title: pandas crosstab vs sklearn confusion_matrix giving different results
body: <p>Just confused why is pandas crosstab returning FP of 1 whereas sklearn's confusion_matrix is returning FP of 0.</p>
  <p>Shouldn't the FP also be 0 as indicated correctly by sklearn? Why is pandas showing it differently? Please clarify.</p>
  <pre><code>import pandas as pd
  from sklearn import metrics
  from sklearn.metrics import confusion_matrix
      
  data = {'y_Actual':    [1, 1, 1, 1, 1, 1, 1],
          'y_Predicted': [0, 1, 1, 0, 1, 0, 1]
         }
  df = pd.DataFrame(data, columns=['y_Actual','y_Predicted'])
  confusion_matrix = 
  pd.crosstab(df['y_Actual'], df['y_Predicted'], rownames=['Actual'], colnames=['Predicted'])  
  print (confusion_matrix)
  </code></pre>
  <p><div class="snippet" data-lang="js" data-hide="false" data-console="true" data-babel="false">
  <div class="snippet-code">
  <pre class="snippet-code-html lang-html prettyprint-override"><code>Predicted  0  1
  Actual         
  1          3  4</code></pre>
  </div>
  </div>
  </p>
  <pre><code>metrics.confusion_matrix(df['y_Actual'], df['y_Predicted'])
  </code></pre>
  <p><div class="snippet" data-lang="js" data-hide="false" data-console="true" data-babel="false">
  <div class="snippet-code">
  <pre class="snippet-code-html lang-html prettyprint-override"><code>array([[0, 0],
         [3, 4]], dtype=int64)</code></pre>
  </div>
  </div>
  </p>