title: Unusual case of unclear difference between axis = 1 and axis = 0 in pd.concat
body: <p>I know the basic difference between the two and have had quite a few practical experiences with them too.
  However, I was experimenting, to find out the columns having more than 0 null values in a data frame for better data cleaning and processing. I was confused by the role <strong>axis</strong> played here.</p>
  <pre><code>null = pd.concat([dfr.isnull().sum()],axis = 1, keys=['NA'])
  </code></pre>
  <p>Where &quot;null&quot; is the dataframe containing the count of null values in the entire dataframe and &quot;NA&quot; is the column these values will be represented under. This code returned a <strong>dataframe</strong> where I could get the columns having more than 0 null values by</p>
  <p><code>null.loc[(null['NA']&gt;0)]</code></p>
  <p>But when I use the code :</p>
  <pre><code>null = pd.concat([dfr.isnull().sum()],axis = 0, keys=['NA'])
  </code></pre>
  <p>I get, what I think, is a series on which <code>null.loc[(null['NA']&gt;0)]</code> doesn't work, apparently.
  I'll attach the pictures of my output for <code>null.head()</code> for both the cases which are surprisingly very identical which confuses me.</p>
  <p><a href="https://i.stack.imgur.com/Bl6vF.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/Bl6vF.png" alt="axis = 0 " /></a></p>
  <p><a href="https://i.stack.imgur.com/pUUh5.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/pUUh5.jpg" alt="axis = 1" /></a></p>