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507 lines
14 KiB
507 lines
14 KiB
from decimal import Decimal
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import numbers
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from sys import maxsize
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import cython
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from cython import Py_ssize_t
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import numpy as np
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cimport numpy as cnp
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from numpy cimport (
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float64_t,
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int64_t,
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ndarray,
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uint8_t,
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)
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cnp.import_array()
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from pandas._libs cimport util
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from pandas._libs.tslibs.nattype cimport (
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c_NaT as NaT,
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checknull_with_nat,
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is_dt64nat,
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is_td64nat,
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)
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from pandas._libs.tslibs.np_datetime cimport (
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get_datetime64_unit,
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get_datetime64_value,
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get_timedelta64_value,
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)
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from pandas._libs.ops_dispatch import maybe_dispatch_ufunc_to_dunder_op
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cdef:
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float64_t INF = <float64_t>np.inf
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float64_t NEGINF = -INF
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int64_t NPY_NAT = util.get_nat()
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bint is_32bit = maxsize <= 2 ** 32
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type cDecimal = Decimal # for faster isinstance checks
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cpdef bint is_matching_na(object left, object right, bint nan_matches_none=False):
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"""
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Check if two scalars are both NA of matching types.
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Parameters
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----------
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left : Any
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right : Any
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nan_matches_none : bool, default False
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For backwards compatibility, consider NaN as matching None.
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Returns
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-------
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bool
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"""
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if left is None:
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if nan_matches_none and util.is_nan(right):
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return True
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return right is None
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elif left is C_NA:
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return right is C_NA
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elif left is NaT:
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return right is NaT
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elif util.is_float_object(left):
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if nan_matches_none and right is None and util.is_nan(left):
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return True
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return (
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util.is_nan(left)
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and util.is_float_object(right)
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and util.is_nan(right)
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)
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elif util.is_complex_object(left):
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return (
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util.is_nan(left)
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and util.is_complex_object(right)
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and util.is_nan(right)
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)
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elif util.is_datetime64_object(left):
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return (
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get_datetime64_value(left) == NPY_NAT
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and util.is_datetime64_object(right)
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and get_datetime64_value(right) == NPY_NAT
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and get_datetime64_unit(left) == get_datetime64_unit(right)
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)
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elif util.is_timedelta64_object(left):
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return (
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get_timedelta64_value(left) == NPY_NAT
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and util.is_timedelta64_object(right)
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and get_timedelta64_value(right) == NPY_NAT
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and get_datetime64_unit(left) == get_datetime64_unit(right)
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)
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elif is_decimal_na(left):
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return is_decimal_na(right)
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return False
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cpdef bint checknull(object val, bint inf_as_na=False):
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"""
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Return boolean describing of the input is NA-like, defined here as any
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of:
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- None
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- nan
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- NaT
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- np.datetime64 representation of NaT
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- np.timedelta64 representation of NaT
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- NA
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- Decimal("NaN")
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Parameters
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----------
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val : object
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inf_as_na : bool, default False
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Whether to treat INF and -INF as NA values.
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Returns
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-------
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bool
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"""
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if val is None or val is NaT or val is C_NA:
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return True
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elif util.is_float_object(val) or util.is_complex_object(val):
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if val != val:
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return True
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elif inf_as_na:
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return val == INF or val == NEGINF
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return False
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elif util.is_timedelta64_object(val):
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return get_timedelta64_value(val) == NPY_NAT
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elif util.is_datetime64_object(val):
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return get_datetime64_value(val) == NPY_NAT
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else:
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return is_decimal_na(val)
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cdef inline bint is_decimal_na(object val):
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"""
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Is this a decimal.Decimal object Decimal("NAN").
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"""
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return isinstance(val, cDecimal) and val != val
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@cython.wraparound(False)
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@cython.boundscheck(False)
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cpdef ndarray[uint8_t] isnaobj(ndarray arr, bint inf_as_na=False):
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"""
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Return boolean mask denoting which elements of a 1-D array are na-like,
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according to the criteria defined in `checknull`:
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- None
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- nan
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- NaT
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- np.datetime64 representation of NaT
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- np.timedelta64 representation of NaT
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- NA
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- Decimal("NaN")
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Parameters
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----------
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arr : ndarray
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Returns
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-------
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result : ndarray (dtype=np.bool_)
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"""
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cdef:
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Py_ssize_t i, n
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object val
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ndarray[uint8_t] result
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assert arr.ndim == 1, "'arr' must be 1-D."
