Drop similar rows of dataframe except for one column
It seems a trivial question, but I don't find anything on the web. I have a dataframe with **inconsistent rows**. In the example below `primary_key` is the primary key so it has to be once in dataset, while `info` is an information of `primary_key`. While for **exactly repeated rows** I want to keep only one of them using a function like `df.drop_duplicates(keep = 'first')`, I don't know which keep in the case in which primary keys (`primary_key`) are the same so refer to the same item, but `info` is different, because they are inconsistent information. In this case I'd like to remove all of these rows with a function like `df.drop_duplicates(subset = "primary_key" , keep = False)`. I don't know how to do this kind of filter because I want to keep 1 row if one case and 0 rows in the other, depending on the values of a specific column within groups of _similar rows_.
 
### Desired behaviour
In:
```bash
     primary_key    info
0    a              1 
1    a              1 #duplicated row of index 0
2    a              2 #similar row of indexes 0 and 1 but inconsistent with info field
3    b              1 
4    b              1 #duplicated row of index 3
```

Out:
```bash
     primary_key    info
1    b              1  #the only primary key with the same value on info field
```

Thank you