<\/span><\/h2>\n\n\n\n\u7a7a\u767d\u5b57\u7b26\u5305\u62ec\u7a7a\u683c\u3001\u5236\u8868\u7b26\u3001\u6362\u884c\u7b26\u7b49\uff0c\u8fd9\u4e9b\u5b57\u7b26\u901a\u5e38\u4e0d\u4f1a\u5f71\u54cd\u6587\u672c\u5185\u5bb9\u7684\u610f\u4e49\uff0c\u4f46\u4f1a\u589e\u52a0\u6570\u636e\u7684\u590d\u6742\u6027\u3002\u4f7f\u7528 strip()<\/code> \u548c replace()<\/code> \u65b9\u6cd5\u53ef\u4ee5\u8f7b\u677e\u53bb\u9664\u8fd9\u4e9b\u5b57\u7b26\u3002<\/p>\n\n\n\ntext = \" Hello, World! \\n\"
clean_text = text.strip() # \u53bb\u9664\u9996\u5c3e\u7a7a\u767d\u5b57\u7b26<\/em>
print(clean_text) # \u8f93\u51fa: Hello, World!<\/em>
text_with_tabs = \"Hello\\tWorld!\"
clean_text = text_with_tabs.replace(\"\\t\", \" \") # \u5c06\u5236\u8868\u7b26\u66ff\u6362\u4e3a\u7a7a\u683c<\/em>
print(clean_text) # \u8f93\u51fa: Hello World!<\/em><\/code><\/pre>\n\n\n\n<\/span>2. \u8f6c\u6362\u4e3a\u5c0f\u5199<\/strong><\/span><\/h2>\n\n\n\n\u5c06\u6240\u6709\u6587\u672c\u8f6c\u6362\u4e3a\u5c0f\u5199\u53ef\u4ee5\u907f\u514d\u56e0\u5927\u5c0f\u5199\u4e0d\u540c\u800c\u5f15\u8d77\u7684\u4e0d\u4e00\u81f4\u95ee\u9898\u3002<\/p>\n\n\n\n
text = \"Hello, World!\"
lower_text = text.lower()
print(lower_text) # \u8f93\u51fa: hello, world!<\/em><\/code><\/pre>\n\n\n\n<\/span>3. \u53bb\u9664\u6807\u70b9\u7b26\u53f7<\/strong><\/span><\/h2>\n\n\n\n\u6807\u70b9\u7b26\u53f7\u901a\u5e38\u4e0d\u4f1a\u5bf9\u6587\u672c\u7684\u8bed\u4e49\u4ea7\u751f\u5b9e\u8d28\u6027\u7684\u5f71\u54cd\uff0c\u4f46\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff08\u5982\u60c5\u611f\u5206\u6790\uff09\u53ef\u80fd\u4f1a\u6709\u5f71\u54cd\u3002\u4f7f\u7528 string<\/code> \u6a21\u5757\u4e2d\u7684 punctuation<\/code> \u53ef\u4ee5\u8f7b\u677e\u53bb\u9664\u6807\u70b9\u7b26\u53f7\u3002<\/p>\n\n\n\nimport string
text = \"Hello, World!\"
clean_text = text.translate(str.maketrans(\"\", \"\", string.punctuation))
print(clean_text) # \u8f93\u51fa: Hello World<\/em><\/code><\/pre>\n\n\n\n<\/span>4. \u5206\u8bcd<\/strong><\/span><\/h2>\n\n\n\n\u5206\u8bcd\u662f\u5c06\u6587\u672c\u5206\u5272\u6210\u5355\u8bcd\u6216\u77ed\u8bed\u7684\u8fc7\u7a0b\u3002\u4f7f\u7528 nltk<\/code> \u5e93\u7684 word_tokenize<\/code> \u65b9\u6cd5\u53ef\u4ee5\u5b9e\u73b0\u8fd9\u4e00\u70b9\u3002<\/p>\n\n\n\nimport nltk
from nltk.tokenize import word_tokenize
nltk.download('punkt')
text = \"Hello, World! This is a test.\"
tokens = word_tokenize(text)
print(tokens) # \u8f93\u51fa: ['Hello', ',', 'World', '!', 'This', 'is', 'a', 'test', '.']<\/em><\/code><\/pre>\n\n\n\n<\/span>5. \u53bb\u9664\u505c\u7528\u8bcd<\/strong><\/span><\/h2>\n\n\n\n\u505c\u7528\u8bcd\u662f\u90a3\u4e9b\u5728\u6587\u672c\u4e2d\u9891\u7e41\u51fa\u73b0\u4f46\u5bf9\u8bed\u4e49\u8d21\u732e\u4e0d\u5927\u7684\u8bcd\u6c47\uff0c\u5982\u201cthe\u201d\u3001\u201cis\u201d\u7b49\u3002\u4f7f\u7528 nltk<\/code> \u5e93\u7684 stopwords<\/code> \u6a21\u5757\u53ef\u4ee5\u53bb\u9664\u8fd9\u4e9b\u8bcd\u3002<\/p>\n\n\n\nfrom nltk.corpus import stopwords
