REF: simplify core.algorithms, reshape.cut (pandas-dev#29385)

pandas/core/algorithms.py

@@ -8,7 +8,7 @@
 
 import numpy as np
 
-from pandas._libs import algos, hashtable as htable, lib
+from pandas._libs import Timestamp, algos, hashtable as htable, lib
 from pandas._libs.tslib import iNaT
 from pandas.util._decorators import Appender, Substitution, deprecate_kwarg
 
@@ -1440,7 +1440,9 @@ def _take_nd_object(arr, indexer, out, axis: int, fill_value, mask_info):
 }
 
 
-def _get_take_nd_function(ndim, arr_dtype, out_dtype, axis: int = 0, mask_info=None):
+def _get_take_nd_function(
+    ndim: int, arr_dtype, out_dtype, axis: int = 0, mask_info=None
+):
     if ndim <= 2:
         tup = (arr_dtype.name, out_dtype.name)
         if ndim == 1:
@@ -1474,7 +1476,7 @@ def func2(arr, indexer, out, fill_value=np.nan):
     return func2
 
 
-def take(arr, indices, axis=0, allow_fill: bool = False, fill_value=None):
+def take(arr, indices, axis: int = 0, allow_fill: bool = False, fill_value=None):
     """
     Take elements from an array.
 
@@ -1568,13 +1570,7 @@ def take(arr, indices, axis=0, allow_fill: bool = False, fill_value=None):
 
 
 def take_nd(
-    arr,
-    indexer,
-    axis=0,
-    out=None,
-    fill_value=np.nan,
-    mask_info=None,
-    allow_fill: bool = True,
+    arr, indexer, axis: int = 0, out=None, fill_value=np.nan, allow_fill: bool = True
 ):
     """
     Specialized Cython take which sets NaN values in one pass
@@ -1597,10 +1593,6 @@ def take_nd(
         maybe_promote to determine this type for any fill_value
     fill_value : any, default np.nan
         Fill value to replace -1 values with
-    mask_info : tuple of (ndarray, boolean)
-        If provided, value should correspond to:
-            (indexer != -1, (indexer != -1).any())
-        If not provided, it will be computed internally if necessary
     allow_fill : boolean, default True
         If False, indexer is assumed to contain no -1 values so no filling
         will be done.  This short-circuits computation of a mask.  Result is
@@ -1611,6 +1603,7 @@ def take_nd(
     subarray : array-like
         May be the same type as the input, or cast to an ndarray.
     """
+    mask_info = None
 
     if is_extension_array_dtype(arr):
         return arr.take(indexer, fill_value=fill_value, allow_fill=allow_fill)
@@ -1632,12 +1625,9 @@ def take_nd(
             dtype, fill_value = maybe_promote(arr.dtype, fill_value)
             if dtype != arr.dtype and (out is None or out.dtype != dtype):
                 # check if promotion is actually required based on indexer
-                if mask_info is not None:
-                    mask, needs_masking = mask_info
-                else:
-                    mask = indexer == -1
-                    needs_masking = mask.any()
-                    mask_info = mask, needs_masking
+                mask = indexer == -1
+                needs_masking = mask.any()
+                mask_info = mask, needs_masking
                 if needs_masking:
                     if out is not None and out.dtype != dtype:
                         raise TypeError("Incompatible type for fill_value")
@@ -1818,12 +1808,12 @@ def searchsorted(arr, value, side="left", sorter=None):
     elif not (
         is_object_dtype(arr) or is_numeric_dtype(arr) or is_categorical_dtype(arr)
     ):
-        from pandas.core.series import Series
-
         # E.g. if `arr` is an array with dtype='datetime64[ns]'
         # and `value` is a pd.Timestamp, we may need to convert value
-        value_ser = Series(value)._values
+        value_ser = array([value]) if is_scalar(value) else array(value)
         value = value_ser[0] if is_scalar(value) else value_ser
+        if isinstance(value, Timestamp) and value.tzinfo is None:
+            value = value.to_datetime64()
 
     result = arr.searchsorted(value, side=side, sorter=sorter)
     return result

pandas/core/reshape/tile.py

@@ -4,6 +4,7 @@
 import numpy as np
 
 from pandas._libs import Timedelta, Timestamp
+from pandas._libs.interval import Interval
 from pandas._libs.lib import infer_dtype
 
 from pandas.core.dtypes.common import (
@@ -18,17 +19,10 @@
     is_scalar,
     is_timedelta64_dtype,
 )
+from pandas.core.dtypes.generic import ABCSeries
 from pandas.core.dtypes.missing import isna
 
-from pandas import (
-    Categorical,
-    Index,
-    Interval,
-    IntervalIndex,
-    Series,
-    to_datetime,
-    to_timedelta,
-)
+from pandas import Categorical, Index, IntervalIndex, to_datetime, to_timedelta
 import pandas.core.algorithms as algos
 import pandas.core.nanops as nanops
 
@@ -206,7 +200,8 @@ def cut(
     # NOTE: this binning code is changed a bit from histogram for var(x) == 0
 
     # for handling the cut for datetime and timedelta objects
-    x_is_series, series_index, name, x = _preprocess_for_cut(x)
+    original = x
+    x = _preprocess_for_cut(x)
     x, dtype = _coerce_to_type(x)
 
     if not np.iterable(bins):
@@ -268,9 +263,7 @@ def cut(
         duplicates=duplicates,
     )
 
-    return _postprocess_for_cut(
-        fac, bins, retbins, x_is_series, series_index, name, dtype
-    )
+    return _postprocess_for_cut(fac, bins, retbins, dtype, original)
 
 
 def qcut(
@@ -333,8 +326,8 @@ def qcut(
     >>> pd.qcut(range(5), 4, labels=False)
     array([0, 0, 1, 2, 3])
     """
-    x_is_series, series_index, name, x = _preprocess_for_cut(x)
-
+    original = x
+    x = _preprocess_for_cut(x)
     x, dtype = _coerce_to_type(x)
 
     if is_integer(q):
@@ -352,9 +345,7 @@ def qcut(
         duplicates=duplicates,
     )
 
-    return _postprocess_for_cut(
-        fac, bins, retbins, x_is_series, series_index, name, dtype
-    )
+    return _postprocess_for_cut(fac, bins, retbins, dtype, original)
 
 
 def _bins_to_cuts(
@@ -544,13 +535,6 @@ def _preprocess_for_cut(x):
     input to array, strip the index information and store it
     separately
     """
-    x_is_series = isinstance(x, Series)
-    series_index = None
-    name = None
-
-    if x_is_series:
-        series_index = x.index
-        name = x.name
 
     # Check that the passed array is a Pandas or Numpy object
     # We don't want to strip away a Pandas data-type here (e.g. datetimetz)
@@ -560,19 +544,17 @@ def _preprocess_for_cut(x):
     if x.ndim != 1:
         raise ValueError("Input array must be 1 dimensional")
 
-    return x_is_series, series_index, name, x
+    return x
 
 
-def _postprocess_for_cut(
-    fac, bins, retbins: bool, x_is_series, series_index, name, dtype
-):
+def _postprocess_for_cut(fac, bins, retbins: bool, dtype, original):
     """
     handles post processing for the cut method where
     we combine the index information if the originally passed
     datatype was a series
     """
-    if x_is_series:
-        fac = Series(fac, index=series_index, name=name)
+    if isinstance(original, ABCSeries):
+        fac = original._constructor(fac, index=original.index, name=original.name)
 
     if not retbins:
         return fac