4-climate-analysis-SQLAlchemy

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.