Script 1379: MediaType Autotagging

Purpose

The Python script automates the tagging of media types and subtypes based on account and campaign information in a dataset.

To Elaborate

The script is designed to automate the process of categorizing media types and subtypes for marketing campaigns based on specific keywords found in account and campaign data. It processes a dataset, identifying patterns in the account names and campaign descriptions to assign appropriate media type and subtype tags. This tagging helps in organizing and analyzing marketing data more efficiently, ensuring that each campaign is correctly classified according to predefined business rules. The script uses a series of conditional checks to match account and campaign details with known media types and subtypes, updating the dataset with these classifications.

Walking Through the Code

  1. Initialization and Setup:
    • The script begins by defining constants for column names used in the dataset, which are crucial for identifying the relevant data fields.
    • Temporary columns for media type and subtype are initialized with NaN values to prepare for tagging.
  2. Tagging Logic:
    • The script uses a series of conditional statements to check for specific keywords in the account and campaign data.
    • For each condition met, it assigns a corresponding media type and subtype to the temporary columns.
    • This logic covers various platforms and media formats, such as ‘Native’, ‘Social’, ‘SEM’, ‘Display’, ‘TV’, ‘Email’, and more, based on the presence of keywords like ‘Taboola’, ‘TikTok’, ‘Google’, etc.
  3. Finalizing the Output:
    • After tagging, 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 analysis or reporting, with each campaign correctly tagged according to the predefined rules.

Vitals

  • Script ID : 1379
  • Client ID / Customer ID: 1306925585 / 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 15:21
  • Last Updated by Anton Antonov on 2024-09-09 15:21
> 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 2024-11-27 06:58:46 GMT

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