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missing.pyx
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import cython
from cython import Py_ssize_t
import numbers
import numpy as np
cimport numpy as cnp
from numpy cimport ndarray, int64_t, uint8_t, float64_t
cnp.import_array()
cimport pandas._libs.util as util
from pandas._libs.tslibs.np_datetime cimport get_datetime64_value, get_timedelta64_value
from pandas._libs.tslibs.nattype cimport (
c_NaT as NaT,
checknull_with_nat,
is_null_datetimelike,
)
from pandas._libs.ops_dispatch import maybe_dispatch_ufunc_to_dunder_op
from pandas.compat import is_platform_32bit
cdef:
float64_t INF = <float64_t>np.inf
float64_t NEGINF = -INF
int64_t NPY_NAT = util.get_nat()
bint is_32bit = is_platform_32bit()
cpdef bint checknull(object val):
"""
Return boolean describing of the input is NA-like, defined here as any
of:
- None
- nan
- NaT
- np.datetime64 representation of NaT
- np.timedelta64 representation of NaT
Parameters
----------
val : object
Returns
-------
bool
Notes
-----
The difference between `checknull` and `checknull_old` is that `checknull`
does *not* consider INF or NEGINF to be NA.
"""
return val is C_NA or is_null_datetimelike(val, inat_is_null=False)
cpdef bint checknull_old(object val):
"""
Return boolean describing of the input is NA-like, defined here as any
of:
- None
- nan
- INF
- NEGINF
- NaT
- np.datetime64 representation of NaT
- np.timedelta64 representation of NaT
Parameters
----------
val : object
Returns
-------
result : bool
Notes
-----
The difference between `checknull` and `checknull_old` is that `checknull`
does *not* consider INF or NEGINF to be NA.
"""
if checknull(val):
return True
elif util.is_float_object(val) or util.is_complex_object(val):
return val == INF or val == NEGINF
return False
cdef inline bint _check_none_nan_inf_neginf(object val):
return val is None or (isinstance(val, float) and
(val != val or val == INF or val == NEGINF))
@cython.wraparound(False)
@cython.boundscheck(False)
cpdef ndarray[uint8_t] isnaobj(ndarray arr):
"""
Return boolean mask denoting which elements of a 1-D array are na-like,
according to the criteria defined in `checknull`:
- None
- nan
- NaT
- np.datetime64 representation of NaT
- np.timedelta64 representation of NaT
Parameters
----------
arr : ndarray
Returns
-------
result : ndarray (dtype=np.bool_)
"""
cdef:
Py_ssize_t i, n
object val
ndarray[uint8_t] result
assert arr.ndim == 1, "'arr' must be 1-D."
n = len(arr)
result = np.empty(n, dtype=np.uint8)
for i in range(n):
val = arr[i]
result[i] = checknull(val)
return result.view(np.bool_)
@cython.wraparound(False)
@cython.boundscheck(False)
def isnaobj_old(arr: ndarray) -> ndarray:
"""
Return boolean mask denoting which elements of a 1-D array are na-like,
defined as being any of:
- None
- nan
- INF
- NEGINF
- NaT
- NA
Parameters
----------
arr : ndarray
Returns
-------
result : ndarray (dtype=np.bool_)
"""
cdef:
Py_ssize_t i, n
object val
ndarray[uint8_t] result
assert arr.ndim == 1, "'arr' must be 1-D."
n = len(arr)
result = np.zeros(n, dtype=np.uint8)
for i in range(n):
val = arr[i]
result[i] = (
val is NaT
or _check_none_nan_inf_neginf(val)
or val is C_NA
)
return result.view(np.bool_)
@cython.wraparound(False)
@cython.boundscheck(False)
def isnaobj2d(arr: ndarray) -> ndarray:
"""
Return boolean mask denoting which elements of a 2-D array are na-like,
according to the criteria defined in `checknull`:
- None
- nan
- NaT
- np.datetime64 representation of NaT
- np.timedelta64 representation of NaT
Parameters
----------
arr : ndarray
Returns
-------
result : ndarray (dtype=np.bool_)
Notes
-----
The difference between `isnaobj2d` and `isnaobj2d_old` is that `isnaobj2d`
does *not* consider INF or NEGINF to be NA.
