title: Add leading zeros to Columns in a Spark Data Frame
body: <p>In short, I'm leveraging spark-xml to do some parsing of XML files. However, using this is removing the leading zeros in all the values I'm interested in. However, I need the final output, which is a DataFrame, to include the leading zeros. I'm unsure/can not figure out a way to add leading zeros to the columns I'm interested in. </p>
  
  <pre><code>val df = spark.read
    .format("com.databricks.spark.xml")
    .option("rowTag", "output")
    .option("excludeAttribute", true)
    .option("allowNumericLeadingZeros", true) //including this does not solve the problem
    .load("pathToXmlFile")
  </code></pre>
  
  <p>Example output that I'm getting</p>
  
  <pre><code>+------+---+--------------------+
  |iD    |val|Code                |
  +------+---+--------------------+
  |1     |44 |9022070536692784476 |
  |2     |66 |-5138930048185086175|
  |3     |25 |805582856291361761  |
  |4     |17 |-9107885086776983000|
  |5     |18 |1993794295881733178 |
  |6     |31 |-2867434050463300064|
  |7     |88 |-4692317993930338046|
  |8     |44 |-4039776869915039812|
  |9     |20 |-5786627276152563542|
  |10    |12 |7614363703260494022 |
  +------+---+--------------------+
  </code></pre>
  
  <p>Desired output</p>
  
  <pre><code>+--------+----+--------------------+
  |iD      |val |Code                |
  +--------+----+--------------------+
  |001     |044 |9022070536692784476 |
  |002     |066 |-5138930048185086175|
  |003     |025 |805582856291361761  |
  |004     |017 |-9107885086776983000|
  |005     |018 |1993794295881733178 |
  |006     |031 |-2867434050463300064|
  |007     |088 |-4692317993930338046|
  |008     |044 |-4039776869915039812|
  |009     |020 |-5786627276152563542|
  |0010    |012 |7614363703260494022 |
  +--------+----+--------------------+
  </code></pre>