{"id":1098881,"date":"2025-01-08T15:25:15","date_gmt":"2025-01-08T07:25:15","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1098881.html"},"modified":"2025-01-08T15:25:17","modified_gmt":"2025-01-08T07:25:17","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e8%bf%9b%e8%a1%8c%e6%96%87%e7%8c%ae%e8%ae%a1%e9%87%8f-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1098881.html","title":{"rendered":"\u5982\u4f55\u7528python\u8fdb\u884c\u6587\u732e\u8ba1\u91cf"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u8fdb\u884c\u6587\u732e\u8ba1\u91cf\"<\/p>\n

\u5982\u4f55\u7528Python\u8fdb\u884c\u6587\u732e\u8ba1\u91cf<\/strong><\/p>\n<\/p>\n

Python\u8fdb\u884c\u6587\u732e\u8ba1\u91cf\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u6570\u636e\u91c7\u96c6\u3001\u6587\u672c\u9884\u5904\u7406\u3001\u6587\u672c\u5206\u6790\u3001\u53ef\u89c6\u5316\u3002<\/strong>\u672c\u6587\u5c06\u8be6\u7ec6\u9610\u8ff0\u5982\u4f55\u7528Python\u8fdb\u884c\u6587\u732e\u8ba1\u91cf\u5206\u6790\uff0c\u5e76\u4ecb\u7ecd\u5e38\u7528\u7684Python\u5e93\u548c\u5de5\u5177\uff0c\u4ee5\u5e2e\u52a9\u7814\u7a76\u8005\u9ad8\u6548\u5730\u5f00\u5c55\u6587\u732e\u8ba1\u91cf\u7814\u7a76\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u6570\u636e\u91c7\u96c6<\/h3>\n<\/p>\n

\u5728\u8fdb\u884c\u6587\u732e\u8ba1\u91cf\u5206\u6790\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u91c7\u96c6\u6587\u732e\u6570\u636e\u3002\u5e38\u7528\u7684\u6587\u732e\u6570\u636e\u6e90\u5305\u62ecGoogle Scholar\u3001PubMed\u3001Web of Science\u7b49\u3002Python\u63d0\u4f9b\u4e86\u591a\u79cd\u5de5\u5177\u548c\u5e93\u6765\u91c7\u96c6\u8fd9\u4e9b\u6570\u636e\u3002<\/p>\n<\/p>\n

1. \u4f7f\u7528Selenium\u81ea\u52a8\u5316\u91c7\u96c6\u6570\u636e<\/h4>\n<\/p>\n

Selenium\u662f\u4e00\u4e2a\u81ea\u52a8\u5316\u6d4b\u8bd5\u5de5\u5177\uff0c\u53ef\u4ee5\u7528\u6765\u6a21\u62df\u6d4f\u89c8\u5668\u64cd\u4f5c\uff0c\u91c7\u96c6\u7f51\u9875\u6570\u636e\u3002\u901a\u8fc7Selenium\uff0c\u6211\u4eec\u53ef\u4ee5\u81ea\u52a8\u767b\u5f55\u6587\u732e\u6570\u636e\u5e93\u7f51\u7ad9\uff0c\u641c\u7d22\u5173\u952e\u8bcd\uff0c\u91c7\u96c6\u6587\u732e\u6570\u636e\u3002<\/p>\n<\/p>\n

from selenium import webdriver<\/p>\n
from selenium.webdriver.common.keys import Keys<\/p>\n
\u8bbe\u7f6eSelenium WebDriver<\/strong><\/h2>\n
driver = webdriver.Chrome(executable_path='path_to_chromedriver')<\/p>\n
\u6253\u5f00\u6587\u732e\u6570\u636e\u5e93\u7f51\u7ad9<\/strong><\/h2>\n
driver.get('https:\/\/scholar.google.com')<\/p>\n
\u641c\u7d22\u5173\u952e\u8bcd<\/strong><\/h2>\n
search_box = driver.find_element_by_name('q')<\/p>\n
search_box.send_keys('machine learning')<\/p>\n
search_box.send_keys(Keys.RETURN)<\/p>\n
\u91c7\u96c6\u6587\u732e\u4fe1\u606f<\/strong><\/h2>\n
results = driver.find_elements_by_css_selector('div.gs_ri')<\/p>\n
for result in results:<\/p>\n
    title = result.find_element_by_css_selector('h3').text<\/p>\n
    authors = result.find_element_by_css_selector('div.gs_a').text<\/p>\n
