sqlalchemy_challenge

Background

A climate analysis on Honolulu, Hawaii.

Objective

Part 1: Analyze and Explore the Climate Data

Use Python and SQLAlchemy to do a basic climate analysis and data exploration of the climate database. Specifically, use SQLAlchemy ORM queries, Pandas, and Matplotlib to do the following:

• Use the SQLAlchemy create_engine() function to connect to the SQLite database.
• Use the SQLAlchemy automap_base() function to reflect the tables into classes, and then save references to the classes named station and measurement.
• Link Python to the database by creating a SQLAlchemy session.
• Perform a precipitation analysis and then a station analysis

Precipitation Analysis

• Find the most recent date in the dataset.
• Using that date, get the previous 12 months of precipitation data by querying the previous 12 months of data.
• Select only the "date" and "prcp" values.
• Load the query results into a Pandas DataFrame, explicitly setting the column names.
• Sort the DataFrame values by "date".
• Plot the results by using the DataFrame plot method

• Use Pandas to print the summary statistics for the precipitation data.

Station Analysis

• Design a query to calculate the total number of stations in the dataset.
• Design a query to find the most-active stations.
• Design a query that calculates the lowest, highest, and average temperatures that filters on the most-active station id found in the previous query.
• Design a query to get the previous 12 months of temperature observation (TOBS) data.

Part 2: Design Climate App

Design a Flask API based on the queries developed above, that:

• Starts at the homepage.
• Lists all the available routes.
• /api/v1.0/precipitation
  • Converts the query results from the precipitation analysis (i.e. retrieve only the last 12 months of data) to a dictionary using date as the key and prcp as the value.
  • Return the JSON representation of the dictionary.
• /api/v1.0/stations
  • Return a JSON list of stations from the dataset.
• /api/v1.0/tobs
  • Query the dates and temperature observations of the most-active station for the previous year of data.
  • Return a JSON list of temperature observations for the previous year.
• /api/v1.0/<start> and /api/v1.0/<start>/<end>
  • Return a JSON list of the minimum temperature, the average temperature, and the maximum temperature for a specified start or start-end range.
  • For a specified start, calculate TMIN, TAVG, and TMAX for all the dates greater than or equal to the start date.
  • For a specified start date and end date, calculate TMIN, TAVG, and TMAX for the dates from the start date to the end date, inclusive.