使用pandas方法统计《红楼梦》中的词汇和人物命名之间的共现关系。代码实现细节如下所示：

``` python
import pandas as pd
from collections import Counter

# 读取《红楼梦》文本，假设存储在'hongloumeng.txt'文件中
with open('hongloumeng.txt', 'r', encoding='utf-8') as file:
    text = file.text().split()

characters = ["贾宝玉", "林黛玉", "薛宝钗"]

words = [word for word in text if word.isalnum()]

co_occurrences = Counter()

for character in characters:
    character_words = [word for word in words if character in word]
    co_occurrences[character] = Counter(character_words)

co_occurrences_df = pd.DataFrame.from_dict(co_occurrences, orient='index').fillna(0).astype(int)

co_occurrences_df.to_csv('co_occurrences.csv', index=True)
```

精简表达并修正语法错误，保留主体逻辑，简化评论措辞
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```python
import pandas as pd
from collections import Counter

# 读取文本并分割成单词
with open('hongloumeng.txt', 'r', encoding='utf-8') as f:
    words = [w for w in f.read().split() if w.isalnum()]

# 定义主要人物
characters = ["贾宝玉", "林黛玉", "薛宝钗"]

# 统计共现关系
co_occur = Counter()
for char in characters:
    co_occur[char] = Counter(w for w in words if char in w)

# 转换为DataFrame并保存
pd.DataFrame(co_occur).fillna(0).astype(int).to_csv('co_occurrences.csv')
```
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