def test_checknull_old(self):
for value in na_vals + sometimes_na_vals:
assert libmissing.checknull_old(value)
for value in inf_vals:
assert libmissing.checknull_old(value)
for value in int_na_vals:
assert not libmissing.checknull_old(value)
for value in never_na_vals:
assert not libmissing.checknull_old(value)
def checknull_old(self):
for value in na_vals:
assert libmissing.checknull_old(value)
for value in inf_vals:
assert libmissing.checknull_old(value)
for value in int_na_vals:
assert not libmissing.checknull_old(value)
for value in sometimes_na_vals:
assert not libmissing.checknull_old(value)
for value in never_na_vals:
assert not libmissing.checknull_old(value)
def _isna_old(obj):
"""Detect missing values. Treat None, NaN, INF, -INF as null.
Parameters
----------
arr: ndarray or object value
Returns
-------
boolean ndarray or boolean
"""
if is_scalar(obj):
return libmissing.checknull_old(obj)
# hack (for now) because MI registers as ndarray
elif isinstance(obj, ABCMultiIndex):
raise NotImplementedError("isna is not defined for MultiIndex")
elif isinstance(obj, (ABCSeries, np.ndarray, ABCIndexClass)):
return _isna_ndarraylike_old(obj)
elif isinstance(obj, ABCGeneric):
return obj._constructor(obj._data.isna(func=_isna_old))
elif isinstance(obj, list):
return _isna_ndarraylike_old(np.asarray(obj, dtype=object))
elif hasattr(obj, "__array__"):
return _isna_ndarraylike_old(np.asarray(obj))
else:
return obj is None
def _isna_old(obj):
"""Detect missing values. Treat None, NaN, INF, -INF as null.
Parameters
----------
arr: ndarray or object value
Returns
-------
boolean ndarray or boolean
"""
if is_scalar(obj):
return libmissing.checknull_old(obj)
# hack (for now) because MI registers as ndarray
elif isinstance(obj, ABCMultiIndex):
raise NotImplementedError("isna is not defined for MultiIndex")
elif isinstance(obj, (ABCSeries, np.ndarray, ABCIndexClass)):
return _isna_ndarraylike_old(obj)
elif isinstance(obj, ABCGeneric):
return obj._constructor(obj._data.isna(func=_isna_old))
elif isinstance(obj, list):
return _isna_ndarraylike_old(np.asarray(obj, dtype=object))
elif hasattr(obj, '__array__'):
return _isna_ndarraylike_old(np.asarray(obj))
else:
return obj is None
def _isna_old(obj):
"""
Detect missing values, treating None, NaN, INF, -INF as null.
Parameters
----------
arr: ndarray or object value
Returns
-------
boolean ndarray or boolean
"""
if is_scalar(obj):
return libmissing.checknull_old(obj)
# hack (for now) because MI registers as ndarray
elif isinstance(obj, ABCMultiIndex):
raise NotImplementedError("isna is not defined for MultiIndex")
elif isinstance(obj, type):
return False
elif isinstance(obj,
(ABCSeries, np.ndarray, ABCIndexClass, ABCExtensionArray)):
return _isna_ndarraylike_old(obj)
elif isinstance(obj, ABCDataFrame):
return obj.isna()
elif isinstance(obj, list):
return _isna_ndarraylike_old(np.asarray(obj, dtype=object))
elif hasattr(obj, "__array__"):
return _isna_ndarraylike_old(np.asarray(obj))
else:
return False
def _isna(obj, inf_as_na: bool = False):
"""
Detect missing values, treating None, NaN or NA as null. Infinite
values will also be treated as null if inf_as_na is True.
Parameters
----------
obj: ndarray or object value
Input array or scalar value.
inf_as_na: bool
Whether to treat infinity as null.
