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##
## name: Auto Tag Campaign with Tipo de Campanha Dimension
## description:
##  
## 
## author: Dana Waidhas 
## created: 2024-03-22
## 
inputDf = dataSourceDict["1"]

# primary data source and columns
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_TIPO_DE_CAMPANHA = 'Tipo de campanha'

# output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_TIPO_DE_CAMPANHA = 'Tipo de campanha'

# user code start here
# Function to extract tag from campaign name
def extract_tag_from_campaign_name(campaign_name):
    """
    Extracts the tag (Tipo de campanha) from the campaign name based on specific keywords.
    
    Args:
    campaign_name (str): The name of the campaign.
    
    Returns:
    str or None: The extracted tag (Tipo de campanha) if found, else None.
    """
    # Convert the campaign name to lowercase for case-insensitive matching
    campaign_name_lower = campaign_name.lower()
    
    # Check for keywords and return the corresponding tag
    if 'curso' in campaign_name_lower:
        return 'Curso'
    elif 'generica' in campaign_name_lower:
        return 'Generica'
    elif 'oportunidade' in campaign_name_lower:
        return 'Oportunidade'
    elif 'competitor' in campaign_name_lower:
        return 'Competitor'
    else:
        return None

# Copy all input rows to output
outputDf = inputDf.copy()

# Iterate through all rows
for index, row in inputDf.iterrows():
    campaign_name = row[RPT_COL_CAMPAIGN]
    
    # Extract tag from campaign name
    tag = extract_tag_from_campaign_name(campaign_name)
    
    # Print campaign and tag information
    print("Campaign [%s] => Tag [%s]" % (campaign_name, tag))
    
    # Update the output dataframe with the extracted tag
    outputDf.at[index, BULK_COL_TIPO_DE_CAMPANHA] = tag

# Drop rows with NaN values in the 'Tipo de campanha' column
outputDf = outputDf.dropna(subset=[BULK_COL_TIPO_DE_CAMPANHA])

# Display the resulting dataframe
if not outputDf.empty:
    print("outputDf", tableize(outputDf))
else:
    print("Empty outputDf")