Script 1555: Script AutoTag Pacing Status

Purpose:

The Python script categorizes pacing percentages into descriptive tags such as ‘On Target’, ‘Under Pacing’, ‘Over Pacing’, etc., based on predefined ranges.

To Elaborate

The script is designed to update a dimension called ‘Impr Pacing’ with descriptive tags that reflect the pacing status of a campaign. It uses predefined ranges to categorize pacing percentages into labels like ‘On Target’, ‘Under Pacing’, ‘Over Pacing’, ‘Significantly Over Pacing’, and ‘Significantly Under Pacing’. These categories help in understanding whether a campaign is performing as expected or if adjustments are needed. The script processes pacing values, cleans them, converts them to percentages, and then assigns the appropriate category based on the defined ranges. This categorization aids in structured budget allocation and campaign performance analysis.

Walking Through the Code

  1. Configurable Pacing Range Definitions:
    • The script begins by defining a dictionary PACING_RANGES that maps descriptive tags to specific numeric ranges. These ranges are user-configurable and determine how pacing values are categorized.
  2. Categorize Pacing Function:
    • The function categorize_pacing takes a pacing value as input, cleans it by removing any percentage signs, and converts it to a float.
    • It scales the value by multiplying by 100 to convert it from a decimal to a percentage.
    • The function iterates over the PACING_RANGES dictionary to find the appropriate category for the pacing value based on the defined ranges.
    • If the value falls within a range, the corresponding category is returned; otherwise, ‘Undefined’ is returned for unexpected cases.
  3. Data Processing:
    • The script reads pacing data from a primary data source and applies the categorize_pacing function to each pacing value in the dataset.
    • The categorized pacing values are stored in a new column ‘Impr. Pacing’ in the output DataFrame.
  4. Output Preparation:
    • The script prepares the output DataFrame by selecting relevant columns and updating the ‘Impr. Pacing’ column with the categorized values.
    • The final output is structured to include account, campaign, campaign ID, and the newly categorized pacing status.

Vitals

  • Script ID : 1555
  • Client ID / Customer ID: 1306928453 / 60270539
  • Action Type: Bulk Upload
  • Item Changed: Campaign
  • Output Columns: Account, Campaign, Campaign ID, Impr. Pacing
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: Grégory Pantaine (gpantaine@marinsoftware.com)
  • Created by Grégory Pantaine on 2024-12-05 14:15
  • Last Updated by Michael Huang on 2025-03-10 02:45
> See it in Action

Python Code

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93

##
## name: Script AutoTag - Pacing Status
## description: Update the 'Impr Pacing' dimension with a worded value,
## such as on target/over pacing etc.
## The breakdown should be as follows:
## if value is betwen -1% & +5% , value output should be set as: 'On Target'
## if value is betwen -1% & -15% , value output should be set as: 'Under Pacing'
## if value is betwen +5% & +20% , value output should be set as: 'Over Pacing'
## if value is above +20% , value output should be set as: 'Significantly Over Pacing'
## if value is below -15% , value output should be set as: 'Significantly Under Pacing'##
##
## author: ChatGPT & Gregory Pantaine
## created: 2024-12-05
##
##
## name: Script AutoTag - Pacing Status
## description: Update the 'Impr Pacing' dimension with a worded value,
## such as on target/over pacing etc.
##
## author: ChatGPT & Gregory Pantaine
## created: 2024-12-05
##




# Configurable Pacing Range Definitions
PACING_RANGES = {
   'On Target': (99, 105),
   'Under Pacing': (85, 99),
   'Over Pacing': (105, 120),
   'Significantly Over Pacing': (120, float('inf')),
   'Significantly Under Pacing': (-float('inf'), 85)
##    'On Target': (-1, 5),
##    'Under Pacing': (-15, -2),
##    'Over Pacing': (6, 20),
##    'Significantly Over Pacing': (21, float('inf')),
##    'Significantly Under Pacing': (-float('inf'), -16)
}


def categorize_pacing(pacing_value):
    try:
        # Convert to string and remove '%' if present
        pacing_value_cleaned = str(pacing_value).replace('%', '').strip()
        
        # Convert to float and scale up if necessary
        pacing_percentage = float(pacing_value_cleaned)  # Convert to float

        # value in report is actually decimal, not percentage, so multiply by 100
        pacing_percentage *= 100  

        for category, (lower, upper) in PACING_RANGES.items():
            if lower <= pacing_percentage < upper:
                # print(f"Value: {pacing_percentage}%, Category: {category}")  # Debug print
                return category
    except (ValueError, TypeError) as e:
        print(f"Error processing value {pacing_value}: {e}")  # Debug print
        pass
    return 'Undefined'  # Fallback for unexpected cases



today = datetime.datetime.now(CLIENT_TIMEZONE).date()


# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_CAMPAIGN_ID = 'Campaign ID'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'
RPT_COL_PACING = 'Pacing %'
RPT_COL_PUB_COST = 'Pub. Cost $'
RPT_COL_STRATEGY = 'Strategy'
RPT_COL_IMPR_PACING = 'Impr. Pacing'


# output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_CAMPAIGN_ID = 'Campaign ID'
BULK_COL_IMPR_PACING = 'Impr. Pacing'
outputDf[BULK_COL_IMPR_PACING] = "<<YOUR VALUE>>"


print(inputDf[RPT_COL_PACING].head())

# Pacing categorization
inputDf[BULK_COL_IMPR_PACING] = inputDf[RPT_COL_PACING].apply(categorize_pacing)
debugDf=inputDf

outputDf = inputDf[[RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, RPT_COL_CAMPAIGN_ID, RPT_COL_IMPR_PACING]]

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

comments powered by Disqus