value_counts() to count NaNs in a dataframe

I have a created a dataframe consisting of two columns. I want to count the number of occurences over these two columns.

The dataframe looks like-

No Name
1 A
1 A
5 T
9 V
Nan M
5 T
1 A

And I want to use value_counts() to get a dataframe like this-

No Name Count
1 A 3
5 T 2
9 V 1
Nan M 1

I tried doing df[["No", "Name"]].value_counts() which counts everything except the nan row. Is there a way to use value_counts() to count Nan as well?

2 Answers

You can use groupby with dropna=False:

df.groupby(['No', 'Name'], dropna=False, as_index=False).size()

Output:

 No Name size
0 1.0 A 3
1 5.0 T 2
2 9.0 V 1
3 NaN M 1

P.S. Interestingly enough, pd.Series.value_counts method also supports dropna argument, but pd.DataFrame.value_counts method does not

You can still use value_counts() but with dropna=False rather than True (the default value), as follows:

df[["No", "Name"]].value_counts(dropna=False)

So, the result will be as follows:

 No Name size
0 1 A 3
1 5 T 2
2 9 V 1
3 NaN M 1