Encontré que el código user2689410 se rompió cuando intenté con window = '1M' ya que el delta en el mes comercial arrojó este error:
AttributeError: 'MonthEnd' object has no attribute 'delta'
Agregué la opción de pasar directamente un delta de tiempo relativo, para que pueda hacer cosas similares para períodos definidos por el usuario.
Gracias por los consejos, aquí está mi intento, espero que sea de utilidad.
def rolling_mean(data, window, min_periods=1, center=False):
""" Function that computes a rolling mean
Reference:
http://stackoverflow.com/questions/15771472/pandas-rolling-mean-by-time-interval
Parameters
----------
data : DataFrame or Series
If a DataFrame is passed, the rolling_mean is computed for all columns.
window : int, string, Timedelta or Relativedelta
int - number of observations used for calculating the statistic,
as defined by the function pd.rolling_mean()
string - must be a frequency string, e.g. '90S'. This is
internally converted into a DateOffset object, and then
Timedelta representing the window size.
Timedelta / Relativedelta - Can directly pass a timedeltas.
min_periods : int
Minimum number of observations in window required to have a value.
center : bool
Point around which to 'center' the slicing.
Returns
-------
Series or DataFrame, if more than one column
"""
def f(x, time_increment):
"""Function to apply that actually computes the rolling mean
:param x:
:return:
"""
if not center:
start_date = x - time_increment + timedelta(0, 0, 1)
end_date = x
else:
start_date = x - time_increment/2 + timedelta(0, 0, 1)
end_date = x + time_increment/2
dslice = col[start_date:end_date]
if dslice.size < min_periods:
return np.nan
else:
return dslice.mean()
data = DataFrame(data.copy())
dfout = DataFrame()
if isinstance(window, int):
dfout = pd.rolling_mean(data, window, min_periods=min_periods, center=center)
elif isinstance(window, basestring):
time_delta = pd.datetools.to_offset(window).delta
idx = Series(data.index.to_pydatetime(), index=data.index)
for colname, col in data.iteritems():
result = idx.apply(lambda x: f(x, time_delta))
result.name = colname
dfout = dfout.join(result, how='outer')
elif isinstance(window, (timedelta, relativedelta)):
time_delta = window
idx = Series(data.index.to_pydatetime(), index=data.index)
for colname, col in data.iteritems():
result = idx.apply(lambda x: f(x, time_delta))
result.name = colname
dfout = dfout.join(result, how='outer')
if dfout.columns.size == 1:
dfout = dfout.ix[:, 0]
return dfout
Y el ejemplo con una ventana de tiempo de 3 días para calcular la media:
from pandas import Series, DataFrame
import pandas as pd
from datetime import datetime, timedelta
import numpy as np
from dateutil.relativedelta import relativedelta
idx = [datetime(2011, 2, 7, 0, 0),
datetime(2011, 2, 7, 0, 1),
datetime(2011, 2, 8, 0, 1, 30),
datetime(2011, 2, 9, 0, 2),
datetime(2011, 2, 10, 0, 4),
datetime(2011, 2, 11, 0, 5),
datetime(2011, 2, 12, 0, 5, 10),
datetime(2011, 2, 12, 0, 6),
datetime(2011, 2, 13, 0, 8),
datetime(2011, 2, 14, 0, 9)]
idx = pd.Index(idx)
vals = np.arange(len(idx)).astype(float)
s = Series(vals, index=idx)
rm = rolling_mean(s, window=relativedelta(days=3))
>>> rm
Out[2]:
2011-02-07 00:00:00 0.0
2011-02-07 00:01:00 0.5
2011-02-08 00:01:30 1.0
2011-02-09 00:02:00 1.5
2011-02-10 00:04:00 3.0
2011-02-11 00:05:00 4.0
2011-02-12 00:05:10 5.0
2011-02-12 00:06:00 5.5
2011-02-13 00:08:00 6.5
2011-02-14 00:09:00 7.5
Name: 0, dtype: float64
rolling_*funciones integradas.