DEPR: string indexing along index for datetimes (#36179)

doc/source/user_guide/timeseries.rst

@@ -579,7 +579,12 @@ This type of slicing will work on a ``DataFrame`` with a ``DatetimeIndex`` as we
 partial string selection is a form of label slicing, the endpoints **will be** included. This
 would include matching times on an included date:
 
+.. warning::
+
+   Indexing ``DataFrame`` rows with strings is deprecated in pandas 1.2.0 and will be removed in a future version.  Use ``frame.loc[dtstring]`` instead.
+
 .. ipython:: python
+   :okwarning:
 
    dft = pd.DataFrame(np.random.randn(100000, 1), columns=['A'],
                       index=pd.date_range('20130101', periods=100000, freq='T'))
@@ -590,24 +595,28 @@ This starts on the very first time in the month, and includes the last date and
 time for the month:
 
 .. ipython:: python
+   :okwarning:
 
    dft['2013-1':'2013-2']
 
 This specifies a stop time **that includes all of the times on the last day**:
 
 .. ipython:: python
+   :okwarning:
 
    dft['2013-1':'2013-2-28']
 
 This specifies an **exact** stop time (and is not the same as the above):
 
 .. ipython:: python
+   :okwarning:
 
    dft['2013-1':'2013-2-28 00:00:00']
 
 We are stopping on the included end-point as it is part of the index:
 
 .. ipython:: python
+   :okwarning:
 
    dft['2013-1-15':'2013-1-15 12:30:00']
 
@@ -631,6 +640,7 @@ We are stopping on the included end-point as it is part of the index:
 Slicing with string indexing also honors UTC offset.
 
 .. ipython:: python
+   :okwarning:
 
     df = pd.DataFrame([0], index=pd.DatetimeIndex(['2019-01-01'], tz='US/Pacific'))
     df
@@ -681,6 +691,7 @@ 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)
@@ -2027,6 +2038,7 @@ 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']
 

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)

doc/source/whatsnew/v1.2.0.rst

@@ -213,6 +213,7 @@ Deprecations
 - Date parser functions :func:`~pandas.io.date_converters.parse_date_time`, :func:`~pandas.io.date_converters.parse_date_fields`, :func:`~pandas.io.date_converters.parse_all_fields` and :func:`~pandas.io.date_converters.generic_parser` from ``pandas.io.date_converters`` are deprecated and will be removed in a future version; use :func:`to_datetime` instead (:issue:`35741`)
 - :meth:`DataFrame.lookup` is deprecated and will be removed in a future version, use :meth:`DataFrame.melt` and :meth:`DataFrame.loc` instead (:issue:`18682`)
 - The :meth:`Index.to_native_types` is deprecated. Use ``.astype(str)`` instead (:issue:`28867`)
+- Deprecated indexing :class:`DataFrame` rows with datetime-like strings ``df[string]``, use ``df.loc[string]`` instead (:issue:`36179`)
 
 .. ---------------------------------------------------------------------------
 

pandas/core/indexing.py

@@ -1,4 +1,5 @@
 from typing import TYPE_CHECKING, Hashable, List, Tuple, Union
+import warnings
 
 import numpy as np
 
@@ -2191,7 +2192,15 @@ def convert_to_index_sliceable(obj: "DataFrame", key):
         # slice here via partial string indexing
         if idx._supports_partial_string_indexing:
             try:
-                return idx._get_string_slice(key)
+                res = idx._get_string_slice(key)
+                warnings.warn(
+                    "Indexing on datetimelike rows with `frame[string]` is "
+                    "deprecated and will be removed in a future version. "
+                    "Use `frame.loc[string]` instead.",
+                    FutureWarning,
+                    stacklevel=3,
+                )
+                return res
             except (KeyError, ValueError, NotImplementedError):
                 return None
 

pandas/tests/indexes/datetimes/test_partial_slicing.py

@@ -228,7 +228,9 @@ 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):
+                        # GH#36179 deprecated this indexing
+                        result = df[ts_string]
                     expected = df[theslice]
                     tm.assert_frame_equal(result, expected)
 

pandas/tests/series/indexing/test_datetime.py

@@ -1,3 +1,6 @@
+"""
+Also test support for datetime64[ns] in Series / DataFrame
+"""
 from datetime import datetime, timedelta
 import re
 
@@ -11,10 +14,6 @@
 from pandas import DataFrame, DatetimeIndex, NaT, Series, Timestamp, date_range
 import pandas._testing as tm
 
-"""
-Also test support for datetime64[ns] in Series / DataFrame
-"""
-
 
 def test_fancy_getitem():
     dti = date_range(
@@ -605,7 +604,9 @@ def test_indexing():
     expected.name = "A"
 
     df = DataFrame(dict(A=ts))
-    result = df["2001"]["A"]
+    with tm.assert_produces_warning(FutureWarning):
+        # GH#36179 string indexing on rows for DataFrame deprecated
+        result = df["2001"]["A"]
     tm.assert_series_equal(expected, result)
 
     # setting
@@ -615,7 +616,9 @@ def test_indexing():
 
     df.loc["2001", "A"] = 1
 
-    result = df["2001"]["A"]
+    with tm.assert_produces_warning(FutureWarning):
+        # GH#36179 string indexing on rows for DataFrame deprecated
+        result = df["2001"]["A"]
     tm.assert_series_equal(expected, result)
 
     # GH3546 (not including times on the last day)