def is_inferred_bool_dtype(arr: ArrayLike) -> bool:
"""
Check if this is a ndarray[bool] or an ndarray[object] of bool objects.
Parameters
----------
arr : np.ndarray or ExtensionArray
Returns
-------
bool
Notes
-----
This does not include the special treatment is_bool_dtype uses for
Categorical.
"""
if not isinstance(arr, np.ndarray):
return False
dtype = arr.dtype
if dtype == np.dtype(bool):
return True
elif dtype == np.dtype("object"):
return lib.is_bool_array(arr)
return False
def is_bool_indexer(key: Any) -> bool:
"""
Check whether `key` is a valid boolean indexer.
Parameters
----------
key : Any
Only list-likes may be considered boolean indexers.
All other types are not considered a boolean indexer.
For array-like input, boolean ndarrays or ExtensionArrays
with ``_is_boolean`` set are considered boolean indexers.
Returns
-------
bool
Whether `key` is a valid boolean indexer.
Raises
------
ValueError
When the array is an object-dtype ndarray or ExtensionArray
and contains missing values.
See Also
--------
check_bool_array_indexer : Check that `key`
is a valid mask for an array, and convert to an ndarray.
"""
na_msg = "cannot mask with array containing NA / NaN values"
if isinstance(key, (ABCSeries, np.ndarray, ABCIndex)) or (
is_array_like(key) and is_extension_array_dtype(key.dtype)
):
if key.dtype == np.object_:
key = np.asarray(values_from_object(key))
if not lib.is_bool_array(key):
if isna(key).any():
raise ValueError(na_msg)
return False
return True
elif is_bool_dtype(key.dtype):
# an ndarray with bool-dtype by definition has no missing values.
# So we only need to check for NAs in ExtensionArrays
if is_extension_array_dtype(key.dtype):
if np.any(key.isna()):
raise ValueError(na_msg)
return True
elif isinstance(key, list):
try:
arr = np.asarray(key)
return arr.dtype == np.bool_ and len(arr) == len(key)
except TypeError: # pragma: no cover
return False
return False
def get_bool_data(self, copy: bool = False) -> ArrayManager:
"""
Select columns that are bool-dtype and object-dtype columns that are all-bool.
Parameters
----------
copy : bool, default False
Whether to copy the blocks
"""
return self._get_data_subset(lambda arr: is_bool_dtype(arr.dtype) or (
is_object_dtype(arr.dtype) and lib.is_bool_array(arr)))
def is_bool_indexer(key: Any) -> bool:
"""
Check whether `key` is a valid boolean indexer.
Parameters
----------
key : Any
Only list-likes may be considered boolean indexers.
All other types are not considered a boolean indexer.
For array-like input, boolean ndarrays or ExtensionArrays
with ``_is_boolean`` set are considered boolean indexers.
Returns
-------
bool
Whether `key` is a valid boolean indexer.
Raises
------
ValueError
When the array is an object-dtype ndarray or ExtensionArray
and contains missing values.
See Also
--------
check_array_indexer : Check that `key` is a valid array to index,
and convert to an ndarray.
"""
if isinstance(key, (ABCSeries, np.ndarray, ABCIndex)) or (
is_array_like(key) and is_extension_array_dtype(key.dtype)
):
if key.dtype == np.object_:
key = np.asarray(key)
if not lib.is_bool_array(key):
na_msg = "Cannot mask with non-boolean array containing NA / NaN values"
if lib.infer_dtype(key) == "boolean" and isna(key).any():
# Don't raise on e.g. ["A", "B", np.nan], see
# test_loc_getitem_list_of_labels_categoricalindex_with_na
raise ValueError(na_msg)
return False
return True
elif is_bool_dtype(key.dtype):
return True
elif isinstance(key, list):
# check if np.array(key).dtype would be bool
if len(key) > 0:
if type(key) is not list:
# GH#42461 cython will raise TypeError if we pass a subclass
key = list(key)
return lib.is_bool_list(key)
return False
def is_bool_indexer(key):
# type: (Any) -> bool
"""
Check whether `key` is a valid boolean indexer.
