Let's extract some data from a public API of Weather to see how it is look.
Once that we have the data, let's see what it comes.
| City | Cloudiness | Country Date | Humidity | Lat | Lng | Max Temp | Wind Speed |
|---|---|---|---|---|---|---|---|
| qaqortoq | 6 | GL | 1619574719 | 70 | 60.7167 | -46.0333 | 41.00 |
| bernardino de campos | 81 | DO | 1619574719 | 77 | 18.8372 | -70.0172 | 77.00 |
| yambio | 21 | SS | 1619574536 | 96 | 4.5721 | 28.3955 | 67.05 |
| mataura | 100 | NZ | 1619574669 | 87 | -46.1927 | 168.8643 | 55.99 |
| gimli | 5 | CA | 1619574720 | 80 | 50.6336 | -96.9907 | 35.60 |
Now let's work on some stadistic information about:
- Cloudiness
- Country
- Date
- Humidity
- Lat
- Lng
- Max Temp
- Wind Speed
OK. now we need some graphs
Trend 1 The mean of the Humidity was 70% and cloudiness mean was 53, thats mean that if you see a lot of clouds the humidity could be high.There was no humidity over 100
Trend 2 There is a breaking point between lattitude and Temperature/Humidity in which latitude 0 is a separetor in both indicators. There was no relation bewteen latitude and wind speed, nothing change changing variables.
Trend 3 Hemisphere trends, north and south are inverse related in temperature and latitude, that means that the more you are close to ecuador you probabaly feel more tempearature. We can say that humidity and latitude have a space relation, as you get latitude you could feel more humidity but a certain level you provably feel nothing more.
Please follow VacationPy folder to reach Map.png to see screenshot of the heatmap.