Returns
-------
boolean ndarray or boolean
"""
if is_scalar(obj):
if inf_as_na:
return libmissing.checknull_old(obj)
else:
return libmissing.checknull(obj)
# hack (for now) because MI registers as ndarray
elif isinstance(obj, ABCMultiIndex):
raise NotImplementedError("isna is not defined for MultiIndex")
elif isinstance(obj, type):
return False
elif isinstance(obj, (np.ndarray, ABCExtensionArray)):
return _isna_array(obj, inf_as_na=inf_as_na)
elif isinstance(obj, ABCIndex):
# Try to use cached isna, which also short-circuits for integer dtypes
# and avoids materializing RangeIndex._values
if not obj._can_hold_na:
return obj.isna()
return _isna_array(obj._values, inf_as_na=inf_as_na)
elif isinstance(obj, ABCSeries):
result = _isna_array(obj._values, inf_as_na=inf_as_na)
# box
result = obj._constructor(result, index=obj.index, name=obj.name, copy=False)
return result
elif isinstance(obj, ABCDataFrame):
return obj.isna()
elif isinstance(obj, list):
return _isna_array(np.asarray(obj, dtype=object), inf_as_na=inf_as_na)
elif hasattr(obj, "__array__"):
return _isna_array(np.asarray(obj), inf_as_na=inf_as_na)
else:
return False
def _isna(obj, inf_as_na: bool = False):
"""
Detect missing values, treating None, NaN or NA as null. Infinite
values will also be treated as null if inf_as_na is True.
Parameters
----------
obj: ndarray or object value
Input array or scalar value.
inf_as_na: bool
Whether to treat infinity as null.
Returns
-------
boolean ndarray or boolean
"""
if is_scalar(obj):
if inf_as_na:
return libmissing.checknull_old(obj)
else:
return libmissing.checknull(obj)
# hack (for now) because MI registers as ndarray
elif isinstance(obj, ABCMultiIndex):
raise NotImplementedError("isna is not defined for MultiIndex")
elif isinstance(obj, type):
return False
elif isinstance(obj, (np.ndarray, ABCExtensionArray)):
# error: Value of type variable "ArrayLike" of "_isna_array" cannot be
# "Union[ndarray, ExtensionArray]"
return _isna_array(obj, inf_as_na=inf_as_na) # type: ignore[type-var]
elif isinstance(obj, (ABCSeries, ABCIndex)):
# error: Value of type variable "ArrayLike" of "_isna_array" cannot be
# "Union[Any, ExtensionArray, ndarray]"
result = _isna_array(obj._values, inf_as_na=inf_as_na) # type: ignore[type-var]
# box
if isinstance(obj, ABCSeries):
result = obj._constructor(
result, index=obj.index, name=obj.name, copy=False
)
return result
elif isinstance(obj, ABCDataFrame):
return obj.isna()
elif isinstance(obj, list):
return _isna_array(np.asarray(obj, dtype=object), inf_as_na=inf_as_na)
elif hasattr(obj, "__array__"):
return _isna_array(np.asarray(obj), inf_as_na=inf_as_na)
else:
return False
def _isna(obj, inf_as_na: bool = False):
"""
Detect missing values, treating None, NaN or NA as null. Infinite
values will also be treated as null if inf_as_na is True.
Parameters
----------
obj: ndarray or object value
Input array or scalar value.
inf_as_na: bool
Whether to treat infinity as null.
Returns
-------
boolean ndarray or boolean
"""
if is_scalar(obj):
if inf_as_na:
return libmissing.checknull_old(obj)
else:
return libmissing.checknull(obj)
# hack (for now) because MI registers as ndarray
elif isinstance(obj, ABCMultiIndex):
raise NotImplementedError("isna is not defined for MultiIndex")
elif isinstance(obj, type):
return False
elif isinstance(obj,
(ABCSeries, np.ndarray, ABCIndexClass, ABCExtensionArray)):
return _isna_ndarraylike(obj, inf_as_na=inf_as_na)
elif isinstance(obj, ABCDataFrame):
return obj.isna()
elif isinstance(obj, list):
return _isna_ndarraylike(np.asarray(obj, dtype=object),
inf_as_na=inf_as_na)
elif hasattr(obj, "__array__"):
return _isna_ndarraylike(np.asarray(obj), inf_as_na=inf_as_na)
else:
return False
