{"id":1092392,"date":"2025-01-08T14:20:42","date_gmt":"2025-01-08T06:20:42","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1092392.html"},"modified":"2025-01-08T14:20:44","modified_gmt":"2025-01-08T06:20:44","slug":"python%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e5%a6%82%e4%bd%95%e5%ae%9e%e7%8e%b0%e8%af%8d%e9%a2%91%e7%bb%9f%e8%ae%a1-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1092392.html","title":{"rendered":"python\u6570\u636e\u5206\u6790\u5982\u4f55\u5b9e\u73b0\u8bcd\u9891\u7edf\u8ba1"},"content":{"rendered":"

\"python\u6570\u636e\u5206\u6790\u5982\u4f55\u5b9e\u73b0\u8bcd\u9891\u7edf\u8ba1\"<\/p>\n

\u5b9e\u73b0Python\u6570\u636e\u5206\u6790\u7684\u8bcd\u9891\u7edf\u8ba1\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u51e0\u4e2a\u5173\u952e\u6b65\u9aa4\u6765\u5b8c\u6210\uff1a\u6570\u636e\u9884\u5904\u7406\u3001\u5206\u8bcd\u5904\u7406\u3001\u7edf\u8ba1\u8bcd\u9891\u3001\u7ed3\u679c\u53ef\u89c6\u5316\u3002<\/strong> \u5176\u4e2d\uff0c\u6570\u636e\u9884\u5904\u7406\u662f\u57fa\u7840\uff0c\u5b83\u51b3\u5b9a\u4e86\u540e\u7eed\u6b65\u9aa4\u7684\u51c6\u786e\u6027\u4e0e\u6709\u6548\u6027\u3002\u4e0b\u9762\u8be6\u7ec6\u63cf\u8ff0\u6570\u636e\u9884\u5904\u7406\u7684\u8fc7\u7a0b\u3002<\/p>\n<\/p>\n

\u6570\u636e\u9884\u5904\u7406<\/strong>\u662f\u8bcd\u9891\u7edf\u8ba1\u7684\u7b2c\u4e00\u6b65\uff0c\u6d89\u53ca\u6e05\u6d17\u548c\u89c4\u8303\u5316\u6587\u672c\u6570\u636e\u3002\u6587\u672c\u6570\u636e\u901a\u5e38\u5305\u542b\u591a\u4f59\u7684\u7a7a\u683c\u3001\u6807\u70b9\u7b26\u53f7\u3001\u7279\u6b8a\u5b57\u7b26\u548c\u5927\u5c0f\u5199\u4e0d\u4e00\u81f4\u7b49\u95ee\u9898\u3002\u901a\u8fc7\u5bf9\u6570\u636e\u8fdb\u884c\u6e05\u6d17\uff0c\u5220\u9664\u591a\u4f59\u5143\u7d20\uff0c\u7edf\u4e00\u5927\u5c0f\u5199\uff0c\u53ef\u4ee5\u63d0\u9ad8\u8bcd\u9891\u7edf\u8ba1\u7684\u51c6\u786e\u6027\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\uff08Regex\uff09\u6765\u53bb\u9664\u6807\u70b9\u7b26\u53f7\u548c\u7279\u6b8a\u5b57\u7b26\uff0c\u4f7f\u7528Python\u5185\u7f6e\u7684\u5b57\u7b26\u4e32\u64cd\u4f5c\u51fd\u6570\u6765\u5904\u7406\u7a7a\u683c\u548c\u5927\u5c0f\u5199\u3002<\/p>\n<\/p>\n

\n

\u4e00\u3001\u6570\u636e\u9884\u5904\u7406<\/h3>\n<\/p>\n

\u6570\u636e\u9884\u5904\u7406\u662f\u6587\u672c\u5206\u6790\u7684\u5173\u952e\u6b65\u9aa4\uff0c\u76f4\u63a5\u5f71\u54cd\u540e\u7eed\u7684\u8bcd\u9891\u7edf\u8ba1\u7ed3\u679c\u3002\u5728\u6b64\u6b65\u9aa4\u4e2d\uff0c\u6211\u4eec\u9700\u8981\u5bf9\u6587\u672c\u6570\u636e\u8fdb\u884c\u6e05\u6d17\u548c\u89c4\u8303\u5316\u5904\u7406\u3002<\/p>\n<\/p>\n

