meteostat.TimeSeries.normalize

In contrast to model data, time series which were recorded by actual weather stations always contain gaps. For instance, because of a temporal outage of some of the sensors. However, when retrieving daily data, for example, you probably expect one record per day. The normalize method makes sure that gaps in the time series are filled.

Parameters

This method does not take any parameters.

Returns

A copy of self

Examples

Hourly

Get normalized weather data for 2018 in Vancouver, BC.

from datetime import datetime
from meteostat import Stations, Hourly

stations = Stations()
stations = stations.nearby(49.2497, -123.1193)
station = stations.fetch(1)

start = datetime(2018, 1, 1)
end = datetime(2018, 12, 31, 23, 59)

data = Hourly(station, start=start, end=end)
data = data.normalize()
data = data.fetch()

print(data)

Daily

Get normalized weather data for 2018 in Vancouver, BC.

from datetime import datetime
from meteostat import Daily

start = datetime(2018, 1, 1)
end = datetime(2018, 12, 31)

data = Daily('71892', start=start, end=end)
data = data.normalize()
data = data.fetch()

print(data)

Monthly

Get normalized weather data for Vancouver, BC.

from datetime import datetime
from meteostat import Stations, Monthly

stations = Stations()
stations = stations.nearby(49.2497, -123.1193)
station = stations.fetch(1)

start = datetime(2000, 1, 1)
end = datetime(2018, 12, 31)

data = Monthly(station, start, end)
data = data.normalize()
data = data.fetch()

print(data)