Script 1381: MediaType Autotagging Group

Purpose:

The Python script automates the tagging of media types and subtypes based on specific account and campaign criteria within a dataset.

To Elaborate

The script is designed to automate the process of categorizing media types and subtypes for various advertising accounts and campaigns. It processes a dataset by examining specific columns related to account names, publisher names, campaigns, and groups. Based on predefined rules, it assigns appropriate media types and subtypes to each entry. This categorization helps in organizing and analyzing advertising data more efficiently, ensuring that each entry is tagged with the correct media type and subtype according to the specified criteria. The script is particularly useful for marketing and advertising teams who need to manage large datasets and require consistent and accurate tagging for reporting and analysis purposes.

Walking Through the Code

  1. Initialization:
    • The script begins by defining constants for column names used in the dataset. These constants help in referencing specific columns throughout the script.
    • Temporary columns for media type and subtype are initialized with ‘Unknown’ values and then set to NaN to prepare for tagging.
  2. Tagging Logic:
    • The script uses a series of conditional statements to check the content of specific columns (Account, Publisher Name, Campaign, Group) in the dataset.
    • For each condition that matches, it assigns a predefined media type and subtype to the corresponding temporary columns.
    • The conditions are based on string matching, such as checking if the account name contains ‘Taboola’ or if the campaign name includes ‘[GDN]’.
  3. Output Preparation:
    • After processing the input data, the script copies the newly assigned media types and subtypes from the temporary columns to the output dataset.
    • The output dataset is then ready for further use, such as reporting or analysis.
  4. User Changeable Parameters:
    • Users can modify the conditions and the corresponding media type and subtype assignments to fit their specific needs.
    • The script allows flexibility in defining what constitutes a match for each media type and subtype, enabling customization based on different datasets or business rules.

Vitals

  • Script ID : 1381
  • Client ID / Customer ID: 1306925587 / 60269545
  • Action Type: Bulk Upload
  • Item Changed: AdGroup
  • Output Columns: Account, Campaign, Group, Media sub-type, Media Type
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: Anton Antonov (aantonov@marinsoftware.com)
  • Created by Anton Antonov on 2024-09-09 15:25
  • Last Updated by Anton Antonov on 2024-09-09 15:25
> See it in Action

Python Code

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RPT_COL_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_GROUP = 'Group'
RPT_COL_MEDIA_SUBTYPE = 'Media sub-type'
RPT_COL_MEDIA_TYPE = 'Media Type'
RPT_COL_ACCOUNT_PUBLISHER_NAME = 'Publisher Name'
RPT_COL_ACCOUNT_PUBLISHER = 'Publisher'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_GROUP = 'Group'
BULK_COL_MEDIA_SUBTYPE = 'Media sub-type'
BULK_COL_MEDIA_TYPE = 'Media Type'
BULK_COL_ACCOUNT_PUBLISHER_NAME = 'Publisher Name'
BULK_COL_ACCOUNT_PUBLISHER = 'Publisher'

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

TMP_MediaType = 'Unknown Media Type'
TMP_SubType = 'Unknown Media sub-type'
# blank out tmp field
inputDf[TMP_MediaType] = numpy.nan
inputDf[TMP_SubType] = numpy.nan

