doublealogic/api-challenge-python

Python API Challenge

Overview

This repository contains two Python scripts: WeatherPy.ipynb and VacationPy.ipynb.

Part I - WeatherPy

This Python script visualizes the weather of 500+ cities across the world of varying distance from the equator. It utilizes citipy, a simple Python library, and the OpenWeatherMap API, to create a representative model of weather across world cities.

To start, I randomly select at least 500 unique (non-repeat) cities based on latitude and longitude. Then I perform a weather check on each of the cities using a series of successive API calls. At the end, I print logs of each city as it's being processed with the city number and city name.

After gathering the data, I display a series of scatter plots to showcase the following relationships:

• Temperature (F) vs. Latitude
• Humidity (%) vs. Latitude
• Cloudiness (%) vs. Latitude
• Wind Speed (mph) vs. Latitude

After each plot I explain what the code is and analyze each relationship.

Next, I ran a linear regression on each relationship, only this time separating them into Northern Hemisphere cities (greater than or equal to 0 degrees latitude) and Southern Hemisphere cities (less than 0 degrees latitude):

• 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

After each pair of plots I explain what the linear regression is modeling such as any relationships I noticed and any other analysis I saw.

For all of the scatter plots above, I save a CSV of all retrieved data and a PNG image for each scatter plot. These PNG images are all located in the Output_Data folder for WeatherPy.

Part II - VacationPy

This script works with weather data to plan future vacations and uses jupyter-gmaps and the Google Places API.

• Creates a heat map that displays the humidity for every city from WeatherPy

• Narrows down the DataFrame to find cities with the following weather conditions:

  • A max temperature lower than 70 degrees but higher than 60.

  • Wind speed less than 5 mph.

  • Cloudiness less than 5.0.

  • Drop any rows that don't contain all three conditions.

• Uses Google Places API to find the first hotel for each city located within 5000 meters of your coordinates.

• Plots the hotels on top of the humidity heatmap with each pin containing the Hotel Name, City, and Country.

Located within the Output_Data folder in VacationPy is a screenshot of the final map figure.