nltk.download('stopwords')
stop_words = set(stopwords.words('english'))
tokens = ['Hello', 'World', 'This', 'is', 'a', 'test']
filtered_tokens = [token for token in tokens if token not in stop_words]
print(filtered_tokens) # \u8f93\u51fa: ['Hello', 'World', 'test']<\/em><\/code><\/pre>\n\n\n\n<\/span>6. \u8bcd\u5e72\u63d0\u53d6<\/strong><\/span><\/h2>\n\n\n\n\u8bcd\u5e72\u63d0\u53d6\u662f\u5c06\u5355\u8bcd\u8fd8\u539f\u4e3a\u5176\u57fa\u672c\u5f62\u5f0f\u7684\u8fc7\u7a0b\u3002\u4f7f\u7528 nltk<\/code> \u5e93\u7684 PorterStemmer<\/code> \u53ef\u4ee5\u5b9e\u73b0\u8fd9\u4e00\u70b9\u3002<\/p>\n\n\n\nfrom nltk.stem import PorterStemmer
stemmer = PorterStemmer()
words = ['running', 'jumps', 'easily']
stemmed_words = [stemmer.stem(word) for word in words]
print(stemmed_words) # \u8f93\u51fa: ['run', 'jump', 'easili']<\/em><\/code><\/pre>\n\n\n\n<\/span>7. \u8bcd\u5f62\u8fd8\u539f<\/strong><\/span><\/h2>\n\n\n\n\u8bcd\u5f62\u8fd8\u539f\u662f\u5c06\u5355\u8bcd\u8fd8\u539f\u4e3a\u5176\u8bcd\u5178\u5f62\u5f0f\u7684\u8fc7\u7a0b\u3002\u4f7f\u7528 nltk<\/code> \u5e93\u7684 WordNetLemmatizer<\/code> \u53ef\u4ee5\u5b9e\u73b0\u8fd9\u4e00\u70b9\u3002<\/p>\n\n\n\nfrom nltk.stem import WordNetLemmatizer
nltk.download('wordnet')
lemmatizer = WordNetLemmatizer()
words = ['running', 'jumps', 'easily']
lemmatized_words = [lemmatizer.lemmatize(word) for word in words]
print(lemmatized_words) # \u8f93\u51fa: ['running', 'jump', 'easily']<\/em><\/code><\/pre>\n\n\n\n<\/span>8. \u53bb\u9664\u6570\u5b57<\/strong><\/span><\/h2>\n\n\n\n\u6570\u5b57\u901a\u5e38\u4e0d\u4f1a\u5bf9\u6587\u672c\u7684\u8bed\u4e49\u4ea7\u751f\u5b9e\u8d28\u6027\u7684\u5f71\u54cd\u3002\u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u53ef\u4ee5\u8f7b\u677e\u53bb\u9664\u6570\u5b57\u3002<\/p>\n\n\n\n
import re
text = \"Hello, World! 123\"
clean_text = re.sub(r'\\d+', '', text)
print(clean_text) # \u8f93\u51fa: Hello, World! <\/em><\/code><\/pre>\n\n\n\n<\/span>9. \u53bb\u9664\u7279\u6b8a\u5b57\u7b26<\/strong><\/span><\/h2>\n\n\n\n\u7279\u6b8a\u5b57\u7b26\u5982 @<\/code>\u3001#<\/code>\u3001$<\/code> \u7b49\u901a\u5e38\u4e0d\u4f1a\u5bf9\u6587\u672c\u7684\u8bed\u4e49\u4ea7\u751f\u5b9e\u8d28\u6027\u7684\u5f71\u54cd\u3002\u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u53ef\u4ee5\u8f7b\u677e\u53bb\u9664\u8fd9\u4e9b\u5b57\u7b26\u3002<\/p>\n\n\n\ntext = \"Hello, @World! #Python $123\"
clean_text = re.sub(r'[^\\w\\s]', '', text)
