I decided to treat myself to a long holiday vacation in Honolulu, Hawaii! To help with my trip planning, I need to do some climate analysis on the area. The following outlines what I need to do.

I used Python and SQLAlchemy to do basic climate analysis and data exploration of my climate database. All of the following analysis were completed using SQLAlchemy ORM queries, Pandas, and Matplotlib.

• I used the provided climate_starter.ipynb and hawaii.sqlite files to complete my climate analysis and data exploration.

• Used SQLAlchemy create_engine to connect to sqlite database.

• Used SQLAlchemy automap_base() to reflect the tables into classes and saved a reference to those classes called Station and Measurement.

• Linked Python to the database by creating an SQLAlchemy session.

• Important Made sure to close out the session at the end of the noteobok.

• Started by finding the most recent date in the data set.

• Using this date, retrieved the last 12 months of precipitation data by querying the 12 preceding months of 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.

  

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

• Designed a query to calculate the total number of stations in the dataset.

• Designed a query to find the most active stations (i.e. which stations have the most rows?).

  • Listed the stations and observation counts in descending order.

  • Found what station id has the highest number of observations

  • Using the most active station id, calculated the lowest, highest, and average temperature.

• Designed a query to retrieve the last 12 months of temperature observation data (TOBS).

  • Filtered by the station with the highest number of observations.

  • Queried the last 12 months of temperature observation data for this station.

  • Plotted the results as a histogram with bins=12.

    

• Closed my session.

Now that the initial analysis is completed, I designed a Flask API based on the queries that I have just developed.

• Used Flask to create my 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 my dictionary.

• /api/v1.0/stations

  • Returned a JSON list of stations from the dataset.

• /api/v1.0/tobs

  • Queried 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/<start> and /api/v1.0/<start>/<end>

  • 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, 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.

• I needed to join the station and measurement tables for some of the queries.

• Used Flask jsonify to convert my API data into a valid JSON response object.

• 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 to perform this portion.

  • Converted the date column format from string to datetime.

  • Set the date column as the DataFrame index

  • Dropped the date column

• 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, if any, is statistically significant.

• I was looking to take a trip from August 1st to August 7th of this year, but was worried that the weather would be less than ideal. Using historical data in the dataset I found out what the temperature has previously looked like.

• The Temperature Analysis II contains a function called calc_temps that accepts a start date and end date in the format %Y-%m-%d. The function returns 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 my trip using the matching dates from a previous year (i.e., used "2017-08-01").

• Plotted the min, avg, and max temperature from my previous query as a bar chart.

  • Used "Trip Avg Temp" as the title.

  • Used the average temperature as the bar height (y value).

  • Used the peak-to-peak (TMAX-TMIN) value as the y error bar (YERR).

  

• Now that I have an idea of the temperature, let's check to see what the rainfall has been, I wouldn't want it to rain the whole time!

• Calculated the rainfall per weather station using the previous year's matching dates.

  • Sorted this in descending order by precipitation amount and list the station, name, latitude, longitude, and elevation.

• Calculated the daily normals for the duration of my trip. Normals were the averages for the min, avg, and max temperatures. Function called daily_normals calculates the daily normals for a specific date. This date string is in the format %m-%d. Used all historic TOBS that match that date string.

  • Set the start and end date of the trip.

  • Used the date to create a range of dates.

  • Stripped off the year and save a list of strings 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 called normals.

• 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.

• Closed out my session.

  

Menne, M.J., I. Durre, R.S. Vose, B.E. Gleason, and T.G. Houston, 2012: An overview of the Global Historical Climatology Network-Daily Database. Journal of Atmospheric and Oceanic Technology, 29, 897-910, https://doi.org/10.1175/JTECH-D-11-00103.1