Parsing In Python

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Python Parser | Working of Python Parse with different Examples

Introduction to Python Parser
In this article, parsing is defined as the processing of a piece of python program and converting these codes into machine language. In general, we can say parse is a command for dividing the given program code into a small piece of code for analyzing the correct syntax. In Python, there is a built-in module called parse which provides an interface between the Python internal parser and compiler, where this module allows the python program to edit the small fragments of code and create the executable program from this edited parse tree of python code. In Python, there is another module known as argparse to parse command-line options.
Working of Python Parse with Examples
In this article, Python parser is mainly used for converting data in the required format, this conversion process is known as parsing. As in many different applications data obtained can have different data formats and these formats might not be suitable to the particular application and here comes the use of parser that means parsing is necessary for such situations. Therefore, parsing is generally defined as the conversion of data with one format to some other format is known as parsing. In parser consists of two parts lexer and a parser and in some cases only parsers are used.
Python parsing is done using various ways such as the use of parser module, parsing using regular expressions, parsing using some string methods such as split() and strip(), parsing using pandas such as reading CSV file to text by using, etc. There is also a concept of argument parsing which means in Python, we have a module named argparse which is used for parsing data with one or more arguments from the terminal or command-line. There are other different modules when working with argument parsings such as getopt, sys, and argparse modules. Now let us below the demonstration for Python parser. In Python, the parser can also be created using few tools such as parser generators and there is a library known as parser combinators that are used for creating parsers.
Now let us see in the below example of how the parser module is used for parsing the given expressions.
Example #1
Code:
import parser
print(“Program to demonstrate parser module in Python”)
print(“\n”)
exp = “5 + 8”
print(“The given expression for parsing is as follows:”)
print(exp)
print(“Parsing of given expression results as: “)
st = (exp)
print(st)
print(“The parsed object is converted to the code object”)
code = mpile()
print(code)
print(“The evaluated result of the given expression is as follows:”)
res = eval(code)
print(res)
Output:
In the above program, we first need to import the parser module, and then we have declared expression to calculate, and to parse this expression we have to use a () function. Then we can evaluate the given expression using eval() function.
In Python, sometimes we get data that consists of date-time format which would be in CSV format or text format. So to parse such formats in proper date-time formats Python provides parse_dates() function. Suppose we have a CSV file that contains data and the data time details are separated with a comma which makes it difficult for reading therefore for such cases we use parse_dates() but before that, we have to import pandas as this function is provided by pandas.
In Python, we can also parse command-line options and arguments using an argparse module which is very user friendly for the command-line interface. Suppose we have Unix commands to execute through python command-line interface such as ls which list all the directories in the current drive and it will take many different arguments also therefore to create such command-line interface we use an argparse module in Python. Therefore, to create a command-line interface in Python we need to do the following; firstly, we have to import an argparse module, then we create an object for holding arguments using ArgumentParser() through the argparse module, later we can add arguments the ArgumentParser() object that will be created and we can run any commands in Python command line. Note as running any commands is not free other than the help command. So here is a small piece of code for how to write the python code to create a command line interface using an argparse module.
import argparse
Now we have created an object using ArgumentParser() and then we can parse the arguments using rse_args() function.
parser = gumentParser()
rse_args()
To add the arguments we can use add_argument() along with passing the argument to this function such as d_argument(“ ls ”). So let us see a small example below.
Example #2
d_argument(“ls”)
args = rse_args()
print()
So in the above program, we can see the screenshot of the output as we cannot use any other commands so it will give an error but when we have an argparse module then we can run the commands in python shell as follows:
$ python –help
usage: [-h] echo
Positional Arguments:
echo
Optional Arguments:
-h, –helpshow this help message and exit
$ python Educba
Educba
Conclusion
In this article, we conclude that Python provides a parsing concept. In this article, we saw that the parsing process is very simple which in general is the process of parting the large string of one type of format for converting this format to another required format is known as parsing. This is done in many different ways in Python using python string methods such as split() or strip(), using python pandas for converting CSV files to text format. In this, we saw that we can even use a parser module for using it as a command-line interface where we can run the commands easily using the argparse module in Python. In the above, we saw how to use argparse and how can we run the commands in Python terminal.
Recommended Articles
This is a guide to Python Parser. Here we also discuss the introduction and working of python parser along with different examples and its code implementation. You may also have a look at the following articles to learn more –
Python Timezone
Python NameError
Python OS Module
Python Event Loop
Parsing text with Python - vipinajayakumar

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Parsing text with Python – vipinajayakumar

