Purpose:

The Python script automatically tags manufacturer names based on keywords found in campaign names within a dataset.

To Elaborate

The script is designed to identify and tag manufacturer names within a dataset by analyzing the campaign names. It uses a predefined set of keywords associated with specific manufacturers, such as “Honda,” “Kawasaki,” and “Yamaha,” among others. When a keyword is detected in a campaign name, the corresponding manufacturer name is assigned to a designated column in the dataset. This process helps in categorizing and organizing data based on manufacturer information, which can be useful for reporting and analysis purposes. The script ensures that all rows from the input dataset are copied to the output, and it checks for the existence of the manufacturer column before assigning values. If no keyword matches are found, an empty string is returned, indicating no manufacturer tag.

Walking Through the Code

1. Initialization and Configuration:
   • The script begins by defining configurable parameters, such as the separator used in account names (SEP) and the index position for tagging (TAG_LOCATION).
   • It sets up the input DataFrame (inputDf) sourced from a data dictionary.
2. Function Definition:
   • A function named tag_manufacturer is defined to determine the manufacturer based on keywords found in the campaign name. It checks for specific keywords and returns the corresponding manufacturer name.
3. Data Processing:
   • The script copies all rows from the input DataFrame to the output DataFrame (outputDf).
   • It ensures the manufacturer column exists in the output DataFrame, creating it if necessary.
4. Application of Tagging Function:
   • The tag_manufacturer function is applied to each campaign name in the output DataFrame, populating the manufacturer column with the appropriate tags.
5. Output Handling:
   • The script checks if the output DataFrame is empty and prints the tableized output if it contains data, otherwise it indicates that the output is empty.

Vitals

• Script ID : 1471
• Client ID / Customer ID: 1306928469 / 60270543
• Action Type: Bulk Upload
• Item Changed: Campaign
• Output Columns: Account, Campaign, Manufacturer
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Grégory Pantaine (gpantaine@marinsoftware.com)
• Created by Grégory Pantaine on 2024-10-31 17:34
• Last Updated by emerryfield@marinsoftware.com on 2024-11-09 00:13

> See it in Action

Python Code

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##
## Name: Auto tagging Manufacturer Dimension
## Description: Return Manufacturer 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_MANUF = 'Manufacturer'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_MANUF = 'Manufacturer'

# Define a function to tag the manufacturer
def tag_manufacturer(campaign_name):
    if 'honda' in campaign_name.lower():
        return "Honda"
    elif 'kawasaki' in campaign_name.lower():
        return "Kawasaki"
    elif 'polaris' in campaign_name.lower():
        return "Polaris"    
    elif 'suzuki' in campaign_name.lower():
        return "Suzuki"
    elif 'ski-doo' in campaign_name.lower():
        return "Ski-Doo"
    elif 'sea-doo' in campaign_name.lower():
        return "Sea-Doo"
    elif 'artic cat' in campaign_name.lower():
        return "Artic Cat"
    elif 'canam' in campaign_name.lower():
        return "CanAM"
    elif 'can-am' in campaign_name.lower():
        return "CanAM"
    elif 'aftermarket' in campaign_name.lower():
        return "Aftermarket"
    elif 'yamaha' in campaign_name.lower():
        return "Yamaha"
    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_MANUF not in outputDf.columns:
    outputDf[BULK_COL_MANUF] = ""

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

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