Script 259: Dimension Update

Purpose:

The Python script assigns campaign dimension values based on specific patterns found in campaign names.

To Elaborate

The script is designed to update campaign dimension values by extracting specific patterns from campaign names. It focuses on two main dimensions: “Strategy//Targeting Type” and “Campaign Tactic.” The script uses regular expressions to identify patterns within the campaign names and assigns these patterns to the respective dimensions. If any changes are detected in these dimensions, the updated campaigns are prepared for output. This process helps in organizing and categorizing campaigns based on their naming conventions, which can be crucial for reporting and analysis purposes.

Walking Through the Code

  1. Pattern Definition:
    • Two regular expression patterns, PATTERN1 and PATTERN2, are defined to match specific substrings within campaign names. PATTERN1 targets strategy or targeting types, while PATTERN2 focuses on campaign tactics.
  2. Pattern Extraction:
    • The script extracts matches for PATTERN1 from the campaign names and assigns them to the “Strategy//Targeting Type” column. Similarly, it extracts matches for PATTERN2 after replacing ‘VID’ with ‘CPV’ in the campaign names and assigns them to the “Campaign Tactic” column.
  3. Change Detection:
    • It checks for any changes in the “Strategy//Targeting Type” and “Campaign Tactic” columns. If changes are detected, the script prepares a DataFrame containing only the updated campaigns.
  4. Output Preparation:
    • If there are updated campaigns, they are printed and stored in outputDf. If no changes are detected, an empty DataFrame is prepared.

Vitals

  • Script ID : 259
  • Client ID / Customer ID: 1306922481 / 2
  • Action Type: Bulk Upload (Preview)
  • Item Changed: Campaign
  • Output Columns: Account, Campaign, Strategy//Targeting Type, Campaign Tactic
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: Jeremy Brown (jbrown@marinsoftware.com)
  • Created by Jeremy Brown on 2023-07-27 16:04
  • Last Updated by vapalussiere@marinsoftware.com on 2023-12-06 04:01
> See it in Action

Python Code

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#
# Assign Campaign Dimension Values according to:
#  - Strategy//Targeting Type
#  - Campaign Tactic
#
# Author: Jeremy Brown
# Date: 2023-07-27

RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_STRATEGY_TARGETING_TYPE = 'Strategy//Targeting Type'
RPT_COL_CAMPAIGN_TACTIC = 'Campaign Tactic'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_STRATEGY_TARGETING_TYPE = 'Strategy-Targeting Type'
BULK_COL_CAMPAIGN_TACTIC = 'Campaign Tactic'

#outputDf[BULK_COL_STRATEGY_TARGETING_TYPE] = "TACTIC"
#outputDf[BULK_COL_CAMPAIGN_TACTIC] = "COST_MODEL"

# define pattern match
PATTERN1 = r'(ISA|ISG|AWR|TRF|YTB|IGR|TIK|YTS)'
PATTERN2 = r'(?:CPV_)?(CPM|CPV|CPC)'

# find all occurrences of the pattern in the Campaign name
inputDf[RPT_COL_STRATEGY_TARGETING_TYPE] = inputDf[RPT_COL_CAMPAIGN].str.extract(PATTERN1).fillna(' ')
inputDf['TEMP_COL'] = inputDf[RPT_COL_CAMPAIGN].str.replace('VID','CPV')

inputDf[RPT_COL_CAMPAIGN_TACTIC] =  inputDf['TEMP_COL'].str.extract(PATTERN2).fillna(' ')
del inputDf['TEMP_COL']
# define tmp column for dimension 'Strategy//Targeting Type' and set to empty
#TMP_TYPE = RPT_COL_STRATEGY_TARGETING_TYPE + '_'
#inputDf[TMP_TYPE] = np.nan

# define tmp column for dimension 'Campaign Tactic' and set to empty
#TMP_TACTIC = RPT_COL_CAMPAIGN_TACTIC + '_'
#inputDf[TMP_TACTIC] = np.nan

# Assign the matches to the respective dimensions based on matches for PATTERN1
#inputDf.loc[inputDf[RPT_COL_CAMPAIGN].apply(lambda x: bool(re.findall(PATTERN1, x))), TMP_TYPE] = MATCH1

# Assign the matches to the respective dimensions based on matches for PATTERN2
#inputDf.loc[inputDf[RPT_COL_CAMPAIGN].apply(lambda x: bool(re.findall(PATTERN2, x))), TMP_TACTIC] = MATCH2


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


# put changed campaigns into outputDf; if none, prepare empty outputDf
if sum(changed) > 0:
  print("== Campaigns with updated Dimension values ==", tableize(inputDf.loc[changed]))

  # only select changed rows
  #cols = [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, RPT_COL_STRATEGY_TARGETING_TYPE, RPT_COL_CAMPAIGN_TACTIC]
  #outputDf = inputDf.loc[ changed, cols ].copy() \
  #                  .rename(columns = { \
  #                    TMP_TYPE: BULK_COL_STRATEGY_TARGETING_TYPE, \
  #                    TMP_TACTIC: BULK_COL_CAMPAIGN_TACTIC \
  #                  })
  outputDf = inputDf.loc[changed]                 
  print("outputDf", tableize(outputDf))

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

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

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