cosette3737 / apipython2

WeatherPy, VacationPy API Homework

APIPython2

WeatherPy, VacationPy API Homework

Figures: Figure 1: We are looking at a scatter plot of Humidity versus latitude in our cities. The data does seem to show a Higher bit of humidity on the higher ranges of latitude. Most datapoints are towards the top of the graph, but they are pretty diverse. Humidity seems to most likely be between 60 to 100 percent, but doesnt necessarily show a relationship. Figure 2: This figure shows Latitude verses temperagure. The data is all located near similar points. The temperatures seems to follow a bell curve looking scatter plot as the higher latitude, the lower the temperature. Figure 3: Shows latitude verses cloudiness. This scatter plot shows a wide variety of data points. It doesnt appear to have any relationship in lattitude verses cloudiness. Figure 4: This scatter plot shows latitude verses wind speed. The datasets show a lower windspeed closer to the higher latitidues, but it is prety scattered so it is hard to tell if there is any relationship. Figure 5 and 6: The scatter plot of Northern and Southern Hemisphere temperature versus latitude and linear regressions shows a relationship between temp and latitude. It appears that there is a positive correlation. As the latitude moves closer to the equater the max temp is increased, showing a direct relationship between the two variables. Figure 7 and 8 looks at the linear regression of Humididty versus latitude. Both of these plots and slopes are very similar, showing a slight correlation between the 2 variables. The data is very spread out, but does appear to have a very small relationship.
Figure 9, 10 Cloudiness verses Latitude using linear regression shows almost 0 slope and no relationship between the latitude and cloudiness have on one another. Figure 11,12 Both show the wind speed versus latitude and the linear regression. The slope of these plots is almost 0 again, showing no relationship between these factors.

The Humididty Maps of the cities shows some clusters of larger areas containig higher humididty as the location becomes closer to the equator, but the data is very spread out. It doesnt appear to show a correlation between the humidity and the lattitude, showing a similar trend as the scatter plot above.

The three trends that are most apparent in this data would be the fact that as the location is closer to the equator, the temperature was higher. We can observe this in the datapoints that are close to one another in the scatterplots, and with the linear regression using the slope as well. The second trend would be that there is no correlation between cloudiness and latitude. The points in these data sets are very scattered and dont appear to have a slope or any correlation. Lastly we can see that Latitude and wind speed dont appear to have a correlation either. Using the information in these datasets we can see the location of a city will mostly have an affect on only the temperature. However this may be because the cities chosen may have not been as equally spread out and possibly not truly random. It also may depend on how many north versus south hemisphere cities were selected.

The hardest part of this assigment was learning how to read the json responses and also learning exactly how to write requests. It is extremely detailed, and requrires alot of practice and repeated attemps. I forgot to put a git.ignore file so i had to make another repository to add the git ignore file.