DEPR: row-based indexing in DataFrame.__getitem__

doc/source/user_guide/timeseries.rst

@@ -574,6 +574,7 @@ partial string selection is a form of label slicing, the endpoints **will be** i
 would include matching times on an included date:
 
 .. ipython:: python
+   :okwarning:
 
    dft = pd.DataFrame(np.random.randn(100000, 1), columns=['A'],
                       index=pd.date_range('20130101', periods=100000, freq='T'))
@@ -677,6 +678,7 @@ If index resolution is second, then the minute-accurate timestamp gives a
 If the timestamp string is treated as a slice, it can be used to index ``DataFrame`` with ``[]`` as well.
 
 .. ipython:: python
+    :okwarning:
 
     dft_minute = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]},
                               index=series_minute.index)
@@ -689,7 +691,11 @@ If the timestamp string is treated as a slice, it can be used to index ``DataFra
 
    To *always* have unambiguous selection, whether the row is treated as a slice or a single selection, use ``.loc``.
 
+   As of version 1.1.0, row-based indexing is deprecated.  Users should always use ``.loc``
+   for label-based row indexing.
+
    .. ipython:: python
+      :okwarning:
 
       dft_minute.loc['2011-12-31 23:59']
 
@@ -1968,6 +1974,7 @@ You can pass in dates and strings to ``Series`` and ``DataFrame`` with ``PeriodI
 Passing a string representing a lower frequency than ``PeriodIndex`` returns partial sliced data.
 
 .. ipython:: python
+   :okwarning:
 
    ps['2011']
 
@@ -1982,6 +1989,7 @@ Passing a string representing a lower frequency than ``PeriodIndex`` returns par
 As with ``DatetimeIndex``, the endpoints will be included in the result. The example below slices data starting from 10:00 to 11:59.
 
 .. ipython:: python
+   :okwarning:
 
    dfp['2013-01-01 10H':'2013-01-01 11H']
 

doc/source/whatsnew/v0.11.0.rst

@@ -367,6 +367,7 @@ Enhancements
   - You can now select with a string from a DataFrame with a datelike index, in a similar way to a Series (:issue:`3070`)
 
     .. ipython:: python
+       :okwarning:
 
         idx = pd.date_range("2001-10-1", periods=5, freq='M')
         ts = pd.Series(np.random.rand(len(idx)), index=idx)

pandas/core/frame.py

@@ -2776,6 +2776,13 @@ def __getitem__(self, key):
         if indexer is not None:
             # either we have a slice or we have a string that can be converted
             #  to a slice for partial-string date indexing
+            if not isinstance(key, slice):
+                warnings.warn(
+                    "row-based slicing on a DataFrame is deprecated and will "
+                    "raise in a future version.  Use `df.loc[key]` instead.",
+                    FutureWarning,
+                    stacklevel=2,
+                )
             return self._slice(indexer, axis=0)
 
         # Do we have a (boolean) DataFrame?
@@ -2926,6 +2933,13 @@ def __setitem__(self, key, value):
         if indexer is not None:
             # either we have a slice or we have a string that can be converted
             #  to a slice for partial-string date indexing
+            if not isinstance(key, slice):
+                warnings.warn(
+                    "row-based slicing on a DataFrame is deprecated and will "
+                    "raise in a future version.  Use `df.loc[key]` instead.",
+                    FutureWarning,
+                    stacklevel=2,
+                )
             return self._setitem_slice(indexer, value)
 
         if isinstance(key, DataFrame) or getattr(key, "ndim", None) == 2:

pandas/core/indexing.py

@@ -1,4 +1,4 @@
-from typing import Hashable, List, Tuple, Union
+from typing import TYPE_CHECKING, Hashable, List, Tuple, Union
 
 import numpy as np
 
@@ -29,6 +29,10 @@
 from pandas.core.indexes.api import Index
 from pandas.core.indexes.base import InvalidIndexError
 
+if TYPE_CHECKING:
+    from pandas import DataFrame
+
+
 # "null slice"
 _NS = slice(None, None)
 
@@ -2167,7 +2171,7 @@ def _tuplify(ndim: int, loc: Hashable) -> Tuple[Union[Hashable, slice], ...]:
     return tuple(_tup)
 
 
-def convert_to_index_sliceable(obj, key):
+def convert_to_index_sliceable(obj: "DataFrame", key):
     """
     If we are index sliceable, then return my slicer, otherwise return None.
     """

pandas/tests/indexes/datetimes/test_partial_slicing.py

@@ -309,7 +309,8 @@ def test_partial_slicing_dataframe(self):
                     tm.assert_series_equal(result, expected)
 
                     # Frame should return slice as well
-                    result = df[ts_string]
+                    with tm.assert_produces_warning(FutureWarning):
+                        result = df[ts_string]
                     expected = df[theslice]
                     tm.assert_frame_equal(result, expected)
 

pandas/tests/series/indexing/test_datetime.py

@@ -641,7 +641,9 @@ def test_indexing():
     expected.name = "A"
 
     df = DataFrame(dict(A=ts))
-    result = df["2001"]["A"]
+    with tm.assert_produces_warning(FutureWarning):
+        ser = df["2001"]
+    result = ser["A"]
     tm.assert_series_equal(expected, result)
 
     # setting
@@ -651,7 +653,9 @@ def test_indexing():
 
     df.loc["2001", "A"] = 1
 
-    result = df["2001"]["A"]
+    with tm.assert_produces_warning(FutureWarning):
+        ser = df["2001"]
+    result = ser["A"]
     tm.assert_series_equal(expected, result)
 
     # GH3546 (not including times on the last day)