Script 619: Pause CCT Groups

Purpose:

The Python script processes and updates group statuses based on keyword and studio conditions to manage campaign groups effectively.

To Elaborate

The script is designed to manage and update the status of campaign groups by evaluating specific conditions related to keywords and studio names. It checks if a keyword consists of a single word and updates its status to ‘PAUSED’ if true. Additionally, it examines if certain studio names are associated with the group and if the group name contains the word ‘phrase’. If both conditions are met, the group’s status is updated to ‘PAUSED - ANIME’. The script ensures that the campaign groups are paused or checked based on these criteria, facilitating structured budget allocation and efficient campaign management.

Walking Through the Code

  1. Data Preparation:
    • The script begins by preparing the input data from a primary data source, focusing on specific columns related to campaigns, groups, and keywords.
    • It initializes output columns with placeholder values for further processing.
  2. Keyword Check:
    • A function check_token_length is defined to determine if a keyword is a single word, marking it as ‘PAUSED’ if true.
    • A DataFrame checkDf is created to apply this function and update the status for single-word keywords.
  3. Group Data Preparation:
    • A separate DataFrame dedupeGroupDf is created to handle group-level data, ensuring each group is represented by a single row.
  4. Data Merging:
    • The script merges the keyword check DataFrame with the group DataFrame to consolidate information for further processing.
  5. Status Updates:
    • The merged DataFrame is updated based on the keyword and group checks, adjusting the status and source template fields accordingly.
  6. Anime Check:
    • The check_conditions function is applied to identify groups associated with specific studios and containing the word ‘phrase’, updating their status to ‘PAUSED - ANIME’.
  7. Final Output:
    • The processed DataFrame is assigned to outputDf, ready for further use or export.

Vitals

  • Script ID : 619
  • Client ID / Customer ID: 1306924343 / 69058
  • Action Type: Bulk Upload
  • Item Changed: AdGroup
  • Output Columns: Account, Campaign, Group, CCT_SourceTemplate_Backup, CCT_sourcetemplate, CCT_Group_pause_check, CCT_Single_keyword_processing, Status
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: Jonathan Reichl (jreichl@marinsoftware.com)
  • Created by Jonathan Reichl on 2023-12-22 10:54
  • Last Updated by Jonathan Reichl on 2023-12-22 19:00
> See it in Action

Python Code

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##
## name: Pause CCT Groups
## description:
##  
## 
## author: 
## created: 2023-12-22
## 

today = datetime.datetime.now(CLIENT_TIMEZONE).date()

# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_KEYWORD = 'Keyword'
RPT_COL_PUBLISHER = 'Publisher'
RPT_COL_CCT_SOURCETEMPLATE = 'CCT_sourcetemplate'
RPT_COL_CCT_SINGLE_KEYWORD_PROCESSING = 'CCT_Single_keyword_processing'
RPT_COL_CCT_GROUP_PAUSE_CHECK = 'CCT_Group_pause_check'
RPT_COL_CCT_SOURCETEMPLATE_BACKUP = 'CCT_SourceTemplate_Backup'
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_GROUP = 'Group'
RPT_COL_PUB_ID = 'Pub. ID'
RPT_COL_GROUP_PUB_ID = 'Group Pub. ID'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_STUDIO = 'Studio'
RPT_COL_GROUP_STATUS = 'Group Status'

# output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_GROUP = 'Group'
BULK_COL_CCT_GROUP_PAUSE_CHECK = 'CCT_Group_pause_check'
BULK_COL_CCT_SINGLE_KEYWORD_PROCESSING = 'CCT_Single_keyword_processing'
BULK_COL_CCT_SOURCETEMPLATE = 'CCT_sourcetemplate'
BULK_COL_CCT_SOURCETEMPLATE_BACKUP = 'CCT_SourceTemplate_Backup'
BULK_COL_STATUS_UPDATE = 'updated_status'
BULK_COL_STATUS = 'Status'
outputDf[BULK_COL_CCT_GROUP_PAUSE_CHECK] = "<<YOUR VALUE>>"
outputDf[BULK_COL_CCT_SINGLE_KEYWORD_PROCESSING] = "<<YOUR VALUE>>"
outputDf[BULK_COL_CCT_SOURCETEMPLATE] = "<<YOUR VALUE>>"
outputDf[BULK_COL_CCT_SOURCETEMPLATE_BACKUP] = "<<YOUR VALUE>>"

