Aggregating the columnar data in Pandas data frame

Question

I have a dataframe as shown below:

col1 = ['a','b','c','a','c','a','b','c','a']
col2 = [1,1,0,1,1,0,1,1,0]
df2 = pd.DataFrame(zip(col1,col2),columns=['name','count'])

    name    count
0   a       1
1   b       1
2   c       0
3   a       1
4   c       1
5   a       0
6   b       1
7   c       1
8   a       0

I am trying to count the number of 0s and 1s corresponding to each element in the 'name' column. So the expected output would look like:

name  zero_count  one_count

a     2           2
b     0           2
c     1           2

So far I tried many scenarios and one that looked promising was:

ser = df2.groupby(['name','count']).size().to_frame().reset_index()
ser
    name    count  0
0   a       0      2
1   a       1      2
2   b       1      2
3   c       0      1
4   c       1      2

What further things can I try to fix this?

Answer 1

#count zeros:
df2.groupby(['name']).agg(lambda x: x.eq(0).sum())
#count ones:
df2.groupby(['name']).agg(lambda x: x.eq(1).sum())

Answer 2

One-liner:

In [982]: df2.groupby(['name','count']).size().reset_index().pivot('name', 'count')
Out[982]: 
         0     
count    0    1
name           
a      2.0  2.0
b      NaN  2.0
c      1.0  2.0

Explanation step-wise:

In [950]: res = df2.groupby(['name','count']).size().reset_index(name='counts')

In [958]: out = res.pivot(index='name', columns='count', values='counts').fillna(0)

In [959]: out.columns = ['zero_count', 'one_count']

In [960]: out
Out[960]: 
      zero_count  one_count
name                       
a            2.0        2.0
b            0.0        2.0
c            1.0        2.0

Answer 3

Try crosstab

pd.crosstab(df2['name'], df2['count'])
Out[40]: 
count  0  1
name       
a      2  2
b      0  2
c      1  2