# Daily Data

Aggregated daily data is very useful when analyzing weather and climate over a longer period of time. The data provided through the Daily class contains only aggregated observations. Model data is not included in those statistics which makes the data very accurate, but causes some gaps in the time series.

# Example

You can use the Daily 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
import matplotlib.pyplot as plt
from meteostat import Stations, Daily

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

# Get daily data
data = Daily('10637', start, end)
data = data.fetch()

# Plot line chart including average, minimum and maximum temperature
data.plot(y=['tavg', 'tmin', 'tmax'])


# Data Structure

Each day is represented by a Pandas DataFrame row which provides the weather data recorded on that day. 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 date Datetime64
tavg The average air temperature in °C Float64
tmin The minimum air temperature in °C Float64
tmax The maximum air temperature in °C Float64
prcp The daily 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 daily sunshine total in minutes (m) Float64
Last Updated: 12/8/2020, 11:28:13 AM