df = pd.DataFrame([['Jhon',15,'A'],['Anna',19,'B'],['Paul',25,'D']])
df. columns = ['Name','Age','Grade']

df
Out[472]: 
   Name  Age Grade
0  Jhon   15     A
1  Anna   19     B
2  Paul   25     D

You can get the index of your row:

i = df[((df.Name == 'jhon') &( df.Age == 15) & (df.Grade == 'A'))].index

and then drop it:

df.drop(i)
Out[474]: 
   Name  Age Grade
1  Anna   19     B
2  Paul   25     D

As @jezrael pointed our, you can also just negate all three:

df[((df.Name != 'jhon') &( df.Age != 15) & (df.Grade != 'A'))]
Out[477]: 
   Name  Age Grade
1  Anna   19     B
2  Paul   25     D

Find the row you want to delete, and use drop.

delete_row = df[df["Int"]==0].index
df = df.drop(delete_row)
print(df)
Code    Int
1   A   1
2   B   1

Further more. you can use iloc to find the row, if you know the position of the column

delete_row = df[df.iloc[:,1]==0].index
df = df.drop(delete_row)

If I'm understanding correctly, it should be as simple as:

df = df[df.line_race != 0]

Use DataFrame.drop and pass it a Series of index labels:

In [65]: df
Out[65]: 
       one  two
one      1    4
two      2    3
three    3    2
four     4    1
    
    
In [66]: df.drop(df.index[[1,3]])
Out[66]: 
       one  two
one      1    4
three    3    2