silenced many warnings in test suite

Author: SaturnFromTitan

pandas/core/algorithms.py

@@ -53,7 +53,11 @@
 )
 from pandas.core.dtypes.missing import isna, na_value_for_dtype
 
-from pandas.core.construction import array, extract_array
+from pandas.core.construction import (
+    array,
+    create_series_with_explicit_index,
+    extract_array,
+)
 from pandas.core.indexers import validate_indices
 
 if TYPE_CHECKING:
@@ -835,7 +839,7 @@ def mode(values, dropna: bool = True) -> "Series":
         warn(f"Unable to sort modes: {err}")
 
     result = _reconstruct_data(result, original.dtype, original)
-    return Series(result)
+    return create_series_with_explicit_index(result)
 
 
 def rank(

pandas/core/apply.py

@@ -16,7 +16,10 @@
 )
 from pandas.core.dtypes.generic import ABCSeries
 
-from pandas.core.construction import create_series_with_explicit_dtype
+from pandas.core.construction import (
+    create_series_with_explicit_dtype,
+    create_series_with_explicit_index,
+)
 
 if TYPE_CHECKING:
     from pandas import DataFrame, Series, Index
@@ -202,15 +205,15 @@ def apply_empty_result(self):
 
         if not should_reduce:
             try:
-                r = self.f(Series([], dtype=np.float64))
+                r = self.f(create_series_with_explicit_index([], dtype=np.float64))
             except Exception:
                 pass
             else:
                 should_reduce = not isinstance(r, Series)
 
         if should_reduce:
             if len(self.agg_axis):
-                r = self.f(Series([], dtype=np.float64))
+                r = self.f(create_series_with_explicit_index([], dtype=np.float64))
             else:
                 r = np.nan
 

pandas/core/construction.py

@@ -674,7 +674,9 @@ def create_series_with_explicit_index(
     if index_if_empty is None:
         index_if_empty = Index([])
 
-    if index is None and isinstance(data, list) and len(data) == 0:
+    # dict's are handled separately in Series.__init__
+    is_relevant_type = is_list_like(data) and not isinstance(data, dict)
+    if index is None and is_relevant_type and len(data) == 0:
         index = index_if_empty
     return Series(
         data=data, index=index, dtype=dtype, name=name, copy=copy, fastpath=fastpath

pandas/core/indexes/base.py

@@ -71,6 +71,7 @@
 from pandas.core.arrays.datetimes import tz_to_dtype, validate_tz_from_dtype
 from pandas.core.base import IndexOpsMixin, PandasObject
 import pandas.core.common as com
+from pandas.core.construction import create_series_with_explicit_index
 from pandas.core.indexers import deprecate_ndim_indexing
 from pandas.core.indexes.frozen import FrozenList
 import pandas.core.missing as missing
@@ -142,9 +143,7 @@ def index_arithmetic_method(self, other):
         if isinstance(other, (ABCSeries, ABCDataFrame, ABCTimedeltaIndex)):
             return NotImplemented
 
-        from pandas import Series
-
-        result = op(Series(self), other)
+        result = op(create_series_with_explicit_index(self), other)
         if isinstance(result, tuple):
             return (Index(result[0]), Index(result[1]))
         return Index(result)

pandas/core/strings.py

@@ -36,7 +36,7 @@
 
 from pandas.core.algorithms import take_1d
 from pandas.core.base import NoNewAttributesMixin
-from pandas.core.construction import extract_array
+from pandas.core.construction import create_series_with_explicit_index, extract_array
 
 if TYPE_CHECKING:
     from pandas.arrays import StringArray
@@ -2180,7 +2180,7 @@ def _wrap_result(
         returns_string=True,
     ):
 
-        from pandas import Index, Series, MultiIndex
+        from pandas import Index, MultiIndex
 
