Use Scrapy crawler to analyze data from novel websites

Use Scrapy crawler to analyze data from novel websites

In the Internet era, a large amount of data is collected by websites. How to use this data for analysis and mining has become an important issue. This article will introduce the use of the Scrapy crawler framework to crawl novel website data and the use of Python for data analysis.

1. Scrapy Framework

Scrapy is a Python framework for crawling website data. It can extract data from websites in an efficient, fast and scalable way. Scrapy is an open source framework that allows us to easily create Spider, Pipeline, DownloaderMiddleware and other modules. For some data mining and large-scale crawling tasks, the Scrapy framework is very popular. .

2. Novel website

The novel website crawled by this article is "Biquge", which is a free online novel reading website. In this website, the novel content is organized by chapters, so the novel chapter content needs to be automatically crawled, and the data can be filtered according to the novel classification.

3. Crawler design

In the Scrapy framework, the crawler is a very important module. It can crawl data for different websites or different pages by defining multiple spiders. . The crawler written in this article is mainly divided into two parts: the novel list and the novel chapter content.

1. Novel list

The novel list refers to the classification, name, author, status and other information of the novel. In the "Biquge" website, each category of novels has a corresponding sub-page. Therefore, when crawling the novel list, first crawl the URL of the novel category, and then traverse the category page to obtain the information of each novel.

2. Novel chapter content

When crawling the chapter content of the novel, the main thing is to obtain the chapter directory of each novel and splice the contents in the chapter directory in order together. In the "Biquge" website, each novel's chapter directory has a corresponding URL, so you only need to obtain the chapter directory URL of each novel, and then obtain the chapter content one by one.

4. Implementation of crawler

Before implementing the crawler, you need to install the Scrapy framework and create a Scrapy project. In the Scrapy project, each crawler needs to define the following parts:

1. #Name

Each crawler has a unique name to distinguish different crawlers. reptile. In this article, we name the crawler "novel_spider".

2. Start_urls

Start URL, which sets the starting point of the crawler.

start_urls = ['http://www.biquge.info/']

3. parse

Crawler parsing method, this method will parse the content returned by each URL in start_urls and extract useful information from it.

In this method, first parse the novel list page, extract the name, author, status and URL information of each novel, and pass this information to the next parse method through the Request object.

def parse(self, response):
    # Get novel classifications
    classifications = response.xpath('//div[@class="nav"]/ul/li')
    for classification in classifications:
        url = classification.xpath('a/@href').extract_first()
        name = classification.xpath('a/text()').extract_first()

        # Get novels in classification
        yield scrapy.Request(url, callback=self.parse_classification, meta={'name': name})

In the sub-level page, obtain the novel content, chapter name and chapter content in sequence. And pass the novel title, chapter name and chapter content information through Item.

def parse_chapter(self, response):
    item = NovelChapter()
    item['novel_name'] = response.meta['novel_name']
    item['chapter_name'] = response.meta['chapter_name']
    item['chapter_content'] = response.xpath('//div[@id="content"]/text()').extract()
    yield item

5. Data Analysis

After obtaining the data, we can use Python and Pandas libraries to analyze the obtained novel data. The following code can perform Pandas data analysis on the novel list.

import pandas as pd

# Load CSV data into dataframe
df = pd.read_csv('./novel.csv')

# Display novel counts by author's name
df.groupby('author_name')[['novel_name']].count().sort_values('novel_name', ascending=False)

6. Summary

Scrapy is a powerful crawler framework that can easily crawl data from websites. This article uses an example of a novel reading website to introduce how to use the Scrapy framework to capture novel classification and chapter content, and use Python and Pandas libraries to analyze the captured data. This technology is widely used for crawling data from other websites, such as news, product information, social media, etc.

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