title: Pandas date range returns "could not convert string to Timestamp" for yyyy-ww
body: <p>I have a dataframe with two columns; <code>Sales</code> and <code>Date</code>. </p>
  
  <pre><code>dataset.head(10)
  
  
          Date    Sales
  0   2015-01-02  34988.0
  1   2015-01-03  32809.0
  2   2015-01-05  9802.0
  3   2015-01-06  15124.0
  4   2015-01-07  13553.0
  5   2015-01-08  14574.0
  6   2015-01-09  20836.0
  7   2015-01-10  28825.0
  8   2015-01-12  6938.0
  9   2015-01-13  11790.0
  </code></pre>
  
  <p>I want to convert the <code>Date</code> column from <code>yyyy-mm-dd</code> (e.g. <code>2015-06-01</code>) to <code>yyyy-ww</code> (e.g. <code>2015-23</code>), so I run the following piece of code: </p>
  
  <pre><code>dataset["Date"] = pd.to_datetime(dataset["Date"]).dt.strftime('%Y-%V')
  </code></pre>
  
  <p>Then I group by my <code>Sales</code> based on weeks, i.e.</p>
  
  <pre><code>data = dataset.groupby(['Date'])["Sales"].sum().reset_index()
  
  data.head(10)
  
      Date    Sales
  0   2015-01 67797.0
  1   2015-02 102714.0
  2   2015-03 107011.0
  3   2015-04 121480.0
  4   2015-05 148098.0
  5   2015-06 132152.0
  6   2015-07 133914.0
  7   2015-08 136160.0
  8   2015-09 185471.0
  9   2015-10 190793.0
  </code></pre>
  
  <p>Now I want to create a date range based on the <code>Date</code> column, since I'm predicting sales based on weeks:</p>
  
  <pre><code>ds = data.Date.values
  ds_pred = pd.date_range(start=ds.min(), periods=len(ds) + num_pred_weeks, 
  freq="W")
  </code></pre>
  
  <p>However I'm getting the following error: <code>could not convert string to Timestamp</code> which I'm not really sure how to fix. So, if I use <code>2015-01-01</code> as the starting date of my date-import I get no error, which makes me realize that I'm using the functions wrong. However, I'm not sure how?</p>
  
  <p>I would like to basically have a date range that spans weekly from the current week and then 52 weeks into the future. </p>