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parser — dateutil 2.8.2 documentation - Read the Docs

parser — dateutil 2.8.2 documentation – Read the Docs

dateutil
This module offers a generic date/time string parser which is able to parse
most known formats to represent a date and/or time.
This module attempts to be forgiving with regards to unlikely input formats,
returning a datetime object even for dates which are ambiguous. If an element
of a date/time stamp is omitted, the following rules are applied:
If AM or PM is left unspecified, a 24-hour clock is assumed, however, an hour
on a 12-hour clock (0 <= hour <= 12) must be specified if AM or PM is specified. If a time zone is omitted, a timezone-naive datetime is returned. If any other elements are missing, they are taken from the time object passed to the parameter default. If this results in a day number exceeding the valid number of days per month, the value falls back to the end of the month. Additional resources about date/time string formats can be found below: A summary of the international standard date and time notation W3C Date and Time Formats Time Formats (Planetary Rings Node) CPAN ParseDate module Java SimpleDateFormat Class Functions¶ (parserinfo=None, **kwargs)[source]¶ Parse a string in one of the supported formats, using the parserinfo parameters. Parameters: timestr – A string containing a date/time stamp. parserinfo – A parserinfo object containing parameters for the parser. If None, the default arguments to the parserinfo constructor are used. The **kwargs parameter takes the following keyword arguments: default – The default datetime object, if this is a datetime object and not None, elements specified in timestr replace elements in the default object. ignoretz – If set True, time zones in parsed strings are ignored and a naive datetime object is returned. tzinfos – Additional time zone names / aliases which may be present in the string. This argument maps time zone names (and optionally offsets from those time zones) to time zones. This parameter can be a dictionary with timezone aliases mapping time zone names to time zones or a function taking two parameters (tzname and tzoffset) and returning a time zone. The timezones to which the names are mapped can be an integer offset from UTC in seconds or a tzinfo object. >>> from import parse
>>> from import gettz
>>> tzinfos = {“BRST”: -7200, “CST”: gettz(“America/Chicago”)}
>>> parse(“2012-01-19 17:21:00 BRST”, tzinfos=tzinfos)
time(2012, 1, 19, 17, 21, tzinfo=tzoffset(u’BRST’, -7200))
>>> parse(“2012-01-19 17:21:00 CST”, tzinfos=tzinfos)
time(2012, 1, 19, 17, 21,
tzinfo=tzfile(‘/usr/share/zoneinfo/America/Chicago’))
This parameter is ignored if ignoretz is set.
dayfirst – Whether to interpret the first value in an ambiguous 3-integer date
(e. g. 01/05/09) as the day (True) or month (False). If
yearfirst is set to True, this distinguishes between YDM and
YMD. If set to None, this value is retrieved from the current
parserinfo object (which itself defaults to False).
yearfirst – Whether to interpret the first value in an ambiguous 3-integer date
(e. 01/05/09) as the year. If True, the first number is taken to
be the year, otherwise the last number is taken to be the year. If
this is set to None, the value is retrieved from the current
fuzzy – Whether to allow fuzzy parsing, allowing for string like “Today is
January 1, 2047 at 8:21:00AM”.
fuzzy_with_tokens – If True, fuzzy is automatically set to True, and the parser
will return a tuple where the first element is the parsed
time datetimestamp and the second element is
a tuple containing the portions of the string which were ignored:
>>> parse(“Today is January 1, 2047 at 8:21:00AM”, fuzzy_with_tokens=True)
(time(2047, 1, 1, 8, 21), (u’Today is ‘, u’ ‘, u’at ‘))
Returns:Returns a time object or, if the
fuzzy_with_tokens option is True, returns a tuple, the
first element being a time object, the second
a tuple containing the fuzzy tokens.
Raises:
ParserError – Raised for invalid or unknown string formats, if the provided
tzinfo is not in a valid format, or if an invalid date would
be created.
OverflowError – Raised if the parsed date exceeds the largest valid C integer on
your system.
classmethod oparse(dt_str)¶
Parse an ISO-8601 datetime string into a time.
An ISO-8601 datetime string consists of a date portion, followed
