- 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
Analyze plot and explain what the code is and analyzing.
- Run linear regression on each relationship, 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
The process 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 a CSV of all retrieved data and a PNG image for each scatter plot.
Use jupyter-gmaps and the Google Places API for this part of the assignment.
-
Create a heat map that displays the humidity for every city from the part I of the homework.
-
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.
-
-
Using Google Places API to find the first hotel for each city located within 5000 meters of your coordinates.
-
Plot the hotels on top of the humidity heatmap with each pin containing the Hotel Name, City, and Country.