Avoid .base

Author: jbrockmendel

pandas/_libs/hashtable_func_helper.pxi.in

@@ -347,8 +347,8 @@ def mode_{{dtype}}({{ctype}}[:] values, bint dropna):
 
     kh_destroy_{{table_type}}(table)
 
-    # Note: For reasons unknown, slicing modes.base works but modes[:j+1].base
-    #  returns an object with an incorrect length
-    return modes.base[:j + 1]  # `.base` to access underlying np.ndarray
+    # Note: For reasons unknown, slicing np.asarray(modes) works but
+    #  np.asarray(modes[:j+1]) returns an object with an incorrect length
+    return np.asarray(modes)[:j + 1]
 
 {{endfor}}

pandas/_libs/lib.pyx

@@ -284,7 +284,7 @@ def dicts_to_array(dicts: list, columns: list):
             else:
                 result[i, j] = onan
 
-    return result.base  # `.base` to access underlying np.ndarray
+    return np.asarray(result)
 
 
 def fast_zip(list ndarrays):
@@ -353,7 +353,7 @@ def get_reverse_indexer(int64_t[:] indexer, Py_ssize_t length):
         if idx != -1:
             rev_indexer[idx] = i
 
-    return rev_indexer.base  # `.base` to access underlying np.ndarray
+    return np.asarray(rev_indexer)
 
 
 @cython.wraparound(False)
@@ -534,7 +534,7 @@ def astype_unicode(arr: ndarray, skipna: bool=False) -> ndarray[object]:
 
         result[i] = arr_i
 
-    return result.base  # `.base` to access underlying np.ndarray
+    return np.asarray(result)
 
 
 @cython.wraparound(False)
@@ -569,7 +569,7 @@ def astype_str(arr: ndarray, skipna: bool = False) -> ndarray[object]:
 
         result[i] = arr_i
 
-    return result.base  # `.base` to access underlying np.ndarray
+    return np.asarray(result)
 
 
 @cython.wraparound(False)
@@ -695,7 +695,7 @@ def row_bool_subset(float64_t[:, :] values,
                 out[pos, j] = values[i, j]
             pos += 1
 
-    return out.base  # `.base` to access underlying np.ndarray
+    return np.asarray(out)
 
 
 @cython.boundscheck(False)
@@ -717,7 +717,7 @@ def row_bool_subset_object(object[:, :] values,
                 out[pos, j] = values[i, j]
             pos += 1
 
-    return out.base  # `.base` to access underlying np.ndarray
+    return np.asarray(out)
 
 
 @cython.boundscheck(False)
@@ -1941,8 +1941,7 @@ def maybe_convert_objects(object[:] objects, bint try_float=0,
 
     if objects is None:
         # Without explicitly raising, groupby.ops _aggregate_series_pure_python
-        #  can pass None and incorrectly raise an AttributeError when trying
-        #  to access `objects.base` below.
+        #  can pass None and end up returning np.array(None) below
         raise TypeError
 
     n = len(objects)
@@ -2053,7 +2052,7 @@ def maybe_convert_objects(object[:] objects, bint try_float=0,
     if seen.datetimetz_:
         if len({getattr(val, 'tzinfo', None) for val in objects}) == 1:
             from pandas import DatetimeIndex
-            return DatetimeIndex(objects.base)
+            return DatetimeIndex(np.asarray(objects))
         seen.object_ = 1
 
     if not seen.object_:
@@ -2118,7 +2117,7 @@ def maybe_convert_objects(object[:] objects, bint try_float=0,
                 elif seen.is_bool:
                     return bools.view(np.bool_)
 
-    return objects.base  # `.base` to access underlying np.ndarray
+    return np.asarray(objects)
 
 
 def map_infer_mask(ndarray arr, object f, uint8_t[:] mask, bint convert=1):
@@ -2161,7 +2160,7 @@ def map_infer_mask(ndarray arr, object f, uint8_t[:] mask, bint convert=1):
                                      convert_datetime=0,
                                      convert_timedelta=0)
 
-    return result.base  # `.base` to access underlying np.ndarray
+    return np.asarray(result)
 
 
 @cython.wraparound(False)
@@ -2203,7 +2202,7 @@ def map_infer(ndarray arr, object f, bint convert=1):
                                      convert_datetime=0,
                                      convert_timedelta=0)
 
-    return result.base  # `.base` to access underlying np.ndarray
+    return np.asarray(result)
 
 
 def to_object_array(rows: list, min_width: int = 0):
@@ -2245,7 +2244,7 @@ def to_object_array(rows: list, min_width: int = 0):
         for j in range(len(row)):
             result[i, j] = row[j]
 
-    return result.base  # `.base` to access underlying np.ndarray
+    return np.asarray(result)
 
 
 def tuples_to_object_array(ndarray[object] tuples):
@@ -2262,7 +2261,7 @@ def tuples_to_object_array(ndarray[object] tuples):
         for j in range(k):
             result[i, j] = tup[j]
 
-    return result.base  # `.base` to access underlying np.ndarray
+    return np.asarray(result)
 
 
 def to_object_array_tuples(rows: list):
@@ -2293,7 +2292,7 @@ def to_object_array_tuples(rows: list):
             for j in range(len(row)):
                 result[i, j] = row[j]
 
-    return result.base  # `.base` to access underlying np.ndarray
+    return np.asarray(result)
 
 
 @cython.wraparound(False)