Skip to content

Latest commit

 

History

History
60 lines (37 loc) · 2.75 KB

README.md

File metadata and controls

60 lines (37 loc) · 2.75 KB
Raw

Python API - What's the Weather Like?

Background

Whether financial, political, or social -- data's true power lies in its ability to answer questions definitively. So let's answer a fundamental question: "What's the weather like as we approach the equator?"

Equator

Part I - WeatherPy

In this example, I will be creating a Python script to visualise the weather of 500+ cities across the world of varying distance from the equator. To accomplish this, I will be utilising a simple Python library, the OpenWeatherMap API to create a representative model of weather across world cities.

First requirement is 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

Second requirement is to run 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

Final notebook will show:

  • Randomly select at least 500 unique (non-repeat) cities based on latitude and longitude.
  • A weather check on each of the cities using a series of successive API calls.
  • A print log of each city as it's being processed with the city number and city name.
  • A CSV of all retrieved data and a PNG image for each scatter plot.

Part II - VacationPy

Now let's work with weather data to plan future vacations. I'll use jupyter-gmaps and the Google Places API for this part of the assignment.

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

    heatmap

  • I'll narrow down the DataFrame to find ideal weather condition. For example:

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

    • Wind speed less than 10 mph.

    • Zero cloudiness.

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

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

  • I'll plot the hotels on top of the humidity heatmap with each pin containing the Hotel Name, City, and Country.

    hotel map