search-restaurants

To find restaurants in a specific city using a pandas dataset, this skill loads bundled data, normalizes city names, and performs a case-insensitive search to return matching eateries. It outputs a table containing Name, Average Cost, Cuisines, Aggregate Rating, and City.

When you search for restaurants by city, the output is a structured table featuring the restaurant Name, Average Cost, Cuisines, Aggregate Rating, and City. This data is retrieved from a bundled dataset to assist with travel planning and dining decisions.

Yes, you can perform a case-insensitive restaurant lookup for travel planning. The workflow normalizes city names and matches them against a bundled dataset, ensuring you retrieve available dining options regardless of capitalization in your query.

No, this restaurant search does not require an external data source or API. It operates entirely on a bundled dataset using a small Pandas-based workflow to load data and match city names for dining recommendations.

The limitation of using a bundled dataset for city-based restaurant search is that results are restricted to the pre-packaged data. You cannot retrieve real-time availability, dynamic pricing, or eateries outside the dataset's existing city entries.