REF: Replace lib.list_to_object_array with construct_1d_array_from_listlike

Author: toobaz

pandas/_libs/lib.pyx

@@ -148,23 +148,6 @@ def item_from_zerodim(object val):
     return util.unbox_if_zerodim(val)
 
 
-@cython.wraparound(False)
-@cython.boundscheck(False)
-cpdef ndarray[object] list_to_object_array(list obj):
-    """
-    Convert list to object ndarray. Seriously can\'t believe
-    I had to write this function.
-    """
-    cdef:
-        Py_ssize_t i, n = len(obj)
-        ndarray[object] arr = np.empty(n, dtype=object)
-
-    for i in range(n):
-        arr[i] = obj[i]
-
-    return arr
-
-
 @cython.wraparound(False)
 @cython.boundscheck(False)
 def fast_unique(ndarray[object] values):

pandas/_libs/src/inference.pyx

@@ -7,6 +7,8 @@ from tslibs.conversion cimport convert_to_tsobject
 from tslibs.timedeltas cimport convert_to_timedelta64
 from tslibs.timezones cimport get_timezone
 from datetime import datetime, timedelta
+from pandas.core.dtypes.cast import construct_1d_array_from_listlike
+
 iNaT = util.get_nat()
 
 cdef bint PY2 = sys.version_info[0] == 2
@@ -349,7 +351,7 @@ def infer_dtype(object value, bint skipna=False):
     else:
         if not isinstance(value, list):
             value = list(value)
-        values = list_to_object_array(value)
+        values = construct_1d_array_from_listlike(value)
 
     values = getattr(values, 'values', values)
     val = _try_infer_map(values)

pandas/core/algorithms.py

@@ -6,7 +6,8 @@
 from warnings import warn, catch_warnings
 import numpy as np
 
-from pandas.core.dtypes.cast import maybe_promote
+from pandas.core.dtypes.cast import (
+    maybe_promote, construct_1d_array_from_listlike)
 from pandas.core.dtypes.generic import (
     ABCSeries, ABCIndex,
     ABCIndexClass, ABCCategorical)
@@ -171,7 +172,7 @@ def _ensure_arraylike(values):
         if inferred in ['mixed', 'string', 'unicode']:
             if isinstance(values, tuple):
                 values = list(values)
-            values = lib.list_to_object_array(values)
+            values = construct_1d_array_from_listlike(values)
         else:
             values = np.asarray(values)
     return values
@@ -401,7 +402,7 @@ def isin(comps, values):
                         .format(values_type=type(values).__name__))
 
     if not isinstance(values, (ABCIndex, ABCSeries, np.ndarray)):
-        values = lib.list_to_object_array(list(values))
+        values = construct_1d_array_from_listlike(list(values))
 
     comps, dtype, _ = _ensure_data(comps)
     values, _, _ = _ensure_data(values, dtype=dtype)

pandas/core/dtypes/cast.py

@@ -42,7 +42,7 @@ def maybe_convert_platform(values):
     """ try to do platform conversion, allow ndarray or list here """
 
     if isinstance(values, (list, tuple)):
-        values = lib.list_to_object_array(list(values))
+        values = construct_1d_array_from_listlike(list(values))
     if getattr(values, 'dtype', None) == np.object_:
         if hasattr(values, '_values'):
             values = values._values

pandas/core/ops.py

@@ -33,7 +33,8 @@
     is_list_like,
     is_scalar,
     _ensure_object)
-from pandas.core.dtypes.cast import maybe_upcast_putmask, find_common_type
+from pandas.core.dtypes.cast import (
+    maybe_upcast_putmask, find_common_type, construct_1d_array_from_listlike)
 from pandas.core.dtypes.generic import (
     ABCSeries,
     ABCDataFrame,
@@ -750,7 +751,7 @@ def wrapper(left, right, name=name, na_op=na_op):
 
 def _comp_method_OBJECT_ARRAY(op, x, y):
     if isinstance(y, list):
-        y = lib.list_to_object_array(y)
+        y = construct_1d_array_from_listlike(y)
     if isinstance(y, (np.ndarray, ABCSeries, ABCIndex)):
         if not is_object_dtype(y.dtype):
             y = y.astype(np.object_)
@@ -901,7 +902,7 @@ def na_op(x, y):
             result = op(x, y)
         except TypeError:
             if isinstance(y, list):
-                y = lib.list_to_object_array(y)
+                y = construct_1d_array_from_listlike(y)
 
             if isinstance(y, (np.ndarray, ABCSeries)):
                 if (is_bool_dtype(x.dtype) and is_bool_dtype(y.dtype)):

pandas/tests/frame/test_constructors.py

@@ -21,8 +21,8 @@
                     MultiIndex, Timedelta, Timestamp,
                     date_range, Categorical)
 import pandas as pd
-import pandas._libs.lib as lib
 import pandas.util.testing as tm
+from pandas.core.dtypes.cast import construct_1d_array_from_listlike
 
 from pandas.tests.frame.common import TestData
 
@@ -1199,7 +1199,7 @@ def test_constructor_from_items(self):
             DataFrame.from_items(row_items, orient='index')
 
         # orient='index', but thar be tuples
-        arr = lib.list_to_object_array(
+        arr = construct_1d_array_from_listlike(
             [('bar', 'baz')] * len(self.mixed_frame))
         self.mixed_frame['foo'] = arr
         row_items = [(idx, list(self.mixed_frame.xs(idx)))

pandas/tests/indexes/test_multi.py

@@ -18,7 +18,7 @@
 from pandas.errors import PerformanceWarning, UnsortedIndexError
 from pandas.core.dtypes.dtypes import CategoricalDtype
 from pandas.core.indexes.base import InvalidIndexError
-from pandas._libs import lib
+from pandas.core.dtypes.cast import construct_1d_array_from_listlike
 from pandas._libs.lib import Timestamp
 
 import pandas.util.testing as tm
@@ -913,7 +913,7 @@ def test_from_product_invalid_input(self):
     def test_from_product_datetimeindex(self):
         dt_index = date_range('2000-01-01', periods=2)
         mi = pd.MultiIndex.from_product([[1, 2], dt_index])
-        etalon = lib.list_to_object_array([(1, pd.Timestamp(
+        etalon = construct_1d_array_from_listlike([(1, pd.Timestamp(
             '2000-01-01')), (1, pd.Timestamp('2000-01-02')), (2, pd.Timestamp(
                 '2000-01-01')), (2, pd.Timestamp('2000-01-02'))])
         tm.assert_numpy_array_equal(mi.values, etalon)
@@ -940,7 +940,7 @@ def test_values_boxed(self):
                   (3, pd.Timestamp('2000-01-03'))]
         mi = pd.MultiIndex.from_tuples(tuples)
         tm.assert_numpy_array_equal(mi.values,
-                                    lib.list_to_object_array(tuples))
+                                    construct_1d_array_from_listlike(tuples))
         # Check that code branches for boxed values produce identical results
         tm.assert_numpy_array_equal(mi.values[:4], mi[:4].values)