Changing the value of a column based on the condition on another column
There is a way that is not optimum for performance, but clear to read and understand. Also, you can use more complex logic and update with different values as you wish.

def myfunc(row):
    if row['incident_zip'] in [this is a list of zip codes]:
        return new_value
    else:
        return row['borough'] #return old value

data['borough'] = data.apply(myfunc, axis=1)


If your logic of update is quite simple, then you can do it like that

mask = df['incident_zip'].isin([this is a list of zip codes])
df[mask]['borough'] = new_value