Step 1 - Climate Analysis and Exploration

Used Python and SQLAlchemy to do basic climate analysis and data exploration of your climate database

• Used SQLAlchemy create_engine to connect to sqlite database

• Used SQLAlchemy automap_base() to reflect tables into classes and saved a reference to those classes called Station and Measurement

• Linked Python to the database by creating an SQLAlchemy session

Precipitation Analysis

• Started by finding the most recent date in the data set

• Using this date, retrieved the last 12 months of precipitation data by querying the 12 preceding months of data

• Selected only the date and prcp values

• Loaded the query results into a Pandas DataFrame and set the index to the date column

• Sorted the DataFrame values by date

• Plotted the results using the DataFrame plot method:

  

• Used Pandas to print the summary statistics for the precipitation data

Station Analysis

• Designed a query to calculate the total number of stations in the dataset

• Designed a query to find the most active stations

  • Listed the stations and observation counts in descending order

  • Which station id has the highest number of observations?

  • Using the most active station id, calculated the lowest, highest, and average temperature

• Designed a query to retrieve the last 12 months of temperature observation data (TOBS)

  • Filtered by the station with the highest number of observations

  • Queried the last 12 months of temperature observation data for this station

  • Plotted the results as a histogram with bins=12.

    

Step 2 - Climate App

Next I design a Flask API based on the queries that you have just developed.

Routes

• /

  • Home page.

  • Listed all routes that are available.

• /api/v1.0/precipitation

  • Converted the query results to a dictionary using date as the key and prcp as the value.

  • Returned the JSON representation of your dictionary.

• /api/v1.0/stations

  • Returned a JSON list of stations from the dataset.

• /api/v1.0/tobs

  • Queried the dates and temperature observations of the most active station for the last year of data.

  • Returned a JSON list of temperature observations (TOBS) for the previous year.

• /api/v1.0/<start> and /api/v1.0/<start>/<end>

  • Returned 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, calculated TMIN, TAVG, and TMAX for all dates greater than and equal to the start date.

  • When given the start and the end date, calculated the TMIN, TAVG, and TMAX for dates between the start and end date inclusive.

Contact

Email: cgrace1011@gmail.com

References

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-11-00103.1

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