Pandas DataFrame Resample

#! /usr/bin/env python3

import pandas as pd
import numpy as np
import datetime

dr = pd.date_range('11/6/2012', periods = 12, freq = 'W-Tue')
df = pd.DataFrame(np.random.randn(len(dr)), index = dr, columns = ['column1'])
df = df.resample(rule = '28D', how = 'mean')

# The start dates for each period
df
#              column1
# 2012-11-06 -0.134757
# 2012-12-04 -0.382133
# 2013-01-01 -0.468343
# 2013-01-29  0.000000

# [4 rows x 1 columns]

# Vacation dates
vaca_days = list(pd.date_range(start = '2012-12-1', periods = 12, freq = 'D'))
vaca_days
# Timestamp('2012-12-01 00:00:00', tz=None),
#  Timestamp('2012-12-02 00:00:00', tz=None),
#  Timestamp('2012-12-03 00:00:00', tz=None),
#  Timestamp('2012-12-04 00:00:00', tz=None),
#  Timestamp('2012-12-05 00:00:00', tz=None),
#  Timestamp('2012-12-06 00:00:00', tz=None),
#  Timestamp('2012-12-07 00:00:00', tz=None),
#  Timestamp('2012-12-08 00:00:00', tz=None),
#  Timestamp('2012-12-09 00:00:00', tz=None),
#  Timestamp('2012-12-10 00:00:00', tz=None),
#  Timestamp('2012-12-11 00:00:00', tz=None),
#  Timestamp('2012-12-12 00:00:00', tz=None)]

# Clearly there should be 3 vacation dates in the 2012-11-06 period
# and 9 vacation dates in the 2013-12-04 period
# ---------------------------
# This gives me wrong numbers, because it tries to start the period on vaca_days[0]
pd.Series(1, vaca_days).resample('28D', how='sum')
# 2012-12-01    12
# 2012-12-29     0
# Freq: 28D, dtype: int64

# No help here
pd.Series(1, vaca_days).resample('28D', how='sum').shift(-1)
# 2012-12-01     0
# 2012-12-29   NaN
# Freq: 28D, dtype: float64

# The only workaround I could figure out: Append on the first start day
vaca_days.append(pd.Timestamp('2012-11-06'))

vaca_series = pd.Series([0] + [1 for each in vaca_days[:-1]], sorted(vaca_days))
vaca_series = vaca_series.resample(rule = '28D', how = sum)

vaca_series
# 2012-11-06    3
# 2012-12-04    9
# 2013-01-01    0
# Freq: 28D, dtype: int64

df['Vacation Days'] = vaca_series
df['Vacation Days'] = df['Vacation Days'].fillna(0)
df

#                   column1  Vacation Days
# 2012-11-06  -4.761727e-01              3
# 2012-12-04   5.715697e-01              9
# 2013-01-01   4.065451e-02              0
# 2013-01-29  2.781364e-309              0

# [4 rows x 2 columns]