Monthly Climate Data¶
We can get temperature and precipitation data averaged by month for 1901 to 2016 from World Bank Climate Change Knowledge Portal. You will need the latitude and longitude of a location from a site such as latlong.net.
latitude = 43.7521
longitude = -79.7614
import pandas as pd
temperature_url = 'https://climateknowledgeportal.worldbank.org/api/data/get-download-data/historical/tas/1901-2016/'+str(latitude)+'$cckp$'+str(longitude)+'/'+str(latitude)+'$cckp$'+str(longitude)
temperature_all = pd.read_csv(temperature_url)
temperature = temperature_all.replace(' Average','',regex=True).replace(' ','',regex=True).rename(columns={'Temperature - (Celsius)':'Temperature (°C)', ' Statistics':'Month'})
precipitation_url = 'https://climateknowledgeportal.worldbank.org/api/data/get-download-data/historical/pr/1901-2016/'+str(latitude)+'$cckp$'+str(longitude)+'/'+str(latitude)+'$cckp$'+str(longitude)
precipitation_all = pd.read_csv(precipitation_url)
precipitation = precipitation_all.replace(' Average','',regex=True).replace(' ','',regex=True).rename(columns={'Rainfall - (MM)':'Precipitation (mm)', ' Statistics':'Month'})
climate = pd.merge(temperature, precipitation).drop(columns=[' Longitude',' Latitude'])
climate
| Temperature (°C) | Year | Month | Precipitation (mm) | |
|---|---|---|---|---|
| 0 | -6.300000 | 1901 | Jan | 44.600002 |
| 1 | -9.900001 | 1901 | Feb | 56.900002 |
| 2 | -2.700000 | 1901 | Mar | 57.600002 |
| 3 | 6.700000 | 1901 | Apr | 56.799999 |
| 4 | 11.900001 | 1901 | May | 80.300003 |
| ... | ... | ... | ... | ... |
| 1387 | 22.400000 | 2016 | Aug | 112.900002 |
| 1388 | 17.600000 | 2016 | Sep | 79.900002 |
| 1389 | 10.500000 | 2016 | Oct | 105.700005 |
| 1390 | 5.700000 | 2016 | Nov | 60.799999 |
| 1391 | -3.200000 | 2016 | Dec | 68.300003 |
1392 rows × 4 columns
