Table of contents
1. Correct understanding of Python crawlers
2. Understand the nature of reptiles
1. Familiar with Python programming
3. Understand the basic principles of web crawlers
4. Learn to use Python crawler library
3. Understand the storage of unstructured data
4. Master various skills to deal with anti-crawling measures for special websites
5. Learn the crawler framework and build an engineered crawler
6. Learn the basics of database and deal with large-scale data storage
7. Distributed crawlers to achieve large-scale concurrent collection
3. Modify the Scrapy configuration
1. Correct understanding of Python crawlers
A Python crawler refers to a program written in the Python programming language to automatically obtain data from the Internet. Python crawlers can automate operations such as accessing websites, obtaining webpage content, parsing webpage data, and storing data, so as to achieve fast and efficient acquisition and processing of data on the Internet.
Python crawlers are widely used in various fields, such as search engines, data analysis, finance, medical care, education and other fields. The advantage of the Python crawler is that it can quickly obtain a large amount of data, and at the same time, it can automatically process the data and improve work efficiency.
2. Understand the nature of reptiles
1. Familiar with Python programming
Python is a high-level programming language that is simple, easy to learn, easy to read, and easy to write. The Python programming language can be used for development in various fields, including web development, data analysis, artificial intelligence, and more. The advantages of the Python programming language are simple syntax, high code readability, rich libraries and tools, and efficient programs can be developed quickly.
2. Understand HTML
HTML is a markup language used to create web pages. HTML is the foundation of Web development, mastering HTML language can help us better understand the structure and content of Web pages. In the Python crawler, we need to use the HTML parsing library to parse the content of the web page to obtain the data we need.
3. Understand the basic principles of web crawlers
A web crawler is an automated program used to obtain data from the Internet. The basic principle of a web crawler is to send a request to the web server through the HTTP protocol, obtain the content of the web page, and then parse the content of the web page to extract the data we need. Web crawlers can automatically access websites, obtain webpage content, analyze webpage data, store data and other operations, so as to achieve fast and efficient acquisition and processing of data on the Internet.
4. Learn to use Python crawler library
The Python crawler library is a toolkit for writing Python crawlers. The Python crawler library includes various powerful libraries, such as Requests, BeautifulSoup, Scrapy, etc. These libraries can help us quickly write Python crawlers, so as to achieve fast and efficient acquisition and processing of data on the Internet.
3. Understand the storage of unstructured data
The data obtained by crawlers is usually unstructured and needs to be processed and stored. Common storage methods include local files, databases, etc.
1. Local files
Storing data to a local file is one of the easiest ways. You can use Python's built-in open() method to open the file, and use the write() method to write data to the file. For example:
with open('data.txt', 'w') as f:
f.write(data)
2. Database
Storing data into a database allows for better management and querying of the data. Commonly used databases in Python include MySQL, MongoDB, etc. Using the Python database driver, you can easily connect to the database and perform data addition, deletion, modification, and query operations. For example, using a MySQL database:
import mysql.connector
mydb = mysql.connector.connect(
host="localhost",
user="yourusername",
password="yourpassword",
database="mydatabase"
)
mycursor = mydb.cursor()
sql = "INSERT INTO customers (name, address) VALUES (%s, %s)"
val = ("John", "Highway 21")
mycursor.execute(sql, val)
mydb.commit()
print(mycursor.rowcount, "record inserted.")
4. Master various skills to deal with anti-crawling measures for special websites
In order to prevent crawlers from causing an excessive burden on the website, many websites will take anti-crawling measures. Crawlers need to deal with these anti-crawling measures in order to obtain data normally.
1. User-Agent
Some websites will judge whether the request comes from a browser based on the User-Agent. Therefore, you can add User-Agent in the request header to simulate the browser's request. For example:
import requests
url = 'http://www.example.com'
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'}
response = requests.get(url, headers=headers)
2. Cookies
Some websites will judge whether the request comes from the same user based on Cookies. Therefore, cookies can be added to the request header to simulate the same user's request. For example:
import requests
url = 'http://www.example.com'
cookies = {'name': 'value'}
response = requests.get(url, cookies=cookies)
3. IP Proxy
Some websites will judge whether the request comes from the same user based on the IP address. Therefore, IP proxy can be used to simulate requests from different IP addresses. For example:
import requests
url = 'http://www.example.com'
proxies = {'http': 'http://10.10.1.10:3128', 'https': 'https://10.10.1.10:1080'}
response = requests.get(url, proxies=proxies)
5. Learn the crawler framework and build an engineered crawler
Using the crawler framework can better manage and maintain the crawler code and improve development efficiency. Common crawler frameworks in Python include Scrapy, PySpider, etc.
Take Scrapy as an example to introduce how to build an engineered crawler.
1. Create a Scrapy project
Create a Scrapy project using the command line tool. For example:
scrapy start project myproject
2. Create Spider
Create spiders using command line tools. For example:
scrapy genspider myspider example.com
3. Write Spider
Write the crawler code in Spider. For example:
import scrapy
class MySpider(scrapy.Spider):
name = 'myspider'
start_urls = ['http://www.example.com']
def parse(self, response):
# Parsing web content
pass
4. Run the Spider
Run the spider using the command line tool. For example:
scrapy crawl myspider
6. Learn the basics of database and deal with large-scale data storage
The data obtained by crawlers is usually very large and requires large-scale data storage. Therefore, it is very important to learn the basics of database.
1. Database type
Commonly used database types are relational databases and non-relational databases. Relational databases such as MySQL, Oracle, etc., non-relational databases such as MongoDB, Redis, etc.
2. Database design
Database design is an important part of database application. According to the characteristics of the data, a reasonable database structure needs to be designed to facilitate data storage and query.
3. Database operation
Commonly used database operation libraries in Python include MySQLdb, pymongo, etc. Using these libraries, you can easily connect to the database and perform data addition, deletion, modification and query operations.
7. Distributed crawlers to achieve large-scale concurrent collection
Distributed crawlers can achieve large-scale concurrent collection and improve crawler efficiency. Commonly used distributed crawler frameworks include Scrapy-Redis, Distributed Spider, etc.
Take Scrapy-Redis as an example to introduce how to implement distributed crawlers.
1. Install Redis
Install Redis using the command line tool. For example:
sudo apt-get install redis-server
2. Install Scrapy-Redis
Install Scrapy-Redis using the command line tool. For example:
pip install scrapy-redis
3. Modify the Scrapy configuration
Add Redis related configuration in the Scrapy configuration file. For example:
SCHEDULER = "scrapy_redis.scheduler.Scheduler"
DUPEFILTER_CLASS = "scrapy_redis.dupefilter.RFPDupeFilter"
REDIS_URL = 'redis://localhost:6379'
4. Write Spider
Write the crawler code in Spider. For example:
import scrapy
from scrapy_redis.spiders import RedisSpider
class MySpider(RedisSpider):
name = 'myspider'
redis_key = 'myspider:start_urls'
def parse(self, response):
# Parsing web content
pass
5. Run the Spider
Run the spider using the command line tool. For example:
scrapy runspider myspider.py
Summarize
This article introduces the basic principles and process of Python crawlers from the perspective of entry to proficiency, and how to deal with anti-crawling measures for special websites, build an engineering crawler framework, and achieve large-scale concurrent collection. I hope this article can help readers better understand Python crawler technology and achieve better results in practice.