pandas multiindex - remove rows based on number of sub index

Here is my dataframe :

df = pd.DataFrame(pd.DataFrame({"C1" : [0.5, 0.9, 0.1, 0.2, 0.3, 0.5, 0.2], 
                            "C2" : [200, 158, 698, 666, 325, 224, 584], 
                            "C3" : [15, 99, 36, 14, 55, 62, 37]},
                           index = pd.MultiIndex.from_tuples([(0,0), (1,0), (1,1), (2,0), (2,1), (3,0), (4,0)], 
                                                         names=['L1','L2'])))

df :

         C1      C2     C3
L1  L2          
0   0   0.5     200     15
1   0   0.9     158     99
    1   0.1     698     36
2   0   0.2     666     14
    1   0.3     325     55
3   0   0.5     224     62
4   0   0.2     584     37

I would like to keep the rows that only have one value in L1 subindex (0 in that case) in order to get something like that :

         C1      C2     C3
L1  L2          
0   0   0.5     200     15
3   0   0.5     224     62
4   0   0.2     584     37

Please, could you let me know if you have any clue to solve this problem ?

Sincerely

Solution

Use GroupBy.transform by first level with any column with GroupBy.size and compare by Series.eq and filter by boolean indexing:

df1 = df[df.groupby(level=0)['C1'].transform('size').eq(1)]

Or extract index of first level by Index.get_level_values and filter with inverted mask by ~ with Index.duplicated and keep=False for all dupes:

df1 = df[~df.index.get_level_values(0).duplicated(keep=False)]