I will visualize weather data for 500+ cities with varying distances from the equator. I will then work with this data to set inputs for my ideal vacation weather and determine a location to visit.
- Python
- JSON
- OpenWeatherMap API
- Google Maps API
- Jupyter Notebook:
- Matplotlib
- Pandas
- Numpy
- requests
- time
- scipy
- citipy
- gmaps
- os
- 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
- Run a linear regression on each relationship, separating them into Northern Hemisphere and Southern Hemisphere:
- 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
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Produce the following in the notebook:
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Randomly selected 500 unique cities based on latitude and longitude.
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A weather check on each of the cities using a series of successive API calls.
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Include a print log of each city as it's being processed with the city number and city name.
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Save a CSV of all retrieved data and a PNG image for each scatter plot.
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Create a heat map that displays the humidity for every city from part I.
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Narrow down the DataFrame to find my ideal weather conditions:
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A max temperature lower than 90 degrees but higher than 75.
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Wind speed less than 10 mph.
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Mild cloudiness.
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Drop any rows that don't contain all three conditions.
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Use Google Places API to find the first hotel for each city located within 5000 meters of my coordinates.
- Plot the hotels on top of the humidity heatmap with each pin containing the hotel name*, city, and country.
