How can I add an empty sparse series to a Pandas dataframe?

Question

I want to add an empty sparse Pandas series to my dataframe. I tried the following but the resulting series is neither sparse, nor of the datatype I intended:

df = pd.DataFrame({"a":[1,2,3]})
df['newcolumn'] = pd.Series(dtype='bool_').to_sparse(fill_value=False)

This adds the 'newcolumn' series to my dataframe but the result looks like this:

   a newcolumn
0  1       NaN
1  2       NaN
2  3       NaN

And newcolumn looks like this:

0    NaN
1    NaN
2    NaN
Name: newcolumn, dtype: object

What I want to see is:

0    False
1    False
2    False
Name: newcolumn, dtype: bool

I can't figure out how to add this without losing my data type and maintaining sparseness.

I recognize there are roundabout ways to achieve this, but I'll be working with very large datasets and I want to avoid creating unnecessary objects in memory.

Answer 1

Per @COLDSPEED's comment, it's not possible to combine a sparse series with a dense dataframe. The solution is to convert the dataframe to be sparse and then add the series.

df = df.to_sparse()
df['newcol'] = pd.SparseSeries([False] * len(df), dtype='bool_', fill_value=False)