1\u3001\u6e05\u6d17\u6570\u636e<\/h4>\n<\/p>\n

\u6e05\u6d17\u6570\u636e\u662f\u4e3a\u4e86\u53bb\u9664\u6587\u672c\u4e2d\u7684\u566a\u97f3\u6570\u636e\uff0c\u8fd9\u4e9b\u6570\u636e\u5305\u62ec\u6807\u70b9\u7b26\u53f7\u3001\u7279\u6b8a\u5b57\u7b26\u3001\u591a\u4f59\u7684\u7a7a\u683c\u548c\u6362\u884c\u7b26\u7b49\u3002\u53ef\u4ee5\u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\uff08Regex\uff09\u8fdb\u884c\u6e05\u6d17\u3002<\/p>\n<\/p>\n

\u4f8b\u5982\uff0c\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u53bb\u9664\u6587\u672c\u4e2d\u7684\u6807\u70b9\u7b26\u53f7\u548c\u7279\u6b8a\u5b57\u7b26\uff1a<\/p>\n<\/p>\n

import re<\/p>\n
def clean_text(text):<\/p>\n
    # \u79fb\u9664\u6807\u70b9\u7b26\u53f7\u548c\u7279\u6b8a\u5b57\u7b26<\/p>\n
    text = re.sub(r'[^\\w\\s]', '', text)<\/p>\n
    # \u79fb\u9664\u591a\u4f59\u7684\u7a7a\u683c<\/p>\n
    text = re.sub(r'\\s+', ' ', text)<\/p>\n
    return text.strip()<\/p>\n
text = "Hello, world! This is a sample text with, punctuation."<\/p>\n
cleaned_text = clean_text(text)<\/p>\n
print(cleaned_text)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u89c4\u8303\u5316\u5904\u7406<\/h4>\n<\/p>\n
\u89c4\u8303\u5316\u5904\u7406\u4e3b\u8981\u662f\u5c06\u6587\u672c\u4e2d\u7684\u5b57\u7b26\u7edf\u4e00\u4e3a\u5c0f\u5199\u6216\u5927\u5199\uff0c\u786e\u4fdd\u540c\u4e00\u4e2a\u8bcd\u5728\u7edf\u8ba1\u65f6\u4e0d\u4f1a\u56e0\u4e3a\u5927\u5c0f\u5199\u4e0d\u540c\u800c\u88ab\u5206\u5f00\u7edf\u8ba1\u3002<\/p>\n<\/p>\n
\u4f8b\u5982\uff0c\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u5c06\u6587\u672c\u5168\u90e8\u8f6c\u6362\u4e3a\u5c0f\u5199\uff1a<\/p>\n<\/p>\n
def normalize_text(text):<\/p>\n
    return text.lower()<\/p>\n
normalized_text = normalize_text(cleaned_text)<\/p>\n
print(normalized_text)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u5206\u8bcd\u5904\u7406<\/h3>\n<\/p>\n
\u5206\u8bcd\u662f\u5c06\u6587\u672c\u5206\u5272\u6210\u4e00\u4e2a\u4e2a\u5355\u8bcd\u6216\u8bcd\u7ec4\u7684\u8fc7\u7a0b\uff0c\u662f\u8bcd\u9891\u7edf\u8ba1\u7684\u57fa\u7840\u3002\u4e0d\u540c\u8bed\u8a00\u7684\u5206\u8bcd\u65b9\u5f0f\u4e0d\u540c\uff0cPython\u4e2d\u5e38\u7528\u7684\u5206\u8bcd\u5e93\u6709nltk<\/code>\u548cjieba<\/code>\u3002<\/p>\n<\/p>\n
1\u3001\u4f7f\u7528nltk\u8fdb\u884c\u5206\u8bcd<\/h4>\n<\/p>\n
nltk<\/code>\uff08Natural Language Toolkit\uff09\u662fPython\u4e2d\u5e38\u7528\u7684\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5e93\uff0c\u9002\u7528\u4e8e\u82f1\u6587\u6587\u672c\u7684\u5206\u8bcd\u5904\u7406\u3002<\/p>\n<\/p>\n
