title: Python: reorganize a dataframe with repeated values appearing in one column.
body: <p>I have a dataframe that looks like this:</p>
  
  <pre><code>     Instrument                  Date  Total Return
  0  KYG2615B1014  2017-11-29T00:00:00Z      0.000000
  1  KYG2615B1014  2017-11-28T00:00:00Z    -10.679612
  2  KYG2615B1014  2017-11-27T00:00:00Z     -8.035714
  3  JP3843250006  2017-11-29T00:00:00Z      0.348086
  4  JP3843250006  2017-11-28T00:00:00Z      0.349301
  5  JP3843250006  2017-11-27T00:00:00Z      0.200000
  </code></pre>
  
  <p>Given that dataframe, I would like to make it look like this:</p>
  
  <pre><code>              11/27/2017  11/28/2017  11/29/2017
  KYG2615B1014   -8.035714  -10.679612    0.000000
  JP3843250006    0.200000    0.349301    0.348086
  </code></pre>
  
  <p>Basically what I want is to place every date as a new column and inside that column, placing the corresponding value. I wouldn't say "filtering" or "deleting" duplicates, I'd say this is much more like rearranging. </p>
  
  <p>Both dataframes were generated by me, but the thing is that to acquire this data I have to make a call to an API. In the 1st dataframe I make only one call and pull all of this data, while in the other I make one call per each date. So 1st is much more efficient than the 2nd and figured it was the right call, but I'm stuck in this part of reorganizing the dataframe to what I need.</p>
  
  <p>I thought of creating an empty dataframe and then populate it, by picking indexes of repeated elements in the 'Instrument' column, use those indexes to get elements from the 'Total Return' column and then place the elements from that chunk of data accordingly, but I don't know how to do it. </p>
  
  <p>If someone can help me, I'll be happy to know. </p>
  
  <p>Not sure if useful at this point, but this how I generated the dataframe (before populating it) in the 2nd version:</p>
  
  <pre><code>import pandas as pd
  import datetime
  
  #Getting a list of dates
  start=datetime.date(2017,11,27)
  end=datetime.date.today() - datetime.timedelta(days=1)
  row_dates=[x.strftime('%m/%d/%Y') for x in pd.bdate_range(start,end).tolist()]
  #getting identifiers to be used on Eikon
  csv_data=pd.read_csv('171128.csv', header=None)
  identifiers=csv_data[0].tolist()
  df=pd.DataFrame(index=identifiers, columns=row_dates)
  </code></pre>