Groupby count of values - pandas

Question

I'm hoping to count specific values from a pandas df. Using below, I'm subsetting Item by Up and grouping Num and Label to count the values in Item . The values in the output are correct but I want to drop Label and include Up in the column headers.

import pandas as pd

df = pd.DataFrame({      
    'Num' : [1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2],
    'Label' : ['A','B','A','B','B','B','A','B','B','A','A','A','B','A','B','A'],   
    'Item' : ['Up','Left','Up','Left','Down','Right','Up','Down','Right','Down','Right','Up','Up','Right','Down','Left'],  
   })

df1 = (df[df['Item'] == 'Up']
      .groupby(['Num','Label'])['Item']
      .count()
      .unstack(fill_value = 0)
      .reset_index()
      )

intended output:

  Num  A_Up  B_Up
    1     3     0
    2     1     1

Answer 1

With your approach, you can include the Item in the grouper.

out = (df[df['Item'] == 'Up'].groupby(['Num','Label','Item']).size()
       .unstack(['Label','Item'],fill_value=0))
out.columns=out.columns.map('_'.join)

print(out)

     A_Up  B_Up
Num            
1       3     0
2       1     1

Answer 2

You can use Groupby.transform to have all column names. Then use df.pivot_table and a list comprehension to get your desired column names.

In [2301]: x = df[df['Item'] == 'Up']
In [2304]: x['c'] = x.groupby(['Num','Label'])['Item'].transform('count')

In [2310]: x = x.pivot_table(index='Num', columns=['Label', 'Item'], aggfunc='first', fill_value=0)
In [2313]: x.columns = [j+'_'+k for i,j,k in x.columns]

In [2314]: x
Out[2314]: 
     A_Up  B_Up
Num            
1       3     0
2       1     1