Nullable Boolean data type¶
New in version 1.0.0.
Indexing with NA values¶
pandas allows indexing with NA values in a boolean array, which are treated as False.
Changed in version 1.0.2.
In [1]: s = pd.Series([1, 2, 3])
In [2]: mask = pd.array([True, False, pd.NA], dtype="boolean")
In [3]: s[mask]
Out[3]:
0 1
dtype: int64
If you would prefer to keep the NA values you can manually fill them with fillna(True).
In [4]: s[mask.fillna(True)]
Out[4]:
0 1
2 3
dtype: int64
Kleene logical operations¶
arrays.BooleanArray implements Kleene Logic (sometimes called three-value logic) for
logical operations like & (and), | (or) and ^ (exclusive-or).
This table demonstrates the results for every combination. These operations are symmetrical, so flipping the left- and right-hand side makes no difference in the result.
Expression |
Result |
|---|---|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
When an NA is present in an operation, the output value is NA only if
the result cannot be determined solely based on the other input. For example,
True | NA is True, because both True | True and True | False
are True. In that case, we don’t actually need to consider the value
of the NA.
On the other hand, True & NA is NA. The result depends on whether
the NA really is True or False, since True & True is True,
but True & False is False, so we can’t determine the output.
This differs from how np.nan behaves in logical operations. pandas treated
np.nan is always false in the output.
In or
In [5]: pd.Series([True, False, np.nan], dtype="object") | True
Out[5]:
0 True
1 True
2 False
dtype: bool
In [6]: pd.Series([True, False, np.nan], dtype="boolean") | True
Out[6]:
0 True
1 True
2 True
dtype: boolean
In and
In [7]: pd.Series([True, False, np.nan], dtype="object") & True
Out[7]:
0 True
1 False
2 False
dtype: bool
In [8]: pd.Series([True, False, np.nan], dtype="boolean") & True
Out[8]:
0 True
1 False
2 <NA>
dtype: boolean