Python API Project

Background

WeatherPy: Utilizes weather data from OpenWeather API to find a correlation between various variables.

VacationPy: Utilizes Google Maps API to find ideal vacation hotels based on set weather.

Part I - WeatherPy

WeatherPy Script

Scatter Plots

• Temperature (F) vs. Latitude

* Humidity (%) vs. Latitude

• Cloudiness (%) vs. Latitude

• Wind Speed (mph) vs. Latitude

Linear Regressions

• Northern Hemisphere - Temperature (F) vs. Latitude

• Southern Hemisphere - Temperature (F) vs. Latitude

• Northern Hemisphere - Humidity (%) vs. Latitude

• Southern Hemisphere - Humidity (%) vs. Latitude

• Northern Hemisphere - Cloudiness (%) vs. Latitude

• Southern Hemisphere - Cloudiness (%) vs. Latitude

* Northern Hemisphere - Wind Speed (mph) vs. Latitude

* Southern Hemisphere - Wind Speed (mph) vs. Latitude

Observations:

1. According to the "Southern Hemisphere - City Latitude vs. Windspeed" linear regression, there appears to be a negative correlation between latitude and windspeed in the Souther Hemisphere cities.

2. There is a negative correlation between City Latitude and Max Temperature for cities in the Northern Hemisphere.

3. There appears to be a positive correlation between City Latitude and Cloudiness in the Northern Hemisphere.

Part II - VacationPy

VacationPy Script

Heatmap

• Narrowing down the DataFrame ideal weather conditions:

  • A max temperature lower than 80 degrees but higher than 70.

  • Wind speed less than 10 mph.

  • Zero cloudiness.

• Plotting the top hotels on top of the humidity heatmap with each pin containing the Hotel Name, City, and Country based on ideal weather conditions.