Purpose:

The Python script identifies and filters paused CCT groups with single-word keywords based on a specific date range.

To Elaborate

The script is designed to process a dataset containing information about CCT (Structured Budget Allocation) groups and their associated keywords. It focuses on identifying groups that have been paused and filters them based on a specific date range. The script extracts relevant information from a column that contains processing details, such as the pause date and reason. It then converts these extracted dates into a datetime format to facilitate comparison. The primary goal is to filter out records where the pause date is within the last five days, allowing users to focus on recent changes in the status of their CCT groups. This functionality is particularly useful for monitoring and managing keyword campaigns effectively.

Walking Through the Code

1. Data Preparation
   • The script begins by defining a dictionary MATCH_TYPE to categorize keywords by their match type (exact, phrase, broad).
   • It retrieves the primary data source into a DataFrame inputDf and specifies key columns related to keywords, publishers, campaigns, and processing details.
2. Data Extraction and Transformation
   • The script extracts the ‘Pause Date’ and ‘Pause Reason’ from the CCT_Single_keyword_processing column by splitting the string data.
   • It converts the extracted ‘Pause Date’ strings into datetime objects for accurate date manipulation and comparison.
3. Date Calculation and Filtering
   • The script calculates a date five days prior to the current date to establish a threshold for filtering.
   • It filters the DataFrame to include only those records where the ‘Pause Date’ is within the last five days, resulting in a filtered DataFrame filtered_df.
4. Output Preparation
   • The filtered DataFrame is assigned to outputDf, which contains the relevant paused CCT groups for further analysis or reporting.

Vitals

• Script ID : 629
• Client ID / Customer ID: 1306924345 / 69058
• Action Type: Email Report
• Item Changed: None
• Output Columns:
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Jonathan Reichl (jreichl@marinsoftware.com)
• Created by Jonathan Reichl on 2023-12-22 16:16
• Last Updated by Jonathan Reichl on 2023-12-22 16:16

> See it in Action

Python Code

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##
## name: Alert paused cct single keywords
## description:
##  
## 
## author: 
## created: 2023-12-19
## 

MATCH_TYPE = {
  'EXACT': 'exact',
  'PHRASE': 'phrase',
  'BROAD': 'broad',
}
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_CAMPAIGN = 'Campaign'
RPT_COL_GROUP = 'Group'
RPT_COL_MATCH_TYPE = 'Match Type'
RPT_COL_STATUS = 'Status'
RPT_COL_KEYWORD_ID = 'Keyword ID'
RPT_COL_PUB_ID = 'Pub. ID'
RPT_COL_CCT_SINGLE_KEYWORD_PROCESSING = 'CCT_Single_keyword_processing'

# output columns and initial values

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




# Splitting and extracting date
inputDf['Pause Date'] = inputDf[RPT_COL_CCT_SINGLE_KEYWORD_PROCESSING].str.split('|').str[2]

inputDf['Pause Reason'] = inputDf[RPT_COL_CCT_SINGLE_KEYWORD_PROCESSING].str.split('|').str[1]


# Convert strings to datetime objects
inputDf['Pause Date'] = pd.to_datetime(inputDf['Pause Date'])


# Calculating 5 days ago from today
five_days_ago = today - datetime.timedelta(days=5)

# Convert the 'five_days_ago' date object to a pandas Timestamp for comparison
five_days_ago = pd.Timestamp(five_days_ago)



# Filtering the DataFrame
filtered_df = inputDf[inputDf['Pause Date'] >= five_days_ago]

outputDf = filtered_df

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