larray-project
diff --git a/‎larray/core/array.py
Lines changed: 16 additions & 7 deletions b/‎larray/core/array.py
Lines changed: 16 additions & 7 deletions
diff --git a/‎larray/tests/test_array.py
Lines changed: 39 additions & 0 deletions b/‎larray/tests/test_array.py
Lines changed: 39 additions & 0 deletions
@@ -1140,6 +1140,13 @@ def _handle_meta(meta, title):
                         f"instead of {type(meta).__name__}")
     return Metadata(meta)
 
+# This prevents a warning in Pandas 1.4 <= version < 2.0 for arrays with object
+# dtype which contain only numeric values. We force Pandas 2.0 behavior
+# (ie use object dtype instead of inferring). See issue #1061.
+def np_array_to_pd_index(array, name=None, tupleize_cols=True):
+    dtype = None if array.dtype.kind != 'O' else object
+    return pd.Index(array, dtype=dtype, name=name, tupleize_cols=tupleize_cols)
+
 
 class Array(ABCArray):
     r"""
@@ -1538,17 +1545,17 @@ def to_frame(self, fold_last_axis_name=False, dropna=None) -> pd.DataFrame:
         a1 b0    4   5
            b1    6   7
         """
-        columns = pd.Index(self.axes[-1].labels)
-        if not fold_last_axis_name:
-            columns.name = self.axes[-1].name
+        last_name = self.axes[-1].name
+        columns_name = None if fold_last_axis_name else last_name
+        columns = np_array_to_pd_index(self.axes[-1].labels, name=columns_name)
         if self.ndim > 1:
             axes_names = self.axes.names[:-1]
             if fold_last_axis_name:
                 tmp = axes_names[-1] if axes_names[-1] is not None else ''
-                if self.axes[-1].name:
-                    axes_names[-1] = f"{tmp}\\{self.axes[-1].name}"
+                if last_name:
+                    axes_names[-1] = f"{tmp}\\{last_name}"
             if self.ndim == 2:
-                index = pd.Index(data=self.axes[0].labels, name=axes_names[0])
+                index = np_array_to_pd_index(self.axes[0].labels, name=axes_names[0])
             else:
                 index = pd.MultiIndex.from_product(self.axes.labels[:-1], names=axes_names)
         else:
@@ -1632,7 +1639,9 @@ def to_series(self, name=None, dropna=False) -> pd.Series:
         elif self.ndim == 1:
             axis = self.axes[0]
             # Note that string labels will be converted to object dtype in the process
-            index = pd.Index(axis.labels, name=axis.name, tupleize_cols=False)
+            # and label arrays with object dtype containing only numeric values will keep
+            # the object dtype.
+            index = np_array_to_pd_index(axis.labels, name=axis.name, tupleize_cols=False)
         else:
             index = pd.MultiIndex.from_product(self.axes.labels, names=self.axes.names)
         series = pd.Series(self.data.reshape(-1), index, name=name)
 
@@ -3761,6 +3761,24 @@ def test_from_lists():
     assert_larray_equal(sorted_arr, expected)
 
 
+def test_to_series():
+    # simple test
+    arr = ndtest(3, dtype=np.int32)
+    res = arr.to_series()
+    expected = pd.Series([0, 1, 2], dtype="int32", index=pd.Index(['a0', 'a1', 'a2'], name='a'))
+    assert res.equals(expected)
+
+    # test for issue #1061 (object dtype labels array produce warning with Pandas1.4+)
+    # We use an explicit int64 type because for some reason, under Linux, summing an int32 array
+    # results in an int64 value, so it is easier to just use a int64 array in the first place so
+    # that the test works on both Windows and Linux
+    arr = ndtest("a=1..3", dtype=np.int64).with_total()[:3]
+    res = arr.to_series()
+    index = pd.Index([1, 2, 3], dtype=object, name='a')
+    expected = pd.Series([0, 1, 2], dtype="int64", index=index)
+    assert res.equals(expected)
+
+
 def test_from_series():
     # Series with Index as index
     expected = ndtest(3)
@@ -3795,6 +3813,27 @@ def test_from_series():
     assert_larray_equal(res, expected)
 
 
+def test_to_frame():
+    # these tests are for issue #1061
+    arr = ndtest("a=0..2").with_total()[:2]
+    df = arr.to_frame()
+    assert df.columns.name == 'a'
+    assert df.columns.to_list() == [0, 1, 2]
+
+    arr = ndtest("a=0..2;b=b0").with_total('a')[:2]
+    df = arr.to_frame()
+    assert df.columns.name == 'b'
+    assert df.columns.to_list() == ['b0']
+    assert df.index.name == 'a'
+    assert df.index.to_list() == [0, 1, 2]
+
+    arr = ndtest("a=0..2;b=b0;c=c0").with_total('a')[:2]
+    df = arr.to_frame()
+    assert df.columns.name == 'c'
+    assert df.columns.to_list() == ['c0']
+    assert df.index.names == ['a', 'b']
+
+
 def test_from_frame():
     # 1) data = scalar
     # ================