【Advanced Chapter】Web Crawler Data Analysis and Visualization: Practical Implementation Using Jupyter Notebook to Display Web Crawler Data Analysis Results

**【Advanced篇】Web Scraping Data Analysis and Visualization in Practice: Utilizing Jupyter Notebook to Display Web Scraping Data Analysis Outcomes**

# 2.1 Web Scraping Fundamentals

### 2.1.1 HTTP Protocol and Web Page Structure

HTTP (Hypertext Transfer Protocol) is the communication protocol used for transferring data over the internet. It defines the means of communication between clients (such as browsers) and servers (such as websites).

Web pages are typically composed of HTML (Hypertext Markup Language) and CSS (Cascading Style Sheets). HTML defines the structure and content of web pages, whereas CSS controls their appearance and layout.

### 2.1.2 Web Page Parsing and Data Extraction

Web page parsing involves the process of decomposing web page content into structured data. This can be achieved using regular expressions, HTML parsers, or specialized libraries such as BeautifulSoup.

Data extraction refers to the process of retrieving the required information from the parsed web page content. This can be done using XPath, CSS selectors, or other techniques.

# 2. Web Scraping Techniques in Action

### 2.1 Web Scraping Fundamentals

#### 2.1.1 HTTP Protocol and Web Page Structure

**HTTP Protocol**

HTTP (Hypertext Transfer Protocol) is the foundational protocol for communication between clients and servers. It defines the format for request and response messages and the manner in which data is transmitted.

**Web Page Structure**

Web pages are commonly written in HTML (Hypertext Markup Language), which defines the content and structure of the web page. HTML elements include titles, paragraphs, lists, and links.

#### 2.1.2 Web Page Parsing and Data Extraction

**Web Page Parsing**

***mon parsers include BeautifulSoup and lxml.

**Data Extraction**

Data extraction is the process of retrieving specific information from the parsed data using regular expressions or XPath.

### 2.2 Distributed Web Scraping Architecture

#### 2.2.1 Principles of Distributed Web Scraping

Distributed web scraping involves the distribution of scraping tasks across multiple worker nodes to enhance scraping efficiency and scalability.

**How it works:**

1. The scheduler assigns tasks to worker nodes.
2. Worker nodes fetch web pages and extract data.
3. Data is stored in a distributed database.

#### 2.2.2 Distributed Web Scraping Frameworks

**Scrapy**

Scrapy is a popular distributed web scraping framework that offers the following functionalities:

- Scheduling and managing scraping tasks
- Parsing web pages and extracting data
- Storing and managing the data

### 2.3 Data Cleaning and Preprocessing

#### 2.3.1 Data Cleaning Methods

**Data Cleaning** is the process of removing errors, inconsistencies, ***mon methods include:

- **Data Validation:** Checking if the data conforms to specific rules.
- **Data Transformation:** Converting data into the required format.
- **Data Imputation:** Filling in missing values with reasonable estimates.

#### 2.3.2 Data Preprocessing Techniques

**Data Preprocessing***mon techniques include:

- **Feature Engineering:** Creating new features or transforming existing ones.
- **Data Standardization:** Scaling or normalizing data to a common range.
- **Data Reduction:** Reducing data dimensions to improve model performance.

# 3.1 Data Exploration and Analysis

Data exploration and analysis are key steps in the data analysis process, aimed at understanding the overall distribution, characteristics, and trends of the data, laying the foundation for subsequent in-depth analysis and decision-making.

#### 3.1.1 Data Visualization

Data visualization is a technique that transforms data into graphical or chart forms, ***mon types of visualizations include:

- **Bar and Column Charts:** Used for comparing data across different categories or groups.
- **Line and Area Charts:** Used to display tren