Скачать или смотреть Resolving scrapy Mathematical Conditions with Non-English Numbers

Learn how to handle non-numeric characters in Scrapy to assess product discounts and ratings effectively.
---
This video is based on the question https://stackoverflow.com/q/63486192/ asked by the user 'Shima Masaeli' ( https://stackoverflow.com/u/13483414/ ) and on the answer https://stackoverflow.com/a/63486755/ provided by the user 'Ryan' ( https://stackoverflow.com/u/4876493/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: scrapy: adding some mathematical conditions

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Resolving scrapy Mathematical Conditions with Non-English Numbers

Building a web scraper with Python's Scrapy framework can be quite liberating, allowing you to extract valuable data from the web. However, sometimes we run into issues, especially when dealing with non-English formatted numbers. If you are trying to scrape products with specific conditions such as minimum star ratings and discounts, you might find yourself facing some frustrating hurdles. Here’s how to efficiently handle this challenge.

The Problem at Hand

Imagine you are working with a Scrapy spider that scrapes products from a website. Your goal is to check:

If a product's star rating is 3.5 or higher.

If the discount percentage is 10% or more.

After scraping, you wish to extract certain elements related to the product. Encountering non-English numerical formats and extra characters like percentage symbols (e.g., %), however, can lead to errors like the following:

[[See Video to Reveal this Text or Code Snippet]]

This problem occurs because Python can't convert strings with non-numerical characters into integers directly. Let's dive into a solution to effectively handle these issues.

A Step-by-Step Solution

1. Understanding the Source of Errors

The original lines in your code that attempt to convert ratings and discounts to integers are likely causing the problem:

[[See Video to Reveal this Text or Code Snippet]]

Using int() directly on a string that includes symbols like % will throw an error.

2. Clean Up the Data

To resolve this issue, you need to make sure that any strings passed to the int() function are cleaned up. This means removing any non-numeric characters.

Updated Code

Replace your existing lines for scraping and converting the data with the following:

[[See Video to Reveal this Text or Code Snippet]]

Explanation of the Changes

.get(): Retrieves the text content from the specified XPath.

.strip(): Removes any leading or trailing whitespace from the scraped data.

.replace('٪', ''): Removes the Persian percentage symbol from the string.

.replace(',', ''): Ensures that any commas used in large numbers (e.g., 1,000) are also removed for correct conversion.

3. Handling Potential Errors

Even after cleaning the strings, there might be instances where no value is fetched (i.e., NoneType). To ensure robustness, modify your code to include checks:

[[See Video to Reveal this Text or Code Snippet]]

This example uses a ternary conditional expression to handle situations where the XPath might return None.

4. Conclusion

With these adjustments, you should be able to prevent errors when converting scraped values into integers. By ensuring that your data is clean and well-formatted, you can seamlessly apply conditions to extract the best products based on your criteria.

Remember, dealing with different languages and formats in web scraping can always be a bit tricky, but with a little careful coding and cleaning, you can overcome these hurdles effectively!

Happy Scraping!