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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.

Fusion

  • 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.

Ironman and CA handshake

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

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