I am trying to figure out the names who only have specific column value and nothing else.
I have tried filtering the rows according to the column value but that isn't what I want, I want the names who only went to eat pizza.
I want names who only had pizza, so my code should return John only and not peter as john only had pizza
Your description is not clear. At first, it looks like a simple .loc will be enough. However, after viewing your picture of sample data, I realized it is not that simple. To get what you want, you need to identify duplicated or non-duplicated names having one Restaurant value only, and pick it. To do this, you need to use nunique and check it eq(1), and assign it a mask m. Finally, using m with slicing to get your desire output:
Your sample data:
In [512]: df
Out[512]:
Name Restaurant
0 john pizza
1 peter kfc
2 john pizza
3 peter pizza
4 peter kfc
5 peter pizza
6 john pizza
m = df.groupby('Name').Restaurant.transform('nunique').eq(1)
df[m]
Out[513]:
Name Res
0 john pizza
2 john pizza
6 john pizza
If you want to show only one row, just chain additional .drop_duplicates
df[m].drop_duplicates()
Out[515]:
Name Restaurant
0 john pizza
