What Is Meant By Parsing
What is data parsing? – ScrapingBee
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07 June, 2021
10 min read
Kevin worked in the web scraping industry for 10 years before co-founding ScrapingBee. He is also the author of the Java Web Scraping Handbook.
Data parsing is the process of taking data in one format and transforming it to another format. You’ll find parsers used everywhere. They are commonly used in compilers when we need to parse computer code and generate machine code.
This happens all the time when developers write code that gets run on hardware. Parsers are also present in SQL engines. SQL engines parse a SQL query, execute it, and return the results.
In the case of web scraping, this usually happens after data has been extracted from a web page via web scraping. Once you’ve scraped data from the web, the next step is making it more readable and better for analysis so that your team can use the results effectively.
A good data parser isn’t constrained to particular formats. You should be able to input any data type and output a different data type. This could mean transforming raw HTML into a JSON object or they might take data scraped from JavaScript rendered pages and change that into a comprehensive CSV file.
Parsers are heavily used in web scraping because the raw HTML we receive isn’t easy to make sense of. We need the data changed into a format that’s interpretable by a person. That might mean generating reports from HTML strings or creating tables to show the most relevant information.
Even though there are multiple uses for parsers, the focus of this blog post will be about data parsing for web scraping because it’s an online activity that thousands of people handle every day.
How to build a data parser
Regardless of what type of data parser you choose, a good parser will figure out what information from an HTML string is useful and based on pre-defined rules. There are usually two steps to the parsing process, lexical analysis and syntactic analysis.
Lexical analysis is the first step in data parsing. It basically creates tokens from a sequence of characters that come into the parser as a string of unstructured data, like HTML. The parser makes the tokens by using lexical units like keywords and delimiters. It also ignores irrelevant information like whitespaces and comments.
After the parser has separated the data between lexical units and the irrelevant information, it discards all of the irrelevant information and passes the relevant information to the next step.
The next part of the data parsing process is syntactic analysis. This is where parse tree building happens. The parser takes the relevant tokens from the lexical analysis step and arranges them into a tree. Any further irrelevant tokens, like semicolons and curly braces, are added to the nesting structure of the tree.
Once the parse tree is finished, then you’re left with relevant information in a structured format that can be saved in any file type. There are several different ways to build a data parser, from creating one programmatically to using existing tools. It depends on your business needs, how much time you have, what your budget is, and a few other factors.
To get started, let’s take a look at HTML parsing libraries.
HTML parsing libraries
HTML parsing libraries are great for adding automation to your web scraping flow. You can connect many of these libraries to your web scraper via API calls and parse data as you receive it.
Here are a few popular HTML parsing libraries:
Scrapy or BeautifulSoup
These are libraries written in Python. BeautifulSoup is a Python library for pulling data out of HTML and XML files. Scrapy is a data parser that can also be used for web scraping. When it comes to web scraping with Python, there are a lot of options available and it depends on how hands-on you want to be.
Cheerio
If you’re used to working with Javascript, Cheerio is a good option. It parses markup and provides an API for manipulating the resulting data structure. You could also use Puppeteer. This can be used to generate screenshots and PDFs of specific pages that can be saved and further parsed with other tools. There are many other JavaScript-based web scrapers and web parsers.
JSoup
For those that work primarily with Java, there are options for you as well. JSoup is one option. It allows you to work with real-world HTML through its API for fetching URLs and extracting and manipulating data. It acts as both a web scraper and a web parser. It can be challenging to find other Java options that are open-source, but it’s definitely worth a look.
Nokogiri
There’s an option for Ruby as well. Take a look at Nokogiri. It allows you to work with HTML and HTML with Ruby. It has an API similar to the other packages in other languages that lets you query the data you’ve retrieved from web scraping. It adds an extra layer of security because it treats all documents as untrusted by default. Data parsing in Ruby can be tricky as it can be harder to find gems you can work with.
Regular expression
Now that you have an idea of what libraries are available for your web scraping and data parsing needs, let’s address a common issue with HTML parsing, regular expressions. Sometimes data isn’t well-formatted inside of an HTML tag and we need to use regular expressions to extract the data we need.
