Script 555: Set Pacing Start & End Date

Purpose

The Python script extracts and updates pacing start and end dates from campaign names in a dataset.

To Elaborate

The script is designed to parse campaign names to extract start and end dates, which are then formatted and stored in a structured manner. It processes a dataset containing campaign information, specifically targeting the ‘Campaign’ column to identify date ranges formatted as “MM/DD/YYYY - MM/DD/YYYY”. The script handles various formats and potential inconsistencies in the campaign names, such as optional spaces and parentheses. Once the dates are extracted and formatted into the ISO format (YYYY-MM-DD), the script updates the dataset with these dates. It also identifies any changes in the pacing dates compared to existing data, ensuring that only modified entries are updated. This process is crucial for maintaining accurate and up-to-date pacing information in marketing campaigns.

Walking Through the Code

  1. Initialization:
    • Temporary columns are created in the input DataFrame to store parsed start and end dates, initialized with NaN values.
  2. Date Extraction Function:
    • A function get_dates_from_campaign_name is defined to extract dates from the ‘Campaign’ column using regular expressions.
    • It attempts to parse dates in both four-digit and two-digit year formats, defaulting to NaN if parsing fails.
  3. Testing:
    • A unit test function test_get_dates_from_campaign_name is implemented to verify the date extraction logic against various campaign name formats.
  4. Date Parsing:
    • The script iterates over each row in the input DataFrame, applying the date extraction function to populate the temporary date columns.
  5. Change Detection:
    • It identifies rows where the parsed dates differ from existing pacing dates, marking these as changed.
  6. Output Preparation:
    • The script filters for changed rows, renames columns appropriately, and prepares the output DataFrame.
    • If no changes are detected, an empty DataFrame is initialized with the necessary columns.

Vitals

  • Script ID : 555
  • Client ID / Customer ID: 1306927177 / 60270139
  • Action Type: Bulk Upload
  • Item Changed: Campaign
  • Output Columns: Account, Campaign, Pacing - End Date, Pacing - Start Date
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: ascott@marinsoftware.com (ascott@marinsoftware.com)
  • Created by ascott@marinsoftware.com on 2023-12-04 16:09
  • Last Updated by ascott@marinsoftware.com on 2024-03-13 16:21
> 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
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110

# 
# Add Dimensions Tag based on Campaign Name
# * `Pacing - Start Date`
# * `Pacing - End Date`
# Example: `Search - Display Campaign & Google Ad Words (2/1/2023 - 1/31/2024)`
#
# Author: Michael S. Huang
# Created: 2023-11-25
#

RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_PACING_START_DATE = 'Pacing - Start Date'
RPT_COL_PACING_END_DATE = 'Pacing - End Date'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_PACING_START_DATE = 'Pacing - Start Date'
BULK_COL_PACING_END_DATE = 'Pacing - End Date'

outputDf[BULK_COL_PACING_START_DATE] = "<<YOUR VALUE>>"
outputDf[BULK_COL_PACING_END_DATE] = "<<YOUR VALUE>>"

# create temp column to store new dim tag and default to empty
TMP_START_DATE = RPT_COL_PACING_START_DATE + '_'
TMP_END_DATE = RPT_COL_PACING_END_DATE + '_'
inputDf[TMP_START_DATE] = np.nan
inputDf[TMP_END_DATE] = np.nan