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n = len(arr)
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result = np.empty(n, dtype=np.uint8)
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for i in range(n):
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val = arr[i]
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result[i] = checknull(val, inf_as_na=inf_as_na)
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return result.view(np.bool_)
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@cython.wraparound(False)
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@cython.boundscheck(False)
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def isnaobj2d(arr: ndarray, inf_as_na: bool = False) -> ndarray:
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"""
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Return boolean mask denoting which elements of a 2-D array are na-like,
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according to the criteria defined in `checknull`:
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- None
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- nan
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- NaT
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- np.datetime64 representation of NaT
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- np.timedelta64 representation of NaT
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- NA
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- Decimal("NaN")
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Parameters
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----------
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arr : ndarray
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Returns
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-------
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result : ndarray (dtype=np.bool_)
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"""
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cdef:
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Py_ssize_t i, j, n, m
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object val
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ndarray[uint8_t, ndim=2] result
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assert arr.ndim == 2, "'arr' must be 2-D."
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n, m = (<object>arr).shape
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result = np.zeros((n, m), dtype=np.uint8)
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for i in range(n):
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for j in range(m):
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val = arr[i, j]
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if checknull(val, inf_as_na=inf_as_na):
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result[i, j] = 1
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return result.view(np.bool_)
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def isposinf_scalar(val: object) -> bool:
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return util.is_float_object(val) and val == INF
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def isneginf_scalar(val: object) -> bool:
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return util.is_float_object(val) and val == NEGINF
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cdef inline bint is_null_datetime64(v):
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# determine if we have a null for a datetime (or integer versions),
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# excluding np.timedelta64('nat')
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if checknull_with_nat(v) or is_dt64nat(v):
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return True
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return False
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cdef inline bint is_null_timedelta64(v):
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# determine if we have a null for a timedelta (or integer versions),
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# excluding np.datetime64('nat')
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if checknull_with_nat(v) or is_td64nat(v):
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return True
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return False
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cdef bint checknull_with_nat_and_na(object obj):
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# See GH#32214
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return checknull_with_nat(obj) or obj is C_NA
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@cython.wraparound(False)
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@cython.boundscheck(False)
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def is_float_nan(values: ndarray) -> ndarray:
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"""
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True for elements which correspond to a float nan
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Returns
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-------
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ndarray[bool]
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"""
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cdef:
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ndarray[uint8_t] result
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Py_ssize_t i, N
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object val
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N = len(values)
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result = np.zeros(N, dtype=np.uint8)
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for i in range(N):
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val = values[i]
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if util.is_nan(val):
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result[i] = True
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return result.view(bool)
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@cython.wraparound(False)
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@cython.boundscheck(False)
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def is_numeric_na(values: ndarray) -> ndarray:
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"""
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Check for NA values consistent with IntegerArray/FloatingArray.
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Similar to a vectorized is_valid_na_for_dtype restricted to numeric dtypes.
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Returns
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-------
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ndarray[bool]
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"""
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cdef:
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ndarray[uint8_t] result
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Py_ssize_t i, N
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object val
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N = len(values)
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result = np.zeros(N, dtype=np.uint8)
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for i in range(N):
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val = values[i]
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if checknull(val):
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if val is None or val is C_NA or util.is_nan(val) or is_decimal_na(val):
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result[i] = True
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else:
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raise TypeError(f"'values' contains non-numeric NA {val}")
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return result.view(bool)
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# -----------------------------------------------------------------------------
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# Implementation of NA singleton
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def _create_binary_propagating_op(name, is_divmod=False):
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def method(self, other):
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if (other is C_NA or isinstance(other, str)
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or isinstance(other, (numbers.Number, np.bool_))
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or isinstance(other, np.ndarray) and not other.shape):
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# Need the other.shape clause to handle NumPy scalars,
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# since we do a setitem on `out` below, which
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# won't work for NumPy scalars.
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if is_divmod:
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return NA, NA
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else:
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return NA
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elif isinstance(other, np.ndarray):
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out = np.empty(other.shape, dtype=object)
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out[:] = NA
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if is_divmod:
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return out, out.copy()
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else:
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return out
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return NotImplemented
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method.__name__ = name
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return method
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def _create_unary_propagating_op(name: str):
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def method(self):
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return NA
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method.__name__ = name
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return method
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cdef class C_NAType:
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pass
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class NAType(C_NAType):
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"""
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NA ("not available") missing value indicator.
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.. warning::
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Experimental: the behaviour of NA can still change without warning.
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.. versionadded:: 1.0.0
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The NA singleton is a missing value indicator defined by pandas. It is
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used in certain new extension dtypes (currently the "string" dtype).
