Project-II-Data-Pipeline

INTRODUCTION:

Brief Overview:

• During this project I have looked at Michelin Star restaurants and their locations.
• The second idea was to donwload and some Web-Scraping about the average Weather in each city furing the year.

Where Did We Get Our Information?:

• Michelin star Restaurant: LINK
• Average weather during the year: LINK

Questions to be answered

Primary Questions:

• What relation does the weather have with Michelin star restaurants & spicy food?

Secondary Questions:

• What kind of Michelin stars exist?
• Which are the countries / Cities that have the most?

Click HERE to go to the presentation.

Cleaning and Merging 🧹🤝

Michelin Restaurant Data (michelin_my_maps.csv)

• Drop Unnecessary Columns: Columns like "Url", "WebsiteUrl", and "PhoneNumber" are dropped.

• Create Combined Column:: Combine "Latitude" and "Longitude" into a single column named "Combined".

• Filter Awards: Only rows with specific Michelin awards like 1, 2, 3 Stars, Bib Gourmand, and Green Star are kept.

• Standardize Price: The 'Price' column is standardized to use dollar signs instead of various currency symbols.

• Split Location: 'Location' is split into two new columns: City and Country. Country names are also standardized.

Weather Data (Web-scraped from Wikipedia)

• Load data: Data is loaded into separate DataFrames for each continent.

• Drop Ref. Column: The reference column is dropped from each continent's DataFrame.

• Add Continent Column A new column indicating the continent is added to each DataFrame.

• Merge DataFrames All continent DataFrames are merged into a single DataFrame.

• Extract Temperatures Temperature data in the columns for each month is extracted and converted to float.

Merging Michelin and Weather Data

• Country-Based Merge The Michelin and weather data are first merged based on the 'Country' column.

• City-Based Filte The merged DataFrame is filtered to match cities.

• Select Relevant Columns Only the relevant columns are kept in the merged DataFrame.