# define function to parse out Start Date and End Date from `Campaign` column of row
# output should be in ISO format YYYY-MM-DD
def get_dates_from_campaign_name(row):
    campaign_name = row['Campaign']
    # Updated regex to handle optional spaces, parentheses, and missing closing parenthesis
    date_pattern_full = r'(\d{1,2}/\d{1,2}/\d{2,4})\s*-\s*?(\d{1,2}/\d{1,2}/\d{2,4})'
    match_full = re.search(date_pattern_full, campaign_name)
    if match_full:
        start_date_str, end_date_str = match_full.groups()
        # Try parsing with four-digit year format
        try:
            start_date = datetime.datetime.strptime(start_date_str, '%m/%d/%Y').strftime('%Y-%m-%d')
            end_date = datetime.datetime.strptime(end_date_str, '%m/%d/%Y').strftime('%Y-%m-%d')
        except ValueError:
            # If it fails, try parsing with two-digit year format
            try:
                start_date = datetime.datetime.strptime(start_date_str, '%m/%d/%y').strftime('%Y-%m-%d')
                end_date = datetime.datetime.strptime(end_date_str, '%m/%d/%y').strftime('%Y-%m-%d')
            except ValueError:
                # If both fail, set to NaN
                start_date, end_date = np.nan, np.nan
    else:
        start_date, end_date = np.nan, np.nan
    return start_date, end_date

def test_get_dates_from_campaign_name():
    campaign_formats = [
        "Traffic_Array Variety Show_3/1/2024-3/31/2024",
        "Traffic_Array Variety Show_(3/1/2024-3/31/2024)",
        "Traffic_Array Variety Show_(3/1/2024-3/31/2024",
        "Traffic_Array Variety Show_3/1/2024-3/31/2024)",
        "Traffic_Array Variety Show_3/1/2024 - 3/31/2024",
        "Traffic_Array Variety Show_(3/1/2024 - 3/31/2024)",
        "Traffic_Array Variety Show_(3/1/2024 - 3/31/2024",
        "Traffic_Array Variety Show_3/1/2024 - 3/31/2024)",
        "Traffic_Array Variety Show_3/1/2024- 3/31/2024",
        "Traffic_Array Variety Show_(3/1/2024- 3/31/2024)",
        "Traffic_Array Variety Show_(3/1/2024- 3/31/2024",
        "Traffic_Array Variety Show_3/1/2024- 3/31/2024)",
        "Traffic_Array Variety Show_3/1/2024 -3/31/2024",
        "Traffic_Array Variety Show_(3/1/2024 -3/31/2024)",
        "Traffic_Array Variety Show_(3/1/2024 -3/31/2024",
        "Traffic_Array Variety Show_3/1/2024 -3/31/2024)"
    ]

    expected_result = ('2024-03-01', '2024-03-31')
    for campaign in campaign_formats:
        row = pd.Series({'Campaign': campaign})
        assert get_dates_from_campaign_name(row) == expected_result, f"Test failed for format: {campaign}"

# Run the unit test
test_get_dates_from_campaign_name()

# parse out Start/End Date from Campaign name
for index, row in inputDf.iterrows():
    start_date, end_date = get_dates_from_campaign_name(row)
    inputDf.at[index, TMP_START_DATE] = start_date
    inputDf.at[index, TMP_END_DATE] = end_date

print("inputDf with parsed tags", tableize(inputDf))

# find changed campaigns
changed = ((inputDf[TMP_START_DATE].notnull() & (inputDf[RPT_COL_PACING_START_DATE] != inputDf[TMP_START_DATE])) | \
          (inputDf[TMP_END_DATE].notnull() & (inputDf[RPT_COL_PACING_END_DATE] != inputDf[TMP_END_DATE])))

print("== Campaigns with Changed Tag ==", tableize(inputDf.loc[changed]))

# only select changed rows
cols = [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, TMP_START_DATE, TMP_END_DATE]
outputDf = inputDf.loc[ changed, cols ].copy() \
                  .rename(columns = { \
                    TMP_START_DATE: BULK_COL_PACING_START_DATE, \
                    TMP_END_DATE: BULK_COL_PACING_END_DATE \
                  })

if not outputDf.empty:
  print("outputDf", tableize(outputDf))
else:
  print("Empty outputDf")
  outputDf = pd.DataFrame(columns=[BULK_COL_ACCOUNT, BULK_COL_CAMPAIGN, BULK_COL_PACING_START_DATE, BULK_COL_PACING_END_DATE])

Post generated on 2024-11-27 06:58:46 GMT

comments powered by Disqus