Whatever your project size is, we will handle it well with all the standards fulfilled! We are here to give 100% satisfaction.
For job seekers, please visit our Career Page or send your resume to hr@actowizsolutions.com
This blog shows important Python libraries for scraping and processing information like pricing, IBAN, and date. It is difficult to process this data type; however, with proper libraries, you can easily do that.
It might look like an easy job to parse currencies, dates, and IBANs. However, think about all the different locales, combinations, and formats. It parses German or USA format dates, scraping decimal values of prices in USD, EUR, or Rupees. An easy job can initially can get very messy!
Fortunately, there are Python libraries that we can utilize rather than coding the rules ourselves.
It is part of preparing data, which is vital for all Machine Learning applications.
Suggested library — dateparser
Here, we parse a date in the German format; we could provide a hint of the library regarding the language for date formats:
d = dateparser.parse('2.Mai 2020', languages=['de'])
The results look great:
2020-05-02 00:00:00
We could try and pass all invalid dates to a library:
d = dateparser.parse('2.Abc 2020', languages=['de'])
Here, we would get such results that are ideal:
None
It’s time to parse the date without providing any hint about a language:
d = dateparser.parse('2020.12.8')
This works well also:
2020-12-08 00:00:00
Suggested library — price-parser
This could get more complicated with pricing parsing, just think about different currencies as well as different ways about how the pricing is written.
Let’s take a test using EUR price as well as comma like a decimal extractor:
p = Price.fromstring("-114,47 €")
The result - we find a number as well as currency symbol:
Price(amount=Decimal('-114.47'), currency='€')
Parse pricing in Russian rubles:
p = Price.fromstring("3 500 руб")
Price(amount=Decimal('11499'), currency='Rs')
Parse pricing in US dollars:
p = Price.fromstring("$1499.99")
Price(amount=Decimal('1499.99'), currency='$')
One more example, without any currency symbol, however with comma like a thousand extractor:
p = Price.fromstring("199,999.00")
The amount gets parsed appropriately:
Price(amount=Decimal('199999.00'), currency=None)
In case, we utilize the point like a decimal extractor:
p = Price.fromstring("199.999,00")
The results are correct also:
Price(amount=Decimal('199999.00'), currency=None)
Suggested library — schwifty
Test German IBAN number:
i = IBAN('DE89 3704 0044 0532 0130 00')
Country(alpha_2='DE', alpha_3='DEU', name='Germany', numeric='276', official_name='Federal Republic of Germany')
Test invalid IBAN:
try: i = IBAN('DE89 3704') print(i.country) except Exception as e: print(e)
Result like it might be anticipated in the case:
Invalid IBAN length
The step of data preparation is among the crucial steps in Machine Learning. Appropriate use of accessible libraries permits streamlining data processing. This blog teaches you how to procedure dates, currencies, and IBANs using web scraping services. For more details, contact Actowiz Solutions now!
Automated price monitoring revolutionizes e-commerce by enabling dynamic pricing, and data-driven decisions to enhance profitability and market competitiveness.
This blog shows how can real estate data scraping help collect comprehensive market data? Discover strategies, tools, and solutions to gather property insights.
This report explores women's fashion trends and pricing strategies in luxury clothing by analyzing data extracted from Gucci's website.
This report explores mastering web scraping Zomato datasets to generate insightful visualizations and perform in-depth analysis for data-driven decisions.
Leverage tyre pricing and market intelligence to gain a competitive edge, optimize strategies, and drive growth in the global tire industry.
Explore how data scraping optimizes ferry schedules and cruise prices, providing actionable insights for businesses to enhance offerings and pricing strategies.
Crumbl is growing sweeter with every bite! Check out thier recently opened locations and see how they are bringing their famous cookies closer to you with our web scraping services. Have you visited one yet
Web scraping enables businesses to access and analyze detailed product specifications from Costco, including prices, descriptions, availability, and reviews. By leveraging this data, companies can gain insights into customer preferences, monitor competitor pricing, and optimize their product offerings for better market performance.