Trying to pull individual values from a dataframe but pulls arrays instead

I have a dataframe which has a list of companies and a date in which a subscription service ends for each company like so:

 Service           Date
Company 1       02-09-2021
Company 2       03-11-2021
Company 3       05-13-2021

My code is here:

plants = []
for i in range(0,14):
    if i <=7:
        dates[i] = dates[i].strftime('%m-%d-%Y')
        plt = df[df.tService < dates[i]].Plant.values
        plants.append(df[df.tService < dates[i]].Plant.values)

for plant in plants:

This dataframe has about 45 entries and what I wanted to do was find the ones that were expiring soon and get notified for it. I am trying to use this to get it to print the date column then compare it to a specific date (like, two weeks from now) but the issue I am running in to is that I get the each entry into my list as an array as below and I thought maybe running through it in a for loop would solve that issue but it doesn’t. I understand how this is happening so my question is is there something I can do to get values from a dataframe not in a list format? I’ve tried taking the list it spits out below and removing any duplicates and it gives the error TypeError: unhashable type: 'numpy.ndarray' so I also can’t get rid of duplicates. Any idea on what I can do here to get the dates below the date I select (so anything that will show up in the next two weeks) in a format that I can print? Because each time I try to print it just prints as ['Wagon Trail' '603Brewery' 'East Brother Beer'] because that is considered one entry.

['Wagon Trail' '603Brewery' 'East Brother Beer']
['Wagon Trail' '603Brewery' 'East Brother Beer']
['Wagon Trail' '603Brewery' 'East Brother Beer']
['Wagon Trail' '603Brewery' 'East Brother Beer']
['Wagon Trail' '603Brewery' 'East Brother Beer']
['Wagon Trail' '603Brewery' 'East Brother Beer']
['Wagon Trail' '603Brewery' 'East Brother Beer']
['Wagon Trail' '603Brewery' 'East Brother Beer']
['Wagon Trail' '603Brewery' 'East Brother Beer']


import pandas as pd
# sample data
df = pd.DataFrame({'Service': [f'Company {x}' for x in range(1, 32)],
                  'Date': pd.date_range('2021-04-10', '2021-05-10')})

# filter your frame where the date minus 14 days is equal to today
new = df[(df['Date'] - pd.Timedelta(days=14)) =='d')]

       Service       Date
24  Company 25 2021-05-04

or you can do something like

# look for dates that are between today and today plus 14 days
new2 = df[df['Date'].between('d'),

       Service       Date
10  Company 11 2021-04-20
11  Company 12 2021-04-21
12  Company 13 2021-04-22
13  Company 14 2021-04-23
14  Company 15 2021-04-24
15  Company 16 2021-04-25
16  Company 17 2021-04-26
17  Company 18 2021-04-27
18  Company 19 2021-04-28
19  Company 20 2021-04-29
20  Company 21 2021-04-30
21  Company 22 2021-05-01
22  Company 23 2021-05-02
23  Company 24 2021-05-03
24  Company 25 2021-05-04