Data Parsing Tools

Data parsing is a general term for the process of scanning individual pages or entire websites to obtain information using special bots (spiders). The obtained information can then be processed and stored in a specific structured format for analysis or other purposes. Today, data parsing is actively used for the following types of tasks:

1. searching for information about potential clients for subsequent cold calls (relevant for companies involved in sales of goods and services, marketing);
2. searching for new real estate listings, start of new projects in this area (relevant for modern real estate agencies);
3. obtaining information about prices and products offered by competitors (relevant for online stores and other e-commerce companies).

For actual web page data parsing, spiders are used, which are essentially programs. Their task is to extract HTML documents from websites and then obtain the needed content, which is sought using specific business logic.

The information obtained through parsing is saved in a specific format for subsequent analysis and decision making. To obtain data and reduce labor costs during subsequent processing while performing such work, three tasks must be solved in advance:

1. choose a specific parsing method (the traditional option here remains using Python frameworks and libraries);
2. scaling the parsing (this question is relevant when studying information from millions of sites at once, when it is necessary to ensure quality code management, collect data, and maintain the storage where it will be located);
3. optimizing information extraction methods (in principle, parsing without the permission of the owners may be considered unauthorized and the spider may be blacklisted, so it is important to follow certain practices when scanning to prevent this).

Basic Data Parsing Methods

For parsing, the most popular language today remains Python, which has a full set of necessary libraries and frameworks. The most sought-after and versatile ones are:

1. HTTP-library;
2. BeautifulSoup;
3. Python Scrapy;
4. Python lxml.html.

HTTP-library is designed to find the content of a specific HTML page directly in the response object. It is very convenient for data parsing and simple to use, providing easily readable code. Additionally, the programmer gets the ability to conveniently and quickly maintain the code and make changes to obtain additional information.

BeautifulSoup is necessary for obtaining data from a page that was extracted using the above-mentioned HTTP-library. It is very simple to use and has a wide range of APIs for parsing the required information, allowing for a quality analysis from headings to links and specific parameters.

Python Scrapy is a framework designed for parsing data on web pages. With it, you can create various types of spiders to obtain the source code on the target site. It scans pages, and then the user can write the logic to obtain the needed data. The base for Scrapy is an asynchronous library based on Python, which allows increasing overall performance through asynchronicity. It is a more efficient tool for parsing compared to BeautifulSoup. It also allows writing custom information parsers, which is not available for the latter library (it performs direct parsing of HTML pages). As a result, Scrapy becomes a very powerful tool for finding the needed information, so to work and use all its capabilities, it is necessary to study the relevant documentation.

Python lxml.html is a library that is fundamentally similar in capabilities to BeautifulSoup. In its standard version for data parsing, it comes with a set of APIs that can be used directly to obtain data and transform it. For example, with this library you can identify all div tags on a page and then perform a specific operation on the child tags contained within them. This can be done by iterating using the iter() function, which allows traversing all descendants inside the parent div tag. Such operations are often difficult to clean and analyze, so quite fine additional tuning may be required.

The tools listed above are quite sufficient for obtaining the necessary information and parsing data from the sites of interest. At the same time, it is important to avoid potential problems related to data processing, "masking" the spider as a "regular" user, which will prevent it from being blocked. However, that is a topic for a slightly different conversation.