{"id":1167392,"date":"2025-01-15T15:44:03","date_gmt":"2025-01-15T07:44:03","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1167392.html"},"modified":"2025-01-15T15:44:06","modified_gmt":"2025-01-15T07:44:06","slug":"python%e5%a6%82%e4%bd%95%e7%88%ac%e5%8e%bb%e8%a1%a8%e6%a0%bc%e6%95%b0%e6%8d%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1167392.html","title":{"rendered":"python\u5982\u4f55\u722c\u53bb\u8868\u683c\u6570\u636e"},"content":{"rendered":"

\"python\u5982\u4f55\u722c\u53bb\u8868\u683c\u6570\u636e\"<\/p>\n

Python\u722c\u53d6\u8868\u683c\u6570\u636e\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528requests\u3001BeautifulSoup\u3001pandas\u3001Scrapy\u7b49\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7\u89e3\u6790HTML\u9875\u9762\u3001\u4f7f\u7528API\u4ee5\u53ca\u5904\u7406PDF\u6587\u4ef6\u7b49\u65b9\u5f0f\u8fdb\u884c\u6570\u636e\u63d0\u53d6\u3002<\/strong>\u5176\u4e2d\uff0c\u4f7f\u7528requests\u548cBeautifulSoup\u5e93\u89e3\u6790HTML\u9875\u9762\u662f\u6700\u5e38\u89c1\u7684\u65b9\u6cd5\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528requests\u548cBeautifulSoup\u5e93\u722c\u53d6\u8868\u683c\u6570\u636e\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528requests\u548cBeautifulSoup\u5e93\u722c\u53d6\u8868\u683c\u6570\u636e<\/h3>\n<\/p>\n

1. \u5b89\u88c5\u76f8\u5173\u5e93<\/h4>\n<\/p>\n

\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5requests\u548cBeautifulSoup\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install requests<\/p>\n
pip install beautifulsoup4<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u53d1\u9001HTTP\u8bf7\u6c42\u5e76\u83b7\u53d6\u7f51\u9875\u5185\u5bb9<\/h4>\n<\/p>\n
\u4f7f\u7528requests\u5e93\u53d1\u9001HTTP\u8bf7\u6c42\u5e76\u83b7\u53d6\u7f51\u9875\u5185\u5bb9\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import requests<\/p>\n
url = 'https:\/\/example.com\/tablepage'<\/p>\n
response = requests.get(url)<\/p>\n
html_content = response.content<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0curl<\/code>\u662f\u8981\u722c\u53d6\u7684\u7f51\u9875\u5730\u5740\uff0cresponse<\/code>\u662f\u83b7\u53d6\u5230\u7684HTTP\u54cd\u5e94\uff0chtml_content<\/code>\u662f\u7f51\u9875\u7684HTML\u5185\u5bb9\u3002<\/p>\n<\/p>\n
3. \u89e3\u6790HTML\u9875\u9762<\/h4>\n<\/p>\n
\u4f7f\u7528BeautifulSoup\u5e93\u89e3\u6790HTML\u9875\u9762\uff0c\u5e76\u627e\u5230\u8868\u683c\u6570\u636e\u6240\u5728\u7684HTML\u5143\u7d20\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
from bs4 import BeautifulSoup<\/p>\n
soup = BeautifulSoup(html_content, 'html.parser')<\/p>\n
table = soup.find('table')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0csoup<\/code>\u662fBeautifulSoup\u5bf9\u8c61\uff0ctable<\/code>\u662f\u627e\u5230\u7684\u8868\u683c\u5143\u7d20\u3002<\/p>\n<\/p>\n
4. \u63d0\u53d6\u8868\u683c\u6570\u636e<\/h4>\n<\/p>\n
\u904d\u5386\u8868\u683c\u7684\u884c\u548c\u5355\u5143\u683c\uff0c\u63d0\u53d6\u8868\u683c\u6570\u636e\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
data = []<\/p>\n
rows = table.find_all('tr')<\/p>\n
for row in rows:<\/p>\n
    cells = row.find_all('td')<\/p>\n
    row_data = [cell.text.strip() for cell in cells]<\/p>\n
    data.append(row_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0crows<\/code>\u662f\u8868\u683c\u7684\u6240\u6709\u884c\uff0ccells<\/code>\u662f\u6bcf\u4e00\u884c\u7684\u6240\u6709\u5355\u5143\u683c\uff0crow_data<\/code>\u662f\u6bcf\u4e00\u884c\u7684\u6570\u636e\uff0cdata<\/code>\u662f\u6574\u4e2a\u8868\u683c\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528pandas\u5e93\u722c\u53d6\u8868\u683c\u6570\u636e<\/h3>\n<\/p>\n
