Jupyter Notebook Database Connection

-Uses the SQLAlchemy create_engine() function to connect to SQLite database

-Uses the SQLAlchemy automap_base() function to reflect tables into classes

-Saves references to the classes named station and measurement

-Links Python to the database by creating a SQLAlchemy session

-Closes session at the end of notebook

Precipitation Analysis

-Creates a query that finds the most recent date in the dataset (8/23/2017)

-Creates a query that collects only the date and precipitation for the last year of data without passing the date as a variable

-Saves the query results to a Pandas DataFrame to create date and precipitation columns

-Sorts the DataFrame by date

-Plots the results by using the DataFrame plot method with date as the x and precipitation as the y variables

-Uses Pandas to print the summary statistics for the precipitation data

Station Analysis

-Designs a query that correctly finds the number of stations in the dataset (9)

-Designs a query that correctly lists the stations and observation counts in descending order and finds the most active station (USC00519281)

-Designs a query that correctly finds the min, max, and average temperatures for the most active station (USC00519281)

-Designs a query to get the previous 12 months of temperature observation (TOBS) data that filters by the station that has the greatest number of observations

-Saves the query results to a Pandas DataFrame

-Correctly plots a histogram with bins=12 for the last year of data using tobs as the column to count

API SQLite Connection & Landing Page

-Correctly generates the engine to the correct sqlite file

-Uses automap_base() and reflect the database schema

-Correctly saves references to the tables in the sqlite file (measurement and station)

-Correctly creates and binds the session between the python app and database

-Displays the available routes on the landing page

API Static Routes

-Includes a precipitation route that:

 -Returns json with the date as the key and the value as the precipitation

 -Only returns the jsonified precipitation data for the last year in the database

-Includes a stations route that:

 -Returns jsonified data of all of the stations in the database

-Includes a tobs route that:

 -Returns jsonified data for the most active station (USC00519281)

 -Only returns the jsonified data for the last year of data

API Dynamic Route

-Includes a start route that:

 -Accepts the start date as a parameter from the URL

 -Returns the min, max, and average temperatures calculated from the given start date to the end of the dataset

-Includes a start/end route that:

 -Accepts the start and end dates as parameters from the URL

 -Returns the min, max, and average temperatures calculated from the given start date to the given end date

Coding Conventions and Formatting

-Places imports at the top of the file, just after any module comments and docstrings, and before module globals and constants

-Names functions and variables with lowercase characters, with words separated by underscores

-Follows DRY (Don't Repeat Yourself) principles, creating maintainable and reusable code

-Uses concise logic and creative engineering where possible

References

Menne, M.J., I. Durre, R.S. Vose, B.E. Gleason, and T.G. Houston, 2012: An overview of the Global Historical Climatology Network-Daily Database. Journal of Atmospheric and Oceanic Technology, 29, 897-910, https://journals.ametsoc.org/view/journals/atot/29/7/jtech-d-11-00103_1.xml Links to an external site.