Purpose:

Automates the process of updating UTM group dimensions based on ad group names by replacing spaces with plus signs.

To Elaborate

The Python script is designed to automate the task of updating UTM group dimensions for advertising data. It processes a dataset containing information about ad campaigns, accounts, and groups, specifically focusing on the ‘utmgroup’ field. The script identifies rows where the ‘utmgroup’ field is not already populated and updates it by transforming the ‘Group’ field. The transformation involves replacing spaces in the group names with plus signs, which is a common requirement for URL parameters in digital marketing. This ensures that the UTM group dimensions are correctly formatted for tracking purposes, facilitating more accurate and structured budget allocation (SBA) analysis.

Walking Through the Code

1. Data Preparation
   • The script begins by defining the primary data source, inputDf, which contains columns for campaign, account, group, and utmgroup.
   • It sets up constants for these columns to ensure consistent referencing throughout the script.
2. Function Definition
   • A function set_utm_group is defined to replace spaces with plus signs in the group names. This function is crucial for formatting the UTM group dimensions correctly.
3. Data Filtering
   • The script filters inputDf to create filteredDf, which only includes rows where the ‘utmgroup’ field is not populated. This ensures that only relevant rows are processed.
4. Data Transformation
   • The outputDf is initialized to include only the filtered rows.
   • The script applies the set_utm_group function to the ‘Group’ column of filteredDf, updating the ‘utmgroup’ column in outputDf with the transformed values.
5. Output
   • Finally, the script prints the first few rows of the outputDf to verify the transformation.

Vitals

• Script ID : 1343
• Client ID / Customer ID: 1306913420 / 60268008
• Action Type: Bulk Upload
• Item Changed: AdGroup
• Output Columns: Account, Campaign, Group, utmgroup
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Kyle Perkins (kyle.perkins@genesys.com)
• Created by Kyle Perkins on 2024-08-20 14:46
• Last Updated by Kyle Perkins on 2024-08-28 17:18

> 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
##
## name: Dimension Update - Groups
## description:
##  
## 
## author: 
## created: 2024-08-20
## 

today = datetime.datetime.now(CLIENT_TIMEZONE).date()

# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_GROUP = 'Group'
RPT_COL_UTMGROUP = 'utmgroup'

# output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_GROUP = 'Group'
BULK_COL_UTMGROUP = 'utmgroup'

# define function to replace ' ' with '+'
def set_utm_group(group_name):
    return group_name.replace(' ', '+')


# filter inputDf to skip rows where both utmgroup is populated and copy rows to new filteredDf dataframe
filteredDf = inputDf[
    ~inputDf[RPT_COL_UTMGROUP].notna()
].copy()

# intialize outputDf so that it only includes rows that have been filtered
outputDf = filteredDf

# apply the functions to each row in the filtered dataframe and set result as the output dataframe value
outputDf[BULK_COL_UTMGROUP] = filteredDf[RPT_COL_GROUP].apply(set_utm_group)

print(tableize(outputDf.head()))

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