The repo contains two notebooks WeatherPy- explores the way weather changes as we approach the equator - and VacationPy - plan future vacations given ideal weather conditions worldwide - . I used Pandas, Numpy, Scipy, JSON, Matplotlib, Requests, CitiPy api and Google API (Geoplaces API, GMAP API) to accomplish my task.
Created a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator. Utilized a simple Python library, the OpenWeatherMap API, and some data filtering to create a representative model of weather across world cities.
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First Objective was to build a series of scatter plots to showcase various relationships:
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Second objective was to run linear regression on each relationship, only this time separating them into Northern Hemisphere (greater than or equal to 0 degrees latitude) and Southern Hemisphere (less than 0 degrees latitude):
Used jupyter-gmaps and the Google Places API to plan future vacations.
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Created a heat map that displays the humidity for every city from the WeatherPy notebook.
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Filtered the dataframe to find ideal weather
- A max temperature lower than 80 degrees but higher than 70.
- Wind speed less than 10 mph.
- Zero cloudiness.
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Used Google Places API to find the first hotel for each city located within 5000 meters of the coordinates in the dataframe.
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Created a map of the hotels on top of the humidity heatmap with each pin containing the Hotel Name, City, and Country.
