Script 781: Set Pacing Start Date & Pacing 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 in a dataset to extract start and end dates, which are then formatted and stored in a structured manner. It identifies campaigns with date ranges embedded in their names, extracts these dates, and converts them into a standardized ISO format (YYYY-MM-DD). The script then compares these parsed dates with existing pacing start and end dates in the dataset to identify any changes. If changes are detected, the script updates the dataset with the new dates. This process ensures that the pacing dates are accurately reflected and up-to-date, which is crucial for campaign management and reporting.

Walking Through the Code

  1. Initialization:
    • The script begins by defining constants for column names used in the input and output dataframes.
    • Temporary columns are created in the input dataframe to store the parsed start and end dates.
  2. Date Extraction Function:
    • A function get_dates_from_campaign_name is defined to extract start and end dates from the campaign name using regular expressions.
    • The function handles various date formats and attempts to parse them into a standardized format, returning NaN if parsing fails.
  3. Testing:
    • A test function test_get_dates_from_campaign_name is implemented to verify the date extraction logic against multiple campaign name formats.
  4. Date Parsing and Comparison:
    • The script iterates over each row in the input dataframe, applying the date extraction function to populate the temporary columns with parsed dates.
    • It identifies rows where the parsed dates differ from the existing pacing dates, marking these as changed.
  5. Output Preparation:
    • The script filters the input dataframe to include only rows with changed pacing dates.
    • It renames the temporary columns to match the output dataframe’s structure and prepares the final output dataframe.
    • If no changes are detected, an empty dataframe is initialized for output.

Vitals

  • Script ID : 781
  • Client ID / Customer ID: 1306927189 / 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 2024-03-08 20:34
  • Last Updated by ascott@marinsoftware.com on 2024-07-16 14:15
> See it in Action

Python Code

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# 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 2025-03-11 01:25:51 GMT

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