1. \u5b89\u88c5\u76f8\u5173\u5e93<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5pandas\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8bfb\u53d6HTML\u8868\u683c\u6570\u636e<\/h4>\n<\/p>\n
\u4f7f\u7528pandas\u5e93\u7684read_html<\/code>\u51fd\u6570\u76f4\u63a5\u8bfb\u53d6HTML\u8868\u683c\u6570\u636e\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
url = 'https:\/\/example.com\/tablepage'<\/p>\n
tables = pd.read_html(url)<\/p>\n
df = tables[0]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0curl<\/code>\u662f\u8981\u722c\u53d6\u7684\u7f51\u9875\u5730\u5740\uff0ctables<\/code>\u662f\u8bfb\u53d6\u5230\u7684\u6240\u6709\u8868\u683c\uff0cdf<\/code>\u662f\u7b2c\u4e00\u4e2a\u8868\u683c\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Scrapy\u6846\u67b6\u722c\u53d6\u8868\u683c\u6570\u636e<\/h3>\n<\/p>\n
1. \u5b89\u88c5Scrapy\u6846\u67b6<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5Scrapy\u6846\u67b6\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install scrapy<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u521b\u5efaScrapy\u9879\u76ee<\/h4>\n<\/p>\n
\u4f7f\u7528Scrapy\u547d\u4ee4\u521b\u5efa\u4e00\u4e2a\u65b0\u7684\u9879\u76ee\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\uff1a<\/p>\n<\/p>\n
scrapy startproject tablecrawler<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u5b9a\u4e49Item<\/h4>\n<\/p>\n
\u5728\u9879\u76ee\u7684items.py<\/code>\u6587\u4ef6\u4e2d\u5b9a\u4e49Item\uff0c\u7528\u4e8e\u5b58\u50a8\u722c\u53d6\u5230\u7684\u6570\u636e\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import scrapy<\/p>\n
class TablecrawlerItem(scrapy.Item):<\/p>\n
    column1 = scrapy.Field()<\/p>\n
    column2 = scrapy.Field()<\/p>\n
    # \u6839\u636e\u8868\u683c\u7684\u5b9e\u9645\u5217\u6570\u5b9a\u4e49\u5b57\u6bb5<\/p>\n
<\/code><\/pre>\n<\/p>\n
4. \u521b\u5efaSpider<\/h4>\n<\/p>\n
\u5728\u9879\u76ee\u7684spiders<\/code>\u76ee\u5f55\u4e0b\u521b\u5efaSpider\uff0c\u7528\u4e8e\u722c\u53d6\u7f51\u9875\u6570\u636e\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import scrapy<\/p>\n
from tablecrawler.items import TablecrawlerItem<\/p>\n
class TableSpider(scrapy.Spider):<\/p>\n
    name = 'tablespider'<\/p>\n
    start_urls = ['https:\/\/example.com\/tablepage']<\/p>\n
    def parse(self, response):<\/p>\n
        rows = response.xpath('\/\/table\/tr')<\/p>\n
        for row in rows:<\/p>\n
            item = TablecrawlerItem()<\/p>\n
            item['column1'] = row.xpath('td[1]\/text()').get()<\/p>\n
            item['column2'] = row.xpath('td[2]\/text()').get()<\/p>\n
            # \u6839\u636e\u8868\u683c\u7684\u5b9e\u9645\u5217\u6570\u63d0\u53d6\u6570\u636e<\/p>\n
            yield item<\/p>\n
<\/code><\/pre>\n<\/p>\n
5. \u8fd0\u884cSpider<\/h4>\n<\/p>\n
\u4f7f\u7528Scrapy\u547d\u4ee4\u8fd0\u884cSpider\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\uff1a<\/p>\n<\/p>\n
scrapy crawl tablespider<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u5904\u7406PDF\u6587\u4ef6\u4e2d\u7684\u8868\u683c\u6570\u636e<\/h3>\n<\/p>\n
