Python The International Space Station Location Project

For this assignment, we are going to utilize an open API to get information about the International Space Station location.

Directions:

• Use the API documentation at this site your API request
• Find out the current location of the ISS and print it to the console
• Find out the names of the current residents of the ISS and print it to the console
• Find out the timestamp of the next 5 times the ISS will pass over Ankeny, IA (You’ll need to determine the lat/long for Ankeny to do this)
• Print these timestamps in the console
• Your outputs should be formatted to answer the questions. This needs to be beyond just printing the json to the console.

Directions:

• After importing the data integration module create a database called weather_tracking
• In the database, create a table called Precipitation
  • The table should have the columns: location (foreign key to county in Location table), date (text), precipitation (float), precip_type(text)
• In the database, create another table called Location
  • The table should have the columns county (primary key)(text), state (text)
• Output the database information in the console with your program (table names and column names)

Using the database structure created in the previous assignment, populate the database with some information.

Directions:

• Gather weather data from at least two different counties for a range of 30 days
• Import this data into your program
• Write this data to your database tables in the Location and Precipitation tables
• Now bring this data back into your program to create a visual of the precipitation data

Continuing to use your existing database, let’s make some changes to it:

Directions

• Create a copy of your database (You can do this in Python or whatever way you prefer)
• If you used inches for your original precipitation, read them in from your database and replace them with millimeters. Do the opposite if you started with millimeters.
• Make the date on each of your readings 1 day earlier (because you realized the reading date was actually the day after the actual precipitation)
• Delete one of your cities and all of the data associated with it. 

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