Concatenate two columns excepting strings from a list

I concatenate two columns for the situation where strings from column 'words' are not present in the column 'sentence'. My code is:

def check(row):
     df['sentence'] = df['sentence'].astype(str)
     df['words'] = df['words'].astype(str)
     left = row['sentence'].split()
     right = row['words'].split()
     unmatched = []
     for y in left:
        word = "".join([x for x in y.lower() if x not in string.punctuation])
        if word not in [v.lower() for v in right]:
           unmatched.append(y)
     return " ".join(unmatched)
mask = df['type'] == 'Is there a match with the Words?'
df.loc[mask, 'new'] = df.loc[mask, :].apply(check, axis=1)
df['new'] = np.where(c, df['new']   ' '   df['words'], df['new'])
df['new'] = df['new'].str.replace('nan', '')
df['new'] = df['new'].fillna("")

Additionally, I want to restrict the concatenation per row if, in column 'words' I have strings present in this list:

restricted = ['not present', 'for sale', 'unknown']

Here is an example of how the result should look like

      words             sentence                    output
0   unknown  This is a new paint       This is a new paint
1     brown   This is a new item  This is a new item brown
2  for sale   The product is new        The product is new

Output given by the code above is:

output
 This is a new paint unknown
 This is a new item brown
 The product is new for sale

CodePudding user response：

Given:

      words             sentence
0   unknown  This is a new paint
1     brown   This is a new item
2  for sale   The product is new

Doing:

restricted = ['not present', 'for sale', 'unknown']
mask = df.words.str.contains('|'.join(restricted))
df['output'] = df.sentence.where(mask, df.sentence   ' '   df.words)
print(df)

Output:

      words             sentence                    output
0   unknown  This is a new paint       This is a new paint
1     brown   This is a new item  This is a new item brown
2  for sale   The product is new        The product is new