Whether financial, political, or social -- data's true power lies in its ability to answer questions definitively. So let's take what you've learned about Python requests, APIs, and JSON traversals to answer a fundamental question: "What's the weather like as we approach the equator?"

Now, we know what you may be thinking: "Duh. It gets hotter..."

But, if pressed, how would you prove it?

In this example, I crated a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator. To accomplish this, I utilized a simple Python library, the OpenWeatherMap API, and a little common sense to create a representative model of weather across world cities.

I built a series of scatter plots to showcase the following relationships, followed by a description of the trend shown:

• Temperature (F) vs. Latitude
• Humidity (%) vs. Latitude
• Cloudiness (%) vs. Latitude
• Wind Speed (mph) vs. Latitude

I then ran 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

I provided a brief analysis of the relationship between each of these factors.

The final notebook provided the following:

• Randomly selected over 500 unique (non-repeat) cities based on latitude and longitude.
• A weather check performed 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 of all retrieved data and a PNG image for each scatter plot.

To showcase my skills working with weather data I generated a list of potential future vacation sites. I used jupyter-gmaps and the Google Places API for this part of the assignment.

• Note: if you have trouble displaying the maps try running jupyter nbextension enable --py gmaps in your environment and retry.

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

  

• I narrowed down the DataFrame to find my ideal weather conditions:

  • A max temperature lower than 79 degrees but higher than 70.

  • Wind speed less than 10 mph.

  • Zero cloudiness

  • Humidity less than 75%

• I then used Google Places API to find the first hotel for each city located within 5000 meters of the coordinates.

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