         # for category, we do the stuff on the categories, so blow it up
         # to the full series again
@@ -2190,7 +2190,9 @@ def _wrap_result(
         if use_codes and self._is_categorical:
             # if self._orig is a CategoricalIndex, there is no .cat-accessor
             result = take_1d(
-                result, Series(self._orig, copy=False).cat.codes, fill_value=fill_value
+                result,
+                create_series_with_explicit_index(self._orig, copy=False).cat.codes,
+                fill_value=fill_value,
             )
 
         if not hasattr(result, "ndim") or not hasattr(result, "dtype"):

pandas/core/tools/datetimes.py

@@ -41,6 +41,7 @@
 from pandas.core import algorithms
 from pandas.core.algorithms import unique
 from pandas.core.arrays.datetimes import tz_to_dtype
+from pandas.core.construction import create_series_with_explicit_index
 
 # ---------------------------------------------------------------------
 # types used in annotations
@@ -764,9 +765,10 @@ def to_datetime(
             if errors == "raise":
                 raise
             # ... otherwise, continue without the cache.
-            from pandas import Series
 
-            cache_array = Series([], dtype=object)  # just an empty array
+            cache_array = create_series_with_explicit_index(
+                [], dtype=object
+            )  # just an empty array
         if not cache_array.empty:
             result = _convert_and_box_cache(arg, cache_array)
         else:

pandas/io/parsers.py

@@ -53,14 +53,14 @@
 
 from pandas.core import algorithms
 from pandas.core.arrays import Categorical
+from pandas.core.construction import create_series_with_explicit_index
 from pandas.core.frame import DataFrame
 from pandas.core.indexes.api import (
     Index,
     MultiIndex,
     RangeIndex,
     ensure_index_from_sequences,
 )
-from pandas.core.series import Series
 from pandas.core.tools import datetimes as tools
 
 from pandas.io.common import (
@@ -3494,14 +3494,20 @@ def _get_empty_meta(columns, index_col, index_names, dtype=None):
     if (index_col is None or index_col is False) or index_names is None:
         index = Index([])
     else:
-        data = [Series([], dtype=dtype[name]) for name in index_names]
+        data = [
+            create_series_with_explicit_index([], dtype=dtype[name])
+            for name in index_names
+        ]
         index = ensure_index_from_sequences(data, names=index_names)
         index_col.sort()
 
         for i, n in enumerate(index_col):
             columns.pop(n - i)
 
-    col_dict = {col_name: Series([], dtype=dtype[col_name]) for col_name in columns}
+    col_dict = {
+        col_name: create_series_with_explicit_index([], dtype=dtype[col_name])
+        for col_name in columns
+    }
 
     return index, columns, col_dict
 

pandas/io/pytables.py

@@ -51,6 +51,7 @@
 from pandas.core.arrays import Categorical, DatetimeArray, PeriodArray
 import pandas.core.common as com
 from pandas.core.computation.pytables import PyTablesExpr, maybe_expression
+from pandas.core.construction import create_series_with_explicit_index
 from pandas.core.indexes.api import ensure_index
 
 from pandas.io.common import stringify_path
@@ -3313,7 +3314,7 @@ def write_metadata(self, key: str, values: np.ndarray):
         key : str
         values : ndarray
         """
-        values = Series(values)
+        values = create_series_with_explicit_index(values)
         self.parent.put(
             self._get_metadata_path(key),
             values,
@@ -4051,7 +4052,9 @@ def read_column(
                     encoding=self.encoding,
                     errors=self.errors,
                 )
-                return Series(_set_tz(col_values[1], a.tz), name=column)
+                return create_series_with_explicit_index(
+                    _set_tz(col_values[1], a.tz), name=column
+                )
 
         raise KeyError(f"column [{column}] not found in the table")
 

pandas/tests/arrays/boolean/test_reduction.py

@@ -2,6 +2,7 @@
 import pytest
 
 import pandas as pd
+from pandas.core.construction import create_series_with_explicit_index
 
 
 @pytest.fixture
@@ -31,7 +32,7 @@ def test_any_all(values, exp_any, exp_all, exp_any_noskip, exp_all_noskip):
     exp_any_noskip = pd.NA if exp_any_noskip is pd.NA else np.bool_(exp_any_noskip)
     exp_all_noskip = pd.NA if exp_all_noskip is pd.NA else np.bool_(exp_all_noskip)
 
