SQLAlchemy

This exercise utIlizes Pandas, SQLAlchemy, SQLite, and Flask to analyze weather data in Hawaii.

Python, SQLAlchemy, and Matplotlib are used to do basic climate analysis and data exploration of the climate database.

• The hawaii.sqlite file is imported into Jupyter Notebook to complete the climate analysis and data exploration.

• SQLAlchemy create_engine connects to the sqlite database.

Link to file: https://github.com/danawoodruff/SQLAlchemy-challenge/blob/main/climate_starter.ipynb

• From the most recent date in the data set retrieve the last 12 months of precipitation data.

• Query results are loaded into a Pandas DataFrame and sorted by date.

• The results are plotted in Matplotlib.

• Pandas is used to generate the summary statistics for the precipitation data.

• Query to calculate the total number of stations in the dataset.

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

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

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

  • Plot the results as a histogram.

A Flask API is designed based on the previous queries.
Link: https://github.com/danawoodruff/SQLAlchemy-challenge/blob/main/app.py

• Home page: List all available routes.

• /api/v1.0/precipitation

  • Return the JSON representation of Precipitation observations.

• /api/v1.0/stations

  • Return 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.

  • 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.

  • 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. Is there a meaningful difference between the temperature in, for example, June and December?

Link to file: https://github.com/danawoodruff/SQLAlchemy-challenge/blob/main/temp_analysis.ipynb

• A paired t-test is designed to compare the means of the same group under two separate scenarios. -An unpaired t-test compares the means of two unrelated groups. -Presumably the temperatures in the months of June and December are independent(unrelated) of one another. -An unpaired test is the more approprite choice.

• The null hypothesis is that there is no statistically significant difference in the mean of June and December temperatures in Hawaii.

• The t-statistic value is 31.6 and the p-value is 3.9e-191 so the null hypothesis is rejected. There is a statistically significant difference between the June and the December mean temperatures in Hawaii.