title: counting returned values of unique( )
body: <p>I have a dataframe,df1</p>
  
  <pre><code>     Date        inp  name   
  0  2017-08-07  2.3.6  ABC 
  1  2017-08-07  2.3.6  ABC      
  2  2017-08-08  2.3.6  TAC         
  3  2017-08-22  2.5.9  TTT         
  4  2017-09-23  0.8.0  TAC         
  5  2017-10-09  2.3.6  ABC         
  6  2017-10-09  2.3.6  TAC
  7  2017-10-09  2.3.6  TAC                  
  8  2017-10-23  0.8.0  TAC         
  9  2017-11-08  6.2.6  ABC        
  </code></pre>
  
  <p>then another dataframe,df2</p>
  
  <pre><code>                2.3.6ABC  2.3.6TAC  2.5.9TTT      
  August            2         1            0            
  September         0         0            0             
  October           1         2            0             
  November          0         0            1             
  </code></pre>
  
  <p>another dataframe,df3</p>
  
  <pre><code>                2.3.6ABC  2.3.6TAC       
  August            2         1                      
  September         0         0                         
  October           1         2                        
  November          0         0                        
  </code></pre>
  
  <p>I want to get the distinct values in column: 'inp' of df1 which in this case is 4. Then, number of columns of df2 and df3 which is 3 and 2 in this case. How can I store the number of columns of the 2 dataframes,df2 and df3  in a new dataframe by renaming the index, then getting the percentage df2 and df3 in df1. It should look like this:</p>
  
  <pre><code>                     noOfColumn      pct
      df2 as sat         3         3/4=75                      
      df3 as nath        2         2/4=50                        
  </code></pre>
  
  <p>I have not been able to go past this point</p>
  
  <pre><code>st=pd.Dataframe(df1)
  st=df['inp'].unique()
  st2=st.count()
  </code></pre>
  
  <p>because I keep getting this: AttributeError: 'numpy.ndarray' object has no attribute 'count'</p>