fix more errors + add to whatsnew v1.4.0

doc/source/whatsnew/v0.20.0.rst

@@ -248,11 +248,12 @@ or purely non-negative, integers. Previously, handling these integers would
 result in improper rounding or data-type casting, leading to incorrect results.
 Notably, a new numerical index, ``UInt64Index``, has been created (:issue:`14937`)
 
-.. ipython:: python
+.. code-block:: ipython
 
-   idx = pd.UInt64Index([1, 2, 3])
-   df = pd.DataFrame({'A': ['a', 'b', 'c']}, index=idx)
-   df.index
+   In [1]: idx = pd.UInt64Index([1, 2, 3])
+   In [2]: df = pd.DataFrame({'A': ['a', 'b', 'c']}, index=idx)
+   In [3]: df.index
+   Out[3]: UInt64Index([1, 2, 3], dtype='uint64')
 
 - Bug in converting object elements of array-like objects to unsigned 64-bit integers (:issue:`4471`, :issue:`14982`)
 - Bug in ``Series.unique()`` in which unsigned 64-bit integers were causing overflow (:issue:`14721`)

doc/source/whatsnew/v1.4.0.rst

@@ -30,18 +30,20 @@ It is now possible to create an index of any numpy int/uint/float dtype using th
     pd.NumericIndex([1, 2, 3], dtype="float32")
 
 In order to maintain backwards compatibility, calls to the base :class:`Index` will in
-pandas 1.x. return :class:`Int64Index`, :class:`UInt64Index` and :class:`Float64Index`.
+pandas 1.x. return :class:`Int64Index`, :class:`UInt64Index` and :class:`Float64Index`, where relevant.
 For example, the code below returns an ``Int64Index`` with dtype ``int64``:
 
 .. code-block:: ipython
 
     In [1]: pd.Index([1, 2, 3], dtype="int8")
     Int64Index([1, 2, 3], dtype='int64')
 
+but will in Pandas 2.0 return a :class:`NumericIndex` with dtype ``int8``.
+
 More generally, for the duration of Pandas 1.x, all operations that until now have
 returned :class:`Int64Index`, :class:`UInt64Index` and :class:`Float64Index` will
-continue to so. This means, that in order to use ``NumericIndex``, you will have to call
-``NumericIndex`` explicitly. For example the below series will have an ``Int64Index``:
+continue to so. This means, that in order to use ``NumericIndex`` in Pandas 1.x, you
+will have to call ``NumericIndex`` explicitly. For example the below series will have an ``Int64Index``:
 
 .. code-block:: ipython
 
@@ -59,9 +61,9 @@ Instead, if you want to use a ``NumericIndex`` in Pandas 1.x, you should do:
 
 In Pandas 2.0, :class:`NumericIndex` will become the default numeric index type and
 ``Int64Index``, ``UInt64Index`` and ``Float64Index`` are therefore deprecated and will
-be removed in pandas 2.0.
+be removed in pandas 2.0, see :ref:`here <whatsnew_140.deprecations.int64_uint64_float64index>` for more.
 
-See :ref:`here <advanced.numericindex>` for more.
+See :ref:`here <advanced.numericindex>` for more about :class:`NumericIndex`.
 
 .. _whatsnew_140.enhancements.styler:
 
