Purpose:

The Python script adjusts SBA targets by applying a target factor to raw targets.

To Elaborate

The Python script is designed to adjust Structured Budget Allocation (SBA) targets by multiplying the raw target values with a specified target factor. This adjustment is crucial for recalibrating budget allocations based on dynamic factors that may influence campaign performance. The script ensures that only campaigns with modified targets are included in the final output, which helps in focusing on campaigns that require attention due to changes in their budget targets. This process is essential for maintaining efficient budget management and ensuring that resources are allocated optimally across different campaigns.

Walking Through the Code

1. Initialization and Setup:
   • The script begins by defining constants for column names used in the input and output data frames. These constants help in maintaining consistency and readability throughout the script.
2. Temporary Field Initialization:
   • A temporary field, TMP_FIELD, is initialized in the output data frame and set to NaN. This field will be used to store intermediate calculations.
3. Calculate New SBA Budget Target:
   • The script calculates the new SBA budget target by multiplying the raw target values (RPT_COL_SBA_RAWTARGET) with the target factor (RPT_COL_SBA_TARGETFACTOR). The result is stored in the TMP_FIELD of the input data frame.
4. Rounding the Calculated Values:
   • The calculated values in TMP_FIELD are rounded to the nearest whole number to ensure that the budget targets are practical and easy to manage.
5. Update Output with New Targets:
   • The new SBA targets are set in the output data frame by copying the rounded values from TMP_FIELD to the BULK_COL_SBA_BUDGETMODELTARGET column.
6. Filter for Changed Targets:
   • The script filters the output data frame to include only those campaigns where the target has changed. This is done by checking for non-null values in TMP_FIELD and ensuring that the new target differs from the existing one. This step ensures that only relevant changes are captured in the final output.

Vitals

• Script ID : 95
• Client ID / Customer ID: 1306923673 / 60269245
• Action Type: Bulk Upload
• Item Changed: Campaign
• Output Columns: Account, Campaign, SBA Budget Model Target
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Jonathan Reichl (jreichl@marinsoftware.com)
• Created by Jonathan Reichl on 2023-05-12 12:46
• Last Updated by Jonathan Reichl on 2023-12-06 04:01

> See it in Action

Python Code

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RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_SBA_BUCKETNAME = 'SBA Bucket Name'
RPT_COL_SBA_RAWTARGET = 'SBA Raw Target'
RPT_COL_SBA_TARGETFACTOR = 'SBA Target Factor'
RPT_COL_SBA_BUDGETMODELTARGET = 'SBA Budget Model Target'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_SBA_BUDGETMODELTARGET = 'SBA Budget Model Target'

#outputDf[BULK_COL_ACCOUNT] = inputDf[RPT_COL_ACCOUNT]



# blank out tmp field
outputDf['TMP_FIELD'] = np.nan

# calculate new sba budget target 
inputDf['TMP_FIELD'] = inputDf[RPT_COL_SBA_TARGETFACTOR] * inputDf[RPT_COL_SBA_RAWTARGET]

#round new value 
inputDf['TMP_FIELD'] = inputDf['TMP_FIELD'].round(0)

# set new sba target in output 
outputDf.loc[:,BULK_COL_SBA_BUDGETMODELTARGET] = inputDf.loc[:, 'TMP_FIELD']

# only include campaigns with changed target  in bulk file
outputDf = outputDf[ inputDf['TMP_FIELD'].notnull() & (inputDf[BULK_COL_SBA_BUDGETMODELTARGET] != inputDf['TMP_FIELD']) ]

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