title: unique combinations of values in selected columns in pandas data frame and count
body: <p>I have my data in pandas data frame as follows:</p>
  
  <pre><code>df1 = pd.DataFrame({'A':['yes','yes','yes','yes','no','no','yes','yes','yes','no'],
                     'B':['yes','no','no','no','yes','yes','no','yes','yes','no']})
  </code></pre>
  
  <p>So, my data looks like this</p>
  
  <pre><code>----------------------------
  index         A        B
  0           yes      yes
  1           yes       no
  2           yes       no
  3           yes       no
  4            no      yes
  5            no      yes
  6           yes       no
  7           yes      yes
  8           yes      yes
  9            no       no
  -----------------------------
  </code></pre>
  
  <p>I would like to transform it to another data frame.  The expected output can be shown in the following python script:</p>
  
  <pre><code>output = pd.DataFrame({'A':['no','no','yes','yes'],'B':['no','yes','no','yes'],'count':[1,2,4,3]})
  </code></pre>
  
  <p>So, my expected output looks like this</p>
  
  <pre><code>--------------------------------------------
  index      A       B       count
  --------------------------------------------
  0         no       no        1
  1         no      yes        2
  2        yes       no        4
  3        yes      yes        3
  --------------------------------------------
  </code></pre>
  
  <p>Actually, I can achieve to find all combinations and count them by using the following command: <code>mytable = df1.groupby(['A','B']).size()</code></p>
  
  <p>However, it turns out that such combinations are in a single column.  I would like to separate each value in a combination into different column and also add one more column for the result of counting.  Is it possible to do that?  May I have your suggestions?  Thank you in advance.</p>