@@ -224,9 +226,41 @@ Other API changes
 
 Deprecations
 ~~~~~~~~~~~~
-- Deprecated :class:`Int64Index` (:issue:`43028`).
-- Deprecated :class:`UInt64Index` (:issue:`43028`).
-- Deprecated :class:`Float64Index` (:issue:`43028`).
+
+.. _whatsnew_140.deprecations.int64_uint64_float64index:
+
+Deprecated Int64Index, UInt64Index & Float64Index
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+:class:`Int64Index`, :class:`UInt64Index` (:issue:`43028`) and :class:`Float64Index` have
+been deprecated in favor of the new :class:`NumericIndex` (:issue:`43028`).
+
+For the duration of Pandas 1.x, in order to maintain backward compatibility, calls to
+:class:`Index` will continue to return :class:`Int64Index`, :class:`UInt64Index` and :class:`Float64Index`
+when given numeric data, but in Pandas 2.0, a :class:`NumericIndex` will be returned.
+
+*Current behavior (Pandas 1.x)*:
+
+.. code-block:: ipython
+
+    In [1]: pd.Index([1, 2, 3], dtype="int32")
+    Out [1]: Int64Index([1, 2, 3], dtype='int64')
+    In [1]: pd.Index([1, 2, 3], dtype="uint64")
+    Out [1]: UInt64Index([1, 2, 3], dtype='uint64')
+
+*Future behavior (Pandas 2.0)*:
+
+.. code-block:: ipython
+
+    In [3]: pd.Index([1, 2, 3], dtype="int32")
+    Out [3]: NumericIndex([1, 2, 3], dtype='int32')
+    In [4]: pd.Index([1, 2, 3], dtype="uint64")
+    Out [4]: NumericIndex([1, 2, 3], dtype='uint64')
+
+
+.. _whatsnew_140.deprecations.other:
+
+Other Deprecations
+^^^^^^^^^^^^^^^^^^
 - Deprecated :meth:`Index.is_type_compatible` (:issue:`42113`)
 - Deprecated ``method`` argument in :meth:`Index.get_loc`, use ``index.get_indexer([label], method=...)`` instead (:issue:`42269`)
 - Deprecated treating integer keys in :meth:`Series.__setitem__` as positional when the index is a :class:`Float64Index` not containing the key, a :class:`IntervalIndex` with no entries containing the key, or a :class:`MultiIndex` with leading :class:`Float64Index` level not containing the key (:issue:`33469`)

pandas/tests/groupby/test_apply.py

@@ -16,6 +16,7 @@
     bdate_range,
 )
 import pandas._testing as tm
+from pandas.core.api import Int64Index
 
 
 def test_apply_issues():
@@ -785,10 +786,10 @@ def test_apply_with_mixed_types():
 
 def test_func_returns_object():
     # GH 28652
-    df = DataFrame({"a": [1, 2]}, index=pd.Int64Index([1, 2]))
+    df = DataFrame({"a": [1, 2]}, index=Int64Index([1, 2]))
     result = df.groupby("a").apply(lambda g: g.index)
     expected = Series(
-        [pd.Int64Index([1]), pd.Int64Index([2])], index=pd.Int64Index([1, 2], name="a")
+        [Int64Index([1]), Int64Index([2])], index=Int64Index([1, 2], name="a")
     )
 
     tm.assert_series_equal(result, expected)

pandas/tests/groupby/test_grouping.py

@@ -14,6 +14,10 @@
     date_range,
 )
 import pandas._testing as tm
+from pandas.core.api import (
+    Float64Index,
+    Int64Index,
+)
 from pandas.core.groupby.grouper import Grouping
 
 # selection
@@ -634,11 +638,11 @@ def test_list_grouper_with_nat(self):
             ),
             (
                 "agg",
-                Series(name=2, dtype=np.float64, index=pd.Float64Index([], name=1)),
+                Series(name=2, dtype=np.float64, index=Float64Index([], name=1)),
             ),
             (
                 "apply",
-                Series(name=2, dtype=np.float64, index=pd.Float64Index([], name=1)),
+                Series(name=2, dtype=np.float64, index=Float64Index([], name=1)),
             ),
         ],
     )
@@ -702,7 +706,7 @@ def test_groupby_multiindex_level_empty(self):
         empty = df[df.value < 0]
         result = empty.groupby("id").sum()
         expected = DataFrame(
-            dtype="float64", columns=["value"], index=pd.Int64Index([], name="id")
+            dtype="float64", columns=["value"], index=Int64Index([], name="id")
         )
         tm.assert_frame_equal(result, expected)
 

pandas/tests/groupby/test_min_max.py

@@ -10,6 +10,7 @@
     Series,
 )
 import pandas._testing as tm
+from pandas.core.api import Int64Index
 
 
 def test_max_min_non_numeric():
@@ -120,7 +121,7 @@ def test_groupby_aggregate_period_column(func):
     df = DataFrame({"a": groups, "b": periods})
 
     result = getattr(df.groupby("a")["b"], func)()
-    idx = pd.Int64Index([1, 2], name="a")
+    idx = Int64Index([1, 2], name="a")
     expected = Series(periods, index=idx, name="b")
 
     tm.assert_series_equal(result, expected)
@@ -134,7 +135,7 @@ def test_groupby_aggregate_period_frame(func):
     df = DataFrame({"a": groups, "b": periods})
 
     result = getattr(df.groupby("a"), func)()
-    idx = pd.Int64Index([1, 2], name="a")
+    idx = Int64Index([1, 2], name="a")
     expected = DataFrame({"b": periods}, index=idx)
 
     tm.assert_frame_equal(result, expected)

pandas/tests/groupby/test_pipe.py

@@ -6,6 +6,7 @@
     Index,
 )
 import pandas._testing as tm
+from pandas.core.api import Int64Index
 
 
 def test_pipe():
@@ -76,6 +77,6 @@ def h(df, arg3):
     ser = pd.Series([1, 1, 2, 2, 3, 3])
     result = ser.groupby(ser).pipe(lambda grp: grp.sum() * grp.count())
 
-    expected = pd.Series([4, 8, 12], index=pd.Int64Index([1, 2, 3]))
+    expected = pd.Series([4, 8, 12], index=Int64Index([1, 2, 3]))
 
     tm.assert_series_equal(result, expected)

pandas/tests/resample/test_resampler_grouper.py

@@ -14,6 +14,7 @@
     Timestamp,
 )
 import pandas._testing as tm
+from pandas.core.api import Int64Index
 from pandas.core.indexes.datetimes import date_range
 
 test_frame = DataFrame(
@@ -324,7 +325,7 @@ def test_consistency_with_window():
 
     # consistent return values with window
     df = test_frame
-    expected = pd.Int64Index([1, 2, 3], name="A")
+    expected = Int64Index([1, 2, 3], name="A")
     result = df.groupby("A").resample("2s").mean()
     assert result.index.nlevels == 2
     tm.assert_index_equal(result.index.levels[0], expected)