Hourly Data

If you want to investigate the weather on a particular day or a short period of time, the Hourly class is a perfect match. It may include model data to fill gaps in the observations.

Example

You can use the Hourly class to retrieve historical data and prepare the records for further processing. For more complex analysis and visulization tasks you can utilize Pandas.

# Import Meteostat library and dependencies
from datetime import datetime
from meteostat import Hourly

# Set time period
start = datetime(2018, 1, 1)
end = datetime(2018, 12, 31, 23, 59)

# Get hourly data
data = Hourly('72219', start, end)
data = data.fetch()

# Print DataFrame
print(data)

API

Parent Class: meteostat.TimeSeries

• meteostat.Hourly
• meteostat.TimeSeries.normalize
• meteostat.TimeSeries.aggregate
• meteostat.TimeSeries.interpolate
• meteostat.TimeSeries.convert
• meteostat.TimeSeries.coverage
• meteostat.TimeSeries.fetch
• meteostat.TimeSeries.count
• meteostat.TimeSeries.stations
• meteostat.TimeSeries.clear_cache

Data Structure

Each hour is represented by a Pandas DataFrame row which provides the weather data recorded at that time. These are the different columns:

Column	Description	Type
station	The Meteostat ID of the weather station (only if query refers to multiple stations)	String
time	The datetime of the observation	Datetime64
temp	The air temperature in °C	Float64
dwpt	The dew point in °C	Float64
rhum	The relative humidity in percent (%)	Float64
prcp	The one hour precipitation total in mm	Float64
snow	The snow depth in mm	Float64
wdir	The average wind direction in degrees (°)	Float64
wspd	The average wind speed in km/h	Float64
wpgt	The peak wind gust in km/h	Float64
pres	The average sea-level air pressure in hPa	Float64
tsun	The one hour sunshine total in minutes (m)	Float64
coco	The weather condition code	Float64