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Say I have the following DataFrame which has a 0/1 entry depending on whether something happened/didn't happen within a certain month.

Y = [0,0,1,1,0,0,0,0,1,1,1]
X = pd.date_range(start = "2010", freq = "MS", periods = len(Y))

df = pd.DataFrame('R': Y,index = X)



 R
2010-01-01 0
2010-02-01 0
2010-03-01 1
2010-04-01 1
2010-05-01 0
2010-06-01 0
2010-07-01 0
2010-08-01 0
2010-09-01 1
2010-10-01 1
2010-11-01 1

What I want is to create a 2nd column that lists the # of months until the next occurrence of a 1.

That is, I need:

 R F
2010-01-01 0 2
2010-02-01 0 1
2010-03-01 1 0
2010-04-01 1 0
2010-05-01 0 4
2010-06-01 0 3
2010-07-01 0 2
2010-08-01 0 1
2010-09-01 1 0
2010-10-01 1 0
2010-11-01 1 0

What I've tried: I haven't gotten far, but I'm able to fill the first bit

A = list(df.index)
T = df[df['R']==1]

a = df.index[0]
b = T.index[0]
c = A.index(b) - A.index(a)

df.loc[a:b, 'F'] = np.linspace(c,0,c+1)

 R F
2010-01-01 0 2.0
2010-02-01 0 1.0
2010-03-01 1 0.0
2010-04-01 1 NaN
2010-05-01 0 NaN
2010-06-01 0 NaN
2010-07-01 0 NaN
2010-08-01 0 NaN
2010-09-01 1 NaN
2010-10-01 1 NaN
2010-11-01 1 NaN

EDIT Probably would have been better to provide an original example that spanned multiple years.

Y = [0,0,1,1,0,0,0,0,1,1,1,0,0,1,1,1,0,1,1,1]
X = pd.date_range(start = "2010", freq = "MS", periods = len(Y))

df = pd.DataFrame('R': Y,index = X)

Here is my way

s=df.R.cumsum()
df.loc[df.R==0,'F']=s.groupby(s).cumcount(ascending=False)+1
df.F.fillna(0,inplace=True)

df
Out[12]: 
 R F
2010-01-01 0 2.0
2010-02-01 0 1.0
2010-03-01 1 0.0
2010-04-01 1 0.0
2010-05-01 0 4.0
2010-06-01 0 3.0
2010-07-01 0 2.0
2010-08-01 0 1.0
2010-09-01 1 0.0
2010-10-01 1 0.0
2010-11-01 1 0.0

Create a series containing your dates, mask this series when your R series is not equal to 1, bfill, and subtract!

u = df.index.to_series()

ii = u.where(df.R.eq(1)).bfill()

12 * (ii.dt.year - u.dt.year) + (ii.dt.month - u.dt.month)

2010-01-01 2
2010-02-01 1
2010-03-01 0
2010-04-01 0
2010-05-01 4
2010-06-01 3
2010-07-01 2
2010-08-01 1
2010-09-01 0
2010-10-01 0
2010-11-01 0
Freq: MS, dtype: int64

Here is a way that worked for me, not as elegant as @user3483203 but it does the job.

df['F'] = 0 
for i in df.index: 
 j = i 
 while df.loc[j, 'R'] == 0: 
 df.loc[i, 'F'] =df.loc[i, 'F'] + 1 
 j=j+1 
df 

################
Out[39]: 
 index R F
0 2010-01-01 0 2
1 2010-02-01 0 1
2 2010-03-01 1 0
3 2010-04-01 1 0
4 2010-05-01 0 4
5 2010-06-01 0 3
6 2010-07-01 0 2
7 2010-08-01 0 1
8 2010-09-01 1 0
9 2010-10-01 1 0
10 2010-11-01 1 0

In [40]: 

My take

s = (df.R.diff().ne(0) | df.R.eq(1)).cumsum()
s.groupby(s).transform(lambda s: np.arange(len(s),0,-1) if len(s)>1 else 0)

2010-01-01 2
2010-02-01 1
2010-03-01 0
2010-04-01 0
2010-05-01 4
2010-06-01 3
2010-07-01 2
2010-08-01 1
2010-09-01 0
2010-10-01 0
2010-11-01 0
Freq: MS, Name: R, dtype: int64