title: What is the reverse operation of `.value_counts()` in pandas dataframe?
body: <p>Starting from a non unique pandas series, one can count the number of each unique value by <code>.value_counts()</code>. </p>
  
  <pre><code>&gt;&gt; col = pd.Series([1.0, 1.0, 2.0, 3.0, 3.0, 3.0])
  
  0    1.0
  1    1.0
  2    2.0
  3    3.0
  4    3.0
  5    3.0
  dtype: object
  
  &gt;&gt; stat = col.value_counts()
  &gt;&gt; stat
  3.0    3
  1.0    2
  2.0    1
  dtype: int64
  </code></pre>
  
  <p>But, if starting from a data frame of two column, one for the unique values, while another for the number of occurrence. (<code>stat</code> in previous example). How to expand those into a single column.</p>
  
  <p>Because I would like to calculate the median, mean, etc of the data in such a dataframe, I think describing a single column is much easier that two. Or is there any method to describe a 'value_count' dataframe derectly without expanding the data? </p>
  
  <pre><code># turn `stat` into col ???
  
  &gt;&gt; col.describe()
  count    6.000000
  mean     2.166667
  std      0.983192
  min      1.000000
  25%      1.250000
  50%      2.500000
  75%      3.000000
  max      3.000000 
  </code></pre>
  
  <hr>
  
  <p>add testing data</p>
  
  <pre><code>&gt;&gt; df = pd.DataFrame({"Name": ["A", "B", "C"], "Value": [1,2,3], "Count": [2, 10, 2]})
  &gt;&gt; df
    Name  Value  Count
  0    A      1      2
  1    B      2      5
  2    C      3      2
  
  df2 = _reverse_count(df)
  &gt;&gt; df2
    Name  Value 
  0    A      1 
  1    A      1
  2    B      2 
  3    B      2
  4    B      2  
  5    B      2  
  6    B      2  
  7    B      2
  8    C      3
  9    C      3  
  </code></pre>