REF: only support object dtype

Author: toobaz

pandas/_libs/src/inference.pyx

@@ -349,8 +349,8 @@ def infer_dtype(object value, bint skipna=False):
     else:
         if not isinstance(value, list):
             value = list(value)
-        from pandas.core.dtypes.cast import construct_1d_array_from_listlike
-        values = construct_1d_array_from_listlike(value)
+        from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
+        values = construct_1d_object_array_from_listlike(value)
 
     values = getattr(values, 'values', values)
     val = _try_infer_map(values)

pandas/core/algorithms.py

@@ -7,7 +7,7 @@
 import numpy as np
 
 from pandas.core.dtypes.cast import (
-    maybe_promote, construct_1d_array_from_listlike)
+    maybe_promote, construct_1d_object_array_from_listlike)
 from pandas.core.dtypes.generic import (
     ABCSeries, ABCIndex,
     ABCIndexClass, ABCCategorical)
@@ -172,7 +172,7 @@ def _ensure_arraylike(values):
         if inferred in ['mixed', 'string', 'unicode']:
             if isinstance(values, tuple):
                 values = list(values)
-            values = construct_1d_array_from_listlike(values)
+            values = construct_1d_object_array_from_listlike(values)
         else:
             values = np.asarray(values)
     return values
@@ -402,7 +402,7 @@ def isin(comps, values):
                         .format(values_type=type(values).__name__))
 
     if not isinstance(values, (ABCIndex, ABCSeries, np.ndarray)):
-        values = construct_1d_array_from_listlike(list(values))
+        values = construct_1d_object_array_from_listlike(list(values))
 
     comps, dtype, _ = _ensure_data(comps)
     values, _, _ = _ensure_data(values, dtype=dtype)

pandas/core/common.py

@@ -21,7 +21,7 @@
 from pandas.core.dtypes.missing import isna, isnull, notnull  # noqa
 from pandas.api import types
 from pandas.core.dtypes import common
-from pandas.core.dtypes.cast import construct_1d_array_from_listlike
+from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
 
 # compat
 from pandas.errors import (  # noqa
@@ -382,7 +382,7 @@ def _asarray_tuplesafe(values, dtype=None):
         return values.values
 
     if isinstance(values, list) and dtype in [np.object_, object]:
-        return construct_1d_array_from_listlike(values)
+        return construct_1d_object_array_from_listlike(values)
 
     result = np.asarray(values, dtype=dtype)
 
@@ -392,7 +392,7 @@ def _asarray_tuplesafe(values, dtype=None):
     if result.ndim == 2:
         # Avoid building an array of arrays:
         values = [tuple(x) for x in values]
-        result = construct_1d_array_from_listlike(values)
+        result = construct_1d_object_array_from_listlike(values)
 
     return result
 

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 = construct_1d_array_from_listlike(list(values))
+        values = construct_1d_object_array_from_listlike(list(values))
     if getattr(values, 'dtype', None) == np.object_:
         if hasattr(values, '_values'):
             values = values._values
@@ -1164,21 +1164,21 @@ def construct_1d_arraylike_from_scalar(value, length, dtype):
     return subarr
 
 
-def construct_1d_array_from_listlike(values, dtype='object'):
+def construct_1d_object_array_from_listlike(values):
     """
-    Transform any list-like object in a 1-dimensional numpy array.
+    Transform any list-like object in a 1-dimensional numpy array of object
+    dtype.
 
     Parameters
     ----------
     values : any iterable which has a len()
-    dtype : dtype, default 'object'
 
     Returns
     -------
-    1-dimensional numpy array of dtype "dtype"
+    1-dimensional numpy array of dtype object
     """
     # numpy will try to interpret nested lists as further dimensions, hence
     # making a 1D array that contains list-likes is a bit tricky:
-    result = np.empty(len(values), dtype=dtype)
+    result = np.empty(len(values), dtype='object')
     result[:] = values
     return result

pandas/core/ops.py

@@ -34,7 +34,8 @@
     is_scalar,
     _ensure_object)
 from pandas.core.dtypes.cast import (
-    maybe_upcast_putmask, find_common_type, construct_1d_array_from_listlike)
+    maybe_upcast_putmask, find_common_type,
+    construct_1d_object_array_from_listlike)
 from pandas.core.dtypes.generic import (
     ABCSeries,
     ABCDataFrame,
@@ -751,7 +752,7 @@ def wrapper(left, right, name=name, na_op=na_op):
 
