title: Pandas Groupby: How to get distinct Column Values
body: <p>Trying to get distinct values for columns after grouping; but am getting a total sum 
  groupby is dropping object distinction and losing leading zeros for column</p>
  
  <p>df = pd.read_csv("trial.txt",sep='|',converters={'zip':str},keep_default_na=True,low_memory=False)</p>
  
  <p>Data: </p>
  
  <pre><code>Emp State   Zip      Jan feb mar 
  
  Int  NY    11111      1   0   1
  
  int  NY    11111      1   1   0
  
  int  NC    09999      2   2   0
  
  int  ON    NH443     2   2   2
  </code></pre>
  
  <p>after </p>
  
  <pre><code>df2 = df.groupby("Zip").count()
  </code></pre>
  
  <p>df2 for zip my output for zip = 11111 
  i'll have the output for all 12 months show 2 2 2.
      Were I would expect 2 1 1
  and zip 09999 shows as 9999. </p>
  
  <p>How what is wrong about the grouping to not get distinct column values. 
       Have account for non-null values (there are no nulls). Column value is only (0 , 1 ,2)</p>