value_counts() to count NaNs in a dataframe

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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?

pandas nan python
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

user_67927430

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
user_67301560
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