title: Getting weekly and daily averages of timestamp data
body: <p>I currently have data on a Spark data frame that is formatted as such:</p>
  
  <pre><code>Timestamp    Number
  .........    ......
  M-D-Y        3
  M-D-Y        4900
  </code></pre>
  
  <p>The timestamp data is in no way uniform or consistent (i.e., I could have one value that is present on March 1, 2015, and the next value in the table be for the date September 1, 2015 ... also, I could have multiple entries per date). </p>
  
  <p>So I wanted to do two things</p>
  
  <ol>
  <li>Calculate the number of entries per week. So I would essentially want a new table that represented the number of rows in which the timestamp column was in the week that the row corresponded to. If there are multiple years present, I would ideally want to average the values per each year to get a single value.</li>
  <li>Average the number column for each week. So for every week of the year, I would have a value that represents the average of the number column (0 if there is no entry within that week). </li>
  </ol>