\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528nltk<\/code>\u8fdb\u884c\u82f1\u6587\u6587\u672c\u7684\u5206\u8bcd\uff1a<\/p>\n<\/p>\n
import nltk<\/p>\n
nltk.download('punkt')<\/p>\n
from nltk.tokenize import word_tokenize<\/p>\n
def tokenize_text(text):<\/p>\n
    return word_tokenize(text)<\/p>\n
tokens = tokenize_text(normalized_text)<\/p>\n
print(tokens)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528jieba\u8fdb\u884c\u5206\u8bcd<\/h4>\n<\/p>\n
jieba<\/code>\u662fPython\u4e2d\u5e38\u7528\u7684\u4e2d\u6587\u5206\u8bcd\u5e93\uff0c\u9002\u7528\u4e8e\u4e2d\u6587\u6587\u672c\u7684\u5206\u8bcd\u5904\u7406\u3002<\/p>\n<\/p>\n
\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528jieba<\/code>\u8fdb\u884c\u4e2d\u6587\u6587\u672c\u7684\u5206\u8bcd\uff1a<\/p>\n<\/p>\n
import jieba<\/p>\n
def tokenize_text_cn(text):<\/p>\n
    return list(jieba.cut(text))<\/p>\n
text_cn = "\u8fd9\u662f\u4e00\u4e2a\u4e2d\u6587\u5206\u8bcd\u7684\u793a\u4f8b\u6587\u672c\u3002"<\/p>\n
tokens_cn = tokenize_text_cn(text_cn)<\/p>\n
print(tokens_cn)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u7edf\u8ba1\u8bcd\u9891<\/h3>\n<\/p>\n
\u5728\u5b8c\u6210\u5206\u8bcd\u4e4b\u540e\uff0c\u63a5\u4e0b\u6765\u5c31\u662f\u7edf\u8ba1\u6bcf\u4e2a\u8bcd\u51fa\u73b0\u7684\u9891\u7387\u3002\u53ef\u4ee5\u4f7f\u7528Python\u7684collections.Counter<\/code>\u7c7b\u6765\u5b8c\u6210\u8bcd\u9891\u7edf\u8ba1\u3002<\/p>\n<\/p>\n
1\u3001\u4f7f\u7528Counter\u7edf\u8ba1\u8bcd\u9891<\/h4>\n<\/p>\n
\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528Counter<\/code>\u7edf\u8ba1\u8bcd\u9891\uff1a<\/p>\n<\/p>\n
from collections import Counter<\/p>\n
def count_word_frequency(tokens):<\/p>\n
    return Counter(tokens)<\/p>\n
word_freq = count_word_frequency(tokens)<\/p>\n
print(word_freq)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u7edf\u8ba1\u4e2d\u6587\u8bcd\u9891<\/h4>\n<\/p>\n
\u540c\u6837\u7684\u65b9\u6cd5\u4e5f\u9002\u7528\u4e8e\u4e2d\u6587\u6587\u672c\u7684\u8bcd\u9891\u7edf\u8ba1\uff1a<\/p>\n<\/p>\n
word_freq_cn = count_word_frequency(tokens_cn)<\/p>\n
print(word_freq_cn)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u7ed3\u679c\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
\u4e3a\u4e86\u66f4\u76f4\u89c2\u5730\u5c55\u793a\u8bcd\u9891\u7edf\u8ba1\u7ed3\u679c\uff0c\u53ef\u4ee5\u4f7f\u7528\u53ef\u89c6\u5316\u5de5\u5177\u8fdb\u884c\u5c55\u793a\u3002Python\u4e2d\u5e38\u7528\u7684\u53ef\u89c6\u5316\u5e93\u6709matplotlib<\/code>\u548cwordcloud<\/code>\u3002<\/p>\n<\/p>\n
1\u3001\u4f7f\u7528matplotlib\u7ed8\u5236\u8bcd\u9891\u76f4\u65b9\u56fe<\/h4>\n<\/p>\n
