how to access rows of df depending on values of another column in another df

I have a df2 and a temp df that has more rows and more columns(some are common) than df2.

df2=pd.DataFrame(data=(['False','False',0.9],['False','True', 0.1],['True','False',0.2],['True','True',0.8]), columns=['Winter?','Sprinkler?', 'Rain?'] )

temp=pd.DataFrame(list(itertools.product([False, True], repeat=len(['Winter?','Sprinkler?', 'Rain?']))),columns=['Winter?','Sprinkler?', 'Rain?'])
temp["p"] = np.nan

I want to get the ‘p’col values from df2 into temp df, in the rows with compatible values between the two (see screenshot below.)

so expected output would be the following df:

output_df = pd.DataFrame(list(itertools.product([False, True], repeat=len(['Winter?','Sprinkler?', 'Rain?']))),columns=['Winter?','Sprinkler?', 'Rain?'])
output_df["p"] = [0.9, 0.1, 0.9, 0.1, 0.2, 0.8, 0.2, 0.8]

This should not be as hard as I’m finding it, I understand that dataframes thrive for these kind of stuff but I have been stuck for quite some days so any help would be much appreciated

Answer

Use merge:

temp = temp.drop(columns='p').merge(df2, on=['Winter?', 'Rain?'], how='left')
print(temp)

# Output:
   Winter?  Sprinkler?  Rain?    p
0    False       False  False  0.9
1    False       False   True  0.1
2    False        True  False  0.9
3    False        True   True  0.1
4     True       False  False  0.2
5     True       False   True  0.8
6     True        True  False  0.2
7     True        True   True  0.8

Note: I dropped the column p from temp because it’s not necessary.

I slightly modified your dataframe df2. If you want to merge data, you must have the same dtype between columns. In your sample, Winter and Rain of df2 are strings while Winter and Rain of temp are booleans.

Setup:

df2 = pd.DataFrame({'Winter?': [False, False, True, True],
                    'Rain?': [False, True, False, True],
                    'p': [0.9, 0.1, 0.2, 0.8]})