title: Python Pandas Addition/subtraction of integers and integer-arrays with Timestamp is no longer supported
body: <p>I am reading data from excel and manipulating the data using python. But dates are coming as integers. I am using the below function &quot;Parse_datevalue&quot; to convert the dates back to date format.</p>
  <p>Ex. of why this function exists:</p>
  <p>5/15/2015 is coming as 42139.00</p>
  <p>So when I am running the code, I am seeing the following error.</p>
  <pre><code>   &quot;errorMessage&quot;: &quot;Addition/subtraction of integers and integer-arrays with Timestamp is no longer supported.  Instead of adding/subtracting `n`, use `n * obj.freq`&quot;,
  </code></pre>
  <p>Below is the code that parses the date and converts it into a DataTime format for salesforce - yyyy-mm-ddThh:mm:ssZ.</p>
  <pre><code>def parse_datevalue(row):
      &quot;&quot;&quot;Parse date from Excel date value to Python timestampz format.
      
      Args:
          row (obj): Row of a dataframe.
      
      Returns:
          Reformatted date.
      &quot;&quot;&quot;
      dt = datetime.fromordinal(datetime(1900, 1, 1).toordinal() + row['ORDERDATE'] - 2) + timedelta(hours=12)
      return dt.strftime('%Y-%m-%dT%H:%M:%SZ')
  </code></pre>