how to loop a dataframe and check if there is the same name as another column

so im just one step ahead of cleaning my dataframe but i encountered some problem when i were to clean the "name" column of this dataframe below:

i wanted to check each of the row if the company values is equal to the company name contained inside the "name" column, if TRUE: ignore it. if FALSE: delete the row
while it may looks simple since the "company name" are just the first index, but my thought is what if there are some names that put the "company name" somewhere in the "name" columns(Not the first index)

ive tried my code below but it doesnt seem to work:

for x in file["company"]:#Loop company
  for i in file["name"]:#Loop name
    i = i.title().split(" ")#Capitalize each word and split name
    for j in i:
      if j == x:#if the "company name" is equal to "company values"
        pass #ignore
      else:
        file.drop("""THAT ROW""") #remove that row

What you want is to drop rows whose name value does not contain its company value.

Let's start with a toy dataframe:

data = [
    {
        'name': 'Hyundai something',
        'company': 'Hyundai',
    },
    {
        'name': 'Tesla Model Y',
        'company': 'Tesla',
    },
    {
        'name': 'something Hyundai',
        'company': 'Hyundai',
    },
    {
        'name': 'something',
        'company': 'Hyundai',
    },
    {
        'name': 'XYZ Ford Car',
        'company': 'Ford',
    },
]
df = pd.DataFrame(data)

Next, we use apply to iterate rows and return True if company value is within name value. Note that I added .lower() to ignore case. You can adjust this to fit what you need.

contained = dd.apply(
    lambda row: row['company'].lower() in row['name'].lower(), 
    axis=1
)

Finally, you can either filter your dataframe by condition, or you can drop the False indices.

df.drop(contained[contained == False].index)

	name	            company
0	Hyundai something	Hyundai
1	Tesla Model Y	    Tesla
2	something Hyundai	Hyundai
4	XYZ Ford Car	    Ford

Use boolean indexing with select rows if contain substring after lowercase and splitting name column in list comprehension for improve performance:

df = file[[x.lower() in y.lower().split() for x, y in zip(file['company'], file['name'])]]