Calculate conditional probability using groupby and shift operations in Pandas dataframe

I have a dataframe with patients and their visits and the presence of a disease in their left and/or right eye is labeled with {0,1} values (0 = not present and 1 = present). The dataset looks like this:

Patient   R L

P_1       0 1

P_1       1 1

P_1       0 1

P_1       0 1

P_1       0 1

P_2       1 1

P_2       0 1

P_2       0 1

P_2       1 1

P_3       0 0

P_3       1 1

P_3       0 0

P_3       0 1

P_3       1 1

P_3       0 1

and so on.....

How can I calculate, for example, the conditional probability of P(R=1 | L=1) using grouby and shift operations in an elegant way?

Solution

IIUC:

df.groupby('L').R.mean()

gives

L
0    0.000000
1    0.384615
Name: R, dtype: float64

So the answer: P(R=1|L=1) = 0.384, and P(R=1|L=0) = 0.

Or if we want to get probability on patients as well:

df.groupby(['Patient','L']).R.mean()

gives:

Patient  L
P_1      1    0.2
P_2      1    0.5
P_3      0    0.0
         1    0.5
Name: R, dtype: float64

so, for example, P(R=1|Patent=P_3, L=1) = 0.5.