I need help in data sanitization problem in tableau - tableau-api

Question

I trying doing the manual sanitization, however I am getting a type mismatch error in performing the calculations.
I also need help in sanitizing the data and getting the insight as per the below instructions:
The column sellerproductcount gives you the count of products in the
form '1-16 of over 100,000 results' , and you can parse out the product count 100,000.
sellerratings - this columns gives you the % and count of positive ratings (e.g. 88% positive
in the last 12 months (118 ratings) ) if parsed correctly
sellerdetails - you can use this text to parse out phone numbers, and email IDs of
merchants, where available, so our team can reach out to them.
businessaddress - this will give you the business locations of the sellers. You can parse them
to identify if a seller is registered in the US , Germany (DE), or China (CN).
Hero Product 1 #ratings and Hero Product 2 #ratings - these 2 columns give you the number of
ratings of the 2 'hero products' or bestselling products of this seller.
I have attached the dataset for the same.
https://docs.google.com/spreadsheets/d/1PSqRCnmFgq7v7RzZaCXXoV0Edp_vM7QO/edit?usp=sharing&ouid=115547990006782902200&rtpof=true&sd=true

Answer 1

Most of this type of data prep can be done with string & RegEx functions like REGEX_MATCH(). Here are a few examples based on the data you shared:
Seller Product Count
INT(REGEXP_EXTRACT([Sellerproductcount], '(\d*,?\d*) results'))
1-16 of over 6,000 results >> 6000
Seller Rating (Percentage)
INT(REGEXP_EXTRACT([Sellerratings], '(\d*)% positive'))
92% positive in the last 12 months (181 ratings) >> 92
Seller Rating (Count)
INT(REGEXP_EXTRACT([Sellerratings], '(\d*) (?:total )?ratings'))
92% positive in the last 12 months (181 ratings) >> 181
Business Country Code
RIGHT([Businessaddress],2)
AM Treptower Park28-30Berlin12435DE >> DE
These examples all have very straightforward patterns that are present in all rows so they can be done pretty easily with one simple calculation. However, something like sellerdetails which is unstructured, inconsistent, and sometimes incomplete will be a bit more of a challenge. You will need to use a couple of different calculations and techniques combined together to find what you are looking for, as well as some manual data prep. Here's an example of how you can pull out email but it won't work for everything:
Email
REGEXP_EXTRACT([Sellerdetails], '([a-zA-Z0-9.!#$%&’*+/=?^_`{|}~-]+#[a-zA-Z0-9-]+(?:\.[a-zA-Z0-9-]+)*)')
Good luck with your data cleaning, I suggest using sites like https://regex101.com/ and https://regexr.com/ to learn more about and help test regular expressions.