Purpose:

The script categorizes Yahoo DSP ad groups into ‘New’, ‘Mature I’, and ‘Mature II’ based on their spending over the previous 7 days.

To Elaborate

The Python script is designed to classify Yahoo DSP ad groups into different maturity levels based on their spending over the past week. The classification is done by evaluating the ‘Pub. Cost $’ for each ad group and assigning a maturity label of ‘New’, ‘Mature I’, or ‘Mature II’. The script specifically targets ad groups within the ‘Native Path’ account. The criteria for classification are as follows: ad groups with a spend between $500 and $750 are labeled as ‘New’, those with a spend between $750 and $1000 are labeled as ‘Mature I’, and those with a spend over $1000 are labeled as ‘Mature II’. The script ensures that only ad groups meeting these criteria are updated with the appropriate maturity label.

Walking Through the Code

1. Data Preparation
   • The script begins by filtering the input data to include only those ad groups associated with the ‘Native Path’ account. This is done by creating a DataFrame npathDf that contains relevant columns for further processing.
2. Criteria Definition
   • Three sets of criteria are defined to determine the maturity level of each ad group:
     • npnew: Identifies ad groups with a spend between $500 and $750 and no existing maturity label.
     • npmature1: Identifies ad groups with a spend between $750 and $1000 that are not already labeled as ‘Mature I’.
     • npmature2: Identifies ad groups with a spend over $1000 that are not already labeled as ‘Mature II’.
3. DataFrame Creation for Each Maturity Level
   • Separate DataFrames are created for each maturity level based on the criteria:
     • npnewDf for ‘New’ ad groups.
     • npmature1Df for ‘Mature I’ ad groups.
     • npmature2Df for ‘Mature II’ ad groups.
   • Each DataFrame is updated to include the appropriate maturity label.
4. DataFrame Merging and Output
   • The script checks for non-empty DataFrames among the newly created ones and merges them into a single outputDf.
   • If no ad groups meet the criteria, an empty DataFrame is created.
   • The final DataFrame is printed, showing the ad groups with their assigned maturity levels.

Vitals

• Script ID : 235
• Client ID / Customer ID: 1306925569 / 60269477
• Action Type: Bulk Upload (Preview)
• Item Changed: AdGroup
• Output Columns: Account, Campaign, Group, Group Maturity
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Byron Porter (bporter@marinsoftware.com)
• Created by Byron Porter on 2023-07-10 14:29
• Last Updated by Byron Porter on 2023-12-06 04:01

> See it in Action

Python Code

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RPT_COL_GROUP = 'Group'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_PUB_COST = 'Pub. Cost $'
RPT_COL_GROUP_MATURITY = 'Group Maturity'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_GROUP = 'Group'
BULK_COL_GROUP_MATURITY = 'Group Maturity'

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

################################# Native Path Tagging ################################# 

# Create Data Frame containing only Native Path campaigns for tagging
npath = inputDf[RPT_COL_ACCOUNT] == 'Native Path'

npathDf = inputDf.loc[npath, [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, RPT_COL_GROUP, RPT_COL_GROUP_MATURITY]].copy()

# Create criteria for what is considered New vs Mature for Yahpp DSP campaigns
npnew = (inputDf[RPT_COL_PUB_COST] >= 500) & (inputDf[RPT_COL_PUB_COST] < 750) & (inputDf[RPT_COL_GROUP_MATURITY].isnull())
npmature1 = (inputDf[RPT_COL_PUB_COST] >= 750) & (inputDf[RPT_COL_PUB_COST] < 1000) & (inputDf[RPT_COL_GROUP_MATURITY] != 'Mature I')
npmature2 = (inputDf[RPT_COL_PUB_COST] >= 1000) & (inputDf[RPT_COL_GROUP_MATURITY] != 'Mature II')

# Use criteria to create new DataFrames. One for each maturity label
npnewDf = npathDf.loc[npnew, [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, RPT_COL_GROUP, RPT_COL_GROUP_MATURITY]]
npnewDf.loc[:, RPT_COL_GROUP_MATURITY] = 'New'

npmature1Df = npathDf.loc[npmature1, [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, RPT_COL_GROUP, RPT_COL_GROUP_MATURITY]]
npmature1Df.loc[:, RPT_COL_GROUP_MATURITY] = 'Mature I'

npmature2Df = npathDf.loc[npmature2, [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, RPT_COL_GROUP, RPT_COL_GROUP_MATURITY]]
npmature2Df.loc[:, RPT_COL_GROUP_MATURITY] = 'Mature II'



# Merges the defined data frames, excluding those that are empty, and prints resulting outputDf
dataframes = [npnewDf, npmature1Df, npmature2Df]
non_empty_dataframes = [df for df in dataframes if not df.empty]

if non_empty_dataframes:
    outputDf = pd.concat(non_empty_dataframes)
    print("outputDf", tableize(outputDf))
else:
    print("Empty outputDf")
    outputDf = pd.DataFrame(columns=[RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, RPT_COL_GROUP, RPT_COL_GROUP_MATURITY])

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