# user code start here
print(tableize(inputDf))

def check_token_length(text):
    tokens = text.split()  # Splitting based on spaces
    if len(tokens) == 1:
        return 'PAUSED'
    else:
        return 'unchanged'

def check_conditions(df):
    # Check if 'RPT_COL_STUDIO' is in (x, y, z)
    studio_values = ['Medialink', 'Muse', 'Studio Pierrot', 'Liden Films']
    studio_check = df[RPT_COL_STUDIO].isin(studio_values)
    
    # Check if 'phrase' (case insensitive) is in 'RPT_COL_GROUP'
    phrase_check = df[RPT_COL_GROUP].str.contains('phrase', case=False, na=False)
    
    # If both conditions are met, return 'PAUSED - ANIME'
    df.loc[studio_check & phrase_check, 'anime_status'] = 'PAUSED - ANIME'


#Build keyword check DF 
check_columns = [BULK_COL_ACCOUNT,BULK_COL_CAMPAIGN,BULK_COL_GROUP,RPT_COL_KEYWORD]
checkDf = inputDf[check_columns].copy()

#where only a single work set to puased 
checkDf[BULK_COL_STATUS_UPDATE] = checkDf[RPT_COL_KEYWORD].apply(check_token_length)


# set  BULK_COL_CCT_Single_word_check for paused objects 
checkDf.loc[checkDf[BULK_COL_STATUS_UPDATE] == 'PAUSED' , BULK_COL_CCT_SINGLE_KEYWORD_PROCESSING] = 'Paused|Single Word|'+ str(today)


print(tableize(checkDf))

# Filter checkDf to only include paused objects e.g. single keywords 
checkDf =  checkDf[checkDf[BULK_COL_STATUS_UPDATE] == 'PAUSED']


# build group DF 
group_columns = [BULK_COL_ACCOUNT,BULK_COL_CAMPAIGN,BULK_COL_GROUP, RPT_COL_CCT_SOURCETEMPLATE, RPT_COL_GROUP_STATUS, RPT_COL_STUDIO] 
dedupeGroupDf = inputDf[group_columns].copy()

#one row per group 
dedupeGroupDf = dedupeGroupDf.drop_duplicates(subset=[BULK_COL_ACCOUNT,BULK_COL_CAMPAIGN,BULK_COL_GROUP], keep='first')


print('dedupeGroupDf')
print(tableize(dedupeGroupDf))

#merge 2 DFs 
merged_df = pd.merge(dedupeGroupDf, checkDf, on=[BULK_COL_ACCOUNT, BULK_COL_CAMPAIGN, BULK_COL_GROUP], how='left')
print('merged_df')
print(tableize(merged_df))

merged_df[BULK_COL_STATUS] = merged_df[RPT_COL_GROUP_STATUS]

merged_df.loc[merged_df[BULK_COL_STATUS_UPDATE] != 'PAUSED' , BULK_COL_CCT_SINGLE_KEYWORD_PROCESSING] = 'Checked'

merged_df[BULK_COL_CCT_SOURCETEMPLATE_BACKUP] = merged_df[BULK_COL_CCT_SOURCETEMPLATE]

merged_df.loc[merged_df[BULK_COL_STATUS_UPDATE] == 'PAUSED' , BULK_COL_CCT_SOURCETEMPLATE] = ''

merged_df.loc[merged_df[BULK_COL_STATUS_UPDATE] == 'PAUSED' , BULK_COL_STATUS] = 'PAUSED'


#apply anime check
check_conditions(merged_df) 

merged_df.loc[merged_df['anime_status'] == 'PAUSED - ANIME' , BULK_COL_CCT_SOURCETEMPLATE] = ''

merged_df.loc[merged_df['anime_status'] == 'PAUSED - ANIME' , BULK_COL_STATUS] = 'PAUSED'

merged_df.loc[merged_df['anime_status'] == 'PAUSED - ANIME' , BULK_COL_CCT_GROUP_PAUSE_CHECK] = 'Paused|Anime|'+str(today)

merged_df.loc[merged_df['anime_status'] != 'PAUSED - ANIME' , BULK_COL_CCT_GROUP_PAUSE_CHECK] = 'Checked'


outputDf = merged_df

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

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