Hourly Weather
If you want to investigate the weather on a particular day or a short period of time, hourly time series data is a great choice.
🚀 Example
Let's fetch some hourly data for Atlanta, USA from January 1 to December 31, 2018:
# Import Meteostat library and dependencies
from datetime import datetime
import meteostat as ms
# Set time period
start = datetime(2018, 1, 1)
end = datetime(2018, 12, 31, 23, 59)
# Get hourly data
ts = ms.hourly(ms.Station(id='72219'), start, end)
df = ts.fetch()
# Print DataFrame
print(df)
This is the output you would get:
temp rhum prcp snwd wdir wspd wpgt pres tsun cldc coco
time
2018-01-01 00:00:00 0.6 63 <NA> <NA> 340 18.4 <NA> 1024.6 <NA> 6 <NA>
2018-01-01 01:00:00 -1.1 66 0.0 <NA> 320 16.6 <NA> 1025.4 <NA> <NA> <NA>
2018-01-01 02:00:00 -1.1 53 0.0 <NA> 330 22.3 <NA> 1025.8 <NA> <NA> <NA>
2018-01-01 03:00:00 -1.1 51 0.0 <NA> 330 25.9 <NA> 1025.6 <NA> <NA> <NA>
2018-01-01 04:00:00 -1.7 51 0.0 <NA> 340 18.4 <NA> 1026.1 <NA> <NA> <NA>
... ... ... ... ... ... ... ... ... ... ... ...
2018-12-31 19:00:00 20.0 84 0.0 <NA> 200 18.4 <NA> 1016.5 <NA> <NA> 4
2018-12-31 20:00:00 21.1 76 0.0 <NA> 190 22.3 <NA> 1015.8 <NA> <NA> 4
2018-12-31 21:00:00 21.1 73 0.0 <NA> 180 27.7 <NA> 1014.7 <NA> <NA> 4
2018-12-31 22:00:00 21.1 68 0.0 <NA> 190 27.7 <NA> 1015.7 <NA> <NA> 7
2018-12-31 23:00:00 20.0 73 0.0 <NA> 180 20.5 <NA> 1015.6 <NA> <NA> 7
[8760 rows x 11 columns]
🌥 Default Parameters
The default parameters for hourly data requests are listed here.