title: Pandas concat resulting in NaN rows?
body: <p>I have two data frames with the same amount of rows: 1434, and I'd like to concatenate them amongst the axis 1:</p>
  
  <pre><code>res = pd.concat([df_resolved, df1], axis=1)
  </code></pre>
  
  <p>The two data frames do not have any columns that have the same name. I'd just like to join them like:</p>
  
  <pre><code>df1:        df2:
  col1 col2 | col3 col4
  1    0    | 9    0
  6    0    | 0    0
  
  =
  concatenated_df:
  col1 col2 col3 col4
  1    0    9    0
  6    0    0    0
  </code></pre>
  
  <p>This works fine on a small example like this, but for some reason I end up with many NaN rows if I try it on my original dataset, which is too big for me to oversee (I'm trying to join 1434x24 and 1434x17458 shaped data frames). So the outcome is kinda like:</p>
  
  <pre><code>concatenated_df:
  col1 col2 col3 col4
  col1 col2 col3 col4
  1    0    9    0
  6    0    0    0
  NaN  NaN  0    0
  </code></pre>
  
  <p>But I don't see why. Do you have any ideas how this can occur? I've tried renaming all the columns in the smaller data frame by appending a _xyz string to the column names, but the issue stays the same.  </p>