def _isnull_ndarraylike(obj):
values = getattr(obj, 'values', obj)
dtype = values.dtype
if is_string_dtype(dtype):
if is_categorical_dtype(values):
from pandas import Categorical
if not isinstance(values, Categorical):
values = values.values
result = values.isnull()
else:
# Working around NumPy ticket 1542
shape = values.shape
if is_string_like_dtype(dtype):
result = np.zeros(values.shape, dtype=bool)
else:
result = np.empty(shape, dtype=bool)
vec = lib.isnullobj(values.ravel())
result[...] = vec.reshape(shape)
elif needs_i8_conversion(obj):
# this is the NaT pattern
result = values.view('i8') == iNaT
else:
result = np.isnan(values)
# box
if isinstance(obj, ABCSeries):
from pandas import Series
result = Series(result, index=obj.index, name=obj.name, copy=False)
return result
def _isnull_ndarraylike(obj):
from pandas import Series
values = np.asarray(obj)
if values.dtype.kind in ('O', 'S', 'U'):
# Working around NumPy ticket 1542
shape = values.shape
if values.dtype.kind in ('S', 'U'):
result = np.zeros(values.shape, dtype=bool)
else:
result = np.empty(shape, dtype=bool)
vec = lib.isnullobj(values.ravel())
result[:] = vec.reshape(shape)
if isinstance(obj, Series):
result = Series(result, index=obj.index, copy=False)
elif values.dtype == np.dtype('M8[ns]'):
# this is the NaT pattern
result = values.view('i8') == lib.iNaT
elif issubclass(values.dtype.type, np.timedelta64):
result = -np.isfinite(values.view('i8'))
else:
result = -np.isfinite(obj)
return result
def _isnull_ndarraylike(obj):
from pandas import Series
values = np.asarray(obj)
if values.dtype.kind in ('O', 'S', 'U'):
# Working around NumPy ticket 1542
shape = values.shape
if values.dtype.kind in ('S', 'U'):
result = np.zeros(values.shape, dtype=bool)
else:
result = np.empty(shape, dtype=bool)
vec = lib.isnullobj(values.ravel())
result[:] = vec.reshape(shape)
if isinstance(obj, Series):
result = Series(result, index=obj.index, copy=False)
elif values.dtype == np.dtype('M8[ns]'):
# this is the NaT pattern
result = values.view('i8') == lib.iNaT
elif issubclass(values.dtype.type, np.timedelta64):
result = -np.isfinite(values.view('i8'))
else:
result = -np.isfinite(obj)
return result
def _isnull_ndarraylike(obj):
values = getattr(obj, 'values', obj)
dtype = values.dtype
if is_string_dtype(dtype):
if is_categorical_dtype(values):
from pandas import Categorical
if not isinstance(values, Categorical):
values = values.values
result = values.isnull()
else:
# Working around NumPy ticket 1542
shape = values.shape
if is_string_like_dtype(dtype):
result = np.zeros(values.shape, dtype=bool)
else:
result = np.empty(shape, dtype=bool)
vec = lib.isnullobj(values.ravel())
result[...] = vec.reshape(shape)
elif needs_i8_conversion(obj):
# this is the NaT pattern
result = values.view('i8') == iNaT
else:
result = np.isnan(values)
# box
if isinstance(obj, ABCSeries):
from pandas import Series
result = Series(result, index=obj.index, name=obj.name, copy=False)
return result
def _isnull_ndarraylike(obj):
from pandas import Series
values = np.asarray(obj)
if values.dtype.kind in ("O", "S"):
# Working around NumPy ticket 1542
shape = values.shape
result = np.empty(shape, dtype=bool)
vec = lib.isnullobj(values.ravel())
result[:] = vec.reshape(shape)
if isinstance(obj, Series):
result = Series(result, index=obj.index, copy=False)
elif values.dtype == np.dtype("M8[ns]"):
# this is the NaT pattern
result = values.view("i8") == lib.iNaT
else:
result = -np.isfinite(obj)
return result
def isnull(obj):
'''
Replacement for numpy.isnan / -numpy.isfinite which is suitable
for use on object arrays.
Parameters
----------
arr: ndarray or object value
Returns
-------
boolean ndarray or boolean
'''
if lib.isscalar(obj):
return lib.checknull(obj)
from pandas.core.generic import PandasObject
from pandas import Series
if isinstance(obj, np.ndarray):
if obj.dtype.kind in ('O', 'S'):
# Working around NumPy ticket 1542
shape = obj.shape
result = np.empty(shape, dtype=bool)
vec = lib.isnullobj(obj.ravel())
result[:] = vec.reshape(shape)
if isinstance(obj, Series):
result = Series(result, index=obj.index, copy=False)
elif obj.dtype == np.dtype('M8[ns]'):
# this is the NaT pattern
result = np.array(obj).view('i8') == lib.iNaT
else:
result = -np.isfinite(obj)
return result
elif isinstance(obj, PandasObject):
# TODO: optimize for DataFrame, etc.
return obj.apply(isnull)
else:
return obj is None
def isnull(obj):
'''
Replacement for numpy.isnan / -numpy.isfinite which is suitable
for use on object arrays.
Parameters
----------
arr: ndarray or object value
Returns
-------
boolean ndarray or boolean
'''
if lib.isscalar(obj):
return lib.checknull(obj)
from pandas.core.generic import PandasObject
from pandas import Series
if isinstance(obj, np.ndarray):
if obj.dtype.kind in ('O', 'S'):
# Working around NumPy ticket 1542
shape = obj.shape
result = np.empty(shape, dtype=bool)
vec = lib.isnullobj(obj.ravel())
result[:] = vec.reshape(shape)
if isinstance(obj, Series):
result = Series(result, index=obj.index, copy=False)
elif obj.dtype == np.dtype('M8[ns]'):
# this is the NaT pattern
result = np.array(obj).view('i8') == lib.iNaT
else:
result = -np.isfinite(obj)
return result
elif isinstance(obj, PandasObject):
# TODO: optimize for DataFrame, etc.
return obj.apply(isnull)
else:
return obj is None
