title: Sum of null and empty values in pandas dataframe
body: <p>I have a dataframe that contains both NULL values and empty strings.</p>
  <p>As follows (with code for repro)</p>
  <pre><code>data = {'vals1':  [None, '100','','200'],
          'vals2': ['100', '100','',''],
           'vals3': ['100', None,'100',None],
          'vals4': ['', '','','']
          }
  
  df = pd.DataFrame (data, columns = ['vals1','vals2','vals3','vals4'])
  </code></pre>
  <p>Output:</p>
  <pre><code>    vals1   vals2   vals3   vals4
  0   None    100     100 
  1   100     100     None    
  2                   100 
  3   200             None    
  </code></pre>
  <p>You'll see there are legit null values (Python treats 'None' as null) but there are also empty strings, denoted by the blanks which are also a legit feature of the dataset.
  We can count the nulls by using <code>df.isnull().sum()</code></p>
  <p>which gives:</p>
  <pre><code>    vals1   vals2   vals3   vals4
  0   True    False   False   False
  1   False   False   True    False
  2   False   False   False   False
  3   False   False   True    False
  </code></pre>
  <p>And I can sum the null values by using <code>df.isnull().sum()</code> which gives:</p>
  <pre><code>   vals1    1
     vals2    0
     vals3    2
     vals4    0
  dtype: int64
  </code></pre>
  <p>However, I also need a way of accounting for the empty values too, such that the output becomes something like:</p>
  <pre><code>       Nulls Empty
  vals1    1    1
  vals2    0    1
  vals3    2    1
  vals4    0    4
  dtype: int64
  </code></pre>
  <p>How can I achieve my desired output?</p>