title: pandas.DataFrame corrwith() method
body: <p>I recently start working with <code>pandas</code>. Can anyone explain me difference in behaviour of function <code>.corrwith()</code> with <code>Series</code> and <code>DataFrame</code>?</p>
  
  <p>Suppose i have one <code>DataFrame</code>:</p>
  
  <pre><code>frame = pd.DataFrame(data={'a':[1,2,3], 'b':[-1,-2,-3], 'c':[10, -10, 10]})
  </code></pre>
  
  <p>And i want calculate correlation between features 'a' and all other features.
  I can do it in the following way:</p>
  
  <pre><code>frame.drop(labels='a', axis=1).corrwith(frame['a'])
  </code></pre>
  
  <p>And result will be:</p>
  
  <pre><code>b   -1.0
  c    0.0
  </code></pre>
  
  <p>But very similar code:</p>
  
  <pre><code>frame.drop(labels='a', axis=1).corrwith(frame[['a']])
  </code></pre>
  
  <p>Generate absolutely different and unacceptable table:</p>
  
  <pre><code>a   NaN
  b   NaN
  c   NaN
  </code></pre>
  
  <p>So, my question is: why in case of <code>DataFrame</code> as second argument we get such strange output?</p>