compare multiple columns of pandas dataframe with one column of another dataframe having different length and index

Question

i have dataframe dd1:

     A   B   C  D    E  
  0  V  10   5  18  20       
  1  W   9  18  11  13      
  2  X   8   7  12   5      
  3  Y   7   9   7   8       
  4  Z   6   5   3  90       

I want to add a column 'Result' in dd1 which should return zero if value in column 'E' is greater than values in dd1 columns A,B,C and D else return that value which is greater than dd1 column E

   A   B   C   D   E  Result
0  V  10   5  18  20       0
1  W   9  18  11  13      18
2  X   8   7  12   5      12
3  Y   7   9   7   8       9
4  Z   6   5   3  90       0

Answer 1

You can compare by DataFrame.lt columns selected by positions by DataFrame.iloc or list of columns names, check if all True s per rows by DataFrame.all and set values by numpy.where to new column:

df1 = df.iloc[:, 1:-1]
#for select by columns names
#df1 = df[['B','C','D']]
df['Result'] = np.where(df1.lt(df['E'], axis=0).all(axis=1), 0, df1.max(axis=1))

Another idea is compare by Series.gt with max values per selected columns and then multiple by Series.mul :

s = df.iloc[:, 1:-1].max(axis=1)
df['Result'] = s.gt(df['E']).mul(s)
print (df)
   A   B   C   D   E  Result
0  V  10   5  18  20       0
1  W   9  18  11  13      18
2  X   8   7  12   5      12
3  Y   7   9   7   8       9
4  Z   6   5   3  90       0

Answer 2

You can get the max value of each row (axis=1), and compare that to the E column. If this condition is True, we return 0, else we return the max value of that row:

df['Result'] = np.where(df.iloc[:, 1:].max(axis=1).eq(df['E']), # our condition
                        0,                                      # value if true
                        df.iloc[:, 1:].max(axis=1))             # value if false

---

print(df)
   A   B   C   D   E  Result
0  V  10   5  18  20       0
1  W   9  18  11  13      18
2  X   8   7  12   5      12
3  Y   7   9   7   8       9
4  Z   6   5   3  90       0

Explanation
numpy.where works as followed:

np.where(condition, value if true, value if false)