The task was to create a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator.
The first requirement was to create a series of scatter plots to showcase the following relationships:
The second requirement was to run linear regression on each relationship. This time, separate the plots into Northern Hemisphere (greater than or equal to 0 degrees latitude) and Southern Hemisphere (less than 0 degrees latitude):
My final jupyter notebook included:
- Randomly selected cities (at least 500) 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 saved CSV file of all retrieved data and a PNG image for each scatter plot.
Planning a future vacation
I used jupyter-gmaps and Google Places API for this part of the assignment.
Tasks for this part of the project consisted of:
- Creating a heat map that displays the humidity for every city from Part I - WeatherPy
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Narrowing down the DataFrame to find my ideal weather condition. It had to have:
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A max temperature lower than 80 degrees but higher than 70.
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Wind speed less than 10 mph.
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Zero cloudiness.
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Dropped any rows that don't contain all three conditions. You want to be sure the weather is ideal.
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Using Google Places API to find the first hotel for each city located within 5000 meters of the cities coordinates.
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Plotting the hotels on top of the humidity heatmap with each pin containing the Hotel Name, City, and Country.
As a final consideration:
I completed my analysis using a Jupyter notebook.