\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528matplotlib<\/code>\u7ed8\u5236\u8bcd\u9891\u76f4\u65b9\u56fe\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
def plot_word_frequency(word_freq):<\/p>\n
    words = list(word_freq.keys())<\/p>\n
    frequencies = list(word_freq.values())<\/p>\n
    plt.figure(figsize=(10, 5))<\/p>\n
    plt.bar(words, frequencies)<\/p>\n
    plt.xlabel('Words')<\/p>\n
    plt.ylabel('Frequency')<\/p>\n
    plt.title('Word Frequency')<\/p>\n
    plt.show()<\/p>\n
plot_word_frequency(word_freq)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528wordcloud\u7ed8\u5236\u8bcd\u4e91<\/h4>\n<\/p>\n
\u8bcd\u4e91\u662f\u4e00\u79cd\u76f4\u89c2\u5c55\u793a\u8bcd\u9891\u7684\u56fe\u5f62\u65b9\u6cd5\uff0c\u53ef\u4ee5\u4f7f\u7528wordcloud<\/code>\u5e93\u751f\u6210\u8bcd\u4e91\u3002<\/p>\n<\/p>\n
\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528wordcloud<\/code>\u751f\u6210\u8bcd\u4e91\uff1a<\/p>\n<\/p>\n
from wordcloud import WordCloud<\/p>\n
def generate_wordcloud(word_freq):<\/p>\n
    wordcloud = WordCloud(width=800, height=400, background_color='white').generate_from_frequencies(word_freq)<\/p>\n
    plt.figure(figsize=(10, 5))<\/p>\n
    plt.imshow(wordcloud, interpolation='bilinear')<\/p>\n
    plt.axis('off')<\/p>\n
    plt.show()<\/p>\n
generate_wordcloud(word_freq)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u7efc\u5408\u5e94\u7528\u5b9e\u4f8b<\/h3>\n<\/p>\n
\u6700\u540e\uff0c\u901a\u8fc7\u4e00\u4e2a\u7efc\u5408\u5e94\u7528\u5b9e\u4f8b\u6765\u5c55\u793a\u5982\u4f55\u5c06\u4e0a\u8ff0\u6b65\u9aa4\u7ed3\u5408\u8d77\u6765\uff0c\u5b8c\u6210\u6570\u636e\u9884\u5904\u7406\u3001\u5206\u8bcd\u3001\u8bcd\u9891\u7edf\u8ba1\u548c\u7ed3\u679c\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
1\u3001\u7efc\u5408\u4ee3\u7801\u5b9e\u4f8b<\/h4>\n<\/p>\n
\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u4e00\u4e2a\u7efc\u5408\u5e94\u7528\u5b9e\u4f8b\uff0c\u4ece\u6570\u636e\u9884\u5904\u7406\u5230\u7ed3\u679c\u53ef\u89c6\u5316\u7684\u5168\u8fc7\u7a0b\uff1a<\/p>\n<\/p>\n
import re<\/p>\n
import nltk<\/p>\n
import jieba<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
from collections import Counter<\/p>\n
from wordcloud import WordCloud<\/p>\n
def clean_text(text):<\/p>\n
    text = re.sub(r'[^\\w\\s]', '', text)<\/p>\n
    text = re.sub(r'\\s+', ' ', text)<\/p>\n
    return text.strip()<\/p>\n
def normalize_text(text):<\/p>\n
    return text.lower()<\/p>\n
def tokenize_text(text):<\/p>\n
    return word_tokenize(text)<\/p>\n
def tokenize_text_cn(text):<\/p>\n
    return list(jieba.cut(text))<\/p>\n