\u6709\u65f6\uff0c\u8868\u683c\u6570\u636e\u53ef\u80fd\u5b58\u5728\u4e8ePDF\u6587\u4ef6\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528pdfplumber<\/code>\u5e93\u6765\u63d0\u53d6\u8868\u683c\u6570\u636e\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5pdfplumber\u5e93<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5pdfplumber\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install pdfplumber<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u63d0\u53d6PDF\u8868\u683c\u6570\u636e<\/h4>\n<\/p>\n
\u4f7f\u7528pdfplumber\u5e93\u63d0\u53d6PDF\u6587\u4ef6\u4e2d\u7684\u8868\u683c\u6570\u636e\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pdfplumber<\/p>\n
file_path = 'path\/to\/your\/file.pdf'<\/p>\n
with pdfplumber.open(file_path) as pdf:<\/p>\n
    first_page = pdf.pages[0]<\/p>\n
    table = first_page.extract_table()<\/p>\n
\u8f6c\u6362\u4e3aDataFrame<\/strong><\/h2>\n
import pandas as pd<\/p>\n
df = pd.DataFrame(table[1:], columns=table[0])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0cfile_path<\/code>\u662fPDF\u6587\u4ef6\u7684\u8def\u5f84\uff0cpdf<\/code>\u662f\u6253\u5f00\u7684PDF\u6587\u4ef6\uff0cfirst_page<\/code>\u662fPDF\u6587\u4ef6\u7684\u7b2c\u4e00\u9875\uff0ctable<\/code>\u662f\u63d0\u53d6\u7684\u8868\u683c\u6570\u636e\uff0cdf<\/code>\u662f\u8f6c\u6362\u540e\u7684DataFrame\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u5904\u7406Excel\u6587\u4ef6\u4e2d\u7684\u8868\u683c\u6570\u636e<\/h3>\n<\/p>\n
\u6709\u65f6\uff0c\u8868\u683c\u6570\u636e\u53ef\u80fd\u5b58\u5728\u4e8eExcel\u6587\u4ef6\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u5e93\u6765\u8bfb\u53d6\u548c\u5904\u7406Excel\u6587\u4ef6\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5\u76f8\u5173\u5e93<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5pandas\u5e93\u548copenpyxl\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install pandas<\/p>\n
pip install openpyxl<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8bfb\u53d6Excel\u8868\u683c\u6570\u636e<\/h4>\n<\/p>\n
\u4f7f\u7528pandas\u5e93\u7684read_excel<\/code>\u51fd\u6570\u8bfb\u53d6Excel\u6587\u4ef6\u4e2d\u7684\u8868\u683c\u6570\u636e\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
file_path = 'path\/to\/your\/file.xlsx'<\/p>\n
df = pd.read_excel(file_path)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0cfile_path<\/code>\u662fExcel\u6587\u4ef6\u7684\u8def\u5f84\uff0cdf<\/code>\u662f\u8bfb\u53d6\u5230\u7684\u8868\u683c\u6570\u636e\u3002<\/p>\n<\/p>\n
\u516d\u3001\u5904\u7406CSV\u6587\u4ef6\u4e2d\u7684\u8868\u683c\u6570\u636e<\/h3>\n<\/p>\n
\u6709\u65f6\uff0c\u8868\u683c\u6570\u636e\u53ef\u80fd\u5b58\u5728\u4e8eCSV\u6587\u4ef6\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u5e93\u6765\u8bfb\u53d6\u548c\u5904\u7406CSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5\u76f8\u5173\u5e93<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5pandas\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8bfb\u53d6CSV\u8868\u683c\u6570\u636e<\/h4>\n<\/p>\n
\u4f7f\u7528pandas\u5e93\u7684read_csv<\/code>\u51fd\u6570\u8bfb\u53d6CSV\u6587\u4ef6\u4e2d\u7684\u8868\u683c\u6570\u636e\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
file_path = 'path\/to\/your\/file.csv'<\/p>\n
df = pd.read_csv(file_path)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0cfile_path<\/code>\u662fCSV\u6587\u4ef6\u7684\u8def\u5f84\uff0cdf<\/code>\u662f\u8bfb\u53d6\u5230\u7684\u8868\u683c\u6570\u636e\u3002<\/p>\n<\/p>\n
\u4e03\u3001\u5904\u7406JSON\u6587\u4ef6\u4e2d\u7684\u8868\u683c\u6570\u636e<\/h3>\n<\/p>\n