print(clean_text) # \u8f93\u51fa: Hello World Python 123<\/em><\/code><\/pre>\n\n\n\n<\/span>10. \u53bb\u9664 HTML \u6807\u7b7e<\/strong><\/span><\/h2>\n\n\n\n\u5982\u679c\u6587\u672c\u6765\u81ea\u7f51\u9875\uff0c\u53ef\u80fd\u5305\u542b HTML \u6807\u7b7e\u3002\u4f7f\u7528 BeautifulSoup<\/code> \u5e93\u53ef\u4ee5\u8f7b\u677e\u53bb\u9664\u8fd9\u4e9b\u6807\u7b7e\u3002<\/p>\n\n\n\nfrom bs4 import BeautifulSoup
html_text = \"<html><body><h1>Hello, World!<\/h1><\/body><\/html>\"
soup = BeautifulSoup(html_text, 'html.parser')
clean_text = soup.get_text()
print(clean_text) # \u8f93\u51fa: Hello, World!<\/em><\/code><\/pre>\n\n\n\n<\/span>11. \u53bb\u9664 URL<\/strong><\/span><\/h2>\n\n\n\nURL \u901a\u5e38\u4e0d\u4f1a\u5bf9\u6587\u672c\u7684\u8bed\u4e49\u4ea7\u751f\u5b9e\u8d28\u6027\u7684\u5f71\u54cd\u3002\u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u53ef\u4ee5\u8f7b\u677e\u53bb\u9664 URL\u3002<\/p>\n\n\n\n
text = \"Check out this link: https:\/\/example.com\"
clean_text = re.sub(r'http\\S+|www.\\S+', '', text)
print(clean_text) # \u8f93\u51fa: Check out this link: <\/em><\/code><\/pre>\n\n\n\n<\/span>12. \u53bb\u9664\u91cd\u590d\u5355\u8bcd<\/strong><\/span><\/h2>\n\n\n\n\u91cd\u590d\u5355\u8bcd\u53ef\u80fd\u4f1a\u589e\u52a0\u6587\u672c\u7684\u590d\u6742\u6027\u3002\u4f7f\u7528\u96c6\u5408\u53ef\u4ee5\u8f7b\u677e\u53bb\u9664\u91cd\u590d\u5355\u8bcd\u3002<\/p>\n\n\n\n
tokens = ['Hello', 'World', 'Hello', 'Python', 'Python']
unique_tokens = list(set(tokens))
print(unique_tokens) # \u8f93\u51fa: ['Hello', 'Python', 'World']<\/em><\/code><\/pre>\n\n\n\n<\/span>13. \u53bb\u9664\u77ed\u8bcd<\/strong><\/span><\/h2>\n\n\n\n\u77ed\u8bcd\u901a\u5e38\u4e0d\u4f1a\u5bf9\u6587\u672c\u7684\u8bed\u4e49\u4ea7\u751f\u5b9e\u8d28\u6027\u7684\u5f71\u54cd\u3002\u53ef\u4ee5\u8bbe\u7f6e\u4e00\u4e2a\u9608\u503c\u6765\u53bb\u9664\u957f\u5ea6\u5c0f\u4e8e\u8be5\u9608\u503c\u7684\u5355\u8bcd\u3002<\/p>\n\n\n\n
tokens = ['Hello', 'World', 'a', 'is', 'Python']
min_length = 3
filtered_tokens = [token for token in tokens if len(token) >= min_length]
print(filtered_tokens) # \u8f93\u51fa: ['Hello', 'World', 'Python']<\/em><\/code><\/pre>\n\n\n\n<\/span>14. \u53bb\u9664\u7f55\u89c1\u8bcd<\/strong><\/span><\/h2>\n\n\n\n\u7f55\u89c1\u8bcd\u53ef\u80fd\u4f1a\u589e\u52a0\u6587\u672c\u7684\u590d\u6742\u6027\u3002\u53ef\u4ee5\u8bbe\u7f6e\u4e00\u4e2a\u9891\u7387\u9608\u503c\u6765\u53bb\u9664\u51fa\u73b0\u6b21\u6570\u5c11\u4e8e\u8be5\u9608\u503c\u7684\u5355\u8bcd\u3002<\/p>\n\n\n\n
from collections import Counter
tokens = ['Hello', 'World', 'Hello', 'Python', 'Python', 'test', 'test', 'test']
word_counts = Counter(tokens)
min_frequency = 2
filtered_tokens = [token for token in tokens if word_counts[token] >= min_frequency]
