title: Extract Year, Month, Day from Unix TimeStamp column in Rust DataFusion DataFrame?
body: <p>I have created a DataFusion DataFrame:</p>
  <pre><code>| asin       | vote | verified | unixReviewTime | reviewText      |
  +------------+------+----------+----------------+-----------------+
  | 0486427706 | 3    | true     | 1381017600     | good            |
  | 0486427707 |      | false    | 1376006400     | excellent       |
  | 0486427707 | 1    | true     | 1459814400     | Did not like it |
  | 0486427708 | 4    | false    | 1376006400     |                 |
  +------------+------+----------+----------------+-----------------+
  </code></pre>
  <p>I was trying to find the solution of following information from the API document, but could not figure it out:</p>
  <ul>
  <li>Convert the <code>unixReviewTime</code> column into Rust Native timestamp</li>
  <li>Extract the Year, Month and Day from the newly created column into separate columns</li>
  </ul>
  <p>Here is how json datafile looks like:</p>
  <pre class="lang-json prettyprint-override"><code>{&quot;asin&quot;: &quot;0486427706&quot;, &quot;vote&quot;: 3, &quot;verified&quot;: true, &quot;unixReviewTime&quot;: 1381017600, &quot;reviewText&quot;: &quot;good&quot;, &quot;overall&quot;: 5.0}
  {&quot;asin&quot;: &quot;0486427707&quot;, &quot;vote&quot;: null, &quot;verified&quot;: false, &quot;unixReviewTime&quot;: 1376006400, &quot;reviewText&quot;: &quot;excellent&quot;, &quot;overall&quot;: 5.0}
  {&quot;asin&quot;: &quot;0486427707&quot;, &quot;vote&quot;: 1, &quot;verified&quot;: true, &quot;unixReviewTime&quot;: 1459814400, &quot;reviewText&quot;: &quot;Did not like it&quot;, &quot;overall&quot;: 2.0}
  {&quot;asin&quot;: &quot;0486427708&quot;, &quot;vote&quot;: 4, &quot;verified&quot;: false, &quot;unixReviewTime&quot;: 1376006400, &quot;reviewText&quot;: null, &quot;overall&quot;: 4.0}
  </code></pre>
  <p>It is very easy to do in pyspark as follows:</p>
  <pre class="lang-py prettyprint-override"><code>from PySpark.sql import functions as fn
  from PySpark.sql.functions import col
  
  main_df = (
      main_df
      .withColumn(
          'reviewed_at',
          fn.from_unixtime(col('unixReviewTime'))
      )
  )
  
  main_df = main_df.withColumn(&quot;reviewed_year&quot;, fn.year(col(&quot;reviewed_at&quot;)))
  main_df = main_df.withColumn(&quot;reviewed_month&quot;, fn.month(col(&quot;reviewed_at&quot;)))
  </code></pre>