def count_word_frequency(tokens):<\/p>\n
    return Counter(tokens)<\/p>\n
def plot_word_frequency(word_freq):<\/p>\n
    words = list(word_freq.keys())<\/p>\n
    frequencies = list(word_freq.values())<\/p>\n
    plt.figure(figsize=(10, 5))<\/p>\n
    plt.bar(words, frequencies)<\/p>\n
    plt.xlabel('Words')<\/p>\n
    plt.ylabel('Frequency')<\/p>\n
    plt.title('Word Frequency')<\/p>\n
    plt.show()<\/p>\n
def generate_wordcloud(word_freq):<\/p>\n
    wordcloud = WordCloud(width=800, height=400, background_color='white').generate_from_frequencies(word_freq)<\/p>\n
    plt.figure(figsize=(10, 5))<\/p>\n
    plt.imshow(wordcloud, interpolation='bilinear')<\/p>\n
    plt.axis('off')<\/p>\n
    plt.show()<\/p>\n
\u793a\u4f8b\u6587\u672c<\/strong><\/h2>\n
text = "Hello, world! This is a sample text with, punctuation."<\/p>\n
\u6570\u636e\u9884\u5904\u7406<\/strong><\/h2>\n
cleaned_text = clean_text(text)<\/p>\n
normalized_text = normalize_text(cleaned_text)<\/p>\n
\u5206\u8bcd\u5904\u7406<\/strong><\/h2>\n
tokens = tokenize_text(normalized_text)<\/p>\n
\u8bcd\u9891\u7edf\u8ba1<\/strong><\/h2>\n
word_freq = count_word_frequency(tokens)<\/p>\n
\u7ed3\u679c\u53ef\u89c6\u5316<\/strong><\/h2>\n
plot_word_frequency(word_freq)<\/p>\n
generate_wordcloud(word_freq)<\/p>\n
\u4e2d\u6587\u6587\u672c\u793a\u4f8b<\/strong><\/h2>\n
text_cn = "\u8fd9\u662f\u4e00\u4e2a\u4e2d\u6587\u5206\u8bcd\u7684\u793a\u4f8b\u6587\u672c\u3002"<\/p>\n
\u6570\u636e\u9884\u5904\u7406<\/strong><\/h2>\n
cleaned_text_cn = clean_text(text_cn)<\/p>\n
\u5206\u8bcd\u5904\u7406<\/strong><\/h2>\n
tokens_cn = tokenize_text_cn(cleaned_text_cn)<\/p>\n
\u8bcd\u9891\u7edf\u8ba1<\/strong><\/h2>\n
word_freq_cn = count_word_frequency(tokens_cn)<\/p>\n
\u7ed3\u679c\u53ef\u89c6\u5316<\/strong><\/h2>\n
plot_word_frequency(word_freq_cn)<\/p>\n
generate_wordcloud(word_freq_cn)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\u548c\u4ee3\u7801\u5b9e\u4f8b\uff0c\u6211\u4eec\u53ef\u4ee5\u5b8c\u6210\u4ece\u6570\u636e\u9884\u5904\u7406\u5230\u8bcd\u9891\u7edf\u8ba1\uff0c\u518d\u5230\u7ed3\u679c\u53ef\u89c6\u5316\u7684\u5168\u8fc7\u7a0b\u3002\u8fd9\u4e9b\u6b65\u9aa4\u5728\u5b9e\u9645\u7684\u6587\u672c\u5206\u6790\u548c\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4efb\u52a1\u4e2d\u662f\u975e\u5e38\u91cd\u8981\u7684\uff0c\u80fd\u591f\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u548c\u5206\u6790\u6587\u672c\u6570\u636e\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u8bcd\u9891\u7edf\u8ba1\uff1f<\/strong>
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