rename to private namespaces

Author: jreback

pandas/core/arrays/_mask.py

@@ -1,12 +1,8 @@
 """A boolean mask interace.
 
-This module provide a numpy-like mask; it can be optionally backed
-by pyarrow.
+This module provides an interface to a numpy / pyarrow boolean mask.
 """
 
-import numpy as np
-from pandas.core.arrays import ExtensionArray
-
 
 class _MaskArray:
     _typ = "maskarray"
@@ -24,112 +20,20 @@ def _reduce(self, method, skipna=True, **kwargs):
         return op(**kwargs)
 
 
-# TODO: The NumpyBoolArray should actually be a full EA.
-
-class NumpyBoolArray(ExtensionArray):
-    """Generic class which can be used to represent missing data.
-    """
-
-    @property
-    def dtype(self):
-        return np.dtype('bool')
-
-    @classmethod
-    def _from_sequence(cls, scalars, dtype=None, copy=False):
-        arr = np.asarray(scalars).astype(np.bool_, copy=False)
-        return cls(arr, copy=copy)
-
-    def __init__(self, mask, copy=True):
-        """
-        Parameters
-        ----------
-        mask : numpy array
-            Mask of missing values.
-        """
-        assert isinstance(mask, np.ndarray)
-        assert mask.dtype == np.bool_
-
-        if copy:
-            mask = mask.copy()
-        self._data = mask
-
-    def __getitem__(self, key):
-        return self._data[key]
-
-    def __setitem__(self, key, value):
-        self._data[key] = value
-
-    def __len__(self):
-        return len(self._data)
-
-    def __eq__(self, other):
-        return np.array(self, copy=False) == np.array(other, copy=False)
-
-    def __array__(self, dtype=None):
-        return self._data
-
-    def __iter__(self):
-        return iter(self._data)
-
-    def __invert__(self):
-        return type(self)(~self._data)
-
-    def __or__(self, other):
-        return np.array(
-            self, copy=False).__or__(np.array(other, copy=False))
-
-    def __ior__(self, other):
-        return np.array(self, copy=False) | np.array(other, copy=False)
-
-    def __and__(self, other):
-        return np.array(self, copy=False).__and__(np.array(other, copy=False))
-
-    def __iand__(self, other):
-        return np.array(self, copy=False) & (np.array(other, copy=False))
-
-    @property
-    def nbytes(self):
-        return self._data.nbytes
-
-    @property
-    def size(self):
-        # TODO: should this be an EA property?
-        return len(self)
-
-    def reshape(self, shape, **kwargs):
-        return np.array(self, copy=False).reshape(shape, **kwargs)
-
-    def astype(self, dtype, copy=False):
-        return np.array(self, copy=False).astype(dtype, copy=copy)
-
-    def any(self):
-        return self._data.any()
-
-    def all(self):
-        return self._data.all()
-
-    def copy(self):
-        return type(self)(self._data.copy())
-
-    def sum(self, axis=None):
-        return np.array(self, copy=False).sum()
-
-    def take(self, indicies, **kwargs):
-        return np.array(self, copy=False).take(indicies)
-
-
 def get_mask_array_type():
     # TODO: protect imports
 
     # if ArrowBoolArray is available use it
     # otherwise use the NumpyMask
     try:
-        from pandas.core.arrays.bool import ArrowBoolArray
+        from pandas.core.arrays._pyarrow_bool import ArrowBoolArray
 
         class MaskArray(_MaskArray, ArrowBoolArray):
             pass
 
     except (ModuleNotFoundError, ImportError):
+        from pandas.core.arrays._numpy_bool import NumpyBoolArray
+
         class MaskArray(_MaskArray, NumpyBoolArray):
             pass
 

