6 Python API Homework

Rice University Data Analytics and Visualization Boot Camp 2020

This repository contains the following scenario:

• "What's the weather like as we approach the equator?"

Part I - WeatherPy

In this notebook, we'll be creating a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator. To accomplish this, we'll be utilizing a simple Python library, the OpenWeatherMap API.

The notebook presents 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

The notebook also presents the linear regression on each relationship separated by Northern Hemisphere (greater than or equal to 0 degrees latitude) and Southern Hemisphere (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

The final notebook has the following features:

• Randomly select N unique (non-repeat) cities based on latitude and longitude.
• Perform a weather check on each of the cities using a series of successive API calls.
• Include a print log of each city as it's being processed with the city number and city name.
• Save a CSV of all retrieved data and a PNG image for each scatter plot.

Part II - VacationPy

In this notebook, the skills used to working with weather data will be useful to plan future vacations. Use jupyter-gmaps and the Google Places API for this part of the assignment.

• Note: Remember that any API usage beyond the $200 credit will be charged to your personal account. You can set quotas and limits to your daily requests to be sure you can't be charged. Check out Google Maps Platform Billing and Manage your cost of use for more information.

• Note: if you having trouble displaying the maps try running jupyter nbextension enable --py gmaps in your environment and retry.

• Create a heat map that displays the humidity for every city from the part I of the homework.

  

• Narrow down the DataFrame to find your ideal weather condition. For example:

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

  • 2-Wind speed less than 10 mph.

  • 3-Zero cloudiness.

  • Drop any rows that don't contain all three conditions. You want to be sure the weather is ideal.

  • Based on the 3 previous specifications, we limit the number of rows returned by the API to 10 values.

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

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