title: Dataframe pivot, lining up new column values without create additional NaN values
body: <p>So I'm trying to create a dataframe from a tab delimited text file, with the goal of using the values from one of the columns at the column headers of the dataframe, and using a second column as the values.  When I try to df.pivot, I get what I want but the new columns adds extra NaN values rather than just lining the new rows up properly.</p>
  <p>The tab delimited text file basically looks like this:</p>
  <pre><code>round1    are you healthy   no
  round1    are you healthy   yes
  round2    are you healthy   yes
  round2    are you healthy   yes
  round3    are you healthy   no
  round3    are you healthy   yes
  </code></pre>
  <p>So I've got this:</p>
  <pre><code> import pandas as pd
      import numpy as np
      df = pd.read_csv('test.txt', sep='\t', usecols=[0,2], names=['colA','colB'])
      df = df.pivot(index=None,columns='colA',values='colB')
      print(df)
  </code></pre>
  <p>Which gives me this:</p>
  <pre><code>colA round1 round2 round3
  0        no    NaN    NaN
  1       yes    NaN    NaN
  2       NaN    yes    NaN
  3       NaN    yes    NaN
  4       NaN    NaN     no
  5       NaN    NaN    yes
  </code></pre>
  <p>But what I'm trying to achieve is this:</p>
  <pre><code>colA round1 round2 round3
  0        no    yes    no
  1       yes    yes    yes
  </code></pre>