    snippet = result.find_element_by_css_selector('div.gs_rs').text<\/p>\n
    print(f'Title: {title}, Authors: {authors}, Snippet: {snippet}')<\/p>\n
\u5173\u95ed\u6d4f\u89c8\u5668<\/strong><\/h2>\n
driver.quit()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4f7f\u7528API\u63a5\u53e3\u91c7\u96c6\u6570\u636e<\/h4>\n<\/p>\n
\u5f88\u591a\u6587\u732e\u6570\u636e\u5e93\u63d0\u4f9bAPI\u63a5\u53e3\uff0c\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u7f16\u7a0b\u65b9\u5f0f\u83b7\u53d6\u6587\u732e\u6570\u636e\u3002\u4f8b\u5982\uff0cPubMed\u63d0\u4f9b\u4e86Entrez Programming Utilities (E-utilities)\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u7684Biopython\u5e93\u6765\u8bbf\u95eePubMed API\u3002<\/p>\n<\/p>\n
from Bio import Entrez<\/p>\n
\u8bbe\u7f6eEntrez emAI<\/a>l<\/strong><\/h2>\n
Entrez.email = 'your_email@example.com'<\/p>\n
\u641c\u7d22\u5173\u952e\u8bcd<\/strong><\/h2>\n
search_term = 'machine learning'<\/p>\n
handle = Entrez.esearch(db='pubmed', term=search_term, retmax=10)<\/p>\n
record = Entrez.read(handle)<\/p>\n
handle.close()<\/p>\n
\u83b7\u53d6\u6587\u732eID\u5217\u8868<\/strong><\/h2>\n
id_list = record['IdList']<\/p>\n
\u83b7\u53d6\u6587\u732e\u4fe1\u606f<\/strong><\/h2>\n
handle = Entrez.efetch(db='pubmed', id=id_list, rettype='abstract', retmode='text')<\/p>\n
abstracts = handle.read()<\/p>\n
handle.close()<\/p>\n
print(abstracts)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u6587\u672c\u9884\u5904\u7406<\/h3>\n<\/p>\n
\u83b7\u53d6\u5230\u6587\u732e\u6570\u636e\u540e\uff0c\u9700\u8981\u5bf9\u6587\u672c\u8fdb\u884c\u9884\u5904\u7406\uff0c\u4ee5\u4fbf\u540e\u7eed\u7684\u5206\u6790\u3002\u5e38\u89c1\u7684\u6587\u672c\u9884\u5904\u7406\u6b65\u9aa4\u5305\u62ec\u5206\u8bcd\u3001\u53bb\u9664\u505c\u7528\u8bcd\u3001\u8bcd\u5e72\u63d0\u53d6\u7b49\u3002<\/p>\n<\/p>\n
1. \u5206\u8bcd\u548c\u53bb\u9664\u505c\u7528\u8bcd<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528NLTK\u5e93\u8fdb\u884c\u5206\u8bcd\u548c\u53bb\u9664\u505c\u7528\u8bcd\u3002NLTK\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5de5\u5177\u548c\u8d44\u6e90\u3002<\/p>\n<\/p>\n
import nltk<\/p>\n
from nltk.corpus import stopwords<\/p>\n
from nltk.tokenize import word_tokenize<\/p>\n
\u4e0b\u8f7d\u505c\u7528\u8bcd<\/strong><\/h2>\n
nltk.download('stopwords')<\/p>\n
nltk.download('punkt')<\/p>\n
\u5206\u8bcd<\/strong><\/h2>\n
text = 'This is a sample text for text processing using NLTK.'<\/p>\n
tokens = word_tokenize(text)<\/p>\n
\u53bb\u9664\u505c\u7528\u8bcd<\/strong><\/h2>\n
stop_words = set(stopwords.words('english'))<\/p>\n
filtered_tokens = [word for word in tokens if word.lower() not in stop_words]<\/p>\n
print(filtered_tokens)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8bcd\u5e72\u63d0\u53d6<\/h4>\n<\/p>\n
\u8bcd\u5e72\u63d0\u53d6\u662f\u5c06\u5355\u8bcd\u8fd8\u539f\u4e3a\u5176\u8bcd\u5e72\u5f62\u5f0f\uff0c\u51cf\u5c11\u8bcd\u5f62\u53d8\u5316\u5e26\u6765\u7684\u566a\u97f3\u3002\u53ef\u4ee5\u4f7f\u7528NLTK\u7684PorterStemmer\u8fdb\u884c\u8bcd\u5e72\u63d0\u53d6\u3002<\/p>\n<\/p>\n
