Purpose:

The Python script clears existing dimension values to ensure only the top 20 keywords are labeled for the next day’s dimension script.

To Elaborate

The Python script is designed to manage keyword data by resetting specific dimension values in a dataset. This is crucial for maintaining a clean slate for the next day’s processing, where only the top 20 keywords should be labeled. By clearing out existing values, the script ensures that the dataset is prepared for accurate and efficient processing in subsequent operations. This is particularly important in environments where keyword performance and allocation need to be tracked and adjusted daily, such as in digital marketing campaigns. The script helps streamline the data preparation process, ensuring that only relevant and prioritized data is carried forward.

Walking Through the Code

1. Initialization of Constants:
   • The script begins by defining several constants that represent column names for both report and bulk data. These constants are used to ensure consistency and avoid hardcoding column names throughout the script.
2. Copying Input DataFrame:
   • The script creates a copy of the input DataFrame (inputDf) and assigns it to outputDf. This is done to preserve the original data while making modifications to the copy.
3. Clearing Dimension Values:
   • The script sets the values in the BULK_COL_TOP_KEYWORDS column of outputDf to an empty string. This effectively clears any existing data in this column, preparing it for new data to be added in future processes.
4. Output:
   • Finally, the script prints the modified DataFrame (outputDf) in a tabular format using the tableize function, allowing for easy visualization of the changes made.

Vitals

• Script ID : 1371
• Client ID / Customer ID: 1306923239 / 60269197
• Action Type: Bulk Upload
• Item Changed: Keyword
• Output Columns: Account, Campaign, Group, Keyword, Match Type, Top Keywords
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Autumn Archibald (aarchibald@marinsoftware.com)
• Created by Autumn Archibald on 2024-09-04 22:12
• Last Updated by Autumn Archibald on 2024-09-04 22:18

> See it in Action

Python Code

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RPT_COL_KEYWORD = 'Keyword'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_GROUP = 'Group'
RPT_COL_MATCH_TYPE = 'Match Type'
RPT_COL_TOP_KEYWORDS = 'Top Keywords'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_GROUP = 'Group'
BULK_COL_KEYWORD = 'Keyword'
BULK_COL_MATCH_TYPE = 'Match Type'
BULK_COL_TOP_KEYWORDS = 'Top Keywords'


# Assign current date to a parameter
today = datetime.datetime.now(CLIENT_TIMEZONE).date()

# blank out existing dimension values so that there are only 20 keywords labeled once the next day's dimension script runs
outputDf = inputDf.copy()
outputDf[BULK_COL_TOP_KEYWORDS] = ""

print("outputDf", tableize(outputDf))

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