Climate Analysis and ExplorationGitHub

Weather Data Visualization Dashboard (Latitude)

Weather Queries

Precipitation Analysis

  • 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.

Station Analysis

  • 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).

Temperature Analysis

  • 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).

Other Analysis

  • 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.

Flask Climate App

Routes

  • /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.