from nltk.stem import PorterStemmer<\/p>\n
\u8bcd\u5e72\u63d0\u53d6<\/strong><\/h2>\n
stemmer = PorterStemmer()<\/p>\n
stemmed_tokens = [stemmer.stem(word) for word in filtered_tokens]<\/p>\n
print(stemmed_tokens)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u6587\u672c\u5206\u6790<\/h3>\n<\/p>\n
\u6587\u672c\u9884\u5904\u7406\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u8fdb\u884c\u5404\u79cd\u6587\u672c\u5206\u6790\uff0c\u5305\u62ec\u8bcd\u9891\u7edf\u8ba1\u3001\u5171\u73b0\u5206\u6790\u3001\u4e3b\u9898\u5efa\u6a21\u7b49\u3002<\/p>\n<\/p>\n
1. \u8bcd\u9891\u7edf\u8ba1<\/h4>\n<\/p>\n
\u8bcd\u9891\u7edf\u8ba1\u662f\u6700\u57fa\u672c\u7684\u6587\u672c\u5206\u6790\u65b9\u6cd5\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u7684collections\u5e93\u6765\u7edf\u8ba1\u8bcd\u9891\u3002<\/p>\n<\/p>\n
from collections import Counter<\/p>\n
\u8bcd\u9891\u7edf\u8ba1<\/strong><\/h2>\n
word_counts = Counter(stemmed_tokens)<\/p>\n
print(word_counts)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u5171\u73b0\u5206\u6790<\/h4>\n<\/p>\n
\u5171\u73b0\u5206\u6790\u662f\u7814\u7a76\u6587\u732e\u4e2d\u8bcd\u6c47\u6216\u4e3b\u9898\u4e4b\u95f4\u7684\u5171\u73b0\u5173\u7cfb\uff0c\u53ef\u4ee5\u4f7f\u7528NetworkX\u5e93\u6765\u6784\u5efa\u548c\u5206\u6790\u5171\u73b0\u7f51\u7edc\u3002<\/p>\n<\/p>\n
import networkx as nx<\/p>\n
\u6784\u5efa\u5171\u73b0\u7f51\u7edc<\/strong><\/h2>\n
G = nx.Graph()<\/p>\n
\u6dfb\u52a0\u8282\u70b9\u548c\u8fb9<\/strong><\/h2>\n
for word1 in stemmed_tokens:<\/p>\n
    for word2 in stemmed_tokens:<\/p>\n
        if word1 != word2:<\/p>\n
            G.add_edge(word1, word2)<\/p>\n
\u53ef\u89c6\u5316\u5171\u73b0\u7f51\u7edc<\/strong><\/h2>\n
nx.draw(G, with_labels=True)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u4e3b\u9898\u5efa\u6a21<\/h4>\n<\/p>\n
\u4e3b\u9898\u5efa\u6a21\u662f\u4ece\u6587\u732e\u4e2d\u81ea\u52a8\u63d0\u53d6\u6f5c\u5728\u4e3b\u9898\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u4f7f\u7528Gensim\u5e93\u8fdb\u884cLDA\uff08Latent Dirichlet Allocation\uff09\u4e3b\u9898\u5efa\u6a21\u3002<\/p>\n<\/p>\n
from gensim import corpora, models<\/p>\n
\u521b\u5efa\u8bcd\u5178<\/strong><\/h2>\n
dictionary = corpora.Dictionary([stemmed_tokens])<\/p>\n
\u521b\u5efa\u8bed\u6599\u5e93<\/strong><\/h2>\n
corpus = [dictionary.doc2bow(stemmed_tokens)]<\/p>\n
LDA\u4e3b\u9898\u5efa\u6a21<\/strong><\/h2>\n
lda_model = models.LdaModel(corpus, num_topics=3, id2word=dictionary, passes=15)<\/p>\n
\u6253\u5370\u4e3b\u9898<\/strong><\/h2>\n
for idx, topic in lda_model.print_topics(-1):<\/p>\n
    print(f'Topic: {idx} \\nWords: {topic}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