\u6709\u65f6\uff0c\u8868\u683c\u6570\u636e\u53ef\u80fd\u5b58\u5728\u4e8eJSON\u6587\u4ef6\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u5e93\u6765\u8bfb\u53d6\u548c\u5904\u7406JSON\u6587\u4ef6\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5\u76f8\u5173\u5e93<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5pandas\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8bfb\u53d6JSON\u8868\u683c\u6570\u636e<\/h4>\n<\/p>\n
\u4f7f\u7528pandas\u5e93\u7684read_json<\/code>\u51fd\u6570\u8bfb\u53d6JSON\u6587\u4ef6\u4e2d\u7684\u8868\u683c\u6570\u636e\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
file_path = 'path\/to\/your\/file.json'<\/p>\n
df = pd.read_json(file_path)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0cfile_path<\/code>\u662fJSON\u6587\u4ef6\u7684\u8def\u5f84\uff0cdf<\/code>\u662f\u8bfb\u53d6\u5230\u7684\u8868\u683c\u6570\u636e\u3002<\/p>\n<\/p>\n
\u516b\u3001\u5904\u7406\u6570\u636e\u5e93\u4e2d\u7684\u8868\u683c\u6570\u636e<\/h3>\n<\/p>\n
\u6709\u65f6\uff0c\u8868\u683c\u6570\u636e\u53ef\u80fd\u5b58\u50a8\u5728\u6570\u636e\u5e93\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528SQLAlchemy<\/code>\u548cpandas<\/code>\u5e93\u6765\u8bfb\u53d6\u548c\u5904\u7406\u6570\u636e\u5e93\u4e2d\u7684\u8868\u683c\u6570\u636e\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5\u76f8\u5173\u5e93<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5SQLAlchemy\u548cpandas\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install SQLAlchemy<\/p>\n
pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8fde\u63a5\u6570\u636e\u5e93\u5e76\u8bfb\u53d6\u8868\u683c\u6570\u636e<\/h4>\n<\/p>\n
\u4f7f\u7528SQLAlchemy\u5e93\u8fde\u63a5\u6570\u636e\u5e93\uff0c\u5e76\u4f7f\u7528pandas\u5e93\u8bfb\u53d6\u8868\u683c\u6570\u636e\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
from sqlalchemy import create_engine<\/p>\n
import pandas as pd<\/p>\n
database_url = 'sqlite:\/\/\/path\/to\/your\/database.db'<\/p>\n
engine = create_engine(database_url)<\/p>\n
query = 'SELECT * FROM table_name'<\/p>\n
df = pd.read_sql(query, engine)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0cdatabase_url<\/code>\u662f\u6570\u636e\u5e93\u7684\u8fde\u63a5URL\uff0cengine<\/code>\u662f\u6570\u636e\u5e93\u5f15\u64ce\uff0cquery<\/code>\u662fSQL\u67e5\u8be2\u8bed\u53e5\uff0cdf<\/code>\u662f\u8bfb\u53d6\u5230\u7684\u8868\u683c\u6570\u636e\u3002<\/p>\n<\/p>\n
\u4e5d\u3001\u5904\u7406API\u8fd4\u56de\u7684\u8868\u683c\u6570\u636e<\/h3>\n<\/p>\n
\u6709\u65f6\uff0c\u8868\u683c\u6570\u636e\u53ef\u80fd\u901a\u8fc7API\u8fd4\u56de\uff0c\u53ef\u4ee5\u4f7f\u7528requests<\/code>\u548cpandas<\/code>\u5e93\u6765\u5904\u7406API\u8fd4\u56de\u7684\u8868\u683c\u6570\u636e\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5\u76f8\u5173\u5e93<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5requests\u548cpandas\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install requests<\/p>\n
pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u53d1\u9001API\u8bf7\u6c42\u5e76\u8bfb\u53d6\u8868\u683c\u6570\u636e<\/h4>\n<\/p>\n
\u4f7f\u7528requests\u5e93\u53d1\u9001API\u8bf7\u6c42\uff0c\u5e76\u4f7f\u7528pandas\u5e93\u8bfb\u53d6\u8868\u683c\u6570\u636e\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import requests<\/p>\n
import pandas as pd<\/p>\n
api_url = 'https:\/\/api.example.com\/data'<\/p>\n
response = requests.get(api_url)<\/p>\n
data = response.json()<\/p>\n
