PERF: JoinUnit.is_na

pandas/core/dtypes/missing.py

@@ -9,7 +9,7 @@
 
 from pandas._libs import lib
 import pandas._libs.missing as libmissing
-from pandas._libs.tslibs import NaT, iNaT
+from pandas._libs.tslibs import NaT, Period, iNaT
 from pandas._typing import ArrayLike, DtypeObj
 
 from pandas.core.dtypes.common import (
@@ -43,6 +43,9 @@
 isposinf_scalar = libmissing.isposinf_scalar
 isneginf_scalar = libmissing.isneginf_scalar
 
+nan_checker = np.isnan
+INF_AS_NA = False
+
 
 def isna(obj):
     """
@@ -188,6 +191,12 @@ def _use_inf_as_na(key):
     """
     inf_as_na = get_option(key)
     globals()["_isna"] = partial(_isna, inf_as_na=inf_as_na)
+    if inf_as_na:
+        globals()["nan_checker"] = lambda x: ~np.isfinite(x)
+        globals()["INF_AS_NA"] = True
+    else:
+        globals()["nan_checker"] = np.isnan
+        globals()["INF_AS_NA"] = False
 
 
 def _isna_ndarraylike(obj, inf_as_na: bool = False):
@@ -602,3 +611,31 @@ def is_valid_nat_for_dtype(obj, dtype: DtypeObj) -> bool:
 
     # must be PeriodDType
     return not isinstance(obj, (np.datetime64, np.timedelta64))
+
+
+def isna_all(arr: ArrayLike) -> bool:
+    """
+    Optimized equivalent to isna(arr).all()
+    """
+    total_len = len(arr)
+
+    # Usually it's enough to check but a small fraction of values to see if
+    #  a block is NOT null, chunks should help in such cases.
+    #  parameters 1000 and 40 were chosen arbitrarily
+    chunk_len = max(total_len // 40, 1000)
+
+    dtype = arr.dtype
+    if dtype.kind == "f":
+        checker = nan_checker
+
+    elif dtype.kind in ["m", "M"] or dtype.type is Period:
+        checker = lambda x: np.asarray(x.view("i8")) == iNaT
+
+    else:
+        checker = lambda x: _isna_ndarraylike(x, inf_as_na=INF_AS_NA)
+
+    for i in range(0, total_len, chunk_len):
+        if not checker(arr[i : i + chunk_len]).all():
+            return False
+
+    return True

pandas/core/internals/concat.py

@@ -21,7 +21,7 @@
     is_timedelta64_dtype,
 )
 from pandas.core.dtypes.concat import concat_compat
-from pandas.core.dtypes.missing import isna
+from pandas.core.dtypes.missing import isna_all
 
 import pandas.core.algorithms as algos
 from pandas.core.arrays import DatetimeArray, ExtensionArray
@@ -223,13 +223,8 @@ def is_na(self):
             values_flat = values
         else:
             values_flat = values.ravel(order="K")
-        total_len = values_flat.shape[0]
-        chunk_len = max(total_len // 40, 1000)
-        for i in range(0, total_len, chunk_len):
-            if not isna(values_flat[i : i + chunk_len]).all():
-                return False
 
-        return True
+        return isna_all(values_flat)
 
     def get_reindexed_values(self, empty_dtype, upcasted_na):
         if upcasted_na is None:
@@ -474,8 +469,8 @@ def _is_uniform_join_units(join_units: List[JoinUnit]) -> bool:
     #  cannot necessarily join
     return (
         # all blocks need to have the same type
-        all(type(ju.block) is type(join_units[0].block) for ju in join_units)
-        and  # noqa
+        all(type(ju.block) is type(join_units[0].block) for ju in join_units)  # noqa
+        and
         # no blocks that would get missing values (can lead to type upcasts)
         # unless we're an extension dtype.
         all(not ju.is_na or ju.block.is_extension for ju in join_units)