You can apply an arbitrary function across a dataframe row using DataFrame.apply.

In your case, you could define a function like:

def conditions(s):
    if (s['discount'] > 20) or (s['tax'] == 0) or (s['total'] > 100):
        return 1
    else:
        return 0

And use it to add a new column to your data:

df_full['Class'] = df_full.apply(conditions, axis=1)

import numpy as np
import pandas as pd

data = [(27450, 27450, 29420,"10/10/2016"),
        (29420 , 36142, 29420, "10/10/2016"),
        (11 , 11, 27450, "10/10/2016")] 
df = pd.DataFrame(data, columns=("User_id","Actor1","Actor2", "Time"))
mask = (df['User_id'] == df['Actor1'])
df['first actor'] = mask.astype(int)
df['other actor'] = np.where(mask, df['Actor2'], df['Actor1'])
print(df)

yields

   User_id  Actor1  Actor2        Time  first actor  other actor
0    27450   27450   29420  10/10/2016            1        29420
1    29420   36142   29420  10/10/2016            0        36142
2       11      11   27450  10/10/2016            1        27450

First create a boolean mask which is True when User_id equals Actor1:

In [51]: mask = (df['User_id'] == df['Actor1']); mask
Out[51]: 
0     True
1    False
2     True
dtype: bool

Converting mask to ints creates the first column:

In [52]: mask.astype(int)
Out[52]: 
0    1
1    0
2    1
dtype: int64

Then use np.where to select between two values. np.where(mask, A, B) returns an array whose ith value is A[i] if mask[i] is True, and B[i] otherwise. Thus, np.where(mask, df['Actor2'], df['Actor1']) takes the value from Actor2 where mask is True, and the value from Actor1 otherwise:

In [53]: np.where(mask, df['Actor2'], df['Actor1'])
Out[53]: array([29420, 36142, 27450])