WARN: Clarify datetime warning when can't parse in accordance with dayfirst

doc/source/whatsnew/v1.5.0.rst

@@ -727,6 +727,7 @@ Other Deprecations
 - Deprecated the ``display.column_space`` global configuration option (:issue:`7576`)
 - Deprecated the argument ``na_sentinel`` in :func:`factorize`, :meth:`Index.factorize`, and :meth:`.ExtensionArray.factorize`; pass ``use_na_sentinel=True`` instead to use the sentinel ``-1`` for NaN values and ``use_na_sentinel=False`` instead of ``na_sentinel=None`` to encode NaN values (:issue:`46910`)
 - Deprecated :meth:`DataFrameGroupBy.transform` not aligning the result when the UDF returned DataFrame (:issue:`45648`)
+- Clarified warning from :func:`to_datetime` when delimited dates can't be parsed in accordance to specified ``dayfirst`` argument (:issue:`46210`)
 
 
 .. ---------------------------------------------------------------------------

pandas/_libs/tslibs/parsing.pyx

@@ -85,8 +85,9 @@ _DEFAULT_DATETIME = datetime(1, 1, 1).replace(hour=0, minute=0,
                                               second=0, microsecond=0)
 
 PARSING_WARNING_MSG = (
-    "Parsing '{date_string}' in {format} format. Provide format "
-    "or specify infer_datetime_format=True for consistent parsing."
+    "Parsing dates in {format} format when dayfirst={dayfirst} was specified. "
+    "This may lead to inconsistently parsed dates! Specify a format "
+    "to ensure consistent parsing."
 )
 
 cdef:
@@ -185,16 +186,16 @@ cdef inline object _parse_delimited_date(str date_string, bint dayfirst):
         if dayfirst and not swapped_day_and_month:
             warnings.warn(
                 PARSING_WARNING_MSG.format(
-                    date_string=date_string,
-                    format='MM/DD/YYYY'
+                    format='MM/DD/YYYY',
+                    dayfirst='True',
                 ),
                 stacklevel=4,
             )
         elif not dayfirst and swapped_day_and_month:
             warnings.warn(
                 PARSING_WARNING_MSG.format(
-                    date_string=date_string,
-                    format='DD/MM/YYYY'
+                    format='DD/MM/YYYY',
+                    dayfirst='False (the default)',
                 ),
                 stacklevel=4,
             )

pandas/tests/io/parser/test_parse_dates.py

@@ -1677,16 +1677,25 @@ def test_parse_delimited_date_swap_with_warning(
 ):
     parser = all_parsers
     expected = DataFrame({0: [expected]}, dtype="datetime64[ns]")
-    warning_msg = (
-        "Provide format or specify infer_datetime_format=True for consistent parsing"
-    )
+    warning_msg = "Specify a format to ensure consistent parsing"
     with tm.assert_produces_warning(UserWarning, match=warning_msg):
         result = parser.read_csv(
             StringIO(date_string), header=None, dayfirst=dayfirst, parse_dates=[0]
         )
     tm.assert_frame_equal(result, expected)
 
 
+def test_parse_multiple_delimited_dates_with_swap_warnings():
+    # GH46210
+    warning_msg = "Specify a format to ensure consistent parsing"
+    with tm.assert_produces_warning(UserWarning, match=warning_msg) as record:
+        pd.to_datetime(["01/01/2000", "31/05/2000", "31/05/2001", "01/02/2000"])
+    assert len({str(warning.message) for warning in record}) == 1
+    # Using set(record) as repetitions of the same warning are suppressed
+    # https://docs.python.org/3/library/warnings.html
+    # and here we care to check that the warning is only shows once to users.
+
+
 def _helper_hypothesis_delimited_date(call, date_string, **kwargs):
     msg, result = None, None
     try:
@@ -1848,12 +1857,14 @@ def test_parse_dates_and_keep_orgin_column(all_parsers):
 def test_dayfirst_warnings():
     # GH 12585
     warning_msg_day_first = (
-        "Parsing '31/12/2014' in DD/MM/YYYY format. Provide "
-        "format or specify infer_datetime_format=True for consistent parsing."
+        r"Parsing dates in DD/MM/YYYY format when dayfirst=False \(the default\) was "
+        r"specified. This may lead to inconsistently parsed dates! Specify a format "
+        r"to ensure consistent parsing."
     )
     warning_msg_month_first = (
-        "Parsing '03/30/2011' in MM/DD/YYYY format. Provide "
-        "format or specify infer_datetime_format=True for consistent parsing."
+        "Parsing dates in MM/DD/YYYY format when dayfirst=True was "
+        "specified. This may lead to inconsistently parsed dates! Specify a format "
+        "to ensure consistent parsing."
     )
 
     # CASE 1: valid input