title: Append list to pandas DataFrame as new row with index
body: <p>Despite of the numerous stack overflow questions on appending data to a dataframe I could not really find an answer to the following.
  I am looking for a straight forward solution to append a list as last row of a dataframe.
  Imagine I have a simple dataframe:</p>
  
  <pre><code> indexlist=['one']
   columnList=list('ABC')
   values=np.array([1,2,3])
   # take care, the values array is a 3x1 size array. 
   # row has to be 1x3 so we have to reshape it
  
  values=values.reshape(1,3)
  df3=pd.DataFrame(values,index=indexlist,columns=columnList)
  print(df3)
  
       A  B  C
  one  1  2  3
  </code></pre>
  
  <p>After some operations I get the following list:</p>
  
  <pre><code>listtwo=[4,5,6]
  </code></pre>
  
  <p>I want to append it at the end of the dataframe.
  I change that list into a series:</p>
  
  <pre><code>oseries=pd.Series(listtwo)
  print(type(oseries))
  oseries.name="two"
  </code></pre>
  
  <p>now, this does not work:</p>
  
  <pre><code>df3.append(oseries)
  </code></pre>
  
  <p>since it gives:</p>
  
  <pre><code>A   B   C   0   1   2
  one 1.0 2.0 3.0 NaN NaN NaN
  two NaN NaN NaN 5.0 6.0 7.0
  </code></pre>
  
  <p>I would like to have the values under A B and C.</p>
  
  <p>I also tried:</p>
  
  <pre><code>df3.append(oseries, columns=list('ABC'))  *** not working ***
  df3.append(oseries, ignore_index=True)  *** working but wrong result
  df3.append(oseries, ignore_index=False) *** working but wrong result
  
  df3.loc[oseries.name]=oseries adds a row with NaN values
  </code></pre>
  
  <p>what I am looking for is
  a) how can I add a list to a particular index name
  b) how can I simple add a row of values out of a list even if I don't have a name for index (leave it empty)</p>