- Use Flask to create your routes.
Routes
/
- Home page.
- List all routes that are available.
/api/v1.0/precipitation
- Convert the query results to a dictionary using
date
as the key andprcp
as the value. - Return the JSON representation of your dictionary.
- Convert the query results to a dictionary using
/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 last year of data.
- Return a JSON list of temperature observations (TOBS) for the previous year.
/api/v1.0/
and/api/v1.0/
/ - Return a JSON list of the minimum temperature, the average temperature, and the max temperature for a given start or start-end range.
- When given the start only, calculate
TMIN
,TAVG
, andTMAX
for all dates greater than and equal to the start date. - When given the start and the end date, calculate the
TMIN
,TAVG
, andTMAX
for dates between the start and end date inclusive.
Hints
- You will need to join the station and measurement tables for some of the queries.
- Use Flask
jsonify
to convert your API data into a valid JSON response object.
Temperature Analysis I
- Hawaii is reputed to enjoy mild weather all year. Is there a meaningful difference between the temperature in, for example, June and December?
- You may either use SQLAlchemy or pandas’s
read_csv()
to perform this portion. - Identify the average temperature in June at all stations across all available years in the dataset. Do the same for December temperature.
- Use the t-test to determine whether the difference in the means, if any, is statistically significant. Will you use a paired t-test, or an unpaired t-test? Why?
Temperature Analysis II
- The starter notebook contains a function called
calc_temps
that will accept a start date and end date in the format%Y-%m-%d
. The function will return the minimum, average, and maximum temperatures for that range of dates. - Use the
calc_temps
function to calculate the min, avg, and max temperatures for your trip using the matching dates from the previous year (i.e., use “2017-01-01” if your trip start date was “2018-01-01”). - Plot the min, avg, and max temperature from your previous query as a bar chart.
- Use the average temperature as the bar height.
- Use the peak-to-peak (TMAX-TMIN) value as the y error bar (YERR).
Daily Rainfall Average
- Calculate the rainfall per weather station using the previous year’s matching dates.
- Calculate the daily normals. Normals are the averages for the min, avg, and max temperatures.
- You are provided with a function called
daily_normals
that will calculate the daily normals for a specific date. This date string will be in the format%m-%d
. Be sure to use all historic TOBS that match that date string. - Create a list of dates for your trip in the format
%m-%d
. Use thedaily_normals
function to calculate the normals for each date string and append the results to a list. - Load the list of daily normals into a Pandas DataFrame and set the index equal to the date.
- Use Pandas to plot an area plot (
stacked=False
) for the daily normals.