Purpose:

The Python script updates a dataset to set the “Historical Quality Score - 1st of the month” dimension with the current quality score value for each keyword.

To Elaborate

The script is designed to update a dataset by setting a specific dimension, “Historical Quality Score - 1st of the month,” with the current quality score for each keyword. This is particularly useful for tracking and analyzing the quality score of keywords over time, specifically capturing the score at the beginning of each month. The script processes data from a primary data source, extracts relevant columns, and then updates the dataset with the current quality score. This allows for historical tracking of quality scores, which can be crucial for performance analysis and optimization in digital marketing campaigns.

Walking Through the Code

1. Initialization and Setup
   • The script begins by defining a dictionary MATCH_TYPE to map match types to their string representations.
   • It retrieves the current date based on the client’s timezone, although this part is not detailed in the summary.
2. Data Source and Column Definitions
   • The script identifies the primary data source and specifies the columns that will be used, such as ‘Keyword’, ‘Account’, ‘Campaign’, ‘Group’, ‘Match Type’, ‘Status’, and ‘Quality Score’.
3. Temporary Column Creation
   • A temporary column, TMP_QUALITY_SCORE, is created in the input DataFrame to store the current quality score values.
4. Setting Dimension Value
   • The script assigns the current quality score from the input DataFrame to the temporary column, effectively preparing it for historical tracking.
5. DataFrame Transformation
   • The input DataFrame is copied to an output DataFrame, where the temporary quality score column is renamed to “Historical Quality Score - 1st of the month.”
6. Output
   • The script concludes by printing the transformed DataFrame, showcasing the updated historical quality scores.

Vitals

• Script ID : 547
• Client ID / Customer ID: 1306926109 / 60269815
• Action Type: Bulk Upload
• Item Changed: Keyword
• Output Columns: Account, Campaign, Group, Keyword, Match Type, Historical Quality Score - 1st of the month
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Byron Porter (bporter@marinsoftware.com)
• Created by Byron Porter on 2023-11-29 15:20
• Last Updated by Byron Porter on 2023-12-06 04:01

> See it in Action

Python Code

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##
## name: Keyword -  1st of Month QS
## description:
##  Sets Historical Quality Score - 1st of Month dimension with current QS value
## 
## author: 
## created: 2023-11-29
## 

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_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_GROUP = 'Group'
RPT_COL_MATCH_TYPE = 'Match Type'
RPT_COL_STATUS = 'Status'
RPT_COL_QUALITY_SCORE = 'Quality Score'

# output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_GROUP = 'Group'
BULK_COL_KEYWORD = 'Keyword'
BULK_COL_MATCH_TYPE = 'Match Type'
BULK_COL_HISTORICAL_QUALITY_SCORE__1ST_OF_THE_MONTH = 'Historical Quality Score - 1st of the month'

# temp column to set historical quality score value
TMP_QUALITY_SCORE = RPT_COL_QUALITY_SCORE + '_'
inputDf[TMP_QUALITY_SCORE] = np.nan


# set dimension value
inputDf[TMP_QUALITY_SCORE] = inputDf[RPT_COL_QUALITY_SCORE]

# copy input dataframe to output
outputDf = inputDf.copy() \
            .rename(columns = { \
                TMP_QUALITY_SCORE: BULK_COL_HISTORICAL_QUALITY_SCORE__1ST_OF_THE_MONTH, \
                })

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

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