df = pd.DataFrame(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0capi_url<\/code>\u662fAPI\u7684URL\uff0cresponse<\/code>\u662fAPI\u7684\u54cd\u5e94\uff0cdata<\/code>\u662fAPI\u8fd4\u56de\u7684\u6570\u636e\uff0cdf<\/code>\u662f\u8f6c\u6362\u540e\u7684DataFrame\u3002<\/p>\n<\/p>\n
\u5341\u3001\u5904\u7406\u7f51\u9875\u4e2d\u7684\u5d4c\u5957\u8868\u683c\u6570\u636e<\/h3>\n<\/p>\n
\u6709\u65f6\uff0c\u8868\u683c\u6570\u636e\u53ef\u80fd\u5d4c\u5957\u5728\u7f51\u9875\u7684\u5176\u4ed6\u5143\u7d20\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528requests<\/code>\u548cBeautifulSoup<\/code>\u5e93\u6765\u5904\u7406\u5d4c\u5957\u8868\u683c\u6570\u636e\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5\u76f8\u5173\u5e93<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5requests\u548cBeautifulSoup\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install requests<\/p>\n
pip install beautifulsoup4<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u53d1\u9001HTTP\u8bf7\u6c42\u5e76\u83b7\u53d6\u7f51\u9875\u5185\u5bb9<\/h4>\n<\/p>\n
\u4f7f\u7528requests\u5e93\u53d1\u9001HTTP\u8bf7\u6c42\u5e76\u83b7\u53d6\u7f51\u9875\u5185\u5bb9\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import requests<\/p>\n
url = 'https:\/\/example.com\/nestedtablepage'<\/p>\n
response = requests.get(url)<\/p>\n
html_content = response.content<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0curl<\/code>\u662f\u8981\u722c\u53d6\u7684\u7f51\u9875\u5730\u5740\uff0cresponse<\/code>\u662f\u83b7\u53d6\u5230\u7684HTTP\u54cd\u5e94\uff0chtml_content<\/code>\u662f\u7f51\u9875\u7684HTML\u5185\u5bb9\u3002<\/p>\n<\/p>\n
3. \u89e3\u6790HTML\u9875\u9762\u5e76\u627e\u5230\u5d4c\u5957\u8868\u683c<\/h4>\n<\/p>\n
\u4f7f\u7528BeautifulSoup\u5e93\u89e3\u6790HTML\u9875\u9762\uff0c\u5e76\u627e\u5230\u5d4c\u5957\u8868\u683c\u6240\u5728\u7684HTML\u5143\u7d20\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
from bs4 import BeautifulSoup<\/p>\n
soup = BeautifulSoup(html_content, 'html.parser')<\/p>\n
nested_table = soup.find('div', class_='nested-table').find('table')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0csoup<\/code>\u662fBeautifulSoup\u5bf9\u8c61\uff0cnested_table<\/code>\u662f\u627e\u5230\u7684\u5d4c\u5957\u8868\u683c\u5143\u7d20\u3002<\/p>\n<\/p>\n
4. \u63d0\u53d6\u5d4c\u5957\u8868\u683c\u6570\u636e<\/h4>\n<\/p>\n
\u904d\u5386\u5d4c\u5957\u8868\u683c\u7684\u884c\u548c\u5355\u5143\u683c\uff0c\u63d0\u53d6\u5d4c\u5957\u8868\u683c\u6570\u636e\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
nested_data = []<\/p>\n
rows = nested_table.find_all('tr')<\/p>\n
for row in rows:<\/p>\n
    cells = row.find_all('td')<\/p>\n
    row_data = [cell.text.strip() for cell in cells]<\/p>\n
    nested_data.append(row_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0crows<\/code>\u662f\u5d4c\u5957\u8868\u683c\u7684\u6240\u6709\u884c\uff0ccells<\/code>\u662f\u6bcf\u4e00\u884c\u7684\u6240\u6709\u5355\u5143\u683c\uff0crow_data<\/code>\u662f\u6bcf\u4e00\u884c\u7684\u6570\u636e\uff0cnested_data<\/code>\u662f\u6574\u4e2a\u5d4c\u5957\u8868\u683c\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
\u5341\u4e00\u3001\u5904\u7406\u52a8\u6001\u52a0\u8f7d\u7684\u8868\u683c\u6570\u636e<\/h3>\n<\/p>\n
