[英]Replacing specific values within a dataframe column
I am running the following code in jupyter notebook which checks strings of text within nametest_df['text']
and returns Persons names. 我在jupyter笔记本中运行以下代码,该代码检查nametest_df['text']
的文本字符串并返回人员名称。 I managed to get this working and would like to push these names to the respective fields within the nametest_df['name']
where currently all values are NaN
. 我设法使此工作正常,并想将这些名称推送到nametest_df['name']
中的各个字段,其中当前所有值均为NaN
。
I tried the Series.replace()
method however all entries within the 'name' column are all showing the same name. 我尝试了Series.replace()
方法,但是“名称”列中的所有条目都显示相同的名称。
Any clue how I can do this efficiently? 有什么线索可以有效地做到这一点吗?
for word in nametest_df['text']:
for sent in nltk.sent_tokenize(word):
tokens = nltk.tokenize.word_tokenize(sent)
tags = st.tag(tokens)
for tag in tags:
if tag[1]=='PERSON':
name = tag[0]
print(name)
nametest_df.name = nametest_df.name.replace({"NaN": name})
Sample nametest_df 样本名称test_df
**text** **name**
0 His name is John NaN
1 I went to the beach NaN
2 My friend is called Fred NaN
Expected output 预期产量
**text** **name**
0 His name is John John
1 I went to the beach NaN
2 My friend is called Fred Fred
Don't try and fill series values one by one. 不要尝试一一填写序列值。 This is inefficient prone to error. 这是低效率的,容易出错。 A better idea is to create a list of names and assign directly. 一个更好的主意是创建一个名称列表并直接分配。
L = []
for word in nametest_df['text']:
for sent in nltk.sent_tokenize(word):
tokens = nltk.tokenize.word_tokenize(sent)
tags = st.tag(tokens)
for tag in tags:
if tag[1]=='PERSON':
L.append(tag[0])
nametest_df.loc[nametest_df['name'].isnull(), 'name'] = L
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