Find how many common missing (nan) values are in DataFrame columns - pandas

Question

I have got DataFrame with about 20 columns and 50000 rows. Small part of this Pandas table is below:
I am looking for some way to count how many missing values are in the same positions (rows) in a few columns.
When number of the columns is known simply code like these:
((df['HomeRemote'].isnull() & df['CompanySize'].isnull()).sum()
probably is the answer, but unfortunately number of the columns to compare could be more than 2. I don't know it, because it depends on situation and that's why I looking for something like "universal" solution (working for any number of columns).
My idea is finding a way to "push" every df[col].isnull() into the FOR loop (where col is name of column), but I have a problem with put '&' between every df[col].isnull().
Maybe anyone here have some other possibility to consider?
If something is not clear enough, please let me know.

Answer 1

Try:
Sample input:
>>> df
A B C D E
0 NaN 1.0 1.0 1.0 1.0
1 NaN NaN NaN 1.0 1.0
2 1.0 1.0 1.0 NaN 1.0
3 1.0 1.0 1.0 1.0 NaN
4 1.0 1.0 NaN 1.0 1.0
5 NaN 1.0 1.0 1.0 1.0
6 1.0 1.0 1.0 NaN 1.0
7 1.0 NaN 1.0 NaN 1.0
8 1.0 1.0 1.0 1.0 1.0
9 1.0 1.0 1.0 1.0 1.0
How many missing values are in the same position in columns A, B, C:
>>> df[['A', 'B', 'C']].isnull().all(axis=1).sum()
1
Step by step:
# Find missing values
>>> df[['A', 'B', 'C']].isnull()
A B C
0 True False False
1 True True True # <- HERE
2 False False False
3 False False False
4 False False True
5 True False False
6 False False False
7 False True False
8 False False False
9 False False False
# Reduce
>>> df[['A', 'B', 'C']].isnull().all(axis=1)
0 False
1 True # <- HERE
2 False
3 False
4 False
5 False
6 False
7 False
8 False
9 False
dtype: bool
# Reduce again
>>> df[['A', 'B', 'C']].isnull().all(axis=1).sum()
1