This project is designed to explore and analyze data of climate database by using SQLAlchemy ORM queries, and Pandas and Matplotlib in Python. A Flask API is created to store all the information.

Retrieve, summarize, and plot the most recent 12 months of precipitation data.

Retrieve, summarize, and plot the most recent 12 months of temperature observation data of the most active station.

The Weather App Home Page includes available API routes as below:

• /api/v1.0/precipitation
• /api/v1.0/stations
• /api/v1.0/tobs
• Enter a start date to retrieve the weather info : /api/v1.0/yyyy-mm-dd
• Enter a start and end date to retrieve the weather info : /api/v1.0/yyyy-mm-dd/yyyy-mm-dd

• T-test was used to calculate the means of two different independent samples: the temperature for June vs temperature for December across all available years in the dataset.

• T-test Results: t = 30.624201480767336, p = 6.622829250184814e-178

• Conclusion: With the p-value is extremely close to 0, there is not sufficient evidence to conclude that there is a significant difference in means between June and December temperatures across all the data years available.

• Choose a date range for a trip and use the calc_temps function to calculate the min, avg, and max temperatures for the trip using the matching dates from the previous year.
• Plot the min, avg, and max temperature from the query as a bar chart.

Calculate the rainfall per weather station using the year's previous matching dates for the trip.

Calculate the daily minimum, maximum, and average temperatures for the trip and plot an area plot with the results.