Is your feature request related to a problem?

With pd.Series.value_counts() it is possible to specify dropna=False, but that argument does not exist in pd.DataFrame.value_counts(). As a consequence, all rows that contain at least one NA element is dropped when using df.value_counts().

Describe the solution you'd like

It should be possible to call df.value_counts() with dropna=False and get a count for each unique row, including rows that have NAs in them.

API breaking implications

Like with pd.Series.value_counts() the default should be dropna=True. This will keep consistency between the two implementations, and leave current behavior unchanged.

Describe alternatives you've considered

Additional context

>>> import pandas as pd >>> s1 = pd.Series([1, 2, 3, pd.NA, 3]) >>> s2 = pd.Series([pd.NA, 1, pd.NA, 4, 2]) >>> s1.value_counts(dropna=False) 3.0 2 NaN 1 1.0 1 2.0 1 dtype: int64 >>> df = pd.DataFrame(zip(s1, s2), columns=['s1', 's2']) >>> df s1 s2 0 1 <NA> 1 2 1 2 3 <NA> 3 <NA> 4 4 3 2 >>> df.value_counts() s1 s2 2 1 1 3 2 1 dtype: int64