Design a query to retrieve the last 12 months of precipitation data.

Select only the date and prcp values.

Load the query results into a Pandas DataFrame and set the index to the date column.

Sort the DataFrame values by date.

Plot the results using the DataFrame plot method.

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

Design a query to calculate the total number of stations.

Design a query to find the most active stations.

Design a query to retrieve the last 12 months of temperature observation data (tobs).

Query and 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).

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.

/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 for the dates and temperature observations from a year from the last data point.
• 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.