print(filtered_tokens) # \u8f93\u51fa: ['Hello', 'Hello', 'Python', 'Python', 'test', 'test', 'test']<\/em><\/code><\/pre>\n\n\n\n<\/span>15. \u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u8fdb\u884c\u590d\u6742\u6e05\u6d17<\/strong><\/span><\/h2>\n\n\n\n\u6b63\u5219\u8868\u8fbe\u5f0f\u662f\u4e00\u79cd\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u53ef\u4ee5\u7528\u4e8e\u590d\u6742\u7684\u6587\u672c\u6e05\u6d17\u4efb\u52a1\u3002\u4f8b\u5982\uff0c\u53bb\u9664\u7279\u5b9a\u6a21\u5f0f\u7684\u5b57\u7b26\u4e32\u3002<\/p>\n\n\n\n
text = \"Hello, World! 123-456-7890\"
clean_text = re.sub(r'\\d{3}-\\d{3}-\\d{4}', 'PHONE', text)
print(clean_text) # \u8f93\u51fa: Hello, World! PHONE<\/em><\/code><\/pre>\n\n\n\n<\/span>\u5b9e\u6218\u6848\u4f8b\uff1a\u6e05\u6d17\u793e\u4ea4\u5a92\u4f53\u8bc4\u8bba<\/strong><\/span><\/h2>\n\n\n\n\u5047\u8bbe\u4f60\u6709\u4e00\u4e2a\u5305\u542b\u793e\u4ea4\u5a92\u4f53\u8bc4\u8bba\u7684\u6570\u636e\u96c6\uff0c\u9700\u8981\u5bf9\u5176\u8fdb\u884c\u6e05\u6d17\u548c\u9884\u5904\u7406\u3002\u6211\u4eec\u5c06\u7efc\u5408\u8fd0\u7528\u4e0a\u8ff0\u6280\u672f\u6765\u5b8c\u6210\u8fd9\u4e2a\u4efb\u52a1\u3002<\/p>\n\n\n\n
import pandas as pd
import re
import string
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from bs4 import BeautifulSoup
# \u4e0b\u8f7d\u5fc5\u8981\u7684NLTK\u8d44\u6e90<\/em>
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')
# \u793a\u4f8b\u6570\u636e<\/em>
data = {
'comment': [
\"Check out this link: https:\/\/example.com\",
\"Hello, @World! #Python $123\",
\"<html><body><h1>Hello, World!<\/h1><\/body><\/html>\",
\"Running jumps easily 123-456-7890\"
]
}
df = pd.DataFrame(data)
def clean_text(text):
# \u53bb\u9664HTML\u6807\u7b7e<\/em>
text = BeautifulSoup(text, 'html.parser').get_text()
# \u53bb\u9664URL<\/em>
text = re.sub(r'http\\S+|www.\\S+', '', text)
# \u53bb\u9664\u7279\u6b8a\u5b57\u7b26<\/em>
text = re.sub(r'[^\\w\\s]', '', text)
# \u53bb\u9664\u6570\u5b57<\/em>
text = re.sub(r'\\d+', '', text)
# \u8f6c\u6362\u4e3a\u5c0f\u5199<\/em>
text = text.lower()
# \u5206\u8bcd<\/em>
tokens = word_tokenize(text)
# \u53bb\u9664\u505c\u7528\u8bcd<\/em>
stop_words = set(stopwords.words('english'))
tokens = [token for token in tokens if token not in stop_words]
# \u8bcd\u5f62\u8fd8\u539f<\/em>
lemmatizer = WordNetLemmatizer()
tokens = [lemmatizer.lemmatize(token) for token in tokens]
# \u53bb\u9664\u77ed\u8bcd<\/em>
tokens = [token for token in tokens if len(token) >= 3]
# \u53bb\u9664\u7f55\u89c1\u8bcd<\/em>
word_counts = Counter(tokens)
min_frequency = 2
tokens = [token for token in tokens if word_counts[token] >= min_frequency]
return ' '.join(tokens)
# \u5e94\u7528\u6e05\u6d17\u51fd\u6570<\/em>
df['cleaned_comment'] = df['comment'].apply(clean_text)
print(df)<\/code><\/pre>\n\n\n\n