SurfsUp

The SurfsUp directory contains climate.ipynb Jupyter Notebook, the app.py Python script, and a Resources folder which contains the hawaii.sqlite database, hawaii_measurements.csv and hawaii_stations.csv.

Analyze and Explore the Climate Data

Climate analysis and data exploration was done on the hawaii.sqlite database. The climate.ipynb Jupyter Notebook was created to perform precipitation analysis and station analysis. The app.py Python script was created to design a climate app with a Flask API with 5 routes with JSON lists of climate information.

Precipitation Analysis

• ORM queries were done to get the date and prcp data for the previous 12 months.

• Pandas DataFrame was created using the queried data.

  	precipitation
  2016-08-23	0.00
  2016-08-23	NaN
  2016-08-23	1.79
  2016-08-23	0.05
  2016-08-23	0.15
  ...	...

• The results were plotted by using the Pandas plot method.

  

• A summary statistics table was printed for the precipitation data for the previous 12 months.

  	precipitation
  count	2021.000000
  mean	0.177279
  std	0.461190
  min	0.000000
  25%	0.000000
  50%	0.020000
  75%	0.130000
  max	6.700000

Station Analysis

• All stations and its number of counts were listed in descending order.

[('USC00519281', 2772),
 ('USC00519397', 2724),
 ('USC00513117', 2709),
 ('USC00519523', 2669),
 ('USC00516128', 2612),
 ('USC00514830', 2202),
 ('USC00511918', 1979),
 ('USC00517948', 1372),
 ('USC00518838', 511)]

• The minimum, average and maximum temperature data were queried for the most active station (by id)

• The last 12 months of temperature observation data for the most active station were queried

  	tobs
  0	77.0
  1	77.0
  2	80.0
  3	80.0
  4	75.0
  ...	...

• The results were binned and plotted in histogram using Pandas plot histogram method.

Design Climate API

A Climate API was created using Flask. The following 5 routes are available routes created using Flask:

1. /(Homepage)

• Start at the homepage.
• List all the available routes.

2. /api/v1.0/precipitation (Precipitation)

• 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.
• Return the JSON representation of your dictionary.

3. /api/v1.0/stations (Stations)

• Return a JSON list of stations from the dataset.

4. /api/v1.0/tobs (TOBS)

• 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.

5. /api/v1.0/<start> and /api/v1.0/<start>/<end> (minimum, average, and maximum temperatures for a specific date range)

• 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 minimum, average, and maximum temperatures for all the dates greater than or equal to the start date.
• For a specified start date and end date, calculate minimum, average, and maximum temperatures for the dates from the start date to the end date, inclusive.