Selecting value in a pandas dataframe column, corresponding to values in other columns

Question

So I'm new to python and I'm working with a dataframe using pandas (can't use packages besides pandas) and I have taken user input (make,model,type,rating) for car info for 6 different cars:

     make    model   type rating
0    ford  mustang  coupe      A
1   chevy   camaro  coupe      B
2    ford   fiesta  sedan      C
3    ford    focus  sedan      A
4    ford   taurus  sedan      B
5  toyota    camry  sedan      B

I wanted conditional probabilities for this data, and I did this using a value_counts dataframe,

print df.groupby('rating')['type'].value_counts()
print df.groupby('rating')['type'].count()
conditional = (df.groupby('rating')['type'].value_counts() / df.groupby('rating')['type'].count()).reset_index(name="Cond")
print conditional

Which resulted in the conditional probabilities I was looking for:

  rating   type  cond
0      A  coupe         0.500000
1      A  sedan         0.500000
2      B  sedan         0.666667
3      B  coupe         0.333333
4      C  sedan         1.000000

Now I need to to print individual probabilities. How would I go about selecting individual probabilities here based on conditions in the 'make' and 'model' columns?

For example on the conditional probability dataframe, the conditional probability P(type=sedan|rating=B) = 0.666667. I want to select and print this individual probability, however I don't want to print based on index (like index 2 on the "cond" column), but by selecting for the value in "cond" for when rating = B and type = sedan

Answer 1

IIUC by using crosstab with normalize

pd.crosstab(df.rating,df.type,normalize='index').stack().reset_index()
Out[36]: 
  rating   type         0
0      A  coupe  0.500000
1      A  sedan  0.500000
2      B  coupe  0.333333
3      B  sedan  0.666667
4      C  coupe  0.000000
5      C  sedan  1.000000