World_Weather_Analysis

Use today's current weather to plan for a spontaneous vacation.

Resources:

• Data: Open Weather Map's Api and saved as CSV files.
• Software:
  • Python 3.7.13, Pandas 1.3.5, Numpy 1.21.5, matplotlib 3.5.1, CitiPy 0.0.5
  • Jupyter Notebook (notebook server 6.4.8, Ipython 7.31.1)
  • API keys (Required): Google Maps
  • API keys (optional): Open Weather Map
    • Current Weather Data retrival step can be skipped if a previous Weather_Database.csv has been saved and you do not mind using older data (from July 23, 2022).

Summary

WeatherPy.ipynb and VacationPy.ipynb were my initial exploring of the data available with Open Weather Map's api and Google Maps API.

• WeatherPy.ipynb generated 1500 datapoints retrived current weather for about 580 cities around the world.
  • made several scatterplot graphs of the data gathered, comparing city Latitude with the different metrics
• In VacationPy.ipynb I graphed the information using google maps' Heatmaps.
• Weather_Database uses 10,000 data points to get weather data for 2,000 to 3,000 cities accros the world.

Vacation_Search Filters that data with user input to find the most likely places to have a vacation.

Vacation_Itinerary is a mock vacation result

Future Changes

• If there are multiple cities that share a name, CityPy does not varify the country code is correct for the GPS location.
  • Naples, USA vs. Naples, Italy
  • Southbridge, New Zealand vs. Southbridge, MA, USA
• Filter for more weather types.
• improve driving directions to the hotel listed. Currently only driving to the city
• try getting forcaste information to plan for several days