World-Weather-Analysis-Python-APIs

This project focuses on the usage of Google API and OpenWeather API to acquire data. After the data retreival process it is filtered by user based critera.

1. Firstly, the Weather_Database.ipynb jupyter notebook generated WeatherPy_Database.csv by using OpenWeather API. This outputs weather in cities close to 2000 number of random latitudes and longitudes generated across the globe.
2. Secondly, the Vacation_Search.ipynb jupyter notebook generated WeatherPy_vacation.csv by using Pandas to filter the user criteria that the user might be likely to visit based off temperature ranges. Applying this filter to our Pandas dataframe, we use Google API to help us generate a list of hotels based on the user's temperature ranges.
3. Finally, based on the previous filteration of the data, we can now allow the user to select upto four cities to create a Vacation_Itinerary.ipynb.

Here is an example of the above:

Additional Resources:

During the course of the project, whilst using the Google API and OpenWeather API and the generated cities.csv, heat maps were also generated based off:

1. City Latitude vs. Max Temeprature
2. City Latitude vs. Humidity
3. City Latitude vs. Cloudiness
4. City Latitude vs. Windspeed