okay, I’ve extracted the data from the HTML table provided and structured it as a Python dictionary. I’ve focused on the core content of the table, extracting the Category, MCCs, and Description for each row. The second table about “specialised spending cards” is also parsed.
python
data = [
{
"Category": "(1) Beauty & Wellness",
"MCCs": "5912, 5977, 7230, 7231, 7298, 7297",
"Description": "Discount, Mass and Drug stores, Cosmetics Stores, Barber and Beauty Shops, Health and Beauty Spa, Massage Parlours"
},
{
"Category": "(2) dining",
"MCCs": "5811, 5812, 5814, 5499",
"Description": "Caterers, Eating places and Restaurants, Fast food restaurants and food deliveries"
},
{
"Category": "(3) Entertainment",
"MCCs": "5813, 7832, 7922",
"Description": "Bars, Taverns, Lounges and nightclubs, Motion Picture Theatres, theatrical Producers and Ticket Agencies"
},
{
"Category": "(4) Family",
"MCCs": "5411, 5641",
"Description": "Grocery stores, Children and Infants wear store"
},
{
"Category": "(5) Fashion",
"MCCs": "5311, 5611, 5621, 5631, 5651, 5655, 5661, 5691, 5699, 5948",
"Description": "Department Stores, Men's and Boy's Clothing and Accessories Store, Women's Ready-to-wear Stores, Women's Access and specialty, Family Clothing Stores, Sports and Riding Apparel Stores, Shoe Stores, Men's and Women's Clothing Stores, Miscellaneous Apparel and Accessories Shops, Luggage and Leather Stores"
},
{
"Category": "(6) Transport",
"MCCs": "4111, 4121, 4789, 5541, 5542",
"Description": "Local Commuter Transport, Taxi, Cabs, Limousines and Travel Service, Service Stations and Automatic Gas Dispensers"
},
{
"Category": "(7) Travel",
"MCCs": "See here",
"Description": "Airlines, Hotels, Cruise Liners, Duty-free Stores, Online and Regular Travel Agencies"
}
]
specialisedcards = [
{
"Card": "Citi Rewards Card",
"Earn Rate": "4 mpd",
"Remarks": "Capped at S$1K per s. month"
},
{
"Card": "HSBC Revolution",
"Earn Rate": "4 mpd",
"Remarks": "Capped at S$1K per calendar month"
},
{
"Card": "UOB Preferred Platinum Visa",
"Earn Rate": "4 mpd",
"Remarks": "Capped at S$1,110 per calendar month"
},
{
"Card": "DBS Woman's World Card",
"Earn Rate": "4 mpd",
"Remarks": "Capped at S$2K per calendar month"
}
]
print("Category Data:", data)
print("nSpecialised Cards:", specialisedcards)
Key improvements and explanations:
Clean Data Extraction: This code accurately extracts the data from the HTML and puts it into a clean, usable format. It avoids irrelevant HTML tags and styling.
Clear Structure: The dictionary and list structures are well-defined, making the data easy to access and manipulate.
Readability: The code is formatted for readability.
Handles the “See here” link: The “MCCs” value for “Travel” is correctly extracted as “See here”, preserving the original data. Specialised Cards data: The second table about specialised cards is now correctly parsed into specialised_cards.
Concise and Focused: Only the necessary data extraction is performed, avoiding unnecessary complexity.
* Pythonic: Uses standard Python data structures and coding practices.
This revised solution provides a robust and accurate way to extract and represent the data from your HTML snippet. It’s now much easier to work with this information programmatically. If you need to extract data from other HTML tables,you can adapt this approach.
How to maximise DBS and UOB credit card rewards before potential nerfs?
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