title: Pandas dataframe value_counts count same values seperately as unique values
body: <p>I try to replace the ? in the dataset with the mode of the column.</p>
  <p>First I check the unique value in that column by using</p>
  <pre><code>df['bare_nucleoli'].value_counts()
  </code></pre>
  <p>This is the result</p>
  <pre><code>1     402
  10    132
  5      30
  2      30
  3      28
  8      21
  4      19
  ?      16
  9       9
  7       8
  6       4
  Name: bare_nucleoli, dtype: int64
  </code></pre>
  <p>So I decided to change the question mark into NaN first in order to check for the mark in other column</p>
  <pre><code>import numpy as np
  df.replace('?',np.nan, inplace = True)
  </code></pre>
  <p>And then change the NaN to the mode of that column, which is 1.</p>
  <pre><code>df['bare_nucleoli'].fillna(1, inplace = True)
  </code></pre>
  <p>The result shows that it changed the NaN value, but it was counted seperately as another unique value like this</p>
  <pre><code>1     402
  10    132
  5      30
  2      30
  3      28
  8      21
  4      19
  1      16
  9       9
  7       8
  6       4
  Name: bare_nucleoli, dtype: int64
  </code></pre>
  <p>What's the reason behind this and how can I change the value and put it in the same group?</p>