What Is Data Parsing and How To Parse Data in Python

The data extracted after web scraping is unstructured and difficult to work with. Then how could you transform this unstructured data into a structured or readable format? This is where a crucial step in web scraping comes in: Data parsing.

This article is all about data parsing, its importance in web scraping, how it is different from data scraping, common data parsing techniques used, and much more.

What Is Data Parsing?

Data parsing in web scraping is a process that transforms unstructured data, like HTML, into structured, readable formats. It involves mainly two steps:

1. Lexical Analysis – Breaks down data into tokens, like numbers or strings
2. Syntactic Analysis – Organizes these tokens into a parse tree and makes the data easier to use

This organization is vital as it integrates data from different sources into a single format. It also simplifies the analysis and supports data-driven decision-making.

What Is a Data Parser? How Does a Data Parser Work?

A data parser is a tool that receives data in one format and returns it in a new format. Data parsing in web scraping relies on data parsers and can be built in different programming languages.

Numerous libraries and APIs are available for parsing data in Python. For instance, Python Pandas for Excel files, Python Requests for handling web requests for APIs, and more complex data interactions.

Now let’s see how a data parser works with an example. To parse an HTML document, the HTML parser will:

• Input an HTML document
• Load and store the HTML as a string
• Extract key information by parsing the HTML string
• Refine and cleanse the data during parsing, as needed
• Output the parsed data as a JSON, YAML, or CSV file, or store it in a SQL or NoSQL database

Why Is Data Parsing in Web Scraping Important?

Web scraping uses data parsing to organize unstructured information from websites into a clear and structured format. This step is crucial because it helps clean the data and make it readable, which is necessary for accurate analysis.

Data parsing in Python greatly affects the quality of the data collected. It cleans up the data, speeds up the processing, helps handle errors, and adapts to changes in website layouts, making the data more useful for analysis and applications.

What Is the Difference Between Data Scraping and Data Parsing?

Data scraping and data parsing are two separate processes in data extraction. Data scraping involves the retrieval of information from websites, including HTML content, metadata, and multimedia elements. It must be approached with careful attention to legal considerations, ensuring ethical data extraction.

On the other hand, data parsing converts unstructured data to a structured format for analysis or database insertion. It deals with predefined data formats such as JSON or XML. Unlike scraping, parsing generally doesn’t involve legal issues unless the data is obtained illegally.

Example of Raw HTML Data

Given is an example of raw HTML data for a simple e-commerce product page. This HTML snippet contains the data generally found on an online store, like basic information about a product:

Copy[
    {
        "productName": "Dratini",
        "price": "£94.00",
        "imageSrc": "path_to_image/dratini.jpg"
    },
    {
        "productName": "Dragonair",
        "price": "£57.00",
        "imageSrc": "path_to_image/dragonair.jpg"
    },
    {
        "productName": "Dragonite",
        "price": "£69.00",
        "imageSrc": "path_to_image/dragonite.jpg"
    },
    // ... other products
]

Some Common Data Parsing Techniques

Data parsing in Python transforms data enhancing manageability and usability for specific applications. There are various parsing techniques used for different data types. Each of these parsing techniques is implemented using various programming languages and libraries designed to handle specific data formats.

• String Parsing

String parsing breaks down data into smaller parts in order to locate and extract relevant data. It is commonly used for tasks like locating specific keywords in a document or obtaining information from URLs.

• Regular Expression (regex) Parsing

Regular expression (regex) parsing uses character sequence patterns, known as regular expressions, in order to extract data from unstructured sources. This method is apt at finding specific patterns of letters and numbers, like phone numbers or email addresses, in text.

• XML Parsing

XML parsing extracts data by deconstructing the document into its elemental components and attributes. This method focuses on XML documents and is effective for data retrieval.

• JSON Parsing

JSON parsing is similar to XML parsing and is for JSON documents. This technique breaks down JSON data into its constituent key-value pairs for information extraction.

• HTML Parsing

HTML parsing extracts data from HTML documents. It involves breaking down the basic HTML structure into parts like tags and attributes, allowing for the retrieval of necessary data.

Popular HTML Parsing Tools

HTML parsing is a popular technique in web data extraction. So many tools are used for HTML parsing to extract information from HTML files. Some of them include:

1. Python HTML Parsers

BeautifulSoup

BeautifulSoup is a highly popular Python library for web scraping and parsing HTML data. It is known for its simplicity and versatility. BeautifulSoup constructs a hierarchical ‘soup’ structure from HTML documents, facilitating easy navigation and data extraction using functions like find_all(), find(), and select().

Here’s how to initiate BeautifulSoup for parsing:

Copypip install beautifulsoup4

from bs4 import BeautifulSoup
import requests

# Example URL
url = 'http://example.com'

# Fetching the webpage
response = requests.get(url)
response.raise_for_status()  # Raises an HTTPError for bad responses

# Using Beautiful Soup to parse the HTML content
soup = BeautifulSoup(response.text, 'html.parser')

# Now you can use `soup` to navigate and search the parsed HTML tree
print(soup.prettify())  # Print the nicely formatted HTML

PyQuery

PyQuery is a Python tool that makes working with HTML documents easier, especially for developers who are familiar with jQuery’s syntax. It allows adding, modifying, and changing the HTML elements using simple commands.

Here’s how to initiate PyQuery for parsing:

Copypip install pyquery

from pyquery import PyQuery as pq
import requests

# Example URL
url = 'http://example.com'

# Fetching the webpage
response = requests.get(url)
response.raise_for_status()  # Raises an HTTPError for bad responses

# Using PyQuery to parse the HTML content
doc = pq(response.text)

# Now you can use `doc` to navigate and search the parsed HTML tree
print(doc)

2. JavaScript HTML Parsers

Cheerio is a tool for JavaScript developers that works quickly and flexibly, using jQuery’s style but on servers. With Cheerio, developers can easily change and move through the structure of HTML documents using familiar commands.

Here’s how to initiate Cheerio for parsing:

Copyimport json
parsed_data = json.loads(output_string)