Skip to content

The WeatherPy Analysis assignment used Python requests, APIs, JSON traversals, and Matplotlib to retrieve and report weather conditions from over 500 cities .

Notifications You must be signed in to change notification settings

KCDataVis/WeatherPy

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

3 Commits
 
 
 
 

Repository files navigation

WeatherPy

In this example, you'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, you'll be utilizing a simple Python library, the OpenWeatherMap API, and a little common sense to create a representative model of weather across world cities.

Your objective is to build 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

Your final notebook must:

  • 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 both a CSV of all data retrieved and png images for each scatter plot.

Copyright

Data Boot Camp © 2018. All Rights Reserved.

About

The WeatherPy Analysis assignment used Python requests, APIs, JSON traversals, and Matplotlib to retrieve and report weather conditions from over 500 cities .

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published