You can build regular expressions to get exactly what you need from difficult data. Tools like regex101 can be an easy way to test out whether you’re targeting the correct data or not. For example, you might want to get your data specifically from all of the paragraph tags on a web page. That regular expression might look something like this:
/
(. *)<\/p>/
The syntax for regular expressions changes slightly depending on which programming language you’re working with. Most of the time, if you’re working with one of the libraries we listed above or something similar, you won’t have to worry about generating regular expressions.
If you aren’t interested in using one of those libraries, you might consider building your own parser. This can be challenging, but potentially worth the effort if you’re working with extremely complex data structures.
Building your own parser
When you need full control over how your data is parsed, building your own tool can be a powerful option. Here are a few things to consider before building your own parser.
A custom parser can be written in any programming language you like. You can make it compatible with other tools you’re using, like a web crawler or web scraper, without worrying about integration issues.
In some cases, it might be cost-effective to build your own tool. If you already have a team of developers in-house, it might not too big of a task for them to accomplish.
You have granular control over everything. If you want to target specific tags or keywords, you can do that. Any time you have an update to your strategy, you won’t have many problems with updating your data parser.
Although on the other hand, there are a few challenges that come with building your own parser.
The HTML of pages is constantly changing. This could become a maintenance issue for your developers. Unless you foresee your parsing tool becoming of huge importance to your business, taking that time from product development might not be effective.
It can be costly to build and maintain your own data parser. If you don’t have a developer team, contracting the work is an option but that could lead to step bills based on developers’ hourly rates. There’s also the cost of ramping up developers that are new to the project as they figure out how things work.
You will also need to buy, build, and maintain a server to host your custom parser on. It has to be fast enough to handle all of the data that you send through it or else you might run into issues with parsing data consistently. You’ll also have to make sure that server stays secure since you might be parsing sensitive data.
Having this level of control can be nice if data parsing is a big part of your business, otherwise, it could add more complexity than is necessary. There are plenty of reasons for wanting a custom parser, just make sure that it’s worth the investment over using an existing tool.
Parsing meta data
There’s also another way to parse web data through a website’s schema. Web schema standards are managed by, a community that promotes schema for structured data on the web. Web schema is used to help search engines understand information on web pages and provide better results.
There are many practical reasons people want to parse schema metadata. For example, companies might want to parse schema for an e-commerce product to find updated prices or descriptions. Journalists could parse certain web pages to get information for their news articles. There are also website that might aggregate data like recipes, how-to guides, and technical articles.
Schema comes in different formats. You’ll hear about JSON-LD, RDFa, and Microdata schema. These are the formats you’ll likely be parsing.
JSON-LD is JavaScript Object Notation for Linked Data. This is made of multi-dimensional arrays. It’s implemented using the standards in terms of SEO. JSON-LD is generally more simple to implement because you can paste the markup directly in an HTML document.
RDFa (Resource Description Framework in Attributes) is recommended by the World Wide Web Consortium (W3C). It’s used to embed RDF statements in XML and HTML. One big difference between this and the other schema types is that RDFa only defines the metasyntax for semantic tagging.
Microdata is a WHATWG HTML specification that’s used to nest metadata inside existing content on web pages. Microdata standards allow developers to design a custom vocabulary or use others like
All of these schema types are easily parsable with a number of tools across different languages. There’s a library from ScrapingHub, another from RDFLib.
We’ve covered a number of existing tools, but there are other great services available. For example, the ScrapingBee Google Search API. This tool allows you to scrape search results in real-time without worrying about server uptime or code maintainance. You only need an API key and a search query to start scraping and parsing web data.
There are many other web scraping tools, like JSoup, Puppeteer, Cheerio, or BeautifulSoup.
A few benefits of purchasing a web parser include:
Using an existing tool is low maintenance.
You don’t have to invest a lot of time with development and configurations.
You’ll have access to support that’s trained specifically to use and troubleshoot that particular tool.
Some of the downsides of purchasing a web parser include:
You won’t have granular control over everything the way your parser handles data. Although you will have some options to choose from.
It could be an expensive upfront cost.
Handling server issues will not be something you need to worry about.
Final thoughts
Parsing data is a common task handling everything from market research to gathering data for machine learning processes. Once you’ve collected your data using a mixture of web crawling and web scraping, it will likely be in an unstructured format. This makes it hard to get insightful meaning from it.
