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To begin, use Python and SQLAlchemy to do basic climate analysis and data exploration of your climate database. All of the following analysis should be completed using SQLAlchemy ORM queries, Pandas, and Matplotlib.
- Use the provided starter notebook and hawaii.sqlite files to complete your climate analysis and data exploration.
- Choose a start date and end date for your trip. Make sure that your vacation range is approximately 3-15 days total.
- Use SQLAlchemy
create_engineto connect to your sqlite database.
- Use SQLAlchemy
automap_base()to reflect your tables into classes and save a reference to those classes called
- Design a query to retrieve the last 12 months of precipitation data.
- Select only the
- Load the query results into a Pandas DataFrame and set the index to the date column.
- Sort the DataFrame values by
- Plot the results using the DataFrame
- Use Pandas to print the summary statistics for the precipitation data.
- Design a query to calculate the total number of stations.
- Design a query to find the most active stations.
- List the stations and observation counts in descending order.
- Which station has the highest number of observations?
- Hint: You will need to use a function such as
func.countin your queries.
- Design a 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 with