# Daily Data

Aggregated daily data is very useful when analyzing weather and climate over medium to long periods of time. It may include model data to fill gaps in the observations.

# 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'])
plt.show()

# API

Parent Class: meteostat.TimeSeries

# 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

← Hourly Data Monthly Data →