Mapping methods across multiple columns in a Pandas DataFrame

Question

I have a Pandas dataframe where the values are lists:

import pandas as pd

DF = pd.DataFrame({\'X\':[[1, 5], [1, 2]], \'Y\':[[1, 2, 5], [1, 3, 5]]})
DF
                  


        

Answer 1

Option 1
set conversion and difference using np.where

df_temp = DF.applymap(set)
DF['x_sub_y'] = np.where(df_temp.X - df_temp.Y, False, True)
DF
        X          Y  x_sub_y
0  [1, 5]  [1, 2, 5]     True
1  [1, 2]  [1, 3, 5]    False

---

Option 2
Faster, astype conversion

DF['x_sub_y'] = ~(DF.X.apply(set) - DF.Y.apply(set)).astype(bool)
DF 
        X          Y  x_sub_y
0  [1, 5]  [1, 2, 5]     True
1  [1, 2]  [1, 3, 5]    False

---

Option 3
Fun with np.vectorize

def foo(x):
     return not x

v = np.vectorize(foo)    
DF['x_sub_y'] = v(DF.X.apply(set) - DF.Y.apply(set)) 
DF
        X          Y  x_sub_y
0  [1, 5]  [1, 2, 5]     True
1  [1, 2]  [1, 3, 5]    False

Extending Scott Boston's answer for speed using the same approach:

def foo(x, y):
    return set(x).issubset(y)

v = np.vectorize(foo)

DF['x_sub_y'] = v(DF.X, DF.Y)
DF
        X          Y  x_sub_y
0  [1, 5]  [1, 2, 5]     True
1  [1, 2]  [1, 3, 5]    False

Small

1000 loops, best of 3: 460 µs per loop           # Before       
10000 loops, best of 3: 103 µs per loop          # After

Large (df * 10000)

1 loop, best of 3: 1.26 s per loop               # Before   
100 loops, best of 3: 13.3 ms per loop           # After