Scrapy crawler practice: crawling QQ space data for social network analysis

In recent years, people's demand for social network analysis has become higher and higher. QQ Zone is one of the largest social networks in China, and its data crawling and analysis are particularly important for social network research. This article will introduce how to use the Scrapy framework to crawl QQ Space data and conduct social network analysis.

1. Introduction to Scrapy

Scrapy is an open source web crawling framework based on Python. It can help us quickly and efficiently collect website data through the Spider mechanism, process and save it. The Scrapy framework consists of five core components: Engine, Scheduler, Downloader, Spider and Project Pipeline. Spider is the core component of crawler logic, which defines how to access the website. Extract data from web pages and how to store the extracted data.

2. Scrapy operation process

1. Create a Scrapy project

Use the command line to enter the directory where you want to create the project, and then enter the following command:

scrapy startproject qq_zone

This command will create a Scrapy project named "qq_zone".

2. Create Spider

In the Scrapy project, we need to create a Spider first. Create a folder named "spiders" in the directory of the project, and create a Python file named "qq_zone_spider.py" under the folder.

In qq_zone_spider.py, we need to first define the basic information of Spider, such as name, starting URL and allowed domain names. The code is as follows:

import scrapy

class QQZoneSpider(scrapy.Spider):
    name = "qq_zone"
    start_urls = ['http://user.qzone.qq.com/xxxxxx']
    allowed_domains = ['user.qzone.qq.com']

It should be noted that start_urls should be replaced with the URL of the QQ space main page to be crawled, and "xxxxxx" should be replaced with the numeric ID of the target QQ number.

Then, we need to define data extraction rules. Since QQ Space is a page rendered through Javascript, we need to use Selenium PhantomJS to obtain page data. The code is as follows:

from scrapy.selector import Selector
from selenium import webdriver

class QQZoneSpider(scrapy.Spider):
    name = "qq_zone"
    start_urls = ['http://user.qzone.qq.com/xxxxxx']
    allowed_domains = ['user.qzone.qq.com']

    def __init__(self):
        self.driver = webdriver.PhantomJS()

    def parse(self, response):
        self.driver.get(response.url)
        sel = Selector(text=self.driver.page_source)
        # 爬取数据的代码

Next, you can use XPath or CSS Selector to extract data from the page according to the page structure.

3. Process data and store

In qq_zone_spider.py, we need to define how to process the extracted data. Scrapy provides a project pipeline mechanism for data processing and storage. We can turn on this mechanism and define the project pipeline in the settings.py file.

Add the following code in the settings.py file:

ITEM_PIPELINES = {
    'qq_zone.pipelines.QQZonePipeline': 300,
}

DOWNLOAD_DELAY = 3

Among them, DOWNLOAD_DELAY is the delay time when crawling the page, which can be adjusted as needed.

Then, create a file named "pipelines.py" in the project root directory and define how to process and store the captured data.

import json

class QQZonePipeline(object):

    def __init__(self):
        self.file = open('qq_zone_data.json', 'w')

    def process_item(self, item, spider):
        line = json.dumps(dict(item)) + "
"
        self.file.write(line)
        return item

    def close_spider(self, spider):
        self.file.close()

In the above code, we use the json module to convert the data into json format and then store it in the "qq_zone_data.json" file.

3. Social network analysis

After the QQ space data capture is completed, we can use the NetworkX module in Python to conduct social network analysis.

NetworkX is a Python library for analyzing complex networks. It provides many powerful tools, such as graph visualization, node and edge attribute settings, community discovery, etc. The following shows a simple social network analysis code:

import json
import networkx as nx
import matplotlib.pyplot as plt

G = nx.Graph()

with open("qq_zone_data.json", "r") as f:
    for line in f:
        data = json.loads(line)
        uid = data["uid"]
        friends = data["friends"]
        for friend in friends:
            friend_name = friend["name"]
            friend_id = friend["id"]
            G.add_edge(uid, friend_id)

# 可视化
pos = nx.spring_layout(G)
nx.draw_networkx_nodes(G, pos, node_size=20)
nx.draw_networkx_edges(G, pos, alpha=0.4)
plt.axis('off')
plt.show()

In the above code, we first read the captured data into memory and use NetworkX to build an undirected graph, in which each node represents A QQ account, each edge represents a friend relationship between the two QQ accounts.

Then, we use the spring layout algorithm to layout the graphics, and finally use matplotlib for visualization.

4. Summary

This article introduces how to use the Scrapy framework for data capture and NetworkX for simple social network analysis. I believe readers have a deeper understanding of the use of Scrapy, Selenium and NetworkX. Of course, crawling QQ space data is only part of social network analysis, and more in-depth exploration and analysis of the data are required in the future.

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