Pandas Degree Days (pandas_degreedays) is a Python package to calculate degree days.
You must provide a Pandas Series with temperature values.
Let's call ts_temp this Serie which looks like:
datetime
2014-03-20 23:00:00 11
2014-03-20 23:30:00 11
2014-03-21 00:00:00 11
2014-03-21 00:30:00 11
2014-03-21 01:00:00 11
2014-03-21 01:30:00 11
...
2014-11-01 20:00:00 12
2014-11-01 20:30:00 12
2014-11-01 21:00:00 12
2014-11-01 21:30:00 12
2014-11-01 22:00:00 12
2014-11-01 22:30:00 12
Name: temp, Length: 10757
You can get a time serie with temperature in sample folder and read it using:
import pandas as pd
filename = 'temperature_sample.xls'
df_temp = pd.read_excel(filename)
df_temp = df_temp.set_index('datetime')
ts_temp = df_temp['temp']
You can also fetch a time serie with temperature from OpenWeatherMap.org. You need to install first openweathermap_requests.
import logging
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
from pandas_degreedays.provider import TemperatureProvider
api_key = 'YOUR_API_KEY'
ts_temp = TemperatureProvider('OpenWeatherMap', api_key=api_key).get_from_coordinates(0.34189, 46.5798114, '20150101', '20150915')
#ts_temp = TemperatureProvider('OpenWeatherMap', api_key=api_key).get_from_place('Poitiers,FR', '20150101', '20150915')
We can see if some data are missing using:
idx = ts_temp.index
s_idx = pd.Series(idx, index=idx)
diff_idx = s_idx-s_idx.shift(1)
s_sampling_period = diff_idx.value_counts()
sampling_period = s_sampling_period.index[0] # most prevalent sampling period
not_sampling_period = (diff_idx != sampling_period) # True / False
We can interpolate linearly missing data using:
from pandas_degreedays import inter_lin_nan
ts_temp = inter_lin_nan(ts_temp, '1H') # interpolates linearly NaN
We can calculate degree days using:
from pandas_degreedays import calculate_dd
df_degreedays = calculate_dd(ts_temp, method='pro', typ='heating', Tref=18.0, group='yearly')
method can be:
'pro'(energy professionals) - this is default calculation method'meteo'
typ (calculation type) can be :
'heating'- this is default calculation type'cooling'
Tref is reference temperature - default value is 18.0
group can be:
'yearly'- this is default grouping option'yearly10'- same as'yearly'but year starts in October (10)'monthly''weekly'None- Any lambda function that can be use and that can be applied to a
datetime:
Example:
from pandas_degreedays import yearly_month
df_degreedays = calculate_dd(ts_temp, method='pro', typ='heating', Tref=18.0, group=lambda dt: yearly_month(dt, 10))
It outputs a Pandas DataFrame with degree days like:
Tmin Tmax Tavg Tref DD DD_cum
2014-03-22 7.0 11.0 9.00 18 9.000000 9.000000
2014-03-23 3.0 12.0 7.50 18 10.500000 19.500000
2014-03-24 0.0 10.0 5.00 18 13.000000 32.500000
2014-03-25 6.0 10.0 8.00 18 10.000000 42.500000
2014-03-26 5.0 12.0 8.50 18 9.500000 52.000000
2014-03-27 2.0 8.0 5.00 18 13.000000 65.000000
... ... ... ... ... ... ...
2014-10-26 5.0 17.0 11.00 18 7.000000 653.547663
2014-10-27 9.0 22.0 15.50 18 3.336923 656.884586
2014-10-28 7.5 20.0 13.75 18 4.544400 661.428986
2014-10-29 8.0 19.0 13.50 18 4.618182 666.047168
2014-10-30 12.0 22.0 17.00 18 1.992000 668.039168
2014-10-31 11.0 24.0 17.50 18 2.143077 670.182245
[224 rows x 6 columns]
You can display plot using:
from pandas_degreedays import plot_temp
plot_temp(ts_temp, df_degreedays)
pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It's a very convenient library to work with time series.
$ pip install pandas_degreedays
Get latest version using Git
$ git clone https://github.com/scls19fr/pandas_degreedays.git
$ cd pandas_degreedays
$ python setup.py install
- Documentation can be found at Read The Docs ;
- Source code and issue tracking can be found at GitHub.
- Feel free to tip me!
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