I have a dataframe that looks like the following
df
city val
0 London 3
1 London -1
2 London -1
3 Paris -5
4 Paris -2
5 Rome 2
6 Rome 2
I want to select only the city that have at least one val < 0. I would like to have the following:
df
city val
0 London 3
1 London -1
2 London -1
3 Paris -5
4 Paris -2
Use loc with groupby and .transform(min):
>>> df.loc[df.groupby('city')['val'].transform(min).lt(0)]
city val
0 London 3
1 London -1
2 London -1
3 Paris -5
4 Paris -2
>>>
Since you're filtering under 0, just filter if the minimum value of each group is lower than 0.
Create mask and filter rows with GroupBy.transform:
df = df[df['val'].lt(1).groupby(df['city']).transform('any')]
print (df)
city val
0 London 3
1 London -1
2 London -1
3 Paris -5
4 Paris -2
Or filter city with at least one row match less like 1 and filter original city column by Series.isin:
df[df['city'].isin(df.loc[df['val'].lt(1), 'city'])]
