I've 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.

To begin, I used Python and SQLAlchemy to do basic climate analysis and data exploration of the climate database.

• The start date of the trip is 01/01/2018 and end date is 14/01/2018.

• This one is a query to retrieve the last 12 months of precipitation data. The query is loaded to a Panadas DataFrame. Then using the DataFrame results, a plot has been created and a the summary statistics also has been added for the precipitation data

  

This one contains some other queries:

• A query to calculate the total number of stations.

• A query to find the most active stations.

  • It lists the stations and observation counts in descending order.

  • Then it finds the station with highest number of observations.

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

  • using filter by the station with the highest number of observations.

  • Then it plots the results as a histogram with bins=12.

    

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

• I used Flask to create my routes.

• /

  • Home page.

  • List all routes that are available.

• /api/v1.0/precipitation

  • It converts the query results to a dictionary using date as the key and prcp as the value.

  • It returns the JSON representation of the dictionary.

• /api/v1.0/stations

  • It returns a JSON list of stations from the dataset.

• /api/v1.0/tobs

  • It queries the dates and temperature observations of the most active station for the last year of data.

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

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

  • They 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 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.

• Hawaii is reputed to enjoy mild weather all year. In this exercise we are trying to test if there is a meaningful difference between the temperature in June and December in Hawaii. As the data of these two months are two independent or unrelated groups, I chose unpaired ttest to run. in this case, the Null hypothesis says that there is no difference between Average Temperatures in June and Dec in Hawaii but the p-value is tremendously less than 5% which means we can reject the null hypthesis. Therefore Temperatures in June and Dec in Hawaii are statistically significant.

• 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.

• I used the calc_temps function to calculate the min, avg, and max temperatures for my trip using the matching dates from the previous year.

• Then added a plot for the min, avg, and max temperature from the previous query as a bar chart.

  • I used the average temperature as the bar height.

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

    

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

• Then I calculated the daily normals. Normals are the averages for the min, avg, and max temperatures.

• The function called daily_normals calculates the daily normals for a specific date. This date string will be in the format %m-%d.

• Then created a list of dates for my trip in the format %m-%d. The daily_normals function is used afterwards to calculate the normals for each date string and append the results to a list.

• Then I loaded the list of daily normals into a Pandas DataFrame and set the index equal to the date.

• Then Pandas is used to plot an area plot (stacked=False) for the daily normals.