\u6587\u672c\u5206\u6790\u7ed3\u679c\u53ef\u4ee5\u901a\u8fc7\u53ef\u89c6\u5316\u7684\u65b9\u5f0f\u5c55\u793a\uff0c\u4ee5\u4fbf\u66f4\u76f4\u89c2\u5730\u7406\u89e3\u548c\u89e3\u91ca\u6570\u636e\u3002\u5e38\u7528\u7684\u53ef\u89c6\u5316\u5de5\u5177\u5305\u62ecMatplotlib\u3001Seaborn\u3001WordCloud\u7b49\u3002<\/p>\n<\/p>\n
1. \u8bcd\u4e91\u56fe<\/h4>\n<\/p>\n
\u8bcd\u4e91\u56fe\u662f\u5c55\u793a\u8bcd\u9891\u7684\u5e38\u7528\u65b9\u6cd5\uff0c\u53ef\u4ee5\u4f7f\u7528WordCloud\u5e93\u751f\u6210\u8bcd\u4e91\u56fe\u3002<\/p>\n<\/p>\n
from wordcloud import WordCloud<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u751f\u6210\u8bcd\u4e91\u56fe<\/strong><\/h2>\n
wordcloud = WordCloud(width=800, height=400, background_color='white').generate_from_frequencies(word_counts)<\/p>\n
\u663e\u793a\u8bcd\u4e91\u56fe<\/strong><\/h2>\n
plt.figure(figsize=(10, 5))<\/p>\n
plt.imshow(wordcloud, interpolation='bilinear')<\/p>\n
plt.axis('off')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8bcd\u9891\u67f1\u72b6\u56fe<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528Matplotlib\u548cSeaborn\u5e93\u7ed8\u5236\u8bcd\u9891\u67f1\u72b6\u56fe\u3002<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
\u7ed8\u5236\u8bcd\u9891\u67f1\u72b6\u56fe<\/strong><\/h2>\n
word_freq = word_counts.most_common(10)<\/p>\n
words, frequencies = zip(*word_freq)<\/p>\n
plt.figure(figsize=(10, 5))<\/p>\n
sns.barplot(x=frequencies, y=words)<\/p>\n
plt.xlabel('Frequency')<\/p>\n
plt.ylabel('Words')<\/p>\n
plt.title('Top 10 Words by Frequency')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u5171\u73b0\u7f51\u7edc\u56fe<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528NetworkX\u5e93\u7ed8\u5236\u5171\u73b0\u7f51\u7edc\u56fe\u3002<\/p>\n<\/p>\n
plt.figure(figsize=(10, 10))<\/p>\n
nx.draw(G, with_labels=True, node_size=50, node_color='skyblue', font_size=10, font_color='black', edge_color='gray')<\/p>\n
plt.title('Co-occurrence Network')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u6848\u4f8b\u5206\u6790<\/h3>\n<\/p>\n
\u4e3a\u4e86\u66f4\u597d\u5730\u7406\u89e3\u5982\u4f55\u7528Python\u8fdb\u884c\u6587\u732e\u8ba1\u91cf\u5206\u6790\uff0c\u6211\u4eec\u901a\u8fc7\u4e00\u4e2a\u5177\u4f53\u7684\u6848\u4f8b\u8fdb\u884c\u6f14\u793a\u3002\u5047\u8bbe\u6211\u4eec\u8981\u5206\u6790\u201c\u673a\u5668\u5b66\u4e60<\/a>\u201d\u9886\u57df\u7684\u6587\u732e\uff0c\u4e3b\u8981\u5173\u6ce8\u6587\u732e\u7684\u53d1\u8868\u8d8b\u52bf\u3001\u4e3b\u8981\u7814\u7a76\u4e3b\u9898\u548c\u7814\u7a76\u70ed\u70b9\u3002<\/p>\n<\/p>\n
1. \u6570\u636e\u91c7\u96c6<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u901a\u8fc7Selenium\u91c7\u96c6Google Scholar\u4e0a\u7684\u201c\u673a\u5668\u5b66\u4e60\u201d\u76f8\u5173\u6587\u732e\u6570\u636e\u3002<\/p>\n<\/p>\n
from selenium import webdriver<\/p>\n
from selenium.webdriver.common.keys import Keys<\/p>\n
\u8bbe\u7f6eSelenium WebDriver<\/strong><\/h2>\n
driver = webdriver.Chrome(executable_path='path_to_chromedriver')<\/p>\n
\u6253\u5f00Google Scholar<\/strong><\/h2>\n
driver.get('https:\/\/scholar.google.com')<\/p>\n
\u641c\u7d22\u5173\u952e\u8bcd<\/strong><\/h2>\n
