title: Pandas asfreq with weekly frequency
body: <p>I have weekly data of logs for some devices. For some device it start on Monday, for some on Wednesday etc. Sometimes there are gaps of ~month in this data, but I want the DataFrame index to still contain rows for each week with NaN value.</p>
  
  <p>I am trying to use <code>asfreq('W')</code> in Python, but I cannot get what I expect.</p>
  
  <p><strong>Example:</strong></p>
  
  <p>What I have:</p>
  
  <pre><code>Date            Some_Value
  ====            ==========
  2019-04-10      2
  2019-04-17      1
  2019-04-24      3
  2019-05-01      1
  2019-05-08      3
  2019-05-15      2
  2019-06-06      3
  2019-06-13      2
  </code></pre>
  
  <p>What I expect/want to have (note 2 new rows with NaNs):</p>
  
  <pre><code>Date            Some_Value
  ====            ==========
  2019-04-10      2
  2019-04-17      1
  2019-04-24      3
  2019-05-01      1
  2019-05-08      3
  2019-05-15      2
  2019-05-22      NaN
  2019-05-30      NaN
  2019-06-06      3
  2019-06-13      2
  </code></pre>
  
  <p>What I get with <code>asfreq('W')</code>:</p>
  
  <pre><code>Date            Some_Value
  ====            ==========
  2019-03-31      NaN
  2019-04-07      NaN
  2019-04-14      NaN
  ...................
  </code></pre>
  
  <p>So, I get all <code>NaN</code> values and the dates from each Sunday. But I do not need dates from each Sunday. I need to take the first date of a DataFrame (of first row in a group in pandas' <code>groupby</code> in case of many time-series) and resample weekly form that first row.</p>
  
  <p>Is it achievable directly with pandas <code>asfreq</code>? With some other pandas method? Or should it be some more complex custom function?</p>
  
  <p>Thanks.</p>