Boolean comparison of a list of substring with a list of string

Question

I have research, but found no answer to the question below.

How can I do a boolean comparison for a list of substrings in a list of strings?

Below is the code:

string = {'strings_1': ['AEAB', 'AC', 'AI'], 
             'strings_2':['BB', 'BA', 'AG'], 
             'strings_3': ['AABD', 'DD', 'PP'], 
             'strings_4': ['AV', 'AB', 'BV']}

df_string = pd.DataFrame(data = string)

substring_list = ['AA', 'AE']

for row in df_string.itertuples(index = False):
    combine_row_str = [row[0], row[1], row[2]]

    #below is the main operation
    print(all(substring in row_str for substring in substring_list for row_str in combine_row_str))

The output I get is:

False
False
False

The output I want is:

True
False
False

Answer 1

Here's one way using pd.DataFrame.sum and a list comprehension:

df = pd.DataFrame(data=string)

lst = ['AA', 'AE']

df['test'] = [all(val in i for val in lst) for i in df.sum(axis=1)]

print(df)

  strings_1 strings_2 strings_3 strings_4   test
0      AEAB        BB      AABD        AV   True
1        AC        BA        DD        AB  False
2        AI        AG        PP        BV  False

Answer 2

Since you are using pandas, you can invoke apply row-wise and str.contains with regex to find if strings do match. The first step is to find if any of the values match the strings in the substring_list:

df_string.apply(lambda x: x.str.contains('|'.join(substring_list)), axis=1)

this returns:

   strings_1  strings_2  strings_3  strings_4
0       True      False       True      False
1      False      False      False      False
2      False      False      False      False

Now, what is not clear though is whether you want to return true if both substrings are present within a row or only either of them. If only either of them, you can simply add any() after the contains() method:

df_string.apply(lambda x: x.str.contains('|'.join(substring_list)).any(), axis=1)

this returns:

0     True
1    False
2    False
dtype: bool

For the second case jpp provides a one line solution with concating row elements into one string, but please note it will not work for corner cases when you have two elems in a row, say, "BBA" and "ABB" and you try to match for "AA". Concated string "BBAABB" will still match "AA", which is wrong. I would like to propose a solution with apply and an extra function, so that code is more readable:

def areAllPresent(vals, patterns):
  result = []
  for pat in patterns:
    result.append(any([pat in val for val in vals]))
  return all(result)

df_string.apply(lambda x: areAllPresent(x.values, substring_list), axis=1)

Due to your sample dataframe it will still return the same result, but it works for cases when matching both is necessary:

0     True
1    False
2    False
dtype: bool