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v# Climate Data Analysis and API Design

Climate Data Analysis and APIs

Tools, Languages, and Libraries Used

  • Python
  • SQLAlchemy
  • Pandas
  • Matplotlib
  • Flask

Analyzing and Exploration

Setup

  • Connect to the SQLite database using SQLAlchemy.
  • Reflect tables into classes using SQLAlchemy automap_base().
  • Create a SQLAlchemy session to link Python to the database.

Precipitation Analysis

  • Find the most recent date in the dataset.
  • Query the previous 12 months of precipitation data based on the most recent date.
    • Select only the "date" and "prcp" values.
    • Load query results into a Pandas DataFrame.
    • Sort DataFrame values by "date".
  • Plot the results using DataFrame plot method.
  • Print summary statistics for the precipitation data.

Station Analysis

  • Design a query to calculate the total number of stations in the dataset.
  • Find the most-active station (station with the most rows).
    • List stations and observation counts in descending order.
    • Query lowest, highest, and average temperatures for the most-active station.
  • Query the previous 12 months of temperature observation (TOBS) data for the most-active station.
  • Plot the results as a histogram with bins=12.

Close Session

  • Close SQLAlchemy session at the end of the notebook.

Desigining the Flask App

Routes

  • /: Homepage, listing all available routes.
  • /api/v1.0/precipitation: Convert precipitation analysis results to a dictionary and return JSON representation.
  • /api/v1.0/stations: Return a JSON list of stations from the dataset.
  • /api/v1.0/tobs: Query temperature observations of the most-active station for the previous year and return JSON list.
  • /api/v1.0/ and /api/v1.0//: Calculate TMIN, TAVG, and TMAX for specified start or start-end range and return JSON list.