pandas/core/arrays/_numpy_bool.py

@@ -0,0 +1,98 @@
+"""
+This module provide a numpy-boolean boolean array
+"""
+
+import numpy as np
+from pandas.core.arrays import ExtensionArray
+
+
+class NumpyBoolArray(ExtensionArray):
+    """Generic class which can be used to represent missing data.
+    """
+
+    @property
+    def dtype(self):
+        return np.dtype('bool')
+
+    @classmethod
+    def _from_sequence(cls, scalars, dtype=None, copy=False):
+        arr = np.asarray(scalars).astype(np.bool_, copy=False)
+        return cls(arr, copy=copy)
+
+    def __init__(self, mask, copy=True):
+        """
+        Parameters
+        ----------
+        mask : numpy array
+            Mask of missing values.
+        """
+        assert isinstance(mask, np.ndarray)
+        assert mask.dtype == np.bool_
+
+        if copy:
+            mask = mask.copy()
+        self._data = mask
+
+    def __getitem__(self, key):
+        return self._data[key]
+
+    def __setitem__(self, key, value):
+        self._data[key] = value
+
+    def __len__(self):
+        return len(self._data)
+
+    def __eq__(self, other):
+        return np.array(self, copy=False) == np.array(other, copy=False)
+
+    def __array__(self, dtype=None):
+        return self._data
+
+    def __iter__(self):
+        return iter(self._data)
+
+    def __invert__(self):
+        return type(self)(~self._data)
+
+    def __or__(self, other):
+        return np.array(
+            self, copy=False).__or__(np.array(other, copy=False))
+
+    def __ior__(self, other):
+        return np.array(self, copy=False) | np.array(other, copy=False)
+
+    def __and__(self, other):
+        return np.array(self, copy=False).__and__(np.array(other, copy=False))
+
+    def __iand__(self, other):
+        return np.array(self, copy=False) & (np.array(other, copy=False))
+
+    @property
+    def nbytes(self):
+        return self._data.nbytes
+
+    @property
+    def size(self):
+        # TODO: should this be an EA property?
+        return len(self)
+
+    def reshape(self, shape, **kwargs):
+        return np.array(self, copy=False).reshape(shape, **kwargs)
+
+    def astype(self, dtype, copy=False):
+        return np.array(self, copy=False).astype(dtype, copy=copy)
+
+    def any(self):
+        return self._data.any()
+
+    def all(self):
+        return self._data.all()
+
+    def copy(self):
+        return type(self)(self._data.copy())
+
+    def sum(self, axis=None):
+        return np.array(self, copy=False).sum()
+
+    def take(self, indicies, **kwargs):
+        return np.array(self, copy=False).take(indicies)

pandas/core/arrays/bool.py renamed to pandas/core/arrays/_pyarrow_bool.py

@@ -3,29 +3,19 @@
 import pytest
 from pandas.util import testing as tm
 
-from pandas.core.arrays._mask import NumpyBoolArray
 
-
-@pytest.fixture
-def numpy_data():
-    return NumpyBoolArray([1, 0, 1])
-
-
-@pytest.fixture
-def arrow_data():
-    pytest.importorskip('pyarrow', minversion="0.10.0")
-    from pandas.core.arrays.bool import ArrowBoolArray
-    return ArrowBoolArray([1, 0, 1])
-
-
-@pytest.fixture(params=['numpy', 'arrow'])
+@pytest.fixture(params=['numpy', 'arrow', 'mask'])
 def mask_type(request):
     if request.param == 'numpy':
+        from pandas.core.arrays._numpy_bool import NumpyBoolArray
         return NumpyBoolArray
     elif request.param == 'arrow':
         pytest.importorskip('pyarrow', minversion="0.10.0")
-        from pandas.core.arrays.bool import ArrowBoolArray
+        from pandas.core.arrays._pyarrow_bool import ArrowBoolArray
         return ArrowBoolArray
+    elif request.param == 'mask':
+        from pandas.core.arrays._mask import get_mask_array_type
+        return get_mask_array_type()
 
 
 @pytest.fixture

pandas/tests/arrays/test_mask.py

@@ -7,7 +7,7 @@
 
 pytest.importorskip('pyarrow', minversion="0.10.0")
 
-from pandas.core.arrays.bool import (
+from pandas.core.arrays._pyarrow_bool import (
     ArrowBoolArray, ArrowBoolDtype)  # isort:skip