Purpose:

The script assigns market identifiers to campaigns based on specific patterns in campaign names.

To Elaborate

The Python script is designed to populate the ‘Market’ dimension of a dataset by analyzing the ‘Campaign’ column. It identifies specific market identifiers within campaign names and assigns corresponding market codes to a temporary field. This process is crucial for categorizing campaigns by market, which can be essential for reporting and analysis. The script ensures that only campaigns with updated market identifiers are included in the final output, facilitating accurate and efficient data management.

Walking Through the Code

1. Initialization and Setup
   • The script begins by defining constants for column names used in the input and output dataframes.
   • A temporary field is created in the input dataframe to store new market identifiers, initialized with NaN values.
2. Market Identification
   • The script checks the ‘Campaign’ column for specific substrings that indicate market regions (e.g., ‘- AUS -‘, ‘- FR -‘) and assigns corresponding market codes (‘AUS’, ‘FR’, etc.) to the temporary field.
3. Data Processing and Output
   • The script prints the input dataframe for verification purposes.
   • It identifies and flags duplicate indices in the dataframe.
   • The new market identifiers are copied to the output dataframe.
   • The output dataframe is filtered to include only those campaigns where the market identifier has changed, ensuring that only relevant updates are processed.

Vitals

• Script ID : 161
• Client ID / Customer ID: 1306922915 / 60269133
• Action Type: Bulk Upload
• Item Changed: Campaign
• Output Columns: Account, Campaign, Market
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Jonathan Reichl (jreichl@marinsoftware.com)
• Created by Jonathan Reichl on 2023-06-05 08:46
• 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_MARKET = 'Market'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_MARKET = 'Market'

#outputDf[BULK_COL_MARKET] = "<<YOUR VALUE>>"

TMP_FIELD = BULK_COL_MARKET + '_new'
# blank out tmp field
inputDf[TMP_FIELD] = numpy.nan



today = datetime.datetime.now(CLIENT_TIMEZONE).date()
print(tableize(inputDf))


inputDf.loc[ (inputDf[RPT_COL_CAMPAIGN].str.contains('- AUS -', case=False)) , TMP_FIELD ] = 'AUS'
inputDf.loc[ (inputDf[RPT_COL_CAMPAIGN].str.contains('- FR -', case=False)) , TMP_FIELD ] = 'FR'
inputDf.loc[ (inputDf[RPT_COL_CAMPAIGN].str.contains('- IRE -', case=False)) , TMP_FIELD ] = 'IRE'
inputDf.loc[ (inputDf[RPT_COL_CAMPAIGN].str.contains('- SA -', case=False)) , TMP_FIELD ] = 'SA'
inputDf.loc[ (inputDf[RPT_COL_CAMPAIGN].str.contains('- UAE -', case=False)) , TMP_FIELD ] = 'UAE'
inputDf.loc[ (inputDf[RPT_COL_CAMPAIGN].str.contains('- UK -', case=False)) , TMP_FIELD ] = 'UK'
inputDf.loc[ (inputDf[RPT_COL_CAMPAIGN].str.contains('- US -', case=False)) , TMP_FIELD ] = 'US'


print(tableize(inputDf))

print(inputDf.index.duplicated())

# copy new strategy to output
outputDf.loc[:,BULK_COL_MARKET] = inputDf.loc[:, TMP_FIELD]

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

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