Script 1319: Scripts AutoTag Hotel Dimension

Purpose:

The script parses campaign names to automatically tag hotel-related campaigns with specific Marin Dimensions tags, handling special cases for certain account names.

To Elaborate

The Python script is designed to automate the process of tagging hotel-related campaigns by parsing campaign names and assigning specific Marin Dimensions tags. It specifically addresses campaigns associated with various YOTEL locations by identifying location codes within the campaign names and assigning the corresponding hotel tag. Additionally, the script handles special cases where the account name contains “derbysoft,” modifying the campaign name accordingly to ensure proper formatting. The script processes input data, applies the tagging logic, and outputs the tagged data, ensuring that only non-empty tags are included in the final output.

Walking Through the Code

  1. Configuration and Input Setup
    • The script begins by defining configurable parameters such as the separator used in account names and the index for tag location.
    • It retrieves input data from a specified data source.
  2. Tagging Function Definition
    • A function get_tag_from_account_name is defined to process each account and campaign name.
    • The function checks for specific location codes within the campaign name and returns the corresponding YOTEL location tag.
    • Special handling is implemented for account names containing “derbysoft,” where the campaign name is modified to ensure proper formatting.
  3. Data Processing and Output
    • The script copies all input rows to an output DataFrame.
    • It ensures the output column for hotel tags exists and applies the tagging function to each row.
    • Rows with non-empty tags are retained in the output, and the final tagged data is displayed or indicated as empty if no tags are assigned.

Vitals

  • Script ID : 1319
  • Client ID / Customer ID: 1306922797 / 60269073
  • Action Type: Bulk Upload (Preview)
  • Item Changed: Campaign
  • Output Columns: Account, Campaign, HotelScriptTest
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: Grégory Pantaine (gpantaine@marinsoftware.com)
  • Created by Grégory Pantaine on 2024-08-12 14:57
  • Last Updated by Grégory Pantaine on 2024-09-24 08:27
> See it in Action

Python Code

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105

##
## Name: Campaign Name - Dimension Auto Tagging: Hotel
## Description:
##  Parse Campaign Name and add Campaign-level Marin Dimensions Tag for Hotel.
##  Handles special cases for "derbysoft" and "Brand" in Account names.
##
## author: Gregory Pantaine
## created: 2024-09-17
##
# Configurable Params - START
SEP = '_'  # Separator used in account names (if applicable)
TAG_LOCATION = 1  # Comes after the first separator (0-based index)
inputDf = dataSourceDict["1"]

# Output columns and initial values
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_HOTEL = 'HotelScriptTest'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_HOTEL = 'HotelScriptTest'

def get_tag_from_account_name(account_name, campaign_name):
    print(f"Processing Account: {account_name}, Campaign: {campaign_name}")  # Debugging line

    # Specific location-based conditions
    if 'AMN' in campaign_name.lower():
        return "YOTEL Amsterdam"
    elif 'BOS' in campaign_name.lower():
        return "YOTEL Boston"
    elif 'CORE' in campaign_name.lower():
        return "YOTEL HQ"    
    elif 'EDI' in campaign_name.lower():
        return "YOTEL Edinburgh"
    elif 'GLA' in campaign_name.lower():
        return "YOTEL Glasgow"
    elif 'IST' in campaign_name.lower():
        return "YOTEL Istanbul"
    elif 'LCL' in campaign_name.lower():
        return "YOTEL London City"
    elif 'LGW' in campaign_name.lower():
        return "YOTEL London Gatwick"
    elif 'MIA' in campaign_name.lower():
        return "YOTEL Miami"
    elif 'MAN' in campaign_name.lower():
        return "YOTEL Manchester"
    elif 'NYC' in campaign_name.lower():
        return "YOTEL New York"
    elif 'CDG' in campaign_name.lower():
        return "YOTEL Paris CDG"
    elif 'POR' in campaign_name.lower():
        return "YOTEL Porto"
    elif 'SFA' in campaign_name.lower():
        return "YOTEL San Francisco"
    elif 'SIN' in campaign_name.lower():
        return "YOTEL Singapore"
    elif 'WDC' in campaign_name.lower():
        return "YOTEL Washington DC"
    elif 'AMS' in campaign_name.lower():
        return "YOTELAIR Amsterdam Schiphol"
    elif 'CHA' in campaign_name.lower():
        return "YOTELAIR Singapore Changi Airport"
    elif 'SRA' in campaign_name.lower():
        return "YOTELPAD London Stratford"

# Check if the account name contains "derbysoft"
    elif 'derbysoft' in account_name.lower():
        # Remove the suffix after the first space or dash
        campaign_name = re.sub(r' - \w+$', '', campaign_name)
        print(f"Modified Campaign Name (Derbysoft): {campaign_name}")  # Debugging line
        
        # Ensure "YOTEL" is all caps and the location is proper case
        if "YOTEL" in campaign_name.upper():
            words = campaign_name.split()
            words[0] = words[0].upper()
            campaign_name = ' '.join([words[0]] + [word.capitalize() for word in words[1:]])
        
        return campaign_name


    # Default case: return the modified account name
    else:
        return None # Skip returning the account name

# Copy all input rows to output
outputDf = inputDf.copy()

# Ensure the output column exists before assigning
if BULK_COL_HOTEL not in outputDf.columns:
    outputDf[BULK_COL_HOTEL] = ""

# Apply the tagging function to each row, considering the special "derbysoft" and "Brand" cases
outputDf[BULK_COL_HOTEL] = outputDf.apply(
    lambda row: get_tag_from_account_name(row[BULK_COL_ACCOUNT], row[BULK_COL_CAMPAIGN]), axis=1
)

# Only include non-empty tags in bulk
outputDf = outputDf.dropna(subset=[BULK_COL_HOTEL])

if not outputDf.empty:
    print("outputDf", tableize(outputDf))
else:
    print("Empty outputDf")

# Configurable Params - END

Post generated on 2025-03-11 01:25:51 GMT

comments powered by Disqus