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SQLAlchemy Climate Analysis With Flask

  • Objective

    Conduct a climate analysis of Honolulu, Hawaii, using Python and SQLAlchemy, and develop a Flask API to serve the results.

    Methods

     Utilized SQLAlchemy to connect to SQLite databases and perform ORM queries.  Analyzed precipitation and temperature data using Pandas and Matplotlib.  Developed a Flask API with endpoints for precipitation, stations, temperature observations, and temperature statistics.

    Findings

     Identified the most recent date and analyzed the previous 12 months of precipitation data.   Determined the most active weather stations and calculated temperature statistics for the most active station.   Created visualizations including a precipitation plot and a temperature histogram.

    Impact

     Provided valuable climate insights for planning a vacation in Honolulu.  Delivered an API that enables users to access climate data programmatically, enhancing data accessibility and utility.

    Challenges

     Managing and querying large datasets efficiently.   Ensuring the Flask API was robust and returned accurate data.

    Tools Used

     Terminal  Jupyter lab  Anaconda  Python  Flask  SQLAlchemy  Pandas