Use Jupyter Notebook Python 3 and save the file in .ipynb
Please comment the code thoroughly as I will be explaining it
Part I - 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 first 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
After each plot add a sentence or too explaining what the code is and analyzing.
Your next objective 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
After each pair of plots explain what the linear regression is modelling such as any relationships you notice and any other analysis you may have.
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 a CSV of all retrieved data and a PNG image for each scatter plot.
Part II - VacationPy
Use jupyter-gmaps and the Google Places API for this part of the assignment.
Note:
if you having trouble displaying the maps try running
jupyter nbextension enable --py gmaps
in your environment and retry.
Create a heat map that displays the humidity for every city from the part I of the homework.
Narrow down the DataFrame to find your 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.
Note:
Feel free to adjust to your specifications but be sure to limit the number of rows returned by your API requests to a reasonable number.
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.