Parameters
----------
key : Any
Only list-likes may be considered boolean indexers.
All other types are not considered a boolean indexer.
For array-like input, boolean ndarrays or ExtensionArrays
with ``_is_boolean`` set are considered boolean indexers.
Returns
-------
bool
Raises
------
ValueError
When the array is an object-dtype ndarray or ExtensionArray
and contains missing values.
"""
na_msg = 'cannot index with vector containing NA / NaN values'
if (isinstance(key, (ABCSeries, np.ndarray, ABCIndex)) or
(is_array_like(key) and is_extension_array_dtype(key.dtype))):
if key.dtype == np.object_:
key = np.asarray(values_from_object(key))
if not lib.is_bool_array(key):
if isna(key).any():
raise ValueError(na_msg)
return False
return True
elif is_bool_dtype(key.dtype):
# an ndarray with bool-dtype by definition has no missing values.
# So we only need to check for NAs in ExtensionArrays
if is_extension_array_dtype(key.dtype):
if np.any(key.isna()):
raise ValueError(na_msg)
return True
elif isinstance(key, list):
try:
arr = np.asarray(key)
return arr.dtype == np.bool_ and len(arr) == len(key)
except TypeError: # pragma: no cover
return False
return False
def test_string_array(nullable_string_dtype, any_string_method, request):
method_name, args, kwargs = any_string_method
if method_name == "decode":
pytest.skip("decode requires bytes.")
if nullable_string_dtype == "arrow_string" and method_name in {
"extract",
"extractall",
}:
reason = "extract/extractall does not yet dispatch to array"
mark = pytest.mark.xfail(reason=reason)
request.node.add_marker(mark)
data = ["a", "bb", np.nan, "ccc"]
a = Series(data, dtype=object)
b = Series(data, dtype=nullable_string_dtype)
expected = getattr(a.str, method_name)(*args, **kwargs)
result = getattr(b.str, method_name)(*args, **kwargs)
if isinstance(expected, Series):
if expected.dtype == "object" and lib.is_string_array(
expected.dropna().values, ):
assert result.dtype == nullable_string_dtype
result = result.astype(object)
elif expected.dtype == "object" and lib.is_bool_array(expected.values,
skipna=True):
assert result.dtype == "boolean"
result = result.astype(object)
elif expected.dtype == "bool":
assert result.dtype == "boolean"
result = result.astype("bool")
elif expected.dtype == "float" and expected.isna().any():
assert result.dtype == "Int64"
result = result.astype("float")
elif isinstance(expected, DataFrame):
columns = expected.select_dtypes(include="object").columns
assert all(result[columns].dtypes == nullable_string_dtype)
result[columns] = result[columns].astype(object)
tm.assert_equal(result, expected)
def is_bool_indexer(key):
if isinstance(key, (ABCSeries, np.ndarray)):
if key.dtype == np.object_:
key = np.asarray(_values_from_object(key))
if not lib.is_bool_array(key):
if isnull(key).any():
raise ValueError('cannot index with vector containing '
'NA / NaN values')
return False
return True
elif key.dtype == np.bool_:
return True
elif isinstance(key, list):
try:
arr = np.asarray(key)
return arr.dtype == np.bool_ and len(arr) == len(key)
except TypeError: # pragma: no cover
return False
return False
def is_bool_indexer(key):
if isinstance(key, (ABCSeries, np.ndarray)):
if key.dtype == np.object_:
key = np.asarray(_values_from_object(key))
if not lib.is_bool_array(key):
if isna(key).any():
raise ValueError('cannot index with vector containing '
'NA / NaN values')
return False
return True
elif key.dtype == np.bool_:
return True
elif isinstance(key, list):
try:
arr = np.asarray(key)
return arr.dtype == np.bool_ and len(arr) == len(key)
except TypeError: # pragma: no cover
return False
return False
