Utilized MongoDB and PyMongo to import, query, and update a database of food establishments.  Converted query results into Pandas DataFrames for detailed analysis.  Performed various data manipulations and analyses, including filtering by hygiene scores and location, and updating data types.

 Identified establishments with specific hygiene scores and high rating values in London.   Found the top establishments with a rating of 5 and lowest hygiene scores near the new restaurant "Penang Flavours."  Determined local authority areas with the highest number of establishments having a hygiene score of 0.

 Provided valuable insights for "Eat Safe, Love" magazine, helping to guide their journalists and food critics in focusing their articles on noteworthy establishments.  Demonstrated effective use of NoSQL databases for real-world data analysis and decision-making.

 Managing and querying a large dataset efficiently.  Ensuring data integrity and accurate data type conversions.

 Terminal  Jupyter lab  Anaconda  Pandas  Python  MongoDB