PeiDay / API_WeatherPy_n_VacationPy

Analyzed the weather of 500+ cities using the OpenWeatherMap API. Displayed results using pandas, linear regressions via matplotlib, and a heatmap using Google Places API.

Python API - What's the Weather Like?

Background

Use Python requests, APIs, and JSON traversals to per form the following:

• The script was coded using Jupyter notebook.

• Two directory were created within this repository: WeatherPy and VacationPy.

• The OpenWeatherMap API and Google Places API were used.

• WeatherPy folder contains:

  • WeatherPy.ipynb script.
  • An image folder that stores PNG files of 4 scatter plots created and linear regression plots created by weatherpy script.
  • weatherpy_cities.csv is the cities outsput from the weatherpy script.

• VacationPyfolder contains:

  • VacationPy.ipynb script.
  • An image folder that stores a heatmap and a marker heatmap that were created by weatherpy script.

Part I - WeatherPy:

Create a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator. 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
• 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

Part II - VacationPy:

Use Jupyter gmaps and the Google Places API to plan future vacations based on weather data.

• Use the result of WeatherPy for finding ideal travel cities for this season. Present the result with a heatmap.
• Find and mark the hotels on the heapmap, see below.