search_box = driver.find_element_by_name('q')<\/p>\n
search_box.send_keys('machine learning')<\/p>\n
search_box.send_keys(Keys.RETURN)<\/p>\n
\u91c7\u96c6\u6587\u732e\u4fe1\u606f<\/strong><\/h2>\n
results = driver.find_elements_by_css_selector('div.gs_ri')<\/p>\n
papers = []<\/p>\n
for result in results:<\/p>\n
    title = result.find_element_by_css_selector('h3').text<\/p>\n
    authors = result.find_element_by_css_selector('div.gs_a').text<\/p>\n
    snippet = result.find_element_by_css_selector('div.gs_rs').text<\/p>\n
    papers.append({'title': title, 'authors': authors, 'snippet': snippet})<\/p>\n
\u5173\u95ed\u6d4f\u89c8\u5668<\/strong><\/h2>\n
driver.quit()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u6587\u672c\u9884\u5904\u7406<\/h4>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u5bf9\u91c7\u96c6\u5230\u7684\u6587\u732e\u6570\u636e\u8fdb\u884c\u6587\u672c\u9884\u5904\u7406\u3002<\/p>\n<\/p>\n
import nltk<\/p>\n
from nltk.corpus import stopwords<\/p>\n
from nltk.tokenize import word_tokenize<\/p>\n
from nltk.stem import PorterStemmer<\/p>\n
nltk.download('stopwords')<\/p>\n
nltk.download('punkt')<\/p>\n
\u5206\u8bcd\u548c\u53bb\u9664\u505c\u7528\u8bcd<\/strong><\/h2>\n
stop_words = set(stopwords.words('english'))<\/p>\n
stemmer = PorterStemmer()<\/p>\n
all_tokens = []<\/p>\n
for paper in papers:<\/p>\n
    tokens = word_tokenize(paper['snippet'])<\/p>\n
    filtered_tokens = [word for word in tokens if word.lower() not in stop_words]<\/p>\n
    stemmed_tokens = [stemmer.stem(word) for word in filtered_tokens]<\/p>\n
    all_tokens.extend(stemmed_tokens)<\/p>\n
print(all_tokens)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u6587\u672c\u5206\u6790<\/h4>\n<\/p>\n
\u8fdb\u884c\u8bcd\u9891\u7edf\u8ba1\u3001\u5171\u73b0\u5206\u6790\u548c\u4e3b\u9898\u5efa\u6a21\u3002<\/p>\n<\/p>\n
from collections import Counter<\/p>\n
import networkx as nx<\/p>\n
from gensim import corpora, models<\/p>\n
\u8bcd\u9891\u7edf\u8ba1<\/strong><\/h2>\n
word_counts = Counter(all_tokens)<\/p>\n
print(word_counts)<\/p>\n
\u5171\u73b0\u5206\u6790<\/strong><\/h2>\n
G = nx.Graph()<\/p>\n
for word1 in all_tokens:<\/p>\n
    for word2 in all_tokens:<\/p>\n
        if word1 != word2:<\/p>\n
            G.add_edge(word1, word2)<\/p>\n
\u4e3b\u9898\u5efa\u6a21<\/strong><\/h2>\n
dictionary = corpora.Dictionary([all_tokens])<\/p>\n
corpus = [dictionary.doc2bow(all_tokens)]<\/p>\n
lda_model = models.LdaModel(corpus, num_topics=3, id2word=dictionary, passes=15)<\/p>\n
for idx, topic in lda_model.print_topics(-1):<\/p>\n
    print(f'Topic: {idx} \\nWords: {topic}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
4. \u53ef\u89c6\u5316<\/h4>\n<\/p>\n
\u6700\u540e\uff0c\u901a\u8fc7\u8bcd\u4e91\u56fe\u3001\u8bcd\u9891\u67f1\u72b6\u56fe\u548c\u5171\u73b0\u7f51\u7edc\u56fe\u5c55\u793a\u5206\u6790\u7ed3\u679c\u3002<\/p>\n<\/p>\n
from wordcloud import WordCloud<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import seaborn as sns<\/p>\n
