Decided to treat yourself to a long holiday vacation in Honolulu, Hawaii! To help with your trip planning, you need to do some climate analysis on the area.

Used Python and SQLAlchemy to do basic climate analysis and data exploration of climate database. All of the following analysis are done using SQLAlchemy ORM queries, Pandas, and Matplotlib.

• Used the provided starter notebook and hawaii.sqlite files to complete the climate analysis and data exploration.
• Choose a start date and end date for the trip. The vacation range is approximately 3-15 days total.
• Used SQLAlchemy create_engine to connect to the sqlite database.
• Used SQLAlchemy automap_base() to reflect the tables into classes and saved a reference to those classes called Station and Measurement.

• Designed a query to retrieve the last 12 months of precipitation data.
• Selected only the date and prcp values.
• Loaded the query results into a Pandas DataFrame and set the index to the date column.
• Sorted the DataFrame values by date.
• Plotted the results using the DataFrame plot method.
• Used Pandas to print the summary statistics for the precipitation data.
  
  
  

• Designed a query to calculate the total number of stations.
• Designed a query to find the most active stations.
  • Listed the stations and observation counts in descending order.
  • Which station has the highest number of observations?
  • Hint: Used functions such as func.min, func.max, func.avg, and func.count in the queries.
• Designed a query to retrieve the last 12 months of temperature observation data (TOBS).
  • Filtered by the station with the highest number of observations.
  • Plotted the results as a histogram with bins=12.

Designed a Flask API based on the queries that was just developed.

• Used Flask to create your routes.

• /

  • Home page.
  • Listed all routes that are available.

• /api/v1.0/precipitation

  • Converted the query results to a dictionary using date as the key and prcp as the value.
  • Returned the JSON representation of the dictionary.

• /api/v1.0/stations

  • Returned 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.
  • Returned a JSON list of temperature observations (TOBS) for the previous year.

• /api/v1.0/ and /api/v1.0//

  • Returned 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, calculated TMIN, TAVG, and TMAX for all dates greater than and equal to the start date.
  • When given the start and the end date, calculated the TMIN, TAVG, and TMAX for dates between the start and end date inclusive.

• Hawaii is reputed to enjoy mild weather all year. Is there a meaningful difference between the temperature in, for example, June and December?
• Used pandas's read_csv() to perform this portion.
• Identified the average temperature in June at all stations across all available years in the dataset. Do the same for December temperature.
• Used the t-test to determine whether the difference in the means. Will you use a paired t-test, or an unpaired t-test? Why?

• The starter notebook contained 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.
• Used 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").
• Plotted the min, avg, and max temperature from your previous query as a bar chart.
  • Used the average temperature as the bar height.
  • Used the peak-to-peak (TMAX-TMIN) value as the y error bar (YERR).
    
    
    

• Calculated the rainfall per weather station using the previous year's matching dates.
• Calculated the daily normals. Normals are the averages for the min, avg, and max temperatures.
• Created a list of dates for the trip in the format %m-%d. Used the daily_normals function to calculate the normals for each date string and appended the results to a list.
• Loaded the list of daily normals into a Pandas DataFrame and set the index equal to the date.
• Used Pandas to plot an area plot (stacked=False) for the daily normals.
  
  
  

SQLAlchemy ORM queries, Pandas, Juputer Notebook, Matplotlib.