SQLAlchemy - Surfs Up!

Languages and libraries used:

Climate Analysis and Exploration

Jupyter notebook, climate.ipynb and hawaii.sqlite files were used to complete the climate analysis and data exploration.
SQLAlchemy create_engine connects to the sqlite database.
SQLAlchemy automap_base() reflects the tables into classes and saves a reference to those classes called Station and Measurement.
Linked Python to the database by creating an SQLAlchemy session.

Found the most recent date in the data set.
- Most recent date: 2017, 08, 23
Used this date to retrieve 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.
Used Pandas to print the summary statistics for the precipitation data.

count 2021.000000
mean 0.177279
std 0.461190
min 0.000000
25% 0.000000
50% 0.020000
75% 0.130000
max 6.700000

Queried to calculate the total number of stations in the dataset.
Queried to find the most active stations.
- Listed the stations and observation counts in descending order.
- Determined which station id had the highest number of observations.
  - Station with most observations: USC00519281
- Used the most active station id, calculated the lowest, highest, and average temperature.
  
  count 352.000000
  mean 73.107955
  std 4.733315
  min 59.000000
  25% 70.000000
  50% 74.000000
  75% 77.000000
  max 83.000000
Queried 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.

Designed a Flask API based on the previously developed queries.

/ Home page
- Available Hawaii API Routes:
  - /api/v1.0/precipitation
  - /api/v1.0/stations
  - /api/v1.0/tobs
  - /api/v1.0/<start>
  - /api/v1.0/<start>/<end>
/api/v1.0/stations
- 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
- 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.
  - With the start only, calculated TMIN, TAVG, and TMAX for all dates greater than and equal to the start date.
  - With the start and the end date, calculated the TMIN, TAVG, and TMAX for dates between the start and end date inclusive.

Determined if there is a meaningful difference between the temperature in June and December in Hawaii.

Identified the average temperature in June and December at all stations across all available years in the dataset.
- June: 74.94411764705882
- December: 71.04152933421226
An independent t-test was performed to determine whether the difference in the means, if any, is statistically significant.
- Ttest_indResult(statistic=31.60372399000329, pvalue=3.9025129038616655e-191)
- Null Hypothesis: Temperatures in Hawaii in June and December have no statistically significant difference.
- Reject the Null Hypothesis
- There is a statistically significant difference in the temperatures in June and December in Hawaii.

Used historical data in the dataset find out what the temperature had previously looked like.
Used the calc_temps function to calculate the min, avg, and max temperatures for the trip using the matching dates from a previous year.
Plotted the min, avg, and max temperature as a bar chart.
- Used the average temperature as the bar height (y value).
- Used the peak-to-peak (TMAX-TMIN) value as the y error bar (YERR).

Used historical data in the dataset to find out what the precipitation had previously looked like
- Calculated the rainfall per weather station using the previous year's dates (i.e. "2017-08-01").
  - Sorted in descending order by precipitation amount and list the station, name, latitude, longitude, and elevation.

Calculated the daily normals. Normals are the averages for the min, avg, and max.
- Set the start and end date of the trip.
  - Used the date to create a range of dates.
- Removed the year and saved a list of strings in the format %m-%d.
- Use the daily_normals function to calculate the normals for each date string and append 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 for the daily normals.