 def _comp_method_OBJECT_ARRAY(op, x, y):
     if isinstance(y, list):
-        y = construct_1d_array_from_listlike(y)
+        y = construct_1d_object_array_from_listlike(y)
     if isinstance(y, (np.ndarray, ABCSeries, ABCIndex)):
         if not is_object_dtype(y.dtype):
             y = y.astype(np.object_)
@@ -902,7 +903,7 @@ def na_op(x, y):
             result = op(x, y)
         except TypeError:
             if isinstance(y, list):
-                y = construct_1d_array_from_listlike(y)
+                y = construct_1d_object_array_from_listlike(y)
 
             if isinstance(y, (np.ndarray, ABCSeries)):
                 if (is_bool_dtype(x.dtype) and is_bool_dtype(y.dtype)):

pandas/tests/dtypes/test_cast.py

@@ -22,7 +22,7 @@
     maybe_convert_string_to_object,
     maybe_convert_scalar,
     find_common_type,
-    construct_1d_array_from_listlike)
+    construct_1d_object_array_from_listlike)
 from pandas.core.dtypes.dtypes import (
     CategoricalDtype,
     DatetimeTZDtype,
@@ -409,23 +409,11 @@ def test_period_dtype(self):
             assert find_common_type([dtype, dtype2]) == np.object
             assert find_common_type([dtype2, dtype]) == np.object
 
-    @pytest.mark.parametrize('dtype', [int, float, str, object, None])
     @pytest.mark.parametrize('datum1', [1, 2., "3", (4, 5), [6, 7], None])
     @pytest.mark.parametrize('datum2', [8, 9., "10", (11, 12), [13, 14], None])
-    def test_cast_1d_array(self, dtype, datum1, datum2):
+    def test_cast_1d_array(self, datum1, datum2):
         data = [datum1, datum2]
-        try:
-            # Conversion to 1d array is possible if requested dtype is object
-            possible = dtype is object
-            # ... or the following succeeds _and_ the result has dimension 1:
-            possible = possible or np.array(data, dtype=dtype).ndim == 1
-            if not possible:
-                exc = ValueError
-        except (ValueError, TypeError) as exception:
-            exc = type(exception)
-
-        if possible:
-            assert list(construct_1d_array_from_listlike(data)) == data
-        else:
-            pytest.raises(exc, construct_1d_array_from_listlike,
-                          data, dtype=dtype)
+        result = construct_1d_object_array_from_listlike(data)
+        # Direct comparison fails: https://github.com/numpy/numpy/issues/10218
+        assert result.dtype == 'object'
+        assert list(result) == data

pandas/tests/frame/test_constructors.py

@@ -22,7 +22,7 @@
                     date_range, Categorical)
 import pandas as pd
 import pandas.util.testing as tm
-from pandas.core.dtypes.cast import construct_1d_array_from_listlike
+from pandas.core.dtypes.cast import construct_1d_object_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 = construct_1d_array_from_listlike(
+        arr = construct_1d_object_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.core.dtypes.cast import construct_1d_array_from_listlike
+from pandas.core.dtypes.cast import construct_1d_object_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 = construct_1d_array_from_listlike([(1, pd.Timestamp(
+        etalon = construct_1d_object_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)
@@ -938,11 +938,11 @@ def test_values_boxed(self):
                   (1, pd.Timestamp('2000-01-04')),
                   (2, pd.Timestamp('2000-01-02')),
                   (3, pd.Timestamp('2000-01-03'))]
-        mi = pd.MultiIndex.from_tuples(tuples)
-        tm.assert_numpy_array_equal(mi.values,
-                                    construct_1d_array_from_listlike(tuples))
+        result = pd.MultiIndex.from_tuples(tuples)
+        expected = construct_1d_object_array_from_listlike(tuples)
+        tm.assert_numpy_array_equal(result.values, expected)
         # Check that code branches for boxed values produce identical results
-        tm.assert_numpy_array_equal(mi.values[:4], mi[:4].values)
+        tm.assert_numpy_array_equal(result.values[:4], result[:4].values)
 
     def test_append(self):
         result = self.index[:3].append(self.index[3:])