Make a new column in pandas dataset base on another column string

I am trying to add a new column to a panda dataset depending on another column containing a specific string. I am doing this while going through a list of csv files.
I have this

csv_files = glob.glob(os.path.join(path, "*.csv"))
print('Found',len(csv_files),'files')

#Headers to be checked
header_list = ['Created Date', 'Order Number', 'Shipping Address', 'Shipping Contact', 'Shipping Email', 'Product Type', 'Quantity', 'Serial', 'Activation Status']

list_of_df = []



for f in csv_files: 
    
    print('File Name:', f.split("\\")[-1])
    
    #read the file
    df = pd.read_csv(f, index_col=None, header=0)

    #check type
    conditions = [
        (df['Product Type'].str.contains('VG').any()),
        (df['Product Type'].str.contains('AG').any()),
        (df['Product Type'].str.contains('CM3').any())
        ]
    values = ['Gateway', 'Asset', 'Camera']

    # check the headers
    import_headers = df.axes[1]
    a = [i for i in import_headers if i not in header_list]
    
    if not a:
        print('Headers are OK, file is imported')
        #Prepare the data
        #Drop columns
        df.drop(df.columns[[2,3,4]], axis=1, inplace=True)

        #Fill Activation Status with 0
        df["Activation Status"] = df["Activation Status"].fillna("0")
        df['Activation Status'] = df['Activation Status'].replace('Activated', '1')
        
        #remove unwanted rows
        df = df.loc[df['Product Type'].str.contains('HW-', regex=True, na=True)]
        
        #add new column
        df['Device Type'] =np.select(conditions, values)
        
        list_of_df.append(df)
    else:
        print('Headers are not OK, file is not imported')
        print('Headers not found:', a)
        print('Headers found:', import_headers)

df = pd.concat(list_of_df, axis=0, ignore_index=True)

Product Type column contains this kind of data: HW-VG54-NAH, HW-CM31, HW-AG46

The new column is added but if the first row contains HW-VG54-NAH it will put the whole column as Gateway, even though there are rows with different product types.
If the first row is HW-CM31 all column will be added as Camera.

With the following toy dataframe:

import pandas as pd

df = pd.DataFrame(
    {
        "Order Number": [9, 8, 0, 5],
        "Product Type": ["HW-VG54-NAH", "HW-CM31", "HW-TU35", "HW-AG46"],
    }
)

print(df)
# Output
   Order Number Product Type
0             9  HW-VG54-NAH
1             8      HW-CM31
2             0      HW-TU35
3             5      HW-AG46

Here is one way to do it, where a dictionary replaces conditions and values in your code:

device_types = {"VG": "Gateway", "AG": "Asset", "CM3": "Camera"}

And you add the new column by using str.extract with a regex expression:

df["Device Type"] = (
    df["Product Type"]
    .str.extract(f"({'|'.join(device_types.keys())})")  # will return 'VG' for first row
    .applymap(lambda x: device_types.get(x, pd.NA))  # will return device_types['VG']
)

Then:

   Order Number Product Type Device Type
0             9  HW-VG54-NAH     Gateway
1             8      HW-CM31      Camera
2             0      HW-TU35        <NA>
3             5      HW-AG46       Asset