• Use Flask to create your 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 TMINTAVG, and TMAX for all dates greater than and equal to the start date.
    • When given the start and the end date, calculate the TMINTAVG, and TMAX for dates between the start and end date inclusive.


  • 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.

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