-    for con in [pd.array, pd.Series]:
+    for con in [pd.array, create_series_with_explicit_index]:
         a = con(values, dtype="boolean")
         assert a.any() is exp_any
         assert a.all() is exp_all

pandas/tests/arrays/integer/test_function.py

@@ -4,6 +4,7 @@
 import pandas as pd
 import pandas._testing as tm
 from pandas.core.arrays import integer_array
+from pandas.core.construction import create_series_with_explicit_index
 
 
 @pytest.mark.parametrize("ufunc", [np.abs, np.sign])
@@ -105,7 +106,7 @@ def test_value_counts_na():
 
 def test_value_counts_empty():
     # https://github.com/pandas-dev/pandas/issues/33317
-    s = pd.Series([], dtype="Int64")
+    s = create_series_with_explicit_index([], dtype="Int64")
     result = s.value_counts()
     # TODO: The dtype of the index seems wrong (it's int64 for non-empty)
     idx = pd.Index([], dtype="object")

pandas/tests/base/test_unique.py

@@ -7,6 +7,7 @@
 
 import pandas as pd
 import pandas._testing as tm
+from pandas.core.construction import create_series_with_explicit_index
 from pandas.tests.base.common import allow_na_ops
 
 
@@ -94,7 +95,9 @@ def test_nunique_null(null_obj, index_or_series_obj):
     else:
         values[0:2] = null_obj
 
-    klass = type(obj)
+    klass = (
+        create_series_with_explicit_index if isinstance(obj, pd.Series) else type(obj)
+    )
     repeated_values = np.repeat(values, range(1, len(values) + 1))
     obj = klass(repeated_values, dtype=obj.dtype)
 

pandas/tests/base/test_value_counts.py

@@ -21,6 +21,7 @@
     TimedeltaIndex,
 )
 import pandas._testing as tm
+from pandas.core.construction import create_series_with_explicit_index
 from pandas.tests.base.common import allow_na_ops
 
 
@@ -180,7 +181,7 @@ def test_value_counts_bins(index_or_series):
     assert s.nunique() == 3
 
     s = klass({}) if klass is dict else klass({}, dtype=object)
-    expected = Series([], dtype=np.int64)
+    expected = create_series_with_explicit_index([], dtype=np.int64)
     tm.assert_series_equal(s.value_counts(), expected, check_index_type=False)
     # returned dtype differs depending on original
     if isinstance(s, Index):

pandas/tests/dtypes/test_common.py

@@ -20,6 +20,7 @@
 import pandas as pd
 import pandas._testing as tm
 from pandas.arrays import SparseArray
+from pandas.core.construction import create_series_with_explicit_index
 
 
 # EA & Actual Dtypes
@@ -236,7 +237,9 @@ def test_is_timedelta64_dtype():
     assert not com.is_timedelta64_dtype("NO DATE")
 
     assert com.is_timedelta64_dtype(np.timedelta64)
-    assert com.is_timedelta64_dtype(pd.Series([], dtype="timedelta64[ns]"))
+    assert com.is_timedelta64_dtype(
+        create_series_with_explicit_index([], dtype="timedelta64[ns]")
+    )
     assert com.is_timedelta64_dtype(pd.to_timedelta(["0 days", "1 days"]))
 
 
@@ -499,7 +502,9 @@ def test_needs_i8_conversion():
     assert not com.needs_i8_conversion(np.array(["a", "b"]))
 
     assert com.needs_i8_conversion(np.datetime64)
-    assert com.needs_i8_conversion(pd.Series([], dtype="timedelta64[ns]"))
+    assert com.needs_i8_conversion(
+        create_series_with_explicit_index([], dtype="timedelta64[ns]")
+    )
     assert com.needs_i8_conversion(pd.DatetimeIndex(["2000"], tz="US/Eastern"))
 
