Daily Weather
Daily time series data is ideal for analyzing weather patterns over longer periods, such as weeks, months, or years.
🚀 Example
Let's fetch some daily temperature data for Frankfurt, Germany in 2018:
# Import Meteostat library and dependencies
from datetime import date
import meteostat as ms
# Set time period
start = date(2018, 1, 1)
end = date(2018, 12, 31)
# Get daily data
ts = ms.daily(ms.Station(id='10637'), start, end)
df = ts.fetch()
# Print DataFrame
print(df)
This is the output you would get:
temp tmin tmax rhum prcp snwd wspd wpgt pres tsun cldc
time
2018-01-01 8.1 6.6 11.2 70 1.1 0 26.6 59.8 1005.0 42 7
2018-01-02 6.4 5.2 8.0 74 5.8 0 22.0 50.8 1011.9 12 7
2018-01-03 8.1 5.3 10.4 72 6.3 0 36.4 83.9 999.1 144 6
2018-01-04 7.6 5.9 10.9 83 8.4 0 20.9 58.7 999.9 0 6
2018-01-05 8.5 7.1 10.2 83 4.8 0 18.7 59.4 1001.8 0 7
... ... ... ... ... ... ... ... ... ... ... ...
2018-12-27 -1.4 -2.1 -0.4 96 0.0 0 6.5 22.3 1030.7 0 8
2018-12-28 -0.5 -1.0 0.3 94 0.0 0 9.7 24.1 1031.8 0 8
2018-12-29 1.3 -0.7 4.6 92 0.8 0 16.9 45.7 1033.8 0 8
2018-12-30 7.2 4.5 8.3 76 0.0 0 17.3 48.6 1033.2 0 8
2018-12-31 6.8 5.6 9.0 94 0.0 0 9.7 43.9 1034.5 0 8
[365 rows x 11 columns]
🌥 Default Parameters
The default parameters for daily data requests are listed here.