Congratulations! You've decided to treat yourself to a long holiday vacation in Honolulu, Hawaii. To help with your trip planning, you decide to do a climate analysis about the area. The following sections outline the steps that you need to take to accomplish this task.
In this section, you’ll use Python and SQLAlchemy to do a basic climate analysis and data exploration of your climate database. Specifically, you’ll use SQLAlchemy ORM queries, Pandas, and Matplotlib. To do so, complete the following steps
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Note that you’ll use the provided files (climate_starter.ipynb and hawaii.sqlite) to complete your climate analysis and data exploration.
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Use the SQLAlchemy create_engine() function to connect to your SQLite database.
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Use the SQLAlchemy automap_base() function to reflect your tables into classes, and then save references to the classes named station and measurement.
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Link Python to the database by creating a SQLAlchemy session.
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Perform a precipitation analysis and then a station analysis by completing the steps in the following two subsections.
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Find the most recent date in the dataset.
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Using that date, get the previous 12 months of precipitation data by querying the previous 12 months of data.
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Select only the "date" and "prcp" values.
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Load the query results into a Pandas DataFrame, and set the index to the "date" column.
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Sort the DataFrame values by "date".
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Plot the results by using the DataFrame plot method, as the following image shows:
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Use Pandas to print the summary statistics for the precipitation data.
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Design a query to calculate the total number of stations in the dataset.
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Design a query to find the most-active stations (that is, the stations that have the most rows). To do so, complete the following steps:
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List the stations and observation counts in descending order.
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Answer the following question: which station id has the greatest number of observations?
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Design a query that calculates the lowest, highest, and average temperatures that filters on the most-active station id found in the previous query.
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Design a query to get the previous 12 months of temperature observation (TOBS) data. To do so, complete the following steps:
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Filter by the station that has the greatest number of observations.
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Query the previous 12 months of TOBS data for that station.
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Plot the results as a histogram with bins=12, as the following image shows:
- Close your session
Now that you’ve completed your initial analysis, you’ll design a Flask API based on the queries that you just developed. To do so, use Flask to create your routes as follows:
- /
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Start at the homepage.
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List all the available routes.
- /api/v1.0/precipitation
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Convert the query results from your precipitation analysis (i.e. retrieve only the last 12 months of data) to a dictionary using date as the key and prcp as the value.
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Return the JSON representation of your dictionary.
- /api/v1.0/stations
- Return a JSON list of stations from the dataset.
- /api/v1.0/tobs
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Query the dates and temperature observations of the most-active station for the previous year of data.
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Return a JSON list of temperature observations for the previous year.
- /api/v1.0/ and /api/v1.0//
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Return a JSON list of the minimum temperature, the average temperature, and the maximum temperature for a specified start or start-end range
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For a specified start, calculate TMIN, TAVG, and TMAX for all the dates greater than or equal to the start date.
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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.
- Join the station and measurement tables for some of the queries.
- Use the Flask jsonify function to convert your API data to a valid JSON response object.
Menne, M.J., I. Durre, R.S. Vose, B.E. Gleason, and T.G. Houston, 2012: An overview of the Global Historical Climatology Network-Daily Database. Journal of Atmospheric and Oceanic Technology, 29, 897-910, https://doi.org/10.1175/JTECH-D-11-00103.1Links to an external site.