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


inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Taboola', na=False)) , TMP_MediaType ] = 'Native'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Taboola', na=False)) , TMP_SubType ] = 'Native'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('TikTok', na=False)) , TMP_MediaType ] = 'Social'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('TikTok', na=False)) , TMP_SubType ] = 'TikTok'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Snapchat', na=False)) , TMP_MediaType ] = 'Social'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Snapchat', na=False)) , TMP_SubType ] = 'Snapchat'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Pinterest', na=False)) , TMP_MediaType ] = 'Social'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Pinterest', na=False)) , TMP_SubType ] = 'Pinterest'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('Facebook', na=False)) , TMP_MediaType ] = 'Social'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('Facebook', na=False)) , TMP_SubType ] = 'Meta'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('BING', na=False)) , TMP_MediaType ] = 'SEM'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('BING', na=False)) , TMP_SubType ] = 'SEM-Bing'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('Google', na=False)) , TMP_MediaType ] = 'SEM'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('Google', na=False)) , TMP_SubType ] = 'SEM-Google'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('Google', na=False)) & (inputDf[RPT_COL_CAMPAIGN].str.contains('\[GDN\]', na=False)), TMP_MediaType ] = 'Display'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('Google', na=False)) & (inputDf[RPT_COL_CAMPAIGN].str.contains('\[GDN\]', na=False)), TMP_SubType ] = 'Standard display'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('Google', na=False)) & (inputDf[RPT_COL_CAMPAIGN].str.contains('\[Demand Gen\]', na=False)), TMP_MediaType ] = 'Display & Online video'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('Google', na=False)) & (inputDf[RPT_COL_CAMPAIGN].str.contains('\[Demand Gen\]', na=False)), TMP_SubType ] = 'Demand Gen'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('Google', na=False)) & (inputDf[RPT_COL_CAMPAIGN].str.contains('\[YT\]', na=False)), TMP_MediaType ] = 'Online video'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('Google', na=False)) & (inputDf[RPT_COL_CAMPAIGN].str.contains('\[YT\]', na=False)), TMP_SubType ] = 'VIDEO-Instream'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT_PUBLISHER_NAME].str.contains('Google', na=False)) & (inputDf[RPT_COL_CAMPAIGN].str.contains('\[YT\]', na=False)) & (inputDf[RPT_COL_CAMPAIGN].str.contains('Bumper', na=False)), TMP_SubType ] = 'VIDEO-Bumper'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Adform', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('TV', na=False)), TMP_MediaType ] = 'TV'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Adform', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('TV', na=False)), TMP_SubType ] = 'TV'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Adform', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[DISPLAY\]', na=False)), TMP_MediaType ] = 'Display'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Adform', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[DISPLAY\]', na=False)), TMP_SubType ] = 'Standard display'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Adform', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[DISPLAY-Premium\]', na=False)), TMP_MediaType ] = 'Display'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Adform', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[DISPLAY-Premium\]', na=False)), TMP_SubType ] = 'Premium display'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Adform', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[Email-Newsletter\]', na=False)), TMP_MediaType ] = 'Email'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Adform', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[Email-Newsletter\]', na=False)), TMP_SubType ] = 'Email-Newsletter'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Adform', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[Native\]', na=False)), TMP_MediaType ] = 'Native'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Adform', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[Native\]', na=False)), TMP_SubType ] = 'Native'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Adform', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[AUDIO - Podcast\]', na=False)), TMP_MediaType ] = 'Online Audio'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Adform', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[AUDIO - Podcast\]', na=False)), TMP_SubType ] = 'Audio-Podcast'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Adform', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[VIDEO-Instream\]', na=False)), TMP_MediaType ] = 'Online Video'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Adform', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[VIDEO-Instream\]', na=False)), TMP_SubType ] = 'VIDEO-Instream'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Adform', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[VIDEO-Bumper\]', na=False)), TMP_MediaType ] = 'Online Video'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Adform', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[VIDEO-Bumper\]', na=False)), TMP_SubType ] = 'VIDEO-Bumper'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Adform', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('OOH', na=False)), TMP_MediaType ] = 'OOH'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Adform', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('OOH', na=False)), TMP_SubType ] = 'OOH'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Adform', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('Radio', na=False)), TMP_MediaType ] = 'Radio'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Adform', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('Radio', na=False)), TMP_SubType ] = 'Radio'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Adform', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('QX.se', na=False)), TMP_MediaType ] = 'Display'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Adform', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('QX.se', na=False)), TMP_SubType ] = 'Standard display'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Adform', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('Print', na=False)), TMP_MediaType ] = 'Print'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Adform', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('Print', na=False)), TMP_SubType ] = 'Print'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Offline', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('OOH', na=False)), TMP_MediaType ] = 'OOH'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Offline', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('OOH', na=False)), TMP_SubType ] = 'OOH'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Offline', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('Radio', na=False)), TMP_MediaType ] = 'Radio'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Offline', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('Radio', na=False)), TMP_SubType ] = 'Radio'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Offline', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('TV', na=False)), TMP_MediaType ] = 'TV'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Offline', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('TV', na=False)), TMP_SubType ] = 'TV'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Offline', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('Print', na=False)), TMP_MediaType ] = 'Print'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Offline', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('Print', na=False)), TMP_SubType ] = 'Print'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Offline', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[DISPLAY\]', na=False)), TMP_MediaType ] = 'Display'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Offline', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[DISPLAY\]', na=False)), TMP_SubType ] = 'Standard display'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Offline', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[DISPLAY-Premium\]', na=False)), TMP_MediaType ] = 'Display'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Offline', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[DISPLAY-Premium\]', na=False)), TMP_SubType ] = 'Premium display'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Offline', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[Email-Newsletter\]', na=False)), TMP_MediaType ] = 'Email'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Offline', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[Email-Newsletter\]', na=False)), TMP_SubType ] = 'Email-Newsletter'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Offline', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[Native\]', na=False)), TMP_MediaType ] = 'Native'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Offline', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[Native\]', na=False)), TMP_SubType ] = 'Native'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Offline', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[AUDIO - Podcast\]', na=False)), TMP_MediaType ] = 'Online Audio'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Offline', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[AUDIO - Podcast\]', na=False)), TMP_SubType ] = 'Audio-Podcast'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Offline', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[VIDEO-Instream\]', na=False)), TMP_MediaType ] = 'Online Video'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Offline', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[VIDEO-Instream\]', na=False)), TMP_SubType ] = 'VIDEO-Instream'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Offline', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[VIDEO-Bumper\]', na=False)), TMP_MediaType ] = 'Online Video'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Offline', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[VIDEO-Bumper\]', na=False)), TMP_SubType ] = 'VIDEO-Bumper'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Email', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[Email-Newsletter\]', na=False)), TMP_MediaType ] = 'Email'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Email', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[Email-Newsletter\]', na=False)), TMP_SubType ] = 'Email-Newsletter'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Email', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[Email-Standalone\]', na=False)), TMP_MediaType ] = 'Email'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Email', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[Email-Standalone\]', na=False)), TMP_SubType ] = 'Email-Standalone'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Email', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[SMS\]', na=False)), TMP_MediaType ] = 'SMS'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Email', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[SMS\]', na=False)), TMP_SubType ] = 'SMS'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Email', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[Quiz\]', na=False)), TMP_MediaType ] = 'Quiz'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Email', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[Quiz\]', na=False)), TMP_SubType ] = 'Quiz-Primetime'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Email', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[Standard display\]', na=False)), TMP_MediaType ] = 'Display'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Email', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[Standard display\]', na=False)), TMP_SubType ] = 'Standard display'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Email', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('Affiliate', na=False)), TMP_MediaType ] = 'Affiliate'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Email', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[Affiliate]', na=False)), TMP_SubType ] = 'Affiliate'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Email', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[Affiliate-cashback]', na=False)), TMP_SubType ] = 'Affiliate-cashback'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Email', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[Affiliate-campaign]', na=False)), TMP_SubType ] = 'Affiliate-campaign'
inputDf.loc[ (inputDf[RPT_COL_ACCOUNT].str.contains('Email', na=False)) & (inputDf[RPT_COL_GROUP].str.contains('\[Affiliate-partner]', na=False)), TMP_SubType ] = 'Affiliate-partner'

print(tableize(inputDf))

#print(inputDf.index.duplicated())

# copy new strategy to output
outputDf.loc[:,BULK_COL_MEDIA_TYPE] = inputDf.loc[:, TMP_MediaType]
outputDf.loc[:,BULK_COL_MEDIA_SUBTYPE] = inputDf.loc[:, TMP_SubType]

#outputDf = outputDf[inputDf[TMP_MediaType].notnull() & inputDf[TMP_SubType].notnull() & ~inputDf[BULK_COL_CAMPAIGN].str.contains('"')]
print(tableize(outputDf))

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

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