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Detailed explanation of Python-based web crawler technology
Detailed explanation of Python-based web crawler technology
With the advent of the Internet and big data era, more and more data are dynamically generated and presented on web pages, which brings new challenges to data collection and processing. At this time, Web crawler technology came into being. Web crawler technology refers to technology that automatically obtains information on the Internet by writing programs. As a powerful programming language, Python has the advantages of being easy to learn, efficient and easy to use, and cross-platform. It has become an important choice in web crawler development.
This article will systematically introduce commonly used web crawler technologies in Python, including request modules, parsing modules, storage modules, etc.
1. Request module
The request module is the core of the web crawler. It can simulate the browser to send requests and obtain the required page content. Commonly used request modules include urllib, Requests and Selenium.
- urllib
urllib is an HTTP request module that comes with Python. It can obtain web page data from the network based on the URL. It supports URL encoding, modification of request headers, post, Cookies and other functions. Commonly used functions include urllib.request.urlopen(), urllib.request.urlretrieve(), urllib.request.build_opener(), etc.
You can get the source code of the website through the urllib.request.urlopen() function:
import urllib.request
response = urllib.request.urlopen('http://www.example.com/')
source_code = response.read().decode('utf-8')
print(source_code)- Requests
Requests is a Python third-party library. It is simpler and easier to use than urllib, and supports cookies, POST, proxy and other functions. Commonly used functions include requests.get(), requests.post(), requests.request(), etc.
You can get the response content through the requests.get() function:
import requests
response = requests.get('http://www.example.com/')
source_code = response.text
print(source_code)- Selenium
Selenium is an automated testing tool, used in web crawlers , it can simulate human operations by starting a browser, and can achieve functions such as obtaining page data dynamically generated by JS. Commonly used functions include selenium.webdriver.Chrome(), selenium.webdriver.Firefox(), selenium.webdriver.PhantomJS(), etc.
Get the web page source code through Selenium:
from selenium import webdriver
browser = webdriver.Chrome() # 打开Chrome浏览器
browser.get('http://www.example.com/')
source_code = browser.page_source # 获取网页源代码
print(source_code)2. Parsing module
After getting the web page source code, the next step is to parse the file. Commonly used parsing modules in Python include regular expressions, BeautifulSoup and PyQuery.
- Regular expression
Regular expression is a magical and powerful tool that can match strings according to patterns and quickly extract the required data. You can use the re module in Python to call regular expressions.
For example, extract all links in the web page:
import re
source_code = """
<!DOCTYPE html>
<html>
<head>
<title>Example</title>
</head>
<body>
<a href="http://www.example.com/">example</a>
<a href="http://www.google.com/">google</a>
</body>
</html>
"""
pattern = re.compile('<a href="(.*?)">(.*?)</a>') # 匹配所有链接
results = re.findall(pattern, source_code)
for result in results:
print(result[0], result[1])- BeautifulSoup
Beautiful Soup is a library in Python that can convert HTML files or XML files are parsed into tree structures to easily obtain data in HTML/XML files. It supports a variety of parsers, the commonly used ones are Python's built-in html.parser, lxml and html5lib.
For example, parse out all links in a web page:
from bs4 import BeautifulSoup
source_code = """
<!DOCTYPE html>
<html>
<head>
<title>Example</title>
</head>
<body>
<a href="http://www.example.com/">example</a>
<a href="http://www.google.com/">google</a>
</body>
</html>
"""
soup = BeautifulSoup(source_code, 'html.parser')
links = soup.find_all('a')
for link in links:
print(link.get('href'), link.string)- PyQuery
PyQuery is a jQuery-like Python library that converts HTML documents Into a structure similar to jQuery, elements in the web page can be directly obtained through CSS selectors. It depends on lxml library.
For example, parse out all the links in the web page:
from pyquery import PyQuery as pq
source_code = """
<!DOCTYPE html>
<html>
<head>
<title>Example</title>
</head>
<body>
<a href="http://www.example.com/">example</a>
<a href="http://www.google.com/">google</a>
</body>
</html>
"""
doc = pq(source_code)
links = doc('a')
for link in links:
print(link.attrib['href'], link.text_content())3. Storage module
After getting the required data, the next step is to store the data locally or in the database middle. Commonly used storage modules in Python include file modules, MySQLdb, pymongo, etc.
- File module
The file module can store data locally. Commonly used file modules include CSV, JSON, Excel, etc. Among them, the CSV module is one of the most commonly used file modules, which can write data into CSV files.
For example, write data to a CSV file:
import csv
filename = 'example.csv'
data = [['name', 'age', 'gender'],
['bob', 25, 'male'],
['alice', 22, 'female']]
with open(filename, 'w', encoding='utf-8', newline='') as f:
writer = csv.writer(f)
for row in data:
writer.writerow(row)- MySQLdb
MySQLdb is a library for Python to connect to the MySQL database, which supports transactions , cursor and other functions.
For example, store data into a MySQL database:
import MySQLdb
conn = MySQLdb.connect(host='localhost', port=3306, user='root',
passwd='password', db='example', charset='utf8')
cursor = conn.cursor()
data = [('bob', 25, 'male'), ('alice', 22, 'female')]
sql = "INSERT INTO users (name, age, gender) VALUES (%s, %s, %s)"
try:
cursor.executemany(sql, data)
conn.commit()
except:
conn.rollback()
cursor.close()
conn.close()- pymongo
pymongo is a library for Python to link to the MongoDB database. It supports a variety of Operations, such as adding, deleting, modifying, checking, etc.
For example, store data in the MongoDB database:
import pymongo
client = pymongo.MongoClient('mongodb://localhost:27017/')
db = client['example']
collection = db['users']
data = [{'name': 'bob', 'age': 25, 'gender': 'male'},
{'name': 'alice', 'age': 22, 'gender': 'female'}]
collection.insert_many(data)4. Summary
Web crawler technology in Python includes request module, parsing module and storage module, etc. Among them, the request module is the core of the web crawler, the parsing module is an important channel for obtaining data, and the storage module is the only way to persist data. Python has the advantages of being easy to learn, efficient and easy to use, and cross-platform in web crawler development, and has become an important choice in web crawler development.
The above is the detailed content of Detailed explanation of Python-based web crawler technology. For more information, please follow other related articles on the PHP Chinese website!
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