Purpose

Python script to add a tag to a campaign name based on a specified separator.

To Elaborate

This Python script solves the problem of adding a tag to a campaign name based on a specified separator. The script takes an input DataFrame with campaign names and existing tags, and adds a new tag to the campaign name if it meets certain conditions. The new tag is extracted from the campaign name using a specified separator. The script then outputs the modified DataFrame with the added tags.

Walking Through the Code

1. The script starts by defining configurable parameters, including the separator and the location of the tag within the campaign name.
2. Next, the script defines constants for the column names in the input and output DataFrames.
3. The script defines a function called get_tag_from_campaign_name that takes a campaign name as input and extracts the tag using the specified separator.
4. The script creates a copy of the input DataFrame as the output DataFrame.
5. The script iterates through each row in the input DataFrame.
6. For each row, the script retrieves the existing tag and campaign name.
7. The script calls the get_tag_from_campaign_name function to extract the tag from the campaign name.
8. The script prints the campaign name and extracted tag.
9. The script checks if the extracted tag is different from the existing tag and if it is not empty.
10. If the conditions are met, the script updates the output DataFrame with the extracted tag.
11. If the conditions are not met, the script updates the output DataFrame with a NaN value.
12. After iterating through all rows, the script filters the output DataFrame to include only rows with non-empty tags.
13. If the output DataFrame is not empty, the script prints the tableized output DataFrame.
14. If the output DataFrame is empty, the script prints “Empty outputDf”.

Vitals

• Script ID : 905
• Client ID / Customer ID: 1306927585 / 60270327
• Action Type: Bulk Upload (Preview)
• Item Changed: Campaign
• Output Columns: Account, Campaign, PostSeparatorTag
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Anton Antonov (aantonov@marinsoftware.com)
• Created by Anton Antonov on 2024-04-10 15:38
• Last Updated by Anton Antonov on 2024-04-10 15:56

> See it in Action

Python Code

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#
# Add Dimensions Tag based on Campaign Name
#
#
# 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_POSTSEPARATORTAG = 'PostSeparatorTag'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_POSTSEPARATORTAG = 'PostSeparatorTag'

# 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_POSTSEPARATORTAG]
  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_POSTSEPARATORTAG] = tag
  else:
    outputDf.at[index, BULK_COL_POSTSEPARATORTAG] = np.nan  

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

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

Post generated on 2024-05-15 07:44:05 GMT