"""
cdef:
Py_ssize_t i, j, n, m
object val
ndarray[uint8_t, ndim=2] result
assert arr.ndim == 2, "'arr' must be 2-D."
n, m = (<object>arr).shape
result = np.zeros((n, m), dtype=np.uint8)
for i in range(n):
for j in range(m):
val = arr[i, j]
if checknull(val):
result[i, j] = 1
return result.view(np.bool_)
@cython.wraparound(False)
@cython.boundscheck(False)
def isnaobj2d_old(arr: ndarray) -> ndarray:
"""
Return boolean mask denoting which elements of a 2-D array are na-like,
according to the criteria defined in `checknull_old`:
- None
- nan
- INF
- NEGINF
- NaT
- np.datetime64 representation of NaT
- np.timedelta64 representation of NaT
Parameters
----------
arr : ndarray
Returns
-------
ndarray (dtype=np.bool_)
Notes
-----
The difference between `isnaobj2d` and `isnaobj2d_old` is that `isnaobj2d`
does *not* consider INF or NEGINF to be NA.
"""
cdef:
Py_ssize_t i, j, n, m
object val
ndarray[uint8_t, ndim=2] result
assert arr.ndim == 2, "'arr' must be 2-D."
n, m = (<object>arr).shape
result = np.zeros((n, m), dtype=np.uint8)
for i in range(n):
for j in range(m):
val = arr[i, j]
if checknull_old(val):
result[i, j] = 1
return result.view(np.bool_)
def isposinf_scalar(val: object) -> bool:
return util.is_float_object(val) and val == INF
def isneginf_scalar(val: object) -> bool:
return util.is_float_object(val) and val == NEGINF
cdef inline bint is_null_datetime64(v):
# determine if we have a null for a datetime (or integer versions),
# excluding np.timedelta64('nat')
if checknull_with_nat(v):
return True
elif util.is_datetime64_object(v):
return get_datetime64_value(v) == NPY_NAT
return False
cdef inline bint is_null_timedelta64(v):
# determine if we have a null for a timedelta (or integer versions),
# excluding np.datetime64('nat')
if checknull_with_nat(v):
return True
elif util.is_timedelta64_object(v):
return get_timedelta64_value(v) == NPY_NAT
return False
cdef inline bint is_null_period(v):
# determine if we have a null for a Period (or integer versions),
# excluding np.datetime64('nat') and np.timedelta64('nat')
return checknull_with_nat(v)
# -----------------------------------------------------------------------------
# Implementation of NA singleton
def _create_binary_propagating_op(name, is_divmod=False):
def method(self, other):
if (other is C_NA or isinstance(other, str)
or isinstance(other, (numbers.Number, np.bool_))
or isinstance(other, np.ndarray) and not other.shape):
# Need the other.shape clause to handle NumPy scalars,
# since we do a setitem on `out` below, which
# won't work for NumPy scalars.
if is_divmod:
return NA, NA
else:
return NA
elif isinstance(other, np.ndarray):
out = np.empty(other.shape, dtype=object)
out[:] = NA
if is_divmod:
return out, out.copy()
else:
return out
return NotImplemented
method.__name__ = name
return method
def _create_unary_propagating_op(name):
def method(self):
return NA
method.__name__ = name
return method
cdef class C_NAType:
pass
class NAType(C_NAType):
"""
NA ("not available") missing value indicator.
.. warning::
Experimental: the behaviour of NA can still change without warning.
.. versionadded:: 1.0.0
The NA singleton is a missing value indicator defined by pandas. It is
used in certain new extension dtypes (currently the "string" dtype).
