Purpose:

The Python script assigns dimension values to campaigns based on their names, categorizing them as “Brand,” “Local,” or “Emergency.”

To Elaborate

The Python script is designed to categorize campaigns by extracting specific dimension values from their names. It identifies whether a campaign is associated with “Brand,” “Local,” or “Emergency” by searching for these keywords within the campaign name. Once identified, these dimension values are assigned to a new column in the dataset. The script also checks for any changes in these dimension values and prepares an output dataset containing only the campaigns with updated dimension values. If no changes are detected, it outputs an empty dataset. This process helps in organizing and filtering campaigns based on predefined categories, facilitating structured budget allocation and targeted analysis.

Walking Through the Code

1. Define Constants and Patterns:

   • The script begins by defining constants for column names such as ‘Campaign’, ‘Account’, and ‘Brand

     Local

     Emergency’.

   • A regular expression pattern (PATTERN1) is defined to match the keywords “Brand”, “Local”, and “Emergency”.
2. Extract Dimension Values:
   • The script uses the str.extract() method to find occurrences of the defined pattern within the ‘Campaign’ column.

   • It assigns these extracted values to the ‘Brand

     Local

     Emergency’ column, filling any non-matching entries with a space.

3. Identify Changes:

   • It checks for changes in the ‘Brand

     Local

     Emergency’ column by comparing it to a space, indicating whether a dimension value has been assigned.

4. Select Relevant Columns:

   • The script filters the dataset to include only the relevant columns: ‘Campaign’, ‘Account’, and ‘Brand

     Local

     Emergency’.

5. Prepare Output Dataset:
   • If changes are detected, the script creates an output dataset containing only the changed campaigns.
   • If no changes are found, it prepares an empty output dataset.
6. User Changeable Parameters:
   • Users can modify the pattern (PATTERN1) to include different keywords for categorization.
   • Users can adjust the column names if the dataset structure changes.

Vitals

• Script ID : 535
• Client ID / Customer ID: 1306917127 / 60268084
• Action Type: Bulk Upload
• Item Changed: Campaign

• Output Columns: Account, Campaign, ___Brand

  Local

  Emergency___

• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: ascott@marinsoftware.com (ascott@marinsoftware.com)
• Created by ascott@marinsoftware.com on 2023-11-17 14:53
• Last Updated by ascott@marinsoftware.com on 2023-12-06 04:01

> See it in Action

Python Code

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#
# Assign Campaign Dimension Values according to:
#  - Brand | Local | Emergency
#
# Author: Jeremy Brown & Adam Scott
# Date: 2023-11-17

RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_BRAND_LOCAL_EMERGENCY = 'Brand | Local | Emergency'


# define pattern match
PATTERN1 = r'(Brand|Local|Emergency)'


# find all occurrences of the pattern in the Campaign name
inputDf[RPT_COL_BRAND_LOCAL_EMERGENCY] = inputDf[RPT_COL_CAMPAIGN].str.extract(PATTERN1).fillna(' ')


# find changed campaigns
changed = inputDf[RPT_COL_BRAND_LOCAL_EMERGENCY].ne(' ') | inputDf[RPT_COL_BRAND_LOCAL_EMERGENCY].ne(' ')

#selecting only relevant cols
inputDf= inputDf[[RPT_COL_CAMPAIGN,RPT_COL_ACCOUNT,RPT_COL_BRAND_LOCAL_EMERGENCY]]

# put changed campaigns into outputDf; if none, prepare empty outputDf
if sum(changed) > 0:
  #print('changed data',tableize(inputDf[changed]))
  outputDf = inputDf[changed]                 
  print("outputDf", tableize(outputDf))

else:
  print("Empty outputDf")
  outputDf = outputDf.iloc[0:0]##

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