Pandas, filter rows which column contain another column

You can use boolean indexing with mask created by apply and in if need filter columns A and B per rows:

#if necessary strip ' in all values
df = df.apply(lambda x: x.str.strip("'"))
#df = df.applymap(lambda x: x.strip("'"))

print (df.apply(lambda x: x.A in x.B, axis=1))
0     True
1     True
2    False
dtype: bool

df = df[df.apply(lambda x: x.A in x.B, axis=1)]
print (df)
     A      B
0  lol  lolec
1  ram  rambo

Difference of solutions – input DataFrame is changed:

print (df)
     A      B
0  lol    pio
1  ram  rambo
2   ki  lolec

print (df[df.apply(lambda x: x.A in x.B, axis=1)])
     A      B
1  ram  rambo

print (df[df['B'].str.contains("|".join(df['A']))])
    A      B
1  ram  rambo
2   ki  lolec

for improve performance use list comprehension:

df = df[[a in b for a, b in zip(df.A, df.B)]]