world_weather_analysis

Project Overview

The goal was to create a vacation itinerary across four different cities. In order to create this I did the following:

First thing I did was generate 2,000 random latitudes and longitudes using the Numpy library.
From there, I constructed a URL so that I could call the OpenWeatherMap API. I also used the citipy module to identify the nearest city based on the coordinates.
After a massive list of cities were generated and added to an empty list, I then added all of that data to a Pandas DataFrame.
Then I saved that data as a CSV file that can be accessed in the future.

My city data was already saved as a CSV file so I imported the file as a DataFrame.
Then I filtered by temperature so that the dataset would be manageable.
After that I cleaned the data up as there some empty names. From there I constructed my Google Directions API URL to call, request, and retrieve data in JSON format.
I saved the vacation data as a CSV.
Then I used the Gmaps marker layer to construct a map with markers for all of my cities. I also applied some formating so that the Hotel Name, City, Country, and Current Description with Max Temp would display.
This was the output:

My vacation data was already saved as a CSV file so I imported the file as a DataFrame.
Then I used the Gmaps marker layer to construct a map with markers for all of my cities. I also applied some formating so that the Hotel Name, City, Country, and Current Description with Max Temp would display.
After that I filtered the data so that only four cities would display. Then I used the Gmaps directions layer to set waypoints between the four cities.
This was the output:
Finally, I added the Gmaps marker layer to the same four cities with the same format/info as the previous marker layers.
This was the output: