REF: combine isnaobj+isnaobj_old (#44505)

Author: jbrockmendel

pandas/_libs/missing.pxd

@@ -6,9 +6,9 @@ from numpy cimport (
 
 cpdef bint is_matching_na(object left, object right, bint nan_matches_none=*)
 
-cpdef bint checknull(object val)
+cpdef bint checknull(object val, bint inf_as_na=*)
 cpdef bint checknull_old(object val)
-cpdef ndarray[uint8_t] isnaobj(ndarray arr)
+cpdef ndarray[uint8_t] isnaobj(ndarray arr, bint inf_as_na=*)
 
 cdef bint is_null_datetime64(v)
 cdef bint is_null_timedelta64(v)

pandas/_libs/missing.pyx

@@ -99,7 +99,7 @@ cpdef bint is_matching_na(object left, object right, bint nan_matches_none=False
     return False
 
 
-cpdef bint checknull(object val):
+cpdef bint checknull(object val, bint inf_as_na=False):
     """
     Return boolean describing of the input is NA-like, defined here as any
     of:
@@ -114,19 +114,16 @@ cpdef bint checknull(object val):
     Parameters
     ----------
     val : object
+    inf_as_na : bool, default False
+        Whether to treat INF and -INF as NA values.
 
     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)
+        or is_null_datetimelike(val, inat_is_null=False, inf_as_na=inf_as_na)
         or is_decimal_na(val)
     )
 
@@ -139,42 +136,12 @@ cdef inline bint is_decimal_na(object val):
 
 
 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
-     - NA
-     - Decimal("NaN")
-
-    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
+    return checknull(val, inf_as_na=True)
 
 
 @cython.wraparound(False)
 @cython.boundscheck(False)
-cpdef ndarray[uint8_t] isnaobj(ndarray arr):
+cpdef ndarray[uint8_t] isnaobj(ndarray arr, bint inf_as_na=False):
     """
     Return boolean mask denoting which elements of a 1-D array are na-like,
     according to the criteria defined in `checknull`:
@@ -205,53 +172,19 @@ cpdef ndarray[uint8_t] isnaobj(ndarray arr):
     result = np.empty(n, dtype=np.uint8)
     for i in range(n):
         val = arr[i]
-        result[i] = checknull(val)
+        result[i] = checknull(val, inf_as_na=inf_as_na)
     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
-     - Decimal("NaN")
-
-    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] = (
-            checknull(val)
-            or util.is_float_object(val) and (val == INF or val == NEGINF)
-        )
-    return result.view(np.bool_)
+    return isnaobj(arr, inf_as_na=True)
 
 
 @cython.wraparound(False)
 @cython.boundscheck(False)
-def isnaobj2d(arr: ndarray) -> ndarray:
+def isnaobj2d(arr: ndarray, inf_as_na: bool = False) -> ndarray:
     """
     Return boolean mask denoting which elements of a 2-D array are na-like,
     according to the criteria defined in `checknull`:
@@ -270,11 +203,6 @@ def isnaobj2d(arr: ndarray) -> 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
@@ -288,55 +216,15 @@ def isnaobj2d(arr: ndarray) -> ndarray:
     for i in range(n):
         for j in range(m):
             val = arr[i, j]
-            if checknull(val):
+            if checknull(val, inf_as_na=inf_as_na):
                 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
-     - NA
-     - Decimal("NaN")
-
-    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_)
+    return isnaobj2d(arr, inf_as_na=True)
 
 
 def isposinf_scalar(val: object) -> bool:

pandas/_libs/tslibs/nattype.pxd

@@ -18,4 +18,4 @@ cdef _NaT c_NaT
 cdef bint checknull_with_nat(object val)
 cdef bint is_dt64nat(object val)
 cdef bint is_td64nat(object val)
-cpdef bint is_null_datetimelike(object val, bint inat_is_null=*)
+cpdef bint is_null_datetimelike(object val, bint inat_is_null=*, bint inf_as_na=*)

pandas/_libs/tslibs/nattype.pyi

@@ -12,7 +12,9 @@ NaT: NaTType
 iNaT: int
 nat_strings: set[str]
 
-def is_null_datetimelike(val: object, inat_is_null: bool = ...) -> bool: ...
+def is_null_datetimelike(
+    val: object, inat_is_null: bool = ..., inf_as_na: bool = ...
+) -> bool: ...
 
 class NaTType(datetime):
     value: np.int64

pandas/_libs/tslibs/nattype.pyx

@@ -1201,6 +1201,7 @@ cdef inline bint checknull_with_nat(object val):
     """
     return val is None or util.is_nan(val) or val is c_NaT
 
+
 cdef inline bint is_dt64nat(object val):
     """
     Is this a np.datetime64 object np.datetime64("NaT").
@@ -1209,6 +1210,7 @@ cdef inline bint is_dt64nat(object val):
         return get_datetime64_value(val) == NPY_NAT
     return False
 
+
 cdef inline bint is_td64nat(object val):
     """
     Is this a np.timedelta64 object np.timedelta64("NaT").
@@ -1218,7 +1220,14 @@ cdef inline bint is_td64nat(object val):
     return False
 
 
-cpdef bint is_null_datetimelike(object val, bint inat_is_null=True):
+cdef:
+    cnp.float64_t INF = <cnp.float64_t>np.inf
+    cnp.float64_t NEGINF = -INF
+
+
+cpdef bint is_null_datetimelike(
+    object val, bint inat_is_null=True, bint inf_as_na=False
+):
     """
     Determine if we have a null for a timedelta/datetime (or integer versions).
 
@@ -1227,6 +1236,8 @@ cpdef bint is_null_datetimelike(object val, bint inat_is_null=True):
     val : object
     inat_is_null : bool, default True
         Whether to treat integer iNaT value as null
+    inf_as_na : bool, default False
+        Whether to treat INF or -INF value as null.
 
     Returns
     -------
@@ -1237,7 +1248,11 @@ cpdef bint is_null_datetimelike(object val, bint inat_is_null=True):
     elif val is c_NaT:
         return True
     elif util.is_float_object(val) or util.is_complex_object(val):
-        return val != val
+        if val != val:
+            return True
+        if inf_as_na:
+            return val == INF or val == NEGINF
+        return False
     elif util.is_timedelta64_object(val):
         return get_timedelta64_value(val) == NPY_NAT
     elif util.is_datetime64_object(val):

pandas/core/dtypes/missing.py

@@ -242,7 +242,7 @@ def _isna_array(values: ArrayLike, inf_as_na: bool = False):
     if not isinstance(values, np.ndarray):
         # i.e. ExtensionArray
         if inf_as_na and is_categorical_dtype(dtype):
-            result = libmissing.isnaobj_old(values.to_numpy())
+            result = libmissing.isnaobj(values.to_numpy(), inf_as_na=inf_as_na)
         else:
             result = values.isna()
     elif is_string_dtype(dtype):
@@ -268,10 +268,7 @@ def _isna_string_dtype(values: np.ndarray, inf_as_na: bool) -> np.ndarray:
         result = np.zeros(values.shape, dtype=bool)
     else:
         result = np.empty(shape, dtype=bool)
-        if inf_as_na:
-            vec = libmissing.isnaobj_old(values.ravel())
-        else:
-            vec = libmissing.isnaobj(values.ravel())
+        vec = libmissing.isnaobj(values.ravel(), inf_as_na=inf_as_na)
 
         result[...] = vec.reshape(shape)