optionally by a time portion – the date and time portions are separated
by a single character separator, which is T in the official
standard. Incomplete date formats (such as YYYY-MM) may not be
combined with a time portion.
Supported date formats are:
Common:
YYYY
YYYY-MM or YYYYMM
YYYY-MM-DD or YYYYMMDD
Uncommon:
YYYY-Www or YYYYWww – ISO week (day defaults to 0)
YYYY-Www-D or YYYYWwwD – ISO week and day
The ISO week and day numbering follows the same logic as
().
Supported time formats are:
hh
hh:mm or hhmm
hh:mm:ss or hhmmss
(Up to 6 sub-second digits)
Midnight is a special case for hh, as the standard supports both
00:00 and 24:00 as a representation. The decimal separator can be
either a dot or a comma.
Caution
Support for fractional components other than seconds is part of the
ISO-8601 standard, but is not currently implemented in this parser.
Supported time zone offset formats are:
Z (UTC)
±HH:MM
±HHMM
±HH
Offsets will be represented as objects,
with the exception of UTC, which will be represented as
Time zone offsets equivalent to UTC (such
as +00:00) will also be represented as
Parameters:dt_str – A string or stream containing only an ISO-8601 datetime string
Returns:Returns a time representing the string.
Unspecified components default to their lowest value.
Warning
As of version 2. 7. 0, the strictness of the parser should not be
considered a stable part of the contract. Any valid ISO-8601 string
that parses correctly with the default settings will continue to
parse correctly in future versions, but invalid strings that
currently fail (e. 2017-01-01T00:00+00:00:00) are not
guaranteed to continue failing in future versions if they encode
a valid date.
New in version 2. 0.
Classes¶
class (dayfirst=False, yearfirst=False)[source]¶
Class which handles what inputs are accepted. Subclass this to customize
the language and acceptable values for each parameter.
yearfirst is set to True, this distinguishes between YDM
and YMD. Default is False.
(e. If True, the first number is taken
to be the year, otherwise the last number is taken to be the year.
Default is False.
AMPM = [(‘am’, ‘a’), (‘pm’, ‘p’)]¶
HMS = [(‘h’, ‘hour’, ‘hours’), (‘m’, ‘minute’, ‘minutes’), (‘s’, ‘second’, ‘seconds’)]¶
JUMP = [‘ ‘, ‘. ‘, ‘, ‘, ‘;’, ‘-‘, ‘/’, “‘”, ‘at’, ‘on’, ‘and’, ‘ad’, ‘m’, ‘t’, ‘of’, ‘st’, ‘nd’, ‘rd’, ‘th’]¶
MONTHS = [(‘Jan’, ‘January’), (‘Feb’, ‘February’), (‘Mar’, ‘March’), (‘Apr’, ‘April’), (‘May’, ‘May’), (‘Jun’, ‘June’), (‘Jul’, ‘July’), (‘Aug’, ‘August’), (‘Sep’, ‘Sept’, ‘September’), (‘Oct’, ‘October’), (‘Nov’, ‘November’), (‘Dec’, ‘December’)]¶
PERTAIN = [‘of’]¶
TZOFFSET = {}¶
UTCZONE = [‘UTC’, ‘GMT’, ‘Z’, ‘z’]¶
WEEKDAYS = [(‘Mon’, ‘Monday’), (‘Tue’, ‘Tuesday’), (‘Wed’, ‘Wednesday’), (‘Thu’, ‘Thursday’), (‘Fri’, ‘Friday’), (‘Sat’, ‘Saturday’), (‘Sun’, ‘Sunday’)]¶
ampm(name)[source]¶
convertyear(year, century_specified=False)[source]¶
Converts two-digit years to year within [-50, 49]
range of self. _year (current local time)
hms(name)[source]¶
jump(name)[source]¶
month(name)[source]¶
pertain(name)[source]¶
tzoffset(name)[source]¶
utczone(name)[source]¶
validate(res)[source]¶
weekday(name)[source]¶
Warnings and Exceptions¶
class [source]¶
Exception subclass used for any failure to parse a datetime string.
This is a subclass of ValueError, and should be raised any time
earlier versions of dateutil would have raised ValueError.
New in version 2. 8. 1.
Raised when the parser finds a timezone it cannot parse into a tzinfo.
New in version 2. 0.
Python Parser | Working of Python Parse with different Examples

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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
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:
/

(. *)

/
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 parser parse

What does parser parse do in Python?

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.

What is a data parser?

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 Dateutil parser?

This module offers a generic date/time string parser which is able to parse most known formats to represent a date and/or time. This module attempts to be forgiving with regards to unlikely input formats, returning a datetime object even for dates which are ambiguous.

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