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"""
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_instance = None
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def __new__(cls, *args, **kwargs):
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if NAType._instance is None:
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NAType._instance = C_NAType.__new__(cls, *args, **kwargs)
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return NAType._instance
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def __repr__(self) -> str:
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return "<NA>"
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def __format__(self, format_spec) -> str:
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try:
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return self.__repr__().__format__(format_spec)
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except ValueError:
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return self.__repr__()
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def __bool__(self):
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raise TypeError("boolean value of NA is ambiguous")
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def __hash__(self):
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# GH 30013: Ensure hash is large enough to avoid hash collisions with integers
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exponent = 31 if is_32bit else 61
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return 2 ** exponent - 1
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def __reduce__(self):
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return "NA"
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# Binary arithmetic and comparison ops -> propagate
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__add__ = _create_binary_propagating_op("__add__")
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__radd__ = _create_binary_propagating_op("__radd__")
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__sub__ = _create_binary_propagating_op("__sub__")
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__rsub__ = _create_binary_propagating_op("__rsub__")
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__mul__ = _create_binary_propagating_op("__mul__")
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__rmul__ = _create_binary_propagating_op("__rmul__")
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__matmul__ = _create_binary_propagating_op("__matmul__")
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__rmatmul__ = _create_binary_propagating_op("__rmatmul__")
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__truediv__ = _create_binary_propagating_op("__truediv__")
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__rtruediv__ = _create_binary_propagating_op("__rtruediv__")
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__floordiv__ = _create_binary_propagating_op("__floordiv__")
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__rfloordiv__ = _create_binary_propagating_op("__rfloordiv__")
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__mod__ = _create_binary_propagating_op("__mod__")
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__rmod__ = _create_binary_propagating_op("__rmod__")
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__divmod__ = _create_binary_propagating_op("__divmod__", is_divmod=True)
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__rdivmod__ = _create_binary_propagating_op("__rdivmod__", is_divmod=True)
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# __lshift__ and __rshift__ are not implemented
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__eq__ = _create_binary_propagating_op("__eq__")
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__ne__ = _create_binary_propagating_op("__ne__")
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__le__ = _create_binary_propagating_op("__le__")
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__lt__ = _create_binary_propagating_op("__lt__")
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__gt__ = _create_binary_propagating_op("__gt__")
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__ge__ = _create_binary_propagating_op("__ge__")
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# Unary ops
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__neg__ = _create_unary_propagating_op("__neg__")
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__pos__ = _create_unary_propagating_op("__pos__")
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__abs__ = _create_unary_propagating_op("__abs__")
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__invert__ = _create_unary_propagating_op("__invert__")
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# pow has special
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def __pow__(self, other):
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if other is C_NA:
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return NA
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elif isinstance(other, (numbers.Number, np.bool_)):
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if other == 0:
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# returning positive is correct for +/- 0.
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return type(other)(1)
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else:
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return NA
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elif isinstance(other, np.ndarray):
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return np.where(other == 0, other.dtype.type(1), NA)
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return NotImplemented
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def __rpow__(self, other):
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if other is C_NA:
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return NA
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elif isinstance(other, (numbers.Number, np.bool_)):
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if other == 1:
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return other
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else:
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return NA
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elif isinstance(other, np.ndarray):
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return np.where(other == 1, other, NA)
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return NotImplemented
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# Logical ops using Kleene logic
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def __and__(self, other):
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if other is False:
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return False
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elif other is True or other is C_NA:
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return NA
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return NotImplemented
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__rand__ = __and__
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def __or__(self, other):
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if other is True:
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return True
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elif other is False or other is C_NA:
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return NA
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return NotImplemented
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__ror__ = __or__
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def __xor__(self, other):
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if other is False or other is True or other is C_NA:
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return NA
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return NotImplemented
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__rxor__ = __xor__
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__array_priority__ = 1000
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_HANDLED_TYPES = (np.ndarray, numbers.Number, str, np.bool_)
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def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
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types = self._HANDLED_TYPES + (NAType,)
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for x in inputs:
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if not isinstance(x, types):
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return NotImplemented
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if method != "__call__":
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raise ValueError(f"ufunc method '{method}' not supported for NA")
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result = maybe_dispatch_ufunc_to_dunder_op(
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self, ufunc, method, *inputs, **kwargs
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)
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if result is NotImplemented:
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# For a NumPy ufunc that's not a binop, like np.logaddexp
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index = [i for i, x in enumerate(inputs) if x is NA][0]
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result = np.broadcast_arrays(*inputs)[index]
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if result.ndim == 0:
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result = result.item()
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if ufunc.nout > 1:
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result = (NA,) * ufunc.nout
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return result
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C_NA = NAType() # C-visible
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NA = C_NA # Python-visible
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