"""
_instance = None
def __new__(cls, *args, **kwargs):
if NAType._instance is None:
NAType._instance = C_NAType.__new__(cls, *args, **kwargs)
return NAType._instance
def __repr__(self) -> str:
return "<NA>"
def __bool__(self):
raise TypeError("boolean value of NA is ambiguous")
def __hash__(self):
# GH 30013: Ensure hash is large enough to avoid hash collisions with integers
exponent = 31 if is_32bit else 61
return 2 ** exponent - 1
def __reduce__(self):
return "NA"
# Binary arithmetic and comparison ops -> propagate
__add__ = _create_binary_propagating_op("__add__")
__radd__ = _create_binary_propagating_op("__radd__")
__sub__ = _create_binary_propagating_op("__sub__")
__rsub__ = _create_binary_propagating_op("__rsub__")
__mul__ = _create_binary_propagating_op("__mul__")
__rmul__ = _create_binary_propagating_op("__rmul__")
__matmul__ = _create_binary_propagating_op("__matmul__")
__rmatmul__ = _create_binary_propagating_op("__rmatmul__")
__truediv__ = _create_binary_propagating_op("__truediv__")
__rtruediv__ = _create_binary_propagating_op("__rtruediv__")
__floordiv__ = _create_binary_propagating_op("__floordiv__")
__rfloordiv__ = _create_binary_propagating_op("__rfloordiv__")
__mod__ = _create_binary_propagating_op("__mod__")
__rmod__ = _create_binary_propagating_op("__rmod__")
__divmod__ = _create_binary_propagating_op("__divmod__", is_divmod=True)
__rdivmod__ = _create_binary_propagating_op("__rdivmod__", is_divmod=True)
# __lshift__ and __rshift__ are not implemented
__eq__ = _create_binary_propagating_op("__eq__")
__ne__ = _create_binary_propagating_op("__ne__")
__le__ = _create_binary_propagating_op("__le__")
__lt__ = _create_binary_propagating_op("__lt__")
__gt__ = _create_binary_propagating_op("__gt__")
__ge__ = _create_binary_propagating_op("__ge__")
# Unary ops
__neg__ = _create_unary_propagating_op("__neg__")
__pos__ = _create_unary_propagating_op("__pos__")
__abs__ = _create_unary_propagating_op("__abs__")
__invert__ = _create_unary_propagating_op("__invert__")
# pow has special
def __pow__(self, other):
if other is C_NA:
return NA
elif isinstance(other, (numbers.Number, np.bool_)):
if other == 0:
# returning positive is correct for +/- 0.
return type(other)(1)
else:
return NA
elif isinstance(other, np.ndarray):
return np.where(other == 0, other.dtype.type(1), NA)
return NotImplemented
def __rpow__(self, other):
if other is C_NA:
return NA
elif isinstance(other, (numbers.Number, np.bool_)):
if other == 1:
return other
else:
return NA
elif isinstance(other, np.ndarray):
return np.where(other == 1, other, NA)
return NotImplemented
# Logical ops using Kleene logic
def __and__(self, other):
if other is False:
return False
elif other is True or other is C_NA:
return NA
return NotImplemented
__rand__ = __and__
def __or__(self, other):
if other is True:
return True
elif other is False or other is C_NA:
return NA
return NotImplemented
__ror__ = __or__
def __xor__(self, other):
if other is False or other is True or other is C_NA:
return NA
return NotImplemented
__rxor__ = __xor__
__array_priority__ = 1000
_HANDLED_TYPES = (np.ndarray, numbers.Number, str, np.bool_)
def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
types = self._HANDLED_TYPES + (NAType,)
for x in inputs:
if not isinstance(x, types):
return NotImplemented
if method != "__call__":
raise ValueError(f"ufunc method '{method}' not supported for NA")
result = maybe_dispatch_ufunc_to_dunder_op(
self, ufunc, method, *inputs, **kwargs
)
if result is NotImplemented:
# For a NumPy ufunc that's not a binop, like np.logaddexp
index = [i for i, x in enumerate(inputs) if x is NA][0]
result = np.broadcast_arrays(*inputs)[index]
if result.ndim == 0:
result = result.item()
if ufunc.nout > 1:
result = (NA,) * ufunc.nout
return result
C_NA = NAType() # C-visible
NA = C_NA # Python-visible