读取附件Who Moved My Cheese.txt中的内容，要求拆分为中英两个文件，文件名为词频数量最多的词

为了读取文本文件"Who Moved My Cheese.txt"的内容并将其拆分成英文和中文两部分文件，我们可以使用Python语言中的`pandas`库来处理文本，并使用`jieba`库进行中文分词。假设文本是纯英文或混合了英文和少量中文，我们首先需要对文本进行预处理，然后计算词频。

以下是步骤：

1. **导入所需库**:

import pandas as pd
from collections import Counter
import re
import jieba

2. **读取文本**:

with open("WhoMovedMyCheese.txt", "r", encoding="utf-8") as file:
    text = file.read()

3. **提取英文部分** (如果文件全英文):

# 如果文本全是英文，可以直接计数
if not any(c.isdigit() for c in text):  # 判断是否有数字，作为英文判断条件
    english_words = re.findall(r'\b\w+\b', text)
else:
    # 如果有中文，先分割英文再计数
    chinese_pattern = "[^\u4e00-\u9fa5]"
    text_split = re.split(chinese_pattern, text)
    english_words = [word for part in text_split if word.isalpha()]

4. **计算英文词频**:

english_word_counts = Counter(english_words)
most_common_english = english_word_counts.most_common(1)  # 获取最常出现的英文单词及其频率

5. **中文分词及计数**:

if any(c.isdigit() for c in text):  # 确定包含中文
    chinese_text = text.translate(str.maketrans('', '', string.punctuation))  # 移除标点符号
    words = jieba.lcut(chinese_text)
    chinese_word_counts = Counter(words)
    most_common_chinese = chinese_word_counts.most_common(1)  # 获取最常出现的中文单词及其频率

6. **保存到文件**:

# 英文部分
eng_word, eng_freq = most_common_english[0]
with open(f"English_Frequency_{eng_word}.txt", "w", encoding="utf-8") as file:
    file.write(f"The most common English word is '{eng_word}' with frequency {eng_freq}.")

# 中文部分（如果有）
chi_word, chi_freq = most_common_chinese[0] if most_common_chinese else ("无", 0)
with open(f"Chinese_Frequency_{chi_word}.txt", "w", encoding="utf-8") as file:
    file.write(f"The most common Chinese character is '{chi_word}' with frequency {chi_freq}.")