I hate parsing files, but it is something that I have had to do at the start of nearly every project. Parsing is not easy, and it can be a stumbling block for beginners. However, once you become comfortable with parsing files, you never have to worry about that part of the problem. That is why I recommend that beginners get comfortable with parsing files early on in their programming education. This article is aimed at Python beginners who are interested in learning to parse text files.
In this article, I will introduce you to my system for parsing files. I will briefly touch on parsing files in standard formats, but what I want to focus on is the parsing of complex text files. What do I mean by complex? Well, we will get to that, young padawan.
For reference, the slide deck that I use to present on this topic is available here. All of the code and the sample text that I use is available in my Github repo here.
Why parse files?
The big picture
Parsing text in standard format
Parsing text using string methods
Parsing text in complex format using regular expressions
Step 1: Understand the input format
Step 2: Import the required packages
Step 3: Define regular expressions
Step 4: Write a line parser
Step 5: Write a file parser
Step 6: Test the parser
Is this the best solution?
Conclusion
First, let us understand what the problem is. Why do we even need to parse files? In an imaginary world where all data existed in the same format, one could expect all programs to input and output that data. There would be no need to parse files. However, we live in a world where there is a wide variety of data formats. Some data formats are better suited to different applications. An individual program can only be expected to cater for a selection of these data formats. So, inevitably there is a need to convert data from one format to another for consumption by different programs. Sometimes data is not even in a standard format which makes things a little harder.
So, what is parsing?
Parse
Analyse (a string or text) into logical syntactic components.
I don’t like the above Oxford dictionary definition. So, here is my alternate definition.
Convert data in a certain format into a more usable format.
With that definition in mind, we can imagine that our input may be in any format. So, the first step, when faced with any parsing problem, is to understand the input data format. If you are lucky, there will be documentation that describes the data format. If not, you may have to decipher the data format for yourselves. That is always fun.
Once you understand the input data, the next step is to determine what would be a more usable format. Well, this depends entirely on how you plan on using the data. If the program that you want to feed the data into expects a CSV format, then that’s your end product. For further data analysis, I highly recommend reading the data into a pandas DataFrame.
If you a Python data analyst then you are most likely familiar with pandas. It is a Python package that provides the DataFrame class and other functions to do insanely powerful data analysis with minimal effort. It is an abstraction on top of Numpy which provides multi-dimensional arrays, similar to Matlab. The DataFrame is a 2D array, but it can have multiple row and column indices, which pandas calls MultiIndex, that essentially allows it to store multi-dimensional data. SQL or database style operations can be easily performed with pandas (Comparison with SQL). Pandas also comes with a suite of IO tools which includes functions to deal with CSV, MS Excel, JSON, HDF5 and other data formats.
Although, we would want to read the data into a feature-rich data structure like a pandas DataFrame, it would be very inefficient to create an empty DataFrame and directly write data to it. A DataFrame is a complex data structure, and writing something to a DataFrame item by item is computationally expensive. It’s a lot faster to read the data into a primitive data type like a list or a dict. Once the list or dict is created, pandas allows us to easily convert it to a DataFrame as you will see later on. The image below shows the standard process when it comes to parsing any file.
If your data is in a standard format or close enough, then there is probably an existing package that you can use to read your data with minimal effort.
For example, let’s say we have a CSV file,
a, b, c
1, 2, 3
4, 5, 6
7, 8, 9
You can handle this easily with pandas.
123
import pandas as pd
df = ad_csv(”)
df
a b c
0 1 2 3
1 4 5 6
2 7 8 9
Python is incredible when it comes to dealing with strings. It is worth internalising all the common string operations. We can use these methods to extract data from a string as you can see in the simple example below.
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my_string = ‘Names: Romeo, Juliet’
# split the string at ‘:’
step_0 = (‘:’)
# get the first slice of the list
step_1 = step_0[1]
# split the string at ‘, ‘
step_2 = (‘, ‘)
# strip leading and trailing edge spaces of each item of the list
step_3 = [() for name in step_2]
# do all the above operations in one go
one_go = [() for name in (‘:’)[1](‘, ‘)]
for idx, item in enumerate([step_0, step_1, step_2, step_3]):
print(“Step {}: {}”(idx, item))
print(“Final result in one go: {}”(one_go))
Step 0: [‘Names’, ‘ Romeo, Juliet’]
Step 1: Romeo, Juliet
Step 2: [‘ Romeo’, ‘ Juliet’]
Step 3: [‘Romeo’, ‘Juliet’]
Final result in one go: [‘Romeo’, ‘Juliet’]
As you saw in the previous two sections, if the parsing problem is simple we might get away with just using an existing parser or some string methods. However, life ain’t always that easy. How do we go about parsing a complex text file?
