33sticks

//ACTION REQUIRED: TAG CONFIGURATION
var killDate = new Date("MM/DD/YYYY");  //INPUT CAMPAIGN END DATE
var vendorName = ""; //INPUT NAME OF MARKETING VENDOR
var requestNum = ""; //INPUT TRACKING REQUEST NUMBER
var tagName = ""; //INPUT NAME OF TAG/PIXEL


//Get current date and remove hours
var todaysDate = new Date();
todaysDate.setHours(0,0,0,0);

33sticks

if(_satellite.getVar('Order Value')){
  	var orderValue;

    var img = new Image();
    
    orderValue = _satellite.getVar('Order Value');
    img.src = "https://marketingpixel.example.org/track/conv/?adid=2dngvjx&order_value=" + orderValue ;
    document.body.appendChild(img);
  }

33sticks

# We will use data structures and data analysis tools provided in Pandas library
import pandas as pd

# Import retail sales data from an Excel Workbook into a data frame
path = '/Documents/analysis/python/examples/2015sales.xlsx'
xlsx = pd.ExcelFile(path)
df = pd.read_excel(xlsx, 'Sheet1')

# Let's add a new boolean column to our dataframe that will identify a duplicated order line item (False=Not a duplicate; True=Duplicate)
df['is_duplicated'] = df.duplicated(['order_id', 'order_item_id'])

33sticks

#import the urlprase library to break url into components
from urlparse import urlparse

#import pandas for data processing
from pandas import DataFrame, Series
import pandas as pd

#import URL data
df = pd.read_csv("/Users/Documents/analysis/my_urls.csv")

33sticks

try {
if (window.dataLayer.page.attributes.length){
  var refinements="";

  for (var i = 0; i < window.dataLayer.page.attributes.length; i++) {
    if (dataLayer.page.attributes[i].attributeInfo){
			refinements += dataLayer.page.attributes[i].attributeInfo.refinementValue + ";";
    }  
  }
  

33sticks

for (var i = 0; i < dataLayer.cart.item.length; i++) {
     s.products += ",;" + dataLayer.cart.item[i].productInfo.productID;
}
s.products = s.products.substr(1);

33sticks

for (var i = 0; i < dataLayer.event.length; i++) {
  if(dataLayer.event[i].eventInfo.eventName == "Checkout"){
     s.events += ",scCheckout";
    break;
  }
}

33sticks

for (var i = 0; i < dataLayer.transaction.item.length; i++) {
 
  var payType = dataLayer.transaction.total.paymentMethods[i].paymentMethod;

  switch(payType) {
      case "CREDIT_CARD":
          s.eVar45 = dataLayer.transaction.total.paymentMethods[i].paymentMethod;
          s.events += ",event27";
          break;
      case "PAYPAL":

33sticks

#import the pandas library
import pandas as pd

#pull adobe report download into a dataframe; ignorning the comment header
df = pd.read_csv("/Users/jasonthompson/Documents/analysis/python/sp/adobe_id.csv", comment="#", header=1)

#export cleaned data to a new csv file
df.to_csv("/Users/jasonthompson/Documents/analysis/python/sp/adobe_id_no-comments.csv", encoding='utf-8')

33sticks

/*YouTube Player updates required by Adobe Video Tracking module */
var n=0;

jQuery('iframe').each(function() {
 
 var src = jQuery(this).attr('src');
 
if(src){
	if (src.indexOf('youtube.com') > -1) {