 
@@ -567,7 +572,7 @@ def test_is_extension_type(check_scipy):
     assert com.is_extension_type(pd.DatetimeIndex(["2000"], tz="US/Eastern"))
 
     dtype = DatetimeTZDtype("ns", tz="US/Eastern")
-    s = pd.Series([], dtype=dtype)
+    s = create_series_with_explicit_index([], dtype=dtype)
     assert com.is_extension_type(s)
 
     if check_scipy:
@@ -596,7 +601,7 @@ def test_is_extension_array_dtype(check_scipy):
     assert com.is_extension_array_dtype(pd.DatetimeIndex(["2000"], tz="US/Eastern"))
 
     dtype = DatetimeTZDtype("ns", tz="US/Eastern")
-    s = pd.Series([], dtype=dtype)
+    s = create_series_with_explicit_index([], dtype=dtype)
     assert com.is_extension_array_dtype(s)
 
     if check_scipy:

pandas/tests/dtypes/test_concat.py

@@ -5,6 +5,7 @@
 import pandas as pd
 from pandas import DatetimeIndex, Period, PeriodIndex, Series, TimedeltaIndex
 import pandas._testing as tm
+from pandas.core.construction import create_series_with_explicit_index
 
 
 @pytest.mark.parametrize(
@@ -83,7 +84,7 @@ def test_get_dtype_kinds_period(to_concat, expected):
 def test_concat_mismatched_categoricals_with_empty():
     # concat_compat behavior on series._values should match pd.concat on series
     ser1 = Series(["a", "b", "c"], dtype="category")
-    ser2 = Series([], dtype="category")
+    ser2 = create_series_with_explicit_index([], dtype="category")
 
     result = _concat.concat_compat([ser1._values, ser2._values])
     expected = pd.concat([ser1, ser2])._values

pandas/tests/dtypes/test_inference.py

@@ -52,6 +52,10 @@
 )
 import pandas._testing as tm
 from pandas.core.arrays import IntegerArray
+from pandas.core.construction import (
+    create_series_with_explicit_dtype,
+    create_series_with_explicit_index,
+)
 
 
 @pytest.fixture(params=[True, False], ids=str)
@@ -77,9 +81,9 @@ def coerce(request):
     ((x for x in [1, 2]), True, "generator"),
     ((_ for _ in []), True, "generator-empty"),
     (Series([1]), True, "Series"),
-    (Series([], dtype=object), True, "Series-empty"),
+    (create_series_with_explicit_dtype([], dtype=object), True, "Series-empty"),
     (Series(["a"]).str, True, "StringMethods"),
-    (Series([], dtype="O").str, True, "StringMethods-empty"),
+    (create_series_with_explicit_dtype([], dtype="O").str, True, "StringMethods-empty"),
     (Index([1]), True, "Index"),
     (Index([]), True, "Index-empty"),
     (DataFrame([[1]]), True, "DataFrame"),
@@ -138,7 +142,7 @@ def __getitem__(self):
 
 
 def test_is_array_like():
-    assert inference.is_array_like(Series([], dtype=object))
+    assert inference.is_array_like(create_series_with_explicit_index([], dtype=object))
     assert inference.is_array_like(Series([1, 2]))
     assert inference.is_array_like(np.array(["a", "b"]))
     assert inference.is_array_like(Index(["2016-01-01"]))
@@ -164,7 +168,7 @@ class DtypeList(list):
         {"a": 1},
         {1, "a"},
         Series([1]),
-        Series([], dtype=object),
+        Series(dtype=object),
         Series(["a"]).str,
         (x for x in range(5)),
     ],

pandas/tests/extension/base/missing.py

@@ -2,6 +2,7 @@
 
 import pandas as pd
 import pandas._testing as tm
+from pandas.core.construction import create_series_with_explicit_index
 
 from .base import BaseExtensionTests
 
@@ -19,7 +20,7 @@ def test_isna(self, data_missing):
 
         # GH 21189
         result = pd.Series(data_missing).drop([0, 1]).isna()
-        expected = pd.Series([], dtype=bool)
+        expected = create_series_with_explicit_index([], dtype=bool)
         self.assert_series_equal(result, expected)
 
     def test_dropna_array(self, data_missing):