp(text))
svo_list = []
for item in svotriples:
subject = str(item[0][-1])
verb = str(item[1][-1])
obj = str(item[2])
svo_list.append([subject, verb, obj])
return svo_list
data = {
'sentence': [
'Thank you so much, Chris.',
"And it's truly a great honor to be here.",
'I have been blown away by this conference.',
'And I say that sincerely.'
]
}
df = pd.DataFrame(data)
df[['subject', 'verb', 'object']] = df['sentence'].apply(svo).apply(pd.Series)
print(df)
英文:
The way you use apply is incorrect. You should create an empty DataFrame to store the SVO triples, you're directly updating the columns of the existing DataFrame in each iteration, which will overwrite the previous values.
Try this way
import pandas as pd
import textacy
import spacy
nlp = spacy.load('en_core_web_sm')
def svo(text):
svotriples = textacy.extract.triples.subject_verb_object_triples(nlp(text))
svo_list = []
for item in svotriples:
subject = str(item[0][-1])
verb = str(item[1][-1])
obj = str(item[2])
svo_list.append([subject, verb, obj])
return svo_list
data = {
'sentence': [
'Thank you so much, Chris.',
"And it's truly a great honor to be here.",
'I have been blown away by this conference.',
'And I say that sincerely.'
]
}
df = pd.DataFrame(data)
df[['subject', 'verb', 'object']] = df['sentence'].apply(svo).apply(pd.Series)
print(df)
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