with open(”) as file:
file_contents = ()
print(file_contents)
Sample text
A selection of students from Riverdale High and Hogwarts took part in a quiz.
Below is a record of their scores.
School = Riverdale High
Grade = 1
Student number, Name
0, Phoebe
1, Rachel
Student number, Score
0, 3
1, 7
Grade = 2
0, Angela
1, Tristan
2, Aurora
0, 6
1, 3
2, 9
School = Hogwarts
0, Ginny
1, Luna
0, 8
0, Harry
1, Hermione
0, 5
1, 10
Grade = 3
0, Fred
1, George
0, 0
1, 0
That’s a pretty complex input file! Phew! The data it contains is pretty simple though as you can see below:
Name Score
School Grade Student number
Hogwarts 1 0 Ginny 8
1 Luna 7
2 0 Harry 5
1 Hermione 10
3 0 Fred 0
1 George 0
Riverdale High 1 0 Phoebe 3
1 Rachel 7
2 0 Angela 6
1 Tristan 3
2 Aurora 9
The sample text looks similar to a CSV in that it uses commas to separate out some information. There is a title and some metadata at the top of the file. There are five variables: School, Grade, Student number, Name and Score. School, Grade and Student number are keys. Name and Score are fields. For a given School, Grade, Student number there is a Name and a Score. In other words, School, Grade, and Student Number together form a compound key.
The data is given in a hierarchical format. First, a School is declared, then a Grade. This is followed by two tables providing Name and Score for each Student number. Then Grade is incremented. This is followed by another set of tables. Then the pattern repeats for another School. Note that the number of students in a Grade or the number of classes in a school are not constant, which adds a bit of complexity to the file. This is just a small dataset. You can easily imagine this being a massive file with lots of schools, grades and students.
It goes without saying that the data format is exceptionally poor. I have done this on purpose. If you understand how to handle this, then it will be a lot easier for you to master simpler formats. It’s not unusual to come across files like this if have to deal with a lot of legacy systems. In the past when those systems were being designed, it may not have been a requirement for the data output to be machine readable. However, nowadays everything needs to be machine-readable!
We will need the Regular expressions module and the pandas package. So, let’s go ahead and import those.
12
import re
In the last step, we imported re, the regular expressions module. What is it though?
Well, earlier on we saw how to use the string methods to extract data from text. However, when parsing complex files, we can end up with a lot of stripping, splitting, slicing and whatnot and the code can end up looking pretty unreadable. That is where regular expressions come in. It is essentially a tiny language embedded inside Python that allows you to say what string pattern you are looking for. It is not unique to Python by the way (treehouse).
You do not need to become a master at regular expressions. However, some basic knowledge of regexes can be very handy in your programming career. I will only teach you the very basics in this article, but I encourage you to do some further study. I also recommend regexper for visualising regular expressions. regex101 is another excellent resource for testing your regular expression.
We are going to need three regexes. The first one, as shown below, will help us to identify the school. Its regular expression is School = (. *)\n. What do the symbols mean?. : Any character
*: 0 or more of the preceding expression
(. *): Placing part of a regular expression inside parentheses allows you to group that part of the expression. So, in this case, the grouped part is the name of the school.
\n: The newline character at the end of the line
We then need a regular expression for the grade. Its regular expression is Grade = (\d+)\n. This is very similar to the previous expression. The new symbols are:
\d: Short for [0-9]
+: 1 or more of the preceding expression
Finally, we need a regular expression to identify whether the table that follows the expression in the text file is a table of names or scores. Its regular expression is (Name|Score). The new symbol is:
|: Logical or statement, so in this case, it means ‘Name’ or ‘Score. ’
We also need to understand a few regular expression functions:
mpile(pattern): Compile a regular expression pattern into a RegexObject.
A RegexObject has the following methods:
match(string): If the beginning of string matches the regular expression, return a corresponding MatchObject instance. Otherwise, return None.
search(string): Scan through string looking for a location where this regular expression produced a match, and return a corresponding MatchObject instance. Return None if there are no matches.
A MatchObject always has a boolean value of True. Thus, we can just use an if statement to identify positive matches. It has the following method:
group(): Returns one or more subgroups of the match. Groups can be referred to by their index. group(0) returns the entire match. group(1) returns the first parenthesized subgroup and so on. The regular expressions we used only have a single group. Easy! However, what if there were multiple groups? It would get hard to remember which number a group belongs to. A Python specific extension allows us to name the groups and refer to them by their name instead. We can specify a name within a parenthesized group (… ) like so: (? P… ).
