Purpose:

The Python script automatically tags PS Clients based on specific keywords found in campaign names.

To Elaborate

The script is designed to automate the process of tagging PS Clients by analyzing campaign names and assigning a client name based on specific keywords. It scans through each campaign name in a dataset and checks for predefined keywords associated with particular clients. If a keyword is found, the corresponding client name is tagged; otherwise, an empty string is returned. This process helps in organizing and categorizing campaigns according to the clients they are associated with, streamlining reporting and analysis tasks. The script ensures that the tagging is consistent and accurate by using a set of rules that map keywords to client names.

Walking Through the Code

1. Initialization and Configuration:
   • The script begins by defining configurable parameters such as SEP for separators and TAG_LOCATION for index-based tagging.
   • It sets up input data from a specified data source.
2. Function Definition:
   • A function tag_psclient is defined to determine the PS Client based on keywords in the campaign name.
   • It checks for specific keywords like ‘kpmg’, ‘icaew’, ‘vodafone’, etc., and returns the corresponding client name.
3. Data Processing:
   • The script copies all input rows to an output DataFrame to preserve the original data structure.
   • It ensures the output column for PS Clients exists, creating it if necessary.
4. Application of Tagging Function:
   • The tag_psclient function is applied to each campaign name in the output DataFrame, populating the PS Clients column with the appropriate tags.
5. Output Handling:
   • The script checks if the output DataFrame is empty and prints the results accordingly, using a formatted table view for non-empty outputs.

Vitals

• Script ID : 1481
• Client ID / Customer ID: 1306927879 / 60270369
• Action Type: Bulk Upload (Preview)
• Item Changed: Campaign
• Output Columns: Account, Campaign, PS Clients
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Grégory Pantaine (gpantaine@marinsoftware.com)
• Created by Grégory Pantaine on 2024-11-05 21:20
• Last Updated by Grégory Pantaine on 2024-11-05 21:30

> See it in Action

Python Code

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##
## Name: Auto tagging PS Clients Dimension
## Description: Return PS Clients Name Based on what's in campaign name.
##
## author: Gregory Pantaine
## created: 2024-10-31
## 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_PSCLIENTDIM = 'PS Clients'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_PSCLIENTDIM = 'PS Clients'

# Define a function to tag the PS client
def tag_psclient(campaign_name):
    # Specific location-based conditions
    if 'kpmg' in campaign_name.lower():
        return "KPMG"
    elif 'stcec' in campaign_name.lower():
        return "KPMG"
    elif 'icaew' in campaign_name.lower():
        return "ICAEW"    
    elif 'vodafone' in campaign_name.lower():
        return "Vodafone"
    elif 'vf' in campaign_name.lower():
        return "Vodafone"
    elif 'unlocked' in campaign_name.lower():
        return "Unlocked"
    elif 'costa' in campaign_name.lower():
        return "Costa"
    elif 'wliot' in campaign_name.lower():
        return "Wliot"
    else:
        return ""  # Return an empty string if no match is found

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

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

# Apply the tag_psclient function to each row in the outputDf DataFrame
outputDf[BULK_COL_PSCLIENTDIM] = outputDf[BULK_COL_CAMPAIGN].apply(tag_psclient)

# Print results if not empty
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