Count Boolean values from Pivot table with pandas - pandas

Question

I have a dataframe df defined like so:
A B C D E F
0 a z l 1 qqq True
1 a z l 2 qqq True
2 a z l 3 qqq False
3 a z r 1 www True
4 a z r 2 www False
5 a z r 2 www False
6 s x 7 2 eee True
7 s x 7 3 eee False
8 s x 7 4 eee True
9 s x 5 1 eee True
10 d c l 1 rrr True
11 d c l 2 rrr False
12 d c r 1 fff False
13 d c r 2 fff True
14 d c r 3 fff True
My goal is to create a table based on the unique values of columns A, B and C so that I am able to count the number of elements of column D and the unique number of elements in column C.
The output looks like this:
D E
A B
a z 6 2
d c 5 2
s x 4 2
Where for example the 6 is how many elements are present in the column A having value a, and 2 indicates the number of unique elements in column E (qqq,wwww).
I was able to achgieve this goal by using the following lines of code:
# Define dataframe
df = pd.DataFrame({'A':['a','a','a','a','a','a','s','s','s','s','d','d','d','d','d'],
'B': ['z','z','z','z','z','z','x','x','x','x','c','c','c','c','c'],
'C': ['l','l','l','r','r','r','7','7','7','5','l','l','r','r','r'],
'D': ['1','2','3','1','2','2','2','3','4','1','1','2','1','2','3'],
'E': ['qqq','qqq','qqq','www','www','www','eee','eee','eee','eee','rrr','rrr','fff','fff','fff'],
'F': [True,True,False,True,False,False,True,False,True,True,True,False,False,True,True]})
# My code so far
a = df.pivot_table(index=['A','B','C'], aggfunc={'E':'nunique', 'D':'count'}).sort_values(by='E')
a = a.pivot_table(index=['A','B'], aggfunc='sum').sort_values(by='E')
The Problem:
Now I would like also to count the number of True or False values present in the dataframe with the same criteria presented before so that the result looks like this:
D E True False
A B
a z 6 2 3 3
d c 5 2 3 2
s x 4 2 3 1
As you can see the number of True values where A=a are 3 and False values are 3 as well.
What is a smart and elegant way to achieve my final goal?

Answer 1

Using your code, you could extend like this:
# My code so far
a = df.pivot_table(index=['A','B','C'], aggfunc={'E':'nunique', 'D':'count','F':sum}).sort_values(by='E').rename(columns={'F':'F_True'})
a = a.pivot_table(index=['A','B'], aggfunc='sum').sort_values(by='E').eval('F_False = D - F_True')
OUtput:
D E F_True F_False
A B
a z 6 2 3.0 3.0
d c 5 2 3.0 2.0
s x 4 2 3.0 1.0

Answer 2

You just need two steps
pd.concat([df.groupby(['A','B','C']).agg({'E': 'nunique', 'D':'size'}).sum(level=[0,1])
,df.groupby(['A','B']).F.value_counts().unstack()],1)
Out[702]:
E D False True
A B
a z 2 6 3 3
d c 2 5 2 3
s x 2 4 1 3
Using value_counts
df.groupby(['A','B']).F.value_counts().unstack()