怎样在dateframe对象一列值的英文进行词云统计中删除特定词并只保留名词

你可以使用Python中的nltk（Natural Language Toolkit）库来实现这个功能。首先，你需要安装nltk库并下载其词性标注器，然后按照以下步骤进行操作：

1. 导入必要的库：

import pandas as pd
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk import pos_tag
from wordcloud import WordCloud

2. 读取数据并去除特定词：

df = pd.read_csv('your_data.csv')

# 去除特定词
stop_words = set(['word1', 'word2', 'word3'])
df['text'] = df['text'].apply(lambda x: ' '.join([word for word in x.split() if word.lower() not in stop_words]))

3. 进行词性标注并只保留名词：

# 进行词性标注并只保留名词
df['text'] = df['text'].apply(lambda x: ' '.join([word for word, pos in pos_tag(word_tokenize(x)) if pos.startswith('N')]))

4. 统计词频并生成词云：

# 统计词频并生成词云
text = ' '.join(df['text'])
wordcloud = WordCloud(width=800, height=800, background_color='white', colormap='RdYlBu').generate(text)

完整代码如下：

import pandas as pd
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk import pos_tag
from wordcloud import WordCloud

# 读取数据并去除特定词
df = pd.read_csv('your_data.csv')
stop_words = set(['word1', 'word2', 'word3'])
df['text'] = df['text'].apply(lambda x: ' '.join([word for word in x.split() if word.lower() not in stop_words]))

# 进行词性标注并只保留名词
df['text'] = df['text'].apply(lambda x: ' '.join([word for word, pos in pos_tag(word_tokenize(x)) if pos.startswith('N')]))

# 统计词频并生成词云
text = ' '.join(df['text'])
wordcloud = WordCloud(width=800, height=800, background_color='white', colormap='RdYlBu').generate(text)

希望能对你有所帮助！