Purpose:

The Python script alerts the Program Manager when the percentage difference in a dataset is 15% or more, either positively or negatively.

To Elaborate

The script is designed to monitor a dataset for significant changes in a specific metric, the “Percentage Difference.” It aims to alert a Program Manager whenever this percentage difference reaches or exceeds a threshold of 15%, whether the change is an increase or a decrease. This functionality is crucial for tracking substantial variations that might require managerial attention or intervention. The script processes input data, evaluates the percentage difference for each entry, and updates an alert column to indicate whether a high increase or decrease has occurred. This alert mechanism helps in quickly identifying and responding to significant changes in the data, ensuring that the Program Manager is informed of potential issues or opportunities.

Walking Through the Code

1. Data Preparation
   • The script begins by loading the primary data source into a DataFrame named inputDf.
   • It defines several column names that are used throughout the script, such as ‘Campaign’, ‘Account’, ‘Campaign Status’, ‘SBA Strategy’, and ‘Percentage Difference’.
2. Clearing Existing Alerts
   • The script clears any existing data in the ‘AUTOMATION_ALERT’ column of the inputDf by setting it to np.nan.
3. Initial Data Display
   • It prints the first few rows of the inputDf to provide an initial view of the data.
4. Applying Alert Criteria
   • The script creates a new DataFrame outputDf and initializes the ‘AUTOMATION_ALERT’ column with np.nan.
   • It defines conditions to check if the ‘Percentage Difference’ is greater than or equal to 15% or less than or equal to -15%.
   • Based on these conditions, it updates the ‘AUTOMATION_ALERT’ column with ‘Alert: High Increase’ or ‘Alert: High Decrease’.
   • If the percentage difference does not meet these criteria, the alert column remains blank.
5. Final Data Display
   • Finally, the script prints the updated outputDf to show the results after applying the alert criteria.

Vitals

• Script ID : 991
• Client ID / Customer ID: 1306926629 / 60270083
• Action Type: Bulk Upload
• Item Changed: Campaign
• Output Columns: Account, Campaign, AUTOMATION_ALERT
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: dwaidhas@marinsoftware.com (dwaidhas@marinsoftware.com)
• Created by dwaidhas@marinsoftware.com on 2024-04-22 17:44
• Last Updated by dwaidhas@marinsoftware.com on 2024-05-07 18:50

> See it in Action

Python Code

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##
## name: Percentage Difference Column Alert
## description:
##  Alerts Program Manager whenever The Percentage Difference is higher or equal to 15%
## 
## author: Dana Waidhas 
## created: 2024-04-22
## 

inputDf = dataSourceDict["1"]

# primary data source and columns
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'
RPT_COL_SBA_STRATEGY = 'SBA Strategy'
RPT_COL_PERCENTAGE_DIFFERENCE = 'Percentage Difference'
RPT_COL_AUTOMATION_ALERT = 'AUTOMATION_ALERT'

# Clear existing data in the 'AUTOMATION_ALERT' column
inputDf[RPT_COL_AUTOMATION_ALERT] = np.nan

# output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_AUTOMATION_ALERT = 'AUTOMATION_ALERT'

print(tableize(inputDf.head()))

# Apply criteria to generate values for 'AUTOMATION_ALERT' column
outputDf['AUTOMATION_ALERT'] = np.nan
should_alert_high = inputDf['Percentage Difference'] >= 0.15
should_alert_low = inputDf['Percentage Difference'] <= -0.15
outputDf.loc[should_alert_high, 'AUTOMATION_ALERT'] = 'Alert: High Increase'
outputDf.loc[should_alert_low, 'AUTOMATION_ALERT'] = 'Alert: High Decrease'
outputDf.loc[~(should_alert_high | should_alert_low), 'AUTOMATION_ALERT'] = 'NaN'  # Blank for other cases


# Print the updated DataFrame
print(tableize(outputDf.head()))

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