Let us first define all the regular expressions. Be sure to use raw strings for regex, i. e., use the subscript r before each pattern.
1234567
# set up regular expressions
# use to visualise these if required
rx_dict = {
‘school’: mpile(r’School = (? P. *)\n’),
‘grade’: mpile(r’Grade = (? P\d+)\n’),
‘name_score’: mpile(r'(? PName|Score)’), }
Then, we can define a function that checks for regex matches.
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def _parse_line(line):
“””
Do a regex search against all defined regexes and
return the key and match result of the first matching regex
for key, rx in ():
match = (line)
if match:
return key, match
# if there are no matches
return None, None
Finally, for the main event, we have the file parser function. It is quite big, but the comments in the code should hopefully help you understand the logic.
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def parse_file(filepath):
Parse text at given filepath
Parameters
———-
filepath: str
Filepath for file_object to be parsed
Returns
——-
data: Frame
Parsed data
data = [] # create an empty list to collect the data
# open the file and read through it line by line
with open(filepath, ‘r’) as file_object:
line = adline()
while line:
# at each line check for a match with a regex
key, match = _parse_line(line)
# extract school name
if key == ‘school’:
school = (‘school’)
# extract grade
if key == ‘grade’:
grade = (‘grade’)
grade = int(grade)
# identify a table header
if key == ‘name_score’:
# extract type of table, i. e., Name or Score
value_type = (‘name_score’)
# read each line of the table until a blank line
while ():
# extract number and value
number, value = ()(‘, ‘)
value = ()
# create a dictionary containing this row of data
row = {
‘School’: school,
‘Grade’: grade,
‘Student number’: number,
value_type: value}
# append the dictionary to the data list
(row)
# create a pandas DataFrame from the list of dicts
data = Frame(data)
# set the School, Grade, and Student number as the index
t_index([‘School’, ‘Grade’, ‘Student number’], inplace=True)
# consolidate df to remove nans
data = oupby()()
# upgrade Score from float to integer
data = (_numeric, errors=’ignore’)
return data
We can use our parser on our sample text like so:
1234
if __name__ == ‘__main__’:
filepath = ”
data = parse(filepath)
print(data)
This is all well and good, and you can see by comparing the input and output by eye that the parser is working correctly. However, the best practice is to always write unittests to make sure your code is doing what you intended it to do. Whenever you write a parser, please ensure that it’s well tested. I have gotten into trouble with my colleagues for using parsers without testing before. Eeek! It’s also worth noting that this does not necessarily need to be the last step. Indeed, lots of programmers preach about Test Driven Development. I have not included a test suite here as I wanted to keep this tutorial concise.
I have been parsing text files for a year and perfected my method over time. Even so, I did some additional research to find out if there was a better solution. Indeed, I owe thanks to various community members who advised me on optimising my code. The community also offered some different ways of parsing the text file. Some of them were clever and exciting. My personal favourite was this one. I presented my sample problem and solution at the forums below:
Reddit post
Stackoverflow post
Code review post
If your problem is even more complex and regular expressions don’t cut it, then the next step would be to consider parsing libraries. Here are a couple of places to start with:
Parsing Horrible Things with Python:
A PyCon lecture by Erik Rose looking at the pros and cons of various parsing libraries.
Parsing in Python: Tools and Libraries:
Tools and libraries that allow you to create parsers when regular expressions are not enough.
Now that you understand how difficult and annoying it can be to parse text files, if you ever find yourselves in the privileged position of choosing a file format, choose it with care. Here are Stanford’s best practices for file formats.
I’d be lying if I said I was delighted with my parsing method, but I’m not aware of another way, of quickly parsing a text file, that is as beginner friendly as what I’ve presented above. If you know of a better solution, I’m all ears! I have hopefully given you a good starting point for parsing a file in Python! I spent a couple of months trying lots of different methods and writing some insanely unreadable code before I finally figured it out and now I don’t think twice about parsing a file. So, I hope I have been able to save you some time. Have fun parsing text with python!
What is data parsing? - ScrapingBee

What is data parsing? – ScrapingBee


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.

Frequently Asked Questions about parsing in python

How do you parse in Python?

Parsing text in complex format using regular expressionsStep 1: Understand the input format. 123. … Step 2: Import the required packages. We will need the Regular expressions module and the pandas package. … Step 3: Define regular expressions. … Step 4: Write a line parser. … Step 5: Write a file parser. … Step 6: Test the parser.Jan 7, 2018

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

Why parse is used in Python?

The parser module provides an interface to Python’s internal parser and byte-code compiler. The primary purpose for this interface is to allow Python code to edit the parse tree of a Python expression and create executable code from this.

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