Dream Vacation Location

A Python API Project

Table of Contents

• About the Project
  • Built With
• Usage
• Getting Started
• Results

About The Project

Background

This project tackles the problem of weather patterns as one moves closer to equator using Python APIs. In the 2nd part, I find my ideal vacation location using data I used to analyze weather patterns.

Part I - Weather patterns around the globe

In this project, I created a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator. To accomplish this, I utilized a simple Python library, the OpenWeatherMap API, and a little common sense to create a representative model of weather across world cities.

First, I created 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

Then, I ran 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):

• 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

In the end, I accomplished the following:

• Randomly select at least 500 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 - Finding ideal vacation location in the world

In this part, I use the weather data to plan future vacations. I used jupyter-gmaps and the Google Places API for this part of the assignment.

I accomplished the following with the data.

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

  

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

  • A max temperature lower than 26 degrees but higher than 19.

  • Wind speed between 2-5 mph.

  • Cloudiness under 10 percent.

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

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

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

  

Built With

• Python
  • Matplotlib
  • Pandas
• Openweather APIs
• Google APIs

Getting Started

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

Results

Based on my data and criteria for ideal vacation weather, my ideal vacation location is Lanzhou, China.

Additional reference materials:

Best-README-Template Retrieved from: https://github.com/othneildrew/Best-README-Template