Using a parser will help you transform this data into any format you want whether it’s JSON or CSV or any data store. You could build your own parser to morph the data into a highly specified format or you could use an existing tool to get your data quickly. Choose the option that will benefit your business the most.
What is Parse? – Definition from Techopedia
What Does Parse Mean?
To parse, in computer science, is where a string of commands – usually a program – is separated into more easily processed components, which are analyzed for correct syntax and then attached to tags that define each component. The computer can then process each program chunk and transform it into machine language.
Techopedia Explains Parse
To parse is to break up a sentence or group of words into separate components, including the definition of each part’s function or form. The technical definition implies the same concept.
Parsing is used in all high-level programming languages. Languages like C++ and Java are parsed by their respective compilers before being transformed into executable machine code. Scripting languages, like PHP and Perl, are parsed by a web server, allowing the correct HTML to be sent to a browser.
Expression Parsing in Data Structure: Types of Notation, Associativity …
Home > Data Science > Expression Parsing in Data Structure: Types of Notation, Associativity & Precedence
Parsing is the process of analysing a string of symbols, expressed in natural or computer languages that will accord formal grammar. Expression Parsing in Data Structure means the evaluation of arithmetic and logical expressions. First, let’s see how an arithmetic expression is written:
9+9
C-b
An expression can be written with constants, variables, and symbols that can act as an operator or parenthesis. All this expression needs to follow a specific set of rules. According to this rule, the parsing of the expression is done based on grammar.
An arithmetic expression is expressed in the form of Notation. Now, there are three ways to write an expression in Arithmetics:
Infix Notation
Prefix (Polish) Notation
Postfix (Reverse-Polish) Notation
However, when the expression is written, the output of the desired expression remains the same. Before getting started with the types of Notation, let’s see what Associativity and Precedence are in expression Parsing in Data Structure.
Read: Graphs in Data Structure
AssociativityPrecedence in the Data Structure Types of Notation 1. Infix Notation2. Prefix Notation3. Postfix NotationConversion between Notations ConclusionWhy choose a Data science course with upGrad?
Associativity
Before you get started, you need to know what Associativity property is; it provides you with the rules to rearrange parentheses in an expression to provide valid proof. This means a rearrangement of the bracket needs to give the same value as the parent equation. It provides a valid rule to replace the operators.
In an expression containing two or more operators, the operation performed does not matter unless the sequence of operands is not interchanged. If the expression is written using the brackets and in infix, changing the position does not change the value.
Because in Indo-European languages, expressions are read from left to right, most infix operators are left-associative; operators are evaluated in the same precedence. Rising in power is the rule used in considering the infix operators. Prefix operators are generally right-associative, and postfix operators are left-associative.
In some languages, operators and operands are given equal value, where the Associativity is not considered making this language sequence explicit. While in some languages, the operators are non-associative, this makes the use of complex expressions necessary for the use of brackets, which increases complexity for programmers.
Precedence in the Data Structure
Order of Precedence means what order the operators need to follow in a statement of expressions. This is commonly used while working with Infix Notation.
In the situation of
The most common but not so obvious rule is that multiplication and division operation must be performed before addition and subtraction. Typically, they are collected in the same fashion, so equal importance is provided for all the operators.
Considering this operation in a logical format, variation is seen in “and” and “or. ” Many languages provide equal importance, where “or” operation is given higher Precedence. Some languages consider multiplication or “&, ” “&” addition “or” the equal Precedence, where most languages provide arithmetic operations with the highest Precedence.
Overloading is caused due to no proper allocation of Precedence. Many languages provide negation (true/false) higher Precedence than vector algebra expressions, while some provide equal Precedence.
Also Read: Data Structure Project Ideas
Types of Notation
Now let us learn how the operator position decides the type of Notation.
1. Infix Notation
In Infix Notation, operators are used in between the operands. While reading an expression, Infix Notation is quite easy for humans. But it’s quite time and space consuming to process an infix argument when it comes to a computer algorithm. g: p + q
Infix Notation needs additional information to perform the evaluation; rules are constructed into the expression language using the operator Associativity, Precedence, and brackets () to override the rules.
For example: p * ( q + r) / s
Associativity rules suggest that expression needs to be performed from left to right, such that multiplication by p is done before the division of q.
Similarly, rules for Precedence suggest that multiplication and division operation is performed before addition and subtraction operation is done.
