How can I create a pandas dataframe column for each part-of-speech tag?

I have a dataset that consists of tokenized, POS-tagged phrases as one column of a dataframe:

I want to create a new column in the dataframe, consisting only of the proper nouns in the previous column:

Right now, I'm trying something like this for a single row:

if 'NNP' in df['Description_POS'][96][0:-1]:
df['Proper Noun'] = df['Description_POS'][96]

But then I don't know how to loop this for each row, and how to obtain the tuple which contains the proper noun. I'm very new right now and at a loss for what to use, so any help would be really appreciated!

Edit: I tried the solution recommended, and it seems to work, but there is an issue.

this was my dataframe: Original dataframe

After implementing the code recommended

df['Proper Nouns'] = df['POS_Description'].apply(
    lambda row: [i[0] for i in row if i[1] == 'NNP'])

it looks like this: Dataframe after creating a proper nouns column

Solution

You can use the apply method, which as the name suggests will apply the given function to every row of the dataframe or series. This will return a series, which you can add as a new column to your dataframe

df['Proper Nouns'] = df['POS_Description'].apply(
    lambda row: [i[0] for i in row if i[1] == 'NNP'])

I am assuming the POS_Description dtype to be a list of tuples.