title: Creating a dataframe of Unique Values for each column from another DataFrame
body: <p>I have a dataframe with some 60+ columns. Out of these, about half are categorical (non-amount columns). Though, some of them have categorical data stored as 1s and 0s, so datatype will be int or float if it has NaN.</p>
  <p>I need to create a new dataframe with selected columns in earlier dataframe as index and unique values as the column.</p>
  <p>Test Data is as under:</p>
  <pre><code>data = pd.DataFrame({'A':['A','B','C','A','B','C','D'],
                   'B':[1,0,1,0,1,0,1],
                   'C':[10,20,30,40,50,60,70],
                   'D':['Y','N','Y','N','Y','N','P']
                  })
  </code></pre>
  <p>I did this to get the selected columns from all columns and get unique values for each column.</p>
  <pre><code>cols = itemgetter(0,1,3)(data.columns)
  uniq_stats = pd.DataFrame(columns=['Val'],index=cols)
  for each in cols:
      uniq_stats.loc[each] = ';'.join(data[each].unique())
  </code></pre>
  <p>However, this fails for those columns where the data is categorical but stored in 1s and 0s, and for those columns where there are Null values.</p>
  <p>Expected Outcome for Above Test Data:</p>
  <pre><code>    Val
  A   A;B;C;D
  B   1;0
  D   Y;N;P
  </code></pre>
  <p>What should I do to get those as well?
  I'd like if Null value is also included in the list of unique values.</p>