title: Pandas .at throwing ValueError: At based indexing on an integer index can only have integer indexers
body: <p>So I have a df where I am extracting one value to store it in another df:</p>
  
  <pre><code>import pandas as pd
  
  # Create data set
  d = {'foo':[100, 111, 222], 
       'bar':[333, 444, 555]}
  df = pd.DataFrame(d)
  
  # Full dataframe:
  print(df)
  
  # Shows:
  #    bar   foo 
  # 0  333   100
  # 1  444   111
  # 2  555   222
  
  df2=pd.DataFrame()
  df2.loc[1,'Name'] = df[df.foo == 222]['foo']
  
  #error: 
  ValueError: Incompatible indexer with Series
  </code></pre>
  
  <p>I'm assuming the last line throws that error because <code>df[df.foo == 222]['foo']</code> is a <code>Series</code>:</p>
  
  <pre><code>2    222
  Name: foo, dtype: int64
  </code></pre>
  
  <p>So I'm trying to get the value itself. I used <code>.at</code> and got this:</p>
  
  <pre><code>print(df[df.foo == 222].loc[:,'bar'].at['bar'])
  
  #ValueError: At based indexing on an integer index can only have integer indexers
  </code></pre>
  
  <p>From what I've read it's <code>iat</code> that uses integer indexers and <code>at</code> uses both label and integer, so what's going on here?</p>