title: Python Pandas concatenate/merge DataFrames using index or column identifiers
body: <p>I would like to use the 'pandas.concat' method to merge two DataFrames, but I don't fully understand all 'pandas.concat' arguments. I've got two DataFrames, which have the same identifying variables in the columns, but differ in one single column.  </p>
  
  <pre><code>import pandas as pd
  dict_data = {'Treatment': ['C', 'C', 'C'], 'Biorep': ['A', 'A', 'A'], 'Techrep': [1, 1, 1], 'AAseq': ['ELVISLIVES', 'ELVISLIVES', 'ELVISLIVES'], 'mz':[500.0, 500.5, 501.0]}
  df_a = pd.DataFrame(dict_data)
  dict_data = {'Treatment': ['C', 'C', 'C'], 'Biorep': ['A', 'A', 'A'], 'Techrep': [1, 1, 1], 'AAseq': ['ELVISLIVES', 'ELVISLIVES', 'ELVISLIVES'], 'inte':[1100.0, 1050.0, 1010.0]}
  df_b = pd.DataFrame(dict_data)
  </code></pre>
  
  <p>df_a</p>
  
  <pre><code>        AAseq   Biorep  Techrep Treatment   mz
  0    ELVISLIVES  A   1   C   500.0
  1    ELVISLIVES  A   1   C   500.5
  2    ELVISLIVES  A   1   C   501.0
  </code></pre>
  
  <p>df_b</p>
  
  <pre><code>    AAseq   Biorep  Techrep Treatment   int
  0    ELVISLIVES  A   1   C   1100
  1    ELVISLIVES  A   1   C   1050
  2    ELVISLIVES  A   1   C   1010
  </code></pre>
  
  <p>I can add the column the following way:</p>
  
  <pre><code>df_m = df_a.copy()
  df_m['inte'] = df_b['inte']
  
       AAseq  Biorep  Techrep Treatment   inte
  0    ELVISLIVES  A   1   C   1100
  1    ELVISLIVES  A   1   C   1050
  2    ELVISLIVES  A   1   C   1010
  </code></pre>
  
  <p>My real data looks much more complex and I'm afraid that the method above could lead to the wrong order of values in the rows (specially since I want to use 'pandas.melt' beforehand).</p>
  
  <p>When using: </p>
  
  <pre><code>dfm = pd.concat([df_a, df_b])
  
       AAseq  Biorep  Techrep Treatment   inte    mz
  0    ELVISLIVES  A   1   C   NaN     500.0
  1    ELVISLIVES  A   1   C   NaN     500.5
  2    ELVISLIVES  A   1   C   NaN     501.0
  0    ELVISLIVES  A   1   C   1100    NaN
  1    ELVISLIVES  A   1   C   1050    NaN
  2    ELVISLIVES  A   1   C   1010    NaN
  </code></pre>
  
  <p>The concatenated DataFrame extends the values rowwise leading to NaN vals. </p>
  
  <p><strong>Question</strong>: How can I achieve the same result (shown above) using 'concat'?</p>
  
  <p>Thank you for your support!</p>