Purpose

The script parses campaign names to extract and assign a tag to each campaign based on a specific naming convention.

To Elaborate

The Python script is designed to automate the process of tagging marketing campaigns by extracting a specific part of the campaign name. The script assumes that campaign names follow a structured format where different components are separated by underscores. The tag, which is a key identifier for the campaign, is located immediately after the first underscore. This tag is then compared to any existing tag in the dataset, and if it differs, the new tag is assigned. This ensures that the campaign data is consistently tagged, facilitating better organization and analysis of marketing efforts.

Walking Through the Code

1. Configurable Parameters:
   • The script begins by defining configurable parameters, such as the separator (SEP) used in campaign names and the position (TAG_LOCATION) of the tag within the name. These parameters can be adjusted by the user to accommodate different naming conventions.
2. Function Definition:
   • A function get_tag_from_campaign_name is defined to extract the tag from the campaign name. It splits the campaign name using the specified separator and retrieves the tag based on the defined position. If the tag cannot be extracted, it defaults to an empty string.
3. Data Processing:
   • The script copies the input data to a new DataFrame to preserve the original data. It then iterates over each row, extracting the campaign name and existing tag.
4. Tag Assignment:
   • For each campaign, the script extracts the new tag and compares it to the existing tag. If the new tag is non-empty and different from the existing tag, it updates the tag in the output DataFrame. If the tags are the same or the new tag is empty, it assigns a NaN value.
5. Output Filtering:
   • The script filters the output DataFrame to include only rows with non-empty tags, ensuring that only relevant data is retained for further processing or analysis.

Vitals

• Script ID : 103
• Client ID / Customer ID: 309909744 / 14196
• Action Type: Bulk Upload (Preview)
• Item Changed: Campaign
• Output Columns: Account, Campaign, Test
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Michael Huang (mhuang@marinsoftware.com)
• Created by Michael Huang on 2023-05-16 22:54
• Last Updated by dwaidhas@marinsoftware.com on 2024-02-01 15:58

> See it in Action

Python Code

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#
# Add Dimensions Tag based on Campaign Name (minimal Pandas)
#
#
# Author: Michael S. Huang
# Date: 2023-05-17
#

########### Configurable Params - START ##########

SEP = '_'
TAG_LOCATION = 1 # comes after first separator

########### Configurable Params - END ###########

RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_TEST = 'Test'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_TEST = 'Test'

# define function to parse tag from campaign name
def get_tag_from_campaign_name(campaign_name):
    vals = campaign_name.split(SEP)
    # assume tag value is after 1st separator
    tag = ''
    try:
      tag = str(vals[TAG_LOCATION])
    except:
      tag = ''

    return tag

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

# loop through all rows
for index, row in inputDf.iterrows():
  existing_tag = row[RPT_COL_TEST]
  campaign_name = row[RPT_COL_CAMPAIGN]
  tag = get_tag_from_campaign_name(campaign_name)
  print("Campaign [%s] => Tag [%s]" % (campaign_name, tag))

  # only tag if it's different than existing tag
  if (len(tag) > 0) &  (tag != existing_tag):
    outputDf.at[index, BULK_COL_TEST] = tag
  else:
    outputDf.at[index, BULK_COL_TEST] = np.nan  

# only include non-empty tags in bulk
outputDf = outputDf.loc[ outputDf[BULK_COL_TEST].notnull() ]

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

Post generated on 2024-11-27 06:58:46 GMT