title: inferSchema using spark.read.format("com.crealytics.spark.excel") is inferring double for a date type column
body: <p>I am working on PySpark (<strong>Python 3.6 and Spark 2.1.1</strong>) and trying to fetch data from an excel file using <strong>spark.read.format("com.crealytics.spark.excel")</strong>, but it is inferring double for a date type column.</p>
  
  <p>Example:</p>
  
  <p>Input -</p>
  
  <pre><code> df = spark.read.format("com.crealytics.spark.excel").\
       option("location", "D:\\Users\\ABC\\Desktop\\TmpData\\Input.xlsm").\
       option("spark.read.simpleMode","true"). \
       option("treatEmptyValuesAsNulls", "true").\
       option("addColorColumns", "false").\ 
       option("useHeader", "true").\  option("inferSchema", "true").\
       load("com.databricks.spark.csv")
  </code></pre>
  
  <p>Result:</p>
  
  <pre><code>Name | Age | Gender | DateOfApplication
  ________________________________________
  X    | 12  |   F    |  5/20/2015
  
  Y    | 15  |   F    |  5/28/2015
  
  Z    | 14  |   F    |  5/29/2015
  </code></pre>
  
  <p><strong>Printing Schema</strong> - </p>
  
  <pre><code>df.printSchema()
  
  root
   |-- Name: string (nullable = true)
   |-- Age: double (nullable = true)
   |-- Gender: string (nullable = true)
   |-- DateOfApplication: double (nullable = true)
  </code></pre>
  
  <p><strong>Doing .show</strong> - </p>
  
  <pre><code>df.show()
  
  Name | Age | Gender | DateOfApplication
  ________________________________________
  X    | 12.0  |   F    |   42144.0
  
  Y    | 15.0  |   F    |   16836.0
  
  Z    | 14.0  |   F    |   42152.0
  </code></pre>
  
  <p>While the reading of the data-set the dates or any other numeric value is being converted to double (special problem with date is that it totally changes the value which is hard to be reverted back to original dates.</p>
  
  <p>Can I please be helped?</p>