\u8bcd\u4e91\u56fe<\/strong><\/h2>\n
wordcloud = WordCloud(width=800, height=400, background_color='white').generate_from_frequencies(word_counts)<\/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
\u8bcd\u9891\u67f1\u72b6\u56fe<\/strong><\/h2>\n
word_freq = word_counts.most_common(10)<\/p>\n
words, frequencies = zip(*word_freq)<\/p>\n
plt.figure(figsize=(10, 5))<\/p>\n
sns.barplot(x=frequencies, y=words)<\/p>\n
plt.xlabel('Frequency')<\/p>\n
plt.ylabel('Words')<\/p>\n
plt.title('Top 10 Words by Frequency')<\/p>\n
plt.show()<\/p>\n
\u5171\u73b0\u7f51\u7edc\u56fe<\/strong><\/h2>\n
plt.figure(figsize=(10, 10))<\/p>\n
nx.draw(G, with_labels=True, node_size=50, node_color='skyblue', font_size=10, font_color='black', edge_color='gray')<\/p>\n
plt.title('Co-occurrence Network')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u6211\u4eec\u8be6\u7ec6\u9610\u8ff0\u4e86\u5982\u4f55\u7528Python\u8fdb\u884c\u6587\u732e\u8ba1\u91cf\u5206\u6790\u7684\u6574\u4e2a\u6d41\u7a0b\uff0c\u5305\u62ec\u6570\u636e\u91c7\u96c6\u3001\u6587\u672c\u9884\u5904\u7406\u3001\u6587\u672c\u5206\u6790\u548c\u53ef\u89c6\u5316\u3002\u5229\u7528Python\u5f3a\u5927\u7684\u5e93\u548c\u5de5\u5177\uff0c\u6211\u4eec\u53ef\u4ee5\u9ad8\u6548\u5730\u91c7\u96c6\u548c\u5206\u6790\u6587\u732e\u6570\u636e\uff0c\u4ece\u800c\u6316\u6398\u6587\u732e\u4e2d\u7684\u6709\u4ef7\u503c\u4fe1\u606f\uff0c\u63ed\u793a\u7814\u7a76\u9886\u57df\u7684\u53d1\u5c55\u8d8b\u52bf\u548c\u70ed\u70b9\u3002\u5e0c\u671b\u672c\u6587\u80fd\u4e3a\u7814\u7a76\u8005\u63d0\u4f9b\u6709\u76ca\u7684\u53c2\u8003\uff0c\u52a9\u529b\u4ed6\u4eec\u5728\u6587\u732e\u8ba1\u91cf\u7814\u7a76\u4e2d\u53d6\u5f97\u66f4\u597d\u7684\u6210\u679c\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u6587\u732e\u8ba1\u91cf\u662f\u4ec0\u4e48\uff0c\u4e3a\u4ec0\u4e48\u8981\u4f7f\u7528Python\u8fdb\u884c\u6587\u732e\u8ba1\u91cf\u5206\u6790\uff1f<\/strong>
\u6587\u732e\u8ba1\u91cf\u662f\u5bf9\u6587\u732e\u8fdb\u884c\u5b9a\u91cf\u5206\u6790\u7684\u5b66\u79d1\uff0c\u4e3b\u8981\u7528\u4e8e\u7814\u7a76\u6587\u732e\u7684\u751f\u4ea7\u3001\u4f20\u64ad\u53ca\u5176\u5f71\u54cd\u529b\u3002\u4f7f\u7528Python\u8fdb\u884c\u6587\u732e\u8ba1\u91cf\u5206\u6790\u7684\u539f\u56e0\u5728\u4e8e\u5176\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u548c\u53ef\u89c6\u5316\u80fd\u529b\u3002Python\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5e93\uff0c\u5982Pandas\u3001NumPy\u548cMatplotlib\uff0c\u80fd\u591f\u8f7b\u677e\u5904\u7406\u5927\u89c4\u6a21\u6587\u732e\u6570\u636e\uff0c\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\u548c\u53ef\u89c6\u5316\u5c55\u793a\uff0c\u4ece\u800c\u5e2e\u52a9\u7814\u7a76\u4eba\u5458\u4e86\u89e3\u6587\u732e\u7684\u8d8b\u52bf\u548c\u6a21\u5f0f\u3002<\/p>\n
\u5982\u4f55\u83b7\u53d6\u6587\u732e\u6570\u636e\u7528\u4e8e\u6587\u732e\u8ba1\u91cf\u5206\u6790\uff1f<\/strong>
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