Group/merge rows when defined columns match and sum up values - pandas

Question

How Do I group/merge rows, where multiple defined columns have the same value and display the sums in other columns not relevant for grouping/merging?
In the below example: If rows have the same values in columns "OrgA" to "OrgF" (text – this refers to an org. structure with departments and sub-departments), group/merge rows and add up the numbers in columns "numA" and "numB".
import pandas as pd
import numpy as np
data = {'orgA': ['A','C','A','C','A','C','A','A','A','L'],
'orgB': ['B',np.nan,'E',np.nan,'B',np.nan,'E','E','E','C'],
'orgC': ['C',np.nan,'D',np.nan,'C',np.nan,'H','D','H','B'],
'orgD': ['D',np.nan,np.nan,np.nan,'D',np.nan,'F',np.nan,'F','S'],
'orgE': ['E',np.nan,np.nan,np.nan,'E',np.nan,np.nan,np.nan,np.nan,'F'],
'orgF': ['F',np.nan,np.nan,np.nan,'F',np.nan,np.nan,np.nan,np.nan,np.nan],
'numA': [1,1,1,1,1,1,1,1,1,1],
'numB': [2,2,2,2,2,2,2,2,2,2]}
df = pd.DataFrame(data)
print(df)
orgA orgB orgC orgD orgE orgF numA numB
0 A B C D E F 1 2
1 C NaN NaN NaN NaN NaN 1 2
2 A E D NaN NaN NaN 1 2
3 C NaN NaN NaN NaN NaN 1 2
4 A B C D E F 1 2
5 C NaN NaN NaN NaN NaN 1 2
6 A E H F NaN NaN 1 2
7 A E D NaN NaN NaN 1 2
8 A E H F NaN NaN 1 2
9 L C B S F NaN 1 2
The output is supposed to look as follows:
orgA orgB orgC orgD orgE orgF numA numB
0 A B C D E F 2 4
1 C NaN NaN NaN NaN NaN 3 6
2 A E D NaN NaN NaN 2 4
3 A E H F NaN NaN 3 6
4 L C B S F NaN 1 2
Many thanks for your ideas in advance!

Answer 1

You can pass a list of column names to groupby, and set dropna to False so that rows containing nans are not dropped. You can also specify sort=False if it is not important to sort the group keys. Applying this to your example, as in
df.groupby(
['orgA', 'orgB', 'orgC', 'orgD', 'orgE', 'orgF'],
dropna=False,
sort=False
).sum()
we get
numA numB
orgA orgB orgC orgD orgE orgF
A B C D E F 2 4
C NaN NaN NaN NaN NaN 3 6
A E D NaN NaN NaN 2 4
H F NaN NaN 2 4
L C B S F NaN 1 2