def test_string_array(nullable_string_dtype, any_string_method):
method_name, args, kwargs = any_string_method
data = ["a", "bb", np.nan, "ccc"]
a = Series(data, dtype=object)
b = Series(data, dtype=nullable_string_dtype)
if method_name == "decode":
with pytest.raises(TypeError, match="a bytes-like object is required"):
getattr(b.str, method_name)(*args, **kwargs)
return
expected = getattr(a.str, method_name)(*args, **kwargs)
result = getattr(b.str, method_name)(*args, **kwargs)
if isinstance(expected, Series):
if expected.dtype == "object" and lib.is_string_array(
expected.dropna().values, ):
assert result.dtype == nullable_string_dtype
result = result.astype(object)
elif expected.dtype == "object" and lib.is_bool_array(expected.values,
skipna=True):
assert result.dtype == "boolean"
result = result.astype(object)
elif expected.dtype == "bool":
assert result.dtype == "boolean"
result = result.astype("bool")
elif expected.dtype == "float" and expected.isna().any():
assert result.dtype == "Int64"
result = result.astype("float")
elif isinstance(expected, DataFrame):
columns = expected.select_dtypes(include="object").columns
assert all(result[columns].dtypes == nullable_string_dtype)
result[columns] = result[columns].astype(object)
tm.assert_equal(result, expected)
def test_string_array(any_string_method):
method_name, args, kwargs = any_string_method
if method_name == "decode":
pytest.skip("decode requires bytes.")
data = ["a", "bb", np.nan, "ccc"]
a = Series(data, dtype=object)
b = Series(data, dtype="string")
expected = getattr(a.str, method_name)(*args, **kwargs)
result = getattr(b.str, method_name)(*args, **kwargs)
if isinstance(expected, Series):
if expected.dtype == "object" and lib.is_string_array(
expected.dropna().values,
):
assert result.dtype == "string"
result = result.astype(object)
elif expected.dtype == "object" and lib.is_bool_array(
expected.values, skipna=True
):
assert result.dtype == "boolean"
result = result.astype(object)
elif expected.dtype == "bool":
assert result.dtype == "boolean"
result = result.astype("bool")
elif expected.dtype == "float" and expected.isna().any():
assert result.dtype == "Int64"
result = result.astype("float")
elif isinstance(expected, DataFrame):
columns = expected.select_dtypes(include="object").columns
assert all(result[columns].dtypes == "string")
result[columns] = result[columns].astype(object)
tm.assert_equal(result, expected)
def is_inferred_bool_dtype(arr: ArrayLike) -> bool:
"""
Check if this is a ndarray[bool] or an ndarray[object] of bool objects.
Parameters
----------
arr : np.ndarray or ExtensionArray
Returns
-------
bool
Notes
-----
This does not include the special treatment is_bool_dtype uses for
Categorical.
"""
if not isinstance(arr, np.ndarray):
return False
dtype = arr.dtype
if dtype == np.dtype(bool):
return True
elif dtype == np.dtype("object"):
result = lib.is_bool_array(arr)
if result:
# GH#46188
warnings.warn(
"In a future version, object-dtype columns with all-bool values "
"will not be included in reductions with bool_only=True. "
"Explicitly cast to bool dtype instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
return result
return False
def hash_array(vals, encoding='utf8', hash_key=None, categorize=True):
"""
Given a 1d array, return an array of deterministic integers.
.. versionadded:: 0.19.2
Parameters
----------
vals : ndarray, Categorical
encoding : string, default 'utf8'
encoding for data & key when strings
hash_key : string key to encode, default to _default_hash_key
categorize : bool, default True
Whether to first categorize object arrays before hashing. This is more
efficient when the array contains duplicate values.