\u6709\u65f6\uff0c\u8868\u683c\u6570\u636e\u53ef\u80fd\u662f\u901a\u8fc7JavaScript\u52a8\u6001\u52a0\u8f7d\u7684\uff0c\u53ef\u4ee5\u4f7f\u7528Selenium<\/code>\u5e93\u6765\u5904\u7406\u52a8\u6001\u52a0\u8f7d\u7684\u8868\u683c\u6570\u636e\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5\u76f8\u5173\u5e93<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5Selenium\u5e93\u548c\u6d4f\u89c8\u5668\u9a71\u52a8\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install selenium<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6b64\u5916\uff0c\u8fd8\u9700\u8981\u4e0b\u8f7d\u6d4f\u89c8\u5668\u9a71\u52a8\uff08\u5982ChromeDriver\uff09\uff0c\u5e76\u5c06\u5176\u6dfb\u52a0\u5230\u7cfb\u7edf\u8def\u5f84\u4e2d\u3002<\/p>\n<\/p>\n
2. \u4f7f\u7528Selenium\u5e93\u52a0\u8f7d\u7f51\u9875\u5e76\u63d0\u53d6\u8868\u683c\u6570\u636e<\/h4>\n<\/p>\n
\u4f7f\u7528Selenium\u5e93\u52a0\u8f7d\u7f51\u9875\uff0c\u5e76\u63d0\u53d6\u52a8\u6001\u52a0\u8f7d\u7684\u8868\u683c\u6570\u636e\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
from selenium import webdriver<\/p>\n
from bs4 import BeautifulSoup<\/p>\n
url = 'https:\/\/example.com\/dynamictablepage'<\/p>\n
driver = webdriver.Chrome()<\/p>\n
driver.get(url)<\/p>\n
html_content = driver.page_source<\/p>\n
driver.quit()<\/p>\n
soup = BeautifulSoup(html_content, 'html.parser')<\/p>\n
table = soup.find('table')<\/p>\n
dynamic_data = []<\/p>\n
rows = table.find_all('tr')<\/p>\n
for row in rows:<\/p>\n
    cells = row.find_all('td')<\/p>\n
    row_data = [cell.text.strip() for cell in cells]<\/p>\n
    dynamic_data.append(row_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0curl<\/code>\u662f\u8981\u722c\u53d6\u7684\u7f51\u9875\u5730\u5740\uff0cdriver<\/code>\u662fSelenium\u7684\u6d4f\u89c8\u5668\u9a71\u52a8\uff0chtml_content<\/code>\u662f\u7f51\u9875\u7684HTML\u5185\u5bb9\uff0csoup<\/code>\u662fBeautifulSoup\u5bf9\u8c61\uff0ctable<\/code>\u662f\u627e\u5230\u7684\u8868\u683c\u5143\u7d20\uff0cdynamic_data<\/code>\u662f\u63d0\u53d6\u5230\u7684\u52a8\u6001\u52a0\u8f7d\u7684\u8868\u683c\u6570\u636e\u3002<\/p>\n<\/p>\n
\u5341\u4e8c\u3001\u5904\u7406\u8868\u683c\u6570\u636e\u4e2d\u7684\u7279\u6b8a\u5b57\u7b26\u548c\u7f16\u7801\u95ee\u9898<\/h3>\n<\/p>\n
\u6709\u65f6\uff0c\u8868\u683c\u6570\u636e\u4e2d\u53ef\u80fd\u5305\u542b\u7279\u6b8a\u5b57\u7b26\u548c\u7f16\u7801\u95ee\u9898\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u5185\u7f6e\u7684\u5b57\u7b26\u4e32\u5904\u7406\u51fd\u6570\u548c\u7f16\u7801\u89e3\u7801\u51fd\u6570\u6765\u5904\u7406\u8fd9\u4e9b\u95ee\u9898\u3002<\/p>\n<\/p>\n
1. \u5904\u7406\u7279\u6b8a\u5b57\u7b26<\/h4>\n<\/p>\n
\u4f7f\u7528Python\u5185\u7f6e\u7684\u5b57\u7b26\u4e32\u5904\u7406\u51fd\u6570\u6765\u5904\u7406\u8868\u683c\u6570\u636e\u4e2d\u7684\u7279\u6b8a\u5b57\u7b26\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
def clean_text(text):<\/p>\n
    return text.replace('\\n', '').replace('\\t', '').strip()<\/p>\n
data = ['\\nexample\\n', '\\texample\\t', ' example ']<\/p>\n
cleaned_data = [clean_text(text) for text in data]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0cclean_text<\/code>\u51fd\u6570\u7528\u4e8e\u6e05\u7406\u6587\u672c\u4e2d\u7684\u7279\u6b8a\u5b57\u7b26\uff0ccleaned_data<\/code>\u662f\u6e05\u7406\u540e\u7684\u8868\u683c\u6570\u636e\u3002<\/p>\n<\/p>\n
2. \u5904\u7406\u7f16\u7801\u95ee\u9898<\/h4>\n<\/p>\n
\u4f7f\u7528Python\u5185\u7f6e\u7684\u7f16\u7801\u89e3\u7801\u51fd\u6570\u6765\u5904\u7406\u8868\u683c\u6570\u636e\u4e2d\u7684\u7f16\u7801\u95ee\u9898\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
text = 'example text'<\/p>\n
encoded_text = text.encode('utf-8')<\/p>\n
