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What Is Parsing of Data?

Parsing involves analyzing strings of data and transforming them into structured formats that software can better understand. As data moves between computer systems, parsing gives meaning and context to raw text or binary streams. It is a key process enabling computation on unstructured datasets.

This in-depth guide will explore all aspects of parsers and data parsing:

Along with core concepts, we‘ll look at practical parsing in Python. Let‘s start from the basics!

What Does a Parser Do?

In simple terms, a parser takes input data and extracts relevant parts based on pre-defined rules. The output is a structured format like a tree or object model.

Some examples of parsing tasks:

  • Extract prices from product pages into a spreadsheet
  • Read log files and identify error messages
  • Analyze source code and build an abstract syntax tree
  • Process HTML/XML and pull out desired elements
  • Parse document formats like PDFs and Word docs
  • Decode data interchange formats like JSON and CSV
  • Execute queries against databases and parse results
  • Analyze natural language questions and identify intents

Parsers utilize different techniques like:

  • Regular expressions – pattern matching for texts
  • XPath – querying XML documents
  • CSS selectors – extracting HTML elements
  • Grammars – defining structure of programming languages
  • Machine learning – statistical parsing of unstructured data

The parser definition contains the logic to identify meaningful bits of the input. Well-designed parsers are robust, efficient, and easy to maintain.

Types of Parsers

There are several categories of parsers, classified by how they analyze the input data:

Lexical Analysis

This breaks input into atomic units called tokens. A common approach is splitting on whitespace and punctuation. Regular expressions are often used in lexical analysis.

Syntactical Analysis

Here, tokens get grouped and rearranged based on syntax rules. Syntax checkers use this to validate program structure.

Semantic Analysis

This stage assigns meaning to expressions and verifies logical correctness. Type checkers are a common example of semantic analysis.

In practice, these phases blend together. But separating lexical, syntactical, and semantic parsing concerns keeps implementations modular.

Parse Trees / Abstract Syntax Trees

Many parsers generate tree structures reflecting the hierarchical nature of language constructs. These enable easier analysis and processing.

Parse Tree

Specification Based Parsing

Parsers can be hand-coded, but are often generated automatically from a grammar specification like BNF. Popular parser generators include YACC, Bison, and ANTLR.

Parser Algorithms

There are a variety of algorithms parsers utilize like:

  • Recursive descent – top down parsing, builds tree directly
  • LL – parses left to right, unlimited lookahead
  • LR – parses left to right, limited lookahead
  • LALR – compromise between LL and LR

Each approach has tradeoffs in complexity, efficiency, and language support.

Parsing Expression Grammars

PEGs provide an alternate specification format that operates on ordered choices. They avoid ambiguities of BNF grammars. PEG parsers directly execute the grammar.

Neural Network Parsers

Recent advances in deep learning have enabled neural network parsers. These statistically learn to parse from large corpora vs needing hand-crafted rules.

So in summary, there are many parser types optimized for different data formats and tradeoffs. Pipelines will often utilize multiple parsing stages.

Building a Parser vs Using a Library

Should you build your own custom parser or use an existing library? Here are some pros and cons of each approach:

Custom Parser


  • Handles proprietary and undocumented formats
  • Optimized for specific use case
  • Full control over processing


  • Major effort to build and maintain
  • Requires significant parsing expertise
  • Hard to match robustness of well-tested libraries

Parser Library


  • Mature solutions for common formats
  • Actively maintained by community
  • Quicker to implement


  • Less flexibility in processing
  • Dependence on 3rd party software
  • Potential performance overhead

If you need to parse standard data types like CSV, JSON, or XML – existing libraries are great. But for one-off situations, a custom parser may make sense.

Parsing Considerations

Here are some best practices for working with parsers:

  • Handle malformed data – Use defensive coding to prevent crashes.
  • Optimize performance – Parse in a single pass, use caching, go parallel.
  • Simplify maintenance – Modular and well-documented code.
  • Validate thoroughly – Have test cases covering edge cases.
  • Support evolution – Make parsers easy to update as needs change.

Well-designed parsers are accurate, efficient, and maintainable.

Parsing in Python

Python has excellent built-in and 3rd party parsing libraries. Let‘s go through some examples.

JSON Parsing

JSON is a ubiquitous data interchange format. Python‘s json module provides simple parsing:

import json

json_string = ‘{"name": "John", "age": 30}‘

data = json.loads(json_string)

print(data[‘name‘]) # Prints "John"

For dealing with larger JSON datasets, ujson provides faster parsing.

XML Parsing

XML is commonly used for document markup and data portability. The xml module comes built-in:

import xml.etree.ElementTree as ET

xml = ‘‘‘<person>

root = ET.fromstring(xml)

print(root.find(‘./name‘).text) # Prints "Chuck"

lxml is a popular 3rd party Python library for XML parsing.

HTML Parsing

To extract data from HTML, Beautiful Soup is an excellent choice:

from bs4 import BeautifulSoup

html = # HTML document string

soup = BeautifulSoup(html, ‘html.parser‘)

links = soup.find_all(‘a‘)
prices = soup.find_all(‘span‘, ‘price‘)

It supports parsing broken HTML and provides methods like find, find_all for traversing the document tree.

Regular Expression Parsing

For simple text processing, regular expressions are a handy tool:

import re

log = "error: Variable x is undefined on line 224"

regex = r"error: (.*) is (.*) on line (\d+)"

matches =, log)

print(matches.groups()) # (‘Variable x‘, ‘undefined‘, ‘224‘)

Groups within the expression extract matched subsequences.

This has covered some of the major parsing approaches in Python. There are libraries for parsing nearly any format including PDF, Excel, Markdown, YAML, and more. Python‘s breadth of mature parsing tools is one factor in its popularity for data tasks.

Real-World Parsing Examples

To illustrate parsing in action, here are some examples across different domains:

  • Scientific research – Parse bioinformatics data formats like FASTA, PDB, UniProt
  • Web development – Process HTML, CSS, JavaScript when rendering pages
  • DevOps – Analyze application logs to identify errors
  • Business analytics – Read and import datasets in formats like CSV, TSV
  • Software engineering – Generate ASTs during compilation for code analysis
  • Machine learning – Parse training datasets to featurize text for models

Parsing enables working with loosely structured data from diverse sources. The next section provides additional resources for learning more about parsers.

Parsing Resources

Here are some useful parsing references:

This guide has provided an in-depth exploration of data parsing. We looked at parser types, use cases, Python libraries, and more. Parsing enables extracting meaningful information from raw datasets. With the right approach, vast streams of data can be transformed into structured formats for analysis and computation.

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