SQLAlchemy- STEP 2

• 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 and prcp as the value.
  
  
  • Return the JSON representation of your 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 last year of data.
  
  
  • Return a JSON list of temperature observations (TOBS) 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 max temperature for a given start or start-end range.
  
  
  • When given the start only, calculate TMIN, TAVG, and TMAX for all dates greater than and equal to the start date.
  
  
  • When given the start and the end date, calculate the TMIN, TAVG, and TMAX 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 the daily_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.