title: Pandas iterrows() row.get() method returning series instead of cell value
body: <p>The situation: Pandas' dataframe's <code>iterrows()</code>'s row behaves differently in two different environments. In one, <code>row.get()</code> returns a value as expected, in the other <code>row.get()</code> itself returns a series - which is unexpected.</p>
  
  <p>The question: Are there pandas settings/some configuration that could cause the following behavior to differ between two environments? I acknowlege not all information is present in this example. What I hope is possible is that, shown the following behavior, someone might recognize that this is the result of some common pattern or, perhaps, settings change.</p>
  
  <p>Details:
  While in the first environment, I iterate through rows via the Pandas' <code>iterrows()</code> method. In each row, I access a given attribute, like so:</p>
  
  <pre><code>print(type(row)) # pandas.series.Series
  
  foo_val = row.get('foo', default=None)
  
  print(type(foo_val)) # float
  print(foo_val) # 234.23
  </code></pre>
  
  <p>Now when I run again, in the second environment; the <code>iterrows()</code> operation returns a <code>row</code> variable. It appears normal but getting (<code>row.get()</code>) each element behaves differently than before. Specifically, a returns value won't be an int, float, str, etc. but rather itself a Series, with the index preserved, such that:</p>
  
  <pre><code>print(type(row)) # pandas.series.Series
  
  foo_val = row.get('foo', default=None)
  
  print(type(foo_val)) # pandas.series.Series
  print(foo_val) 
  # foo      234.23
  # Name: 0, dtype: object
  
  foo_val_v2 = foo_val.get('foo', default=None)
  
  print(type(foo_val_v2)) # float
  print(foo_val_v2) # 234.23
  </code></pre>
  
  <p>Final thoughts:
  I seem to recall at some point that you're able to configure pandas to return indices with row values, but maybe I'm imagining things.</p>
  
  <p>I should also note that printing <code>row</code> in both situations renders identical results, as does logging each column type, row type, etc. That is, I can assert that the row type is the same, the column types within the parent dataframe are the same, and the type for each row element is the same.</p>