在nlp_lab下,编写Python代码,从给定的文本文件中统计出最常出现的三个单词,单词长度大于等于5),不使用jieba
时间: 2024-09-06 13:03:21 浏览: 49
要从给定的文本文件中统计最常出现的三个长度大于等于5的单词,可以使用`open()`函数读取文件内容,然后利用`split()`函数去除标点并分割成单词,接着使用`Counter`来计算词频,最后筛选出长度大于等于5的单词并按照频率降序排列。下面是一个示例:
```python
import re
from collections import Counter
# 假设文件名为'nlp_lab.txt'
with open('nlp_lab.txt', 'r') as file:
text = file.read().lower() # 转为小写以便统计大小写无关的词频
# 移除标点并分割单词
words = re.findall(r'\b\w{5,}\b', text)
# 计算词频
word_counts = Counter(words)
# 取出最常见的三个单词
most_common_words = word_counts.most_common(3)
for word, count in most_common_words:
print(f"{word}: {count}")
```
相关问题
在nlp_lab下,编写Python代码,从给定的文本文件中统计出最常出现的三个单词(英文单词长度大于等于5)。
要在`nlp_lab`目录下编写Python代码以统计最长的三个英文单词,我们可以使用`nltk`库进行文本处理,包括分词和去除停用词。以下是一个简单的示例:
```python
import nltk
from nltk.corpus import stopwords
from collections import Counter
# 加载必要的资源
nltk.download('punkt')
nltk.download('stopwords')
def top_three_words(file_path):
with open(file_path, 'r') as file:
text = file.read().lower()
# 分词
words = nltk.word_tokenize(text)
# 删除停用词
stop_words = set(stopwords.words('english'))
filtered_words = [word for word in words if word.isalpha() and len(word) >= 5 and word not in stop_words]
# 统计并获取最常见的三个单词
word_counts = Counter(filtered_words).most_common(3)
return word_counts
# 使用文件路径替换'nlp_lab/your_text_file.txt'
top_words = top_three_words('nlp_lab/your_text_file.txt')
for word, count in top_words:
print(f"'{word}': {count} times")
```
这段代码会读取指定的文本文件,对其进行分词、移除停用词(长度小于5的单词和非字母字符),然后计算并返回最常见的三个单词及其出现次数。
在nlp_lab下,编写Python代码,从给定的文本文件中统计出最常出现的三个单词
要在nlp_lab环境中,你可以按照以下步骤编写Python代码来从文本文件中统计出现频率最高的三个单词:
1. 导入所需的库:
```python
from collections import Counter
import jieba
```
2. 读取文本文件(假设文件名为`text_file.txt`)并加载内容:
```python
with open('text_file.txt', 'r', encoding='utf-8') as file:
text = file.read()
```
3. 使用jieba进行中文分词:
```python
words = jieba.lcut(text)
```
4. 计算词频并存储在Counter对象中:
```python
word_counts = Counter(words)
```
5. 获取出现频率最高的三个单词:
```python
most_common_words = word_counts.most_common(3)
```
6. 打印结果:
```python
for word, count in most_common_words:
print(f"{word}: {count}")
```
完整的代码示例:
```python
# ... (之前的导入部分)
with open('text_file.txt', 'r', encoding='utf-8') as file:
text = file.read()
words = jieba.lcut(text)
word_counts = Counter(words)
most_common_words = word_counts.most_common(3)
for word, count in most_common_words:
print(f"{word}: {count}")
```