title: Compare multiple dataframes' columns names with original one's in Pandas
body: <p>Let's say I have a dataframe <code>df</code> with headers <code>a, b, c, d</code>.</p>
  <p>I want to compare other <code>dfs (df1, df2, df3, ...)</code> columns name with it. I need all the <code>dfs</code>'s columns name should be exactly identical as <code>df</code> (<strong>Please note the different order of columns names should be not considered as different column names</strong>).</p>
  <p>For example:</p>
  <p>Original dataframe:</p>
  <pre><code>df = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
                     columns=['a', 'b', 'c'])
  
  col = ['a', 'b', 'c']
  </code></pre>
  <p>dfs:</p>
  <pre><code>df1 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
                     columns=['a', 'c', 'b'])
  </code></pre>
  <p>Returns <code>identical columns name</code>;</p>
  <pre><code>df2 = pd.DataFrame(np.array([[1, 2, 3, 10], [4, 5, 6, 11], [7, 8, 9, 12]]),
                     columns=['a', 'c', 'e', 'b'])
  </code></pre>
  <p>Returns <code>extra columns in dataframe</code>;</p>
  <pre><code>df3 = pd.DataFrame(np.array([[1, 2], [4, 5], [7, 8]]),
                     columns=['a', 'c'])
  </code></pre>
  <p>Returns <code>missing columns in dataframe</code>;</p>
  <pre><code>df4 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
                     columns=['a', '*c', 'b'])
  </code></pre>
  <p>Returns <code>errors in dataframe's column names</code>;</p>
  <pre><code>df5 = pd.DataFrame(np.array([[1, 2, 3, 9], [4, 5, 6, 9], [7, 8, 9, 10]]),
                     columns=['a', 'b', 'b', 'c'])
  </code></pre>
  <p>returns <code>extra columns in dataframe</code>.</p>
  <p>If it's too complicated, it's also OK returning <code>columns names are incorrect</code> for all kinds of errors.</p>
  <p>How could I do that in Pandas? Thanks.</p>