title: Does groupby automatically group over all non numeric columns in pandas?
body: <p>I have a sample of a dataset below (only showing the first couple rows but there are 193 rows):</p>
  
  <pre><code>country,beer_servings,spirit_servings,wine_servings,total_litres_of_pure_alcohol,continent
  Afghanistan,0,0,0,0.0,Asia
  Albania,89,132,54,4.9,Europe
  Algeria,25,0,14,0.7,Africa
  Andorra,245,138,312,12.4,Europe
  Angola,217,57,45,5.9,Africa
  Antigua &amp; Barbuda,102,128,45,4.9,North America
  ...
  </code></pre>
  
  <p>When I run this: <code>drinks.groupby('continent').head()</code></p>
  
  <p>I get back a dataframe with 30 rows. But in those 30 rows I still have duplicate names for the <code>continent</code>. For example in the image below you can see that <code>Europe</code> is repeated two times (at rows 1 and 3):</p>
  
  <p><a href="https://i.stack.imgur.com/VISVL.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/VISVL.png" alt="enter image description here"></a> </p>
  
  <p>I am not able to understand why I am still having two rows with the same continent when I grouped by continent originally?</p>
  
  <p>In this case is the <code>groupby</code> operation also grouping by the <code>country</code> even though I never specified it in the <code>groupby</code> function? Since I know in SQL you are supposed to use an aggregate function like <code>max</code>, <code>min</code>, <code>sum</code>, etc. But in this case I don't have to pass in an aggregate function and I get the result above. </p>