Climate Analysis in Hawaii

I have decided to treat myself to a long holiday vacation in Honolulu, Hawaii. To help with my trip planning, I decide to do a climate analysis about the area. The following sections outline the steps that I will need to take to accomplish this task.

Part 1: Analyze and Explore the Climate Data

In this section, I will use Python and SQLAlchemy to do a basic climate analysis and data exploration of my climate database. Specifically, I wil use SQLAlchemy ORM queries, Pandas, and Matplotlib. To do so, I will complete the following steps:

• Note that I will use the provided files (climate_starter.ipynb and hawaii.sqlite) to complete my climate analysis and data exploration.
• Use the SQLAlchemy create_engine() function to connect to my SQLite database.
• Use the SQLAlchemy automap_base() function to reflect my tables into classes, and then save references to the classes named station and measurement.
• Link Python code to the database by creating a SQLAlchemy session.
• Perform a precipitation analysis and then a station analysis by completing the steps in the following two subsections.

Precipitation Analysis

• Find the most recent date in the dataset.
• Using that date, get the previous 12 months of precipitation data by querying the previous 12 months of data.
• Select only the "date" and "prcp" values.
• Load the query results into a Pandas DataFrame, and set the index to the "date" column.
• Sort the DataFrame values by "date".
• Plot the results by using the DataFrame plot method.
• Use Pandas to print the summary statistics for the precipitation data.

Station Analysis

• Design a query to calculate the total number of stations in the dataset.
• Design a query to find the most-active stations (that is, the stations that have the most rows).
• List the stations and observation counts in descending order.

Answer the following question: which station id has the greatest number of observations?

• Using the most-active station id, calculate the lowest, highest, and average temperatures.
• Design a query to get the previous 12 months of temperature observation (TOBS) data. To do so, complete the following steps:
• Filter by the station that has the greatest number of observations.
• Query the previous 12 months of TOBS data for that station.
• Plot the results as a histogram with bins=12.

Part 2: Design Your Climate App

Now that I have completed my initial analysis, I will design a Flask API based on the queries that I just developed. To do so, I will use Flask to create my routes as follows:

• Start at the homepage.
• List all the available routes.
• Convert the query results to a dictionary by using date as the key and prcp as the value.
• Return the JSON representation of your dictionary.
• Return a JSON list of stations from the dataset.
• Query the dates and temperature observations of the most-active station for the previous year of data.
• Return a JSON list of temperature observations for the previous year.
• Return a JSON list of the minimum temperature, the average temperature, and the maximum temperature for a specified start or start-end range.
• For a specified start, calculate TMIN, TAVG, and TMAX for all the dates greater than or equal to the start date.
• For a specified start date and end date, calculate TMIN, TAVG, and TMAX for the dates from the start date to the end date, inclusive.

Programs used

Python, SQLAlchemy ORM queries, Pandas, Matplotlib, VS Code, Jupyter Notebook, Flask API, JSON