Skip to content

Latest commit

 

History

History
45 lines (33 loc) · 2.5 KB

README.md

File metadata and controls

45 lines (33 loc) · 2.5 KB
Raw

Python - API Project

This project is broken down into two parts, WeatherPy and VacationPy.

Part 1: WeatherPy

In this section, I'll create a Python script to visualize the weather of over 500 cities of varying distances from the equator. I'll use a simple Python library [citipy Python library] (https://pypi.org/project/citipy/), the [OpenWeatherMap API] (https://openweathermap.org/api), and my problem-solving skills to create a representative model of weather across cities.

The first steps are to create 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 next set of steps is to compute the linear regression for each relationship. This time, separate the plots 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

Finally, I will complete the following steps:

  • Randomly select at least 500 unique (not repeated) cities based on latitude and longitude.
  • Perform a weather check on each of the cities by 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 2: VacationPy

Now, I will use my weather data skills to plan future vacations. I will use Jupyter gmaps and the Google Places API for this part of the project.

  1. Create a heat map that displays the humidity for every city from Part 1

  2. Narrow down the DataFrame to find my ideal weather condition. For example: - A max temperature lower than 80 degrees but higher than 70 - Wind speed less than 10 mph - Zero cloudiness

  3. Drop any rows that don't satisfy all three conditions; I want to be sure that the weather is ideal.

  4. For each city, use the Google Places API to find the first hotel located within 5,000 meters of my coordinates.

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