.. versionadded:: 0.20.0
Returns
-------
1d uint64 numpy array of hash values, same length as the vals
"""
if not hasattr(vals, 'dtype'):
raise TypeError("must pass a ndarray-like")
if hash_key is None:
hash_key = _default_hash_key
# For categoricals, we hash the categories, then remap the codes to the
# hash values. (This check is above the complex check so that we don't ask
# numpy if categorical is a subdtype of complex, as it will choke.
if is_categorical_dtype(vals.dtype):
return _hash_categorical(vals, encoding, hash_key)
# we'll be working with everything as 64-bit values, so handle this
# 128-bit value early
if np.issubdtype(vals.dtype, np.complex128):
return hash_array(vals.real) + 23 * hash_array(vals.imag)
# First, turn whatever array this is into unsigned 64-bit ints, if we can
# manage it.
if is_bool_array(vals):
vals = vals.astype('u8')
elif (is_datetime64_dtype(vals) or
is_timedelta64_dtype(vals)):
vals = vals.view('i8').astype('u8', copy=False)
elif (is_numeric_dtype(vals) and vals.dtype.itemsize <= 8):
vals = vals.view('u{}'.format(vals.dtype.itemsize)).astype('u8')
else:
# With repeated values, its MUCH faster to categorize object dtypes,
# then hash and rename categories. We allow skipping the categorization
# when the values are known/likely to be unique.
if categorize:
codes, categories = factorize(vals, sort=False)
cat = Categorical(codes, Index(categories),
ordered=False, fastpath=True)
return _hash_categorical(cat, encoding, hash_key)
try:
vals = _hash.hash_object_array(vals, hash_key, encoding)
except TypeError:
# we have mixed types
vals = _hash.hash_object_array(vals.astype(str).astype(object),
hash_key, encoding)
# Then, redistribute these 64-bit ints within the space of 64-bit ints
vals ^= vals >> 30
vals *= np.uint64(0xbf58476d1ce4e5b9)
vals ^= vals >> 27
vals *= np.uint64(0x94d049bb133111eb)
vals ^= vals >> 31
return vals
def hash_array(vals, encoding='utf8', hash_key=None, categorize=True):
"""
Given a 1d array, return an array of deterministic integers.
.. versionadded:: 0.19.2
Parameters
----------
vals : ndarray, Categorical
encoding : string, default 'utf8'
encoding for data & key when strings
hash_key : string key to encode, default to _default_hash_key
categorize : bool, default True
Whether to first categorize object arrays before hashing. This is more
efficient when the array contains duplicate values.
.. versionadded:: 0.20.0
Returns
-------
1d uint64 numpy array of hash values, same length as the vals
"""
if not hasattr(vals, 'dtype'):
raise TypeError("must pass a ndarray-like")
if hash_key is None:
hash_key = _default_hash_key
# For categoricals, we hash the categories, then remap the codes to the
# hash values. (This check is above the complex check so that we don't ask
# numpy if categorical is a subdtype of complex, as it will choke.
if is_categorical_dtype(vals.dtype):
return _hash_categorical(vals, encoding, hash_key)
# we'll be working with everything as 64-bit values, so handle this
# 128-bit value early
if np.issubdtype(vals.dtype, np.complex128):
return hash_array(vals.real) + 23 * hash_array(vals.imag)
# First, turn whatever array this is into unsigned 64-bit ints, if we can
# manage it.
if is_bool_array(vals):
vals = vals.astype('u8')
elif (is_datetime64_dtype(vals) or is_timedelta64_dtype(vals)):
vals = vals.view('i8').astype('u8', copy=False)
elif (is_numeric_dtype(vals) and vals.dtype.itemsize <= 8):
vals = vals.view('u{}'.format(vals.dtype.itemsize)).astype('u8')
else:
# With repeated values, its MUCH faster to categorize object dtypes,
# then hash and rename categories. We allow skipping the categorization
# when the values are known/likely to be unique.
if categorize:
codes, categories = factorize(vals, sort=False)
cat = Categorical(codes,
Index(categories),
ordered=False,
fastpath=True)
return _hash_categorical(cat, encoding, hash_key)
try:
vals = _hash.hash_object_array(vals, hash_key, encoding)
except TypeError:
# we have mixed types
vals = _hash.hash_object_array(
vals.astype(str).astype(object), hash_key, encoding)
# Then, redistribute these 64-bit ints within the space of 64-bit ints
vals ^= vals >> 30
vals *= np.uint64(0xbf58476d1ce4e5b9)
vals ^= vals >> 27
vals *= np.uint64(0x94d049bb133111eb)
vals ^= vals >> 31
return vals