2. Prefix Notation
Here operator is written first, followed by an operand. It is also named as Polish Notation. g. +pq
E. g: p * ( q + r) / s
Evaluation needs to be performed from left-to-right, and brackets do not alter or change the equation pattern. Here, addition needs to be completed before multiplication because the position “+” is left of “*. ”
Here, every operator performs operations on values that are immediate to the left of them. For example, the “+” above uses the “q” and “r. ” We can sum up brackets to make this overt:
((p (q r +) *) s /)
Thus, the “()” considers and uses the two values after immediately preceding “p”, and the result of the +. Similarly, the “/” uses the outcome of the multiplication expression and the “s”.
3. Postfix Notation
Postfix Notation, primarily the operand, is written, followed by an operator. It is also named as Reverse Polish Notation, e. g., pq+
As for Postfix, the same as the Prefix operation of the expression is left-to-right and “()” are unnecessary. Here, operators perform on the two nearest values from the right. In the below example, brackets are added unnecessarily, to clear that there is no impact on the evaluation.
(/ (* p (+ q r)) s)
Here “operator evaluation is from left-to-right” operation values are to their right, and if values themselves involve calculations, then there is a change in the order of evaluation. Taking the example listed above, see the “/” is the primary operator on the left.
It waits until the multiplication operation is completed. And primarily, the multiplication operation needs to be performed before the division calculation is started (and from the above example, it is clear that addition operation needs to be completed before the multiplication operation).
Because Postfix Notation operators use the value to its right; any values involving calculations will have the calculation already completed as we move to the left. So, we can conclude that expression calculation is not as same as the Prefix operator operation.
To highlight all three Notations, the operands come in the same order, and operators need to be moved to provide the right meaning during the calculation. This needs to be considered particularly when considering asymmetric operators “-” and “/”to make it clear p-q is ever q-r unless they have the same value; the values are equivalent to “pq -” or “- pq”
P+q ≡ +pq ≡ pq+
E. :
Infix- p * q + r / s
Prefix – pq * rs / +
Post fix – + * pq / rs
First, to perform the operation, multiply p and q and later divide r by s and, at last, add the results.
Below table briefs between the three Notations,
Polish Notation
Reverse polish notation
p+q
+pq
pq+
(p+q)*r
+*pq
pqr+*
p*(q+r)
*p+qr
pqr*+ +
p÷q+r÷s
+÷pq÷rs
pq÷rs÷+
(p-q)*(r-s)
*-pq-rs
pq-rs-*
Conversion between Notations
*To provide clear insight, the brackets are added in the expression,
Infix
Postfix
Prefix
( (p * q) + (r / s))
( (pq *) (r s /) +)
(+ (* pq) (/ rs))
((p * (q + r)) / s)
( (p (q r +) *) s /)
(/ (* p (+ qr)) s)
(p * (q + (r / s)))
(p (q (r s /) +) *)
(* p (+ q (/ rs)))
You can start converting directly in the bracketed forms by operators in the bracket, e. (m + n) or (mn +) or (+ mn). Now repeat this in all the operators by removing the unwanted brackets.
Now use this trick showed above to convert and parse trees – the equivalent parse trees for each node are:
Checkout: Data Structure & Algorithm in Python
Conclusion
Expression Parsing in Data Structure, Infix, Postfix, and Prefix Notations in Arithmetic expressions are quite different but have the same ways of writing expressions. Knowledge of these is essential in writing programs.
In a computer programming language, the expression is considered and parsed from the string. The Associativity and Precedence rule quite change in different languages.
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Frequently Asked Questions about what is meant by parsing
What is parsing of data?
Data parsing is the process of taking data in one format and transforming it to another format. … You’ll find parsers used everywhere. They are commonly used in compilers when we need to parse computer code and generate machine code.Jun 7, 2021
What is meant by parsing in programming?
To parse, in computer science, is where a string of commands – usually a program – is separated into more easily processed components, which are analyzed for correct syntax and then attached to tags that define each component. The computer can then process each program chunk and transform it into machine language.Mar 23, 2017
What is meant by parsing in data structure?
Parsing is the process of analysing a string of symbols, expressed in natural or computer languages that will accord formal grammar. Expression Parsing in Data Structure means the evaluation of arithmetic and logical expressions.Oct 7, 2020
