meteostat.TimeSeries.interpolate

The interpolate method closes gaps in time series by applying an interpolation algorithm. Please make sure to first normalize your data before interpolating.

Parameters

The limit parameter specifies the maximum number of consecutive NaN values which should be filled.

Parameter	Description	Type	Default
limit	Maximum number of missing consecutive values to fill	Integer	3

Returns

A copy of self

Examples

Hourly

Get normalized 2018 weather data for Vancouver, BC and close gaps of up two three consecutive hours.

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.interpolate()
data = data.fetch()

print(data)

Daily

Get normalized 2018 weather data for Vancouver, BC and close gaps of up two three consecutive days.

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.interpolate()
data = data.fetch()

print(data)

Monthly

Get normalized weather data for Vancouver, BC and close gaps of up two three consecutive months.

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.interpolate()
data = data.fetch()

print(data)