decoded_text = encoded_text.decode('utf-8')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0cencoded_text<\/code>\u662f\u7f16\u7801\u540e\u7684\u6587\u672c\uff0cdecoded_text<\/code>\u662f\u89e3\u7801\u540e\u7684\u6587\u672c\u3002<\/p>\n<\/p>\n
\u5341\u4e09\u3001\u5904\u7406\u8868\u683c\u6570\u636e\u4e2d\u7684\u7f3a\u5931\u503c<\/h3>\n<\/p>\n
\u6709\u65f6\uff0c\u8868\u683c\u6570\u636e\u4e2d\u53ef\u80fd\u5b58\u5728\u7f3a\u5931\u503c\uff0c\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u5e93\u6765\u5904\u7406\u7f3a\u5931\u503c\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5\u76f8\u5173\u5e93<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5pandas\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u586b\u5145\u7f3a\u5931\u503c<\/h4>\n<\/p>\n
\u4f7f\u7528pandas\u5e93\u7684fillna<\/code>\u51fd\u6570\u586b\u5145\u8868\u683c\u6570\u636e\u4e2d\u7684\u7f3a\u5931\u503c\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = {'column1': [1, 2, None], 'column2': [None, 2, 3]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
df_filled = df.fillna(0)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0cdata<\/code>\u662f\u5305\u542b\u7f3a\u5931\u503c\u7684\u8868\u683c\u6570\u636e\uff0cdf<\/code>\u662f\u5305\u542b\u7f3a\u5931\u503c\u7684DataFrame\uff0cdf_filled<\/code>\u662f\u586b\u5145\u7f3a\u5931\u503c\u540e\u7684DataFrame\u3002<\/p>\n<\/p>\n
3. \u5220\u9664\u7f3a\u5931\u503c<\/h4>\n<\/p>\n
\u4f7f\u7528pandas\u5e93\u7684dropna<\/code>\u51fd\u6570\u5220\u9664\u8868\u683c\u6570\u636e\u4e2d\u7684\u7f3a\u5931\u503c\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = {'column1': [1, 2, None], 'column2': [None, 2, 3]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
df_dropped = df.dropna()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0cdata<\/code>\u662f\u5305\u542b\u7f3a\u5931\u503c\u7684\u8868\u683c\u6570\u636e\uff0cdf<\/code>\u662f\u5305\u542b\u7f3a\u5931\u503c\u7684DataFrame\uff0cdf_dropped<\/code>\u662f\u5220\u9664\u7f3a\u5931\u503c\u540e\u7684DataFrame\u3002<\/p>\n<\/p>\n
\u5341\u56db\u3001\u5904\u7406\u8868\u683c\u6570\u636e\u4e2d\u7684\u91cd\u590d\u503c<\/h3>\n<\/p>\n
\u6709\u65f6\uff0c\u8868\u683c\u6570\u636e\u4e2d\u53ef\u80fd\u5b58\u5728\u91cd\u590d\u503c\uff0c\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u5e93\u6765\u5904\u7406\u91cd\u590d\u503c\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5\u76f8\u5173\u5e93<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5pandas\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u5220\u9664\u91cd\u590d\u503c<\/h4>\n<\/p>\n
\u4f7f\u7528pandas\u5e93\u7684drop_duplicates<\/code>\u51fd\u6570\u5220\u9664\u8868\u683c\u6570\u636e\u4e2d\u7684\u91cd\u590d\u503c\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = {'column1': [1, 2, 2], 'column2': [3, 2, 2]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
df_unique = df.drop_duplicates()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0cdata<\/code>\u662f\u5305\u542b\u91cd\u590d\u503c\u7684\u8868\u683c\u6570\u636e\uff0cdf<\/code>\u662f\u5305\u542b\u91cd\u590d\u503c\u7684DataFrame\uff0cdf_unique<\/code>\u662f\u5220\u9664\u91cd\u590d\u503c\u540e\u7684DataFrame\u3002<\/p>\n<\/p>\n
\u5341\u4e94\u3001\u5904\u7406\u8868\u683c\u6570\u636e\u4e2d\u7684\u65e5\u671f\u548c\u65f6\u95f4<\/h3>\n<\/p>\n
\u6709\u65f6\uff0c\u8868\u683c\u6570\u636e\u4e2d\u53ef\u80fd\u5305\u542b\u65e5\u671f\u548c\u65f6\u95f4\uff0c\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u5e93\u6765\u5904\u7406\u65e5\u671f\u548c\u65f6\u95f4\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5\u76f8\u5173\u5e93<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5pandas\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u89e3\u6790\u65e5\u671f\u548c\u65f6\u95f4<\/h4>\n<\/p>\n
