pandas_degreedays 0.0.4

Pandas degree days

Welcome to pandas_degreedays’s documentation!

pandas_degreedays

Pandas Degree Days (pandas_degreedays) is a Python package to calculate degree days.

Usage

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
ts_temp = TemperatureProvider('OpenWeatherMap', api_key='').get_from_coordinates(0.34189, 46.5798114, '20120601', '20141215')

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:

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)

About Pandas

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.

Install

From Python package index

$ pip install pandas_degreedays

From source

Get latest version using Git

$ git clone https://github.com/scls19fr/pandas_degreedays.git
$ cd pandas_degreedays
$ python setup.py install

Links

• Documentation can be found at Read The Docs ;

• Source code and issue tracking can be found at GitHub.

• Feel free to tip me!