\u4f7f\u7528pandas\u5e93\u7684to_datetime<\/code>\u51fd\u6570\u89e3\u6790\u8868\u683c\u6570\u636e\u4e2d\u7684\u65e5\u671f\u548c<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u722c\u53d6\u7f51\u9875\u4e0a\u7684\u8868\u683c\u6570\u636e\uff1f<\/strong>
\u4f7f\u7528Python\u722c\u53d6\u7f51\u9875\u8868\u683c\u6570\u636e\u901a\u5e38\u9700\u8981\u501f\u52a9\u4e00\u4e9b\u5e93\uff0c\u5982BeautifulSoup\u548cpandas\u3002\u9996\u5148\uff0c\u4f7f\u7528requests\u5e93\u83b7\u53d6\u7f51\u9875\u5185\u5bb9\uff0c\u7136\u540e\u5229\u7528BeautifulSoup\u89e3\u6790HTML\uff0c\u5b9a\u4f4d\u5230\u8868\u683c\u5143\u7d20\u3002\u63a5\u4e0b\u6765\uff0c\u53ef\u4ee5\u5c06\u63d0\u53d6\u7684\u6570\u636e\u8f6c\u6362\u4e3apandas DataFrame\uff0c\u65b9\u4fbf\u540e\u7eed\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u3002\u5177\u4f53\u6b65\u9aa4\u5305\u62ec\u5b89\u88c5\u5fc5\u8981\u7684\u5e93\u3001\u53d1\u9001\u8bf7\u6c42\u3001\u89e3\u6790HTML\u3001\u627e\u5230\u8868\u683c\u5e76\u63d0\u53d6\u6570\u636e\u3002<\/p>\n
\u722c\u53d6\u8868\u683c\u6570\u636e\u65f6\u9700\u8981\u6ce8\u610f\u54ea\u4e9b\u6cd5\u5f8b\u548c\u9053\u5fb7\u95ee\u9898\uff1f<\/strong>
\u5728\u722c\u53d6\u7f51\u7ad9\u7684\u6570\u636e\u524d\uff0c\u52a1\u5fc5\u68c0\u67e5\u8be5\u7f51\u7ad9\u7684robots.txt\u6587\u4ef6\uff0c\u4e86\u89e3\u54ea\u4e9b\u5185\u5bb9\u5141\u8bb8\u722c\u53d6\u3002\u9075\u5faa\u7f51\u7ad9\u7684\u4f7f\u7528\u6761\u6b3e\uff0c\u907f\u514d\u5bf9\u7f51\u7ad9\u9020\u6210\u8d1f\u62c5\u3002\u6b64\u5916\uff0c\u5c0a\u91cd\u6570\u636e\u9690\u79c1\u548c\u7248\u6743\u95ee\u9898\uff0c\u786e\u4fdd\u4e0d\u4fb5\u72af\u4ed6\u4eba\u7684\u5408\u6cd5\u6743\u76ca\u3002<\/p>\n
\u5982\u4f55\u5904\u7406\u722c\u53d6\u5230\u7684\u8868\u683c\u6570\u636e\u4ee5\u4fbf\u8fdb\u4e00\u6b65\u5206\u6790\uff1f<\/strong>
\u4e00\u65e6\u6210\u529f\u722c\u53d6\u8868\u683c\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528pandas\u5e93\u5bf9\u6570\u636e\u8fdb\u884c\u6e05\u6d17\u548c\u5904\u7406\u3002\u53ef\u4ee5\u5220\u9664\u7f3a\u5931\u503c\u3001\u91cd\u547d\u540d\u5217\u3001\u8f6c\u6362\u6570\u636e\u7c7b\u578b\u7b49\uff0c\u4ee5\u4fbf\u4e3a\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u505a\u597d\u51c6\u5907\u3002\u6570\u636e\u5904\u7406\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528matplotlib\u6216seaborn\u7b49\u5e93\u8fdb\u884c\u53ef\u89c6\u5316\u5206\u6790\uff0c\u4ece\u800c\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u722c\u53d6\u8868\u683c\u6570\u636e\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528requests\u3001BeautifulSoup\u3001pandas\u3001Scrapy […]","protected":false},"author":3,"featured_media":1167397,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1167392"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1167392"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1167392\/revisions"}],"predecessor-version":[{"id":1167398,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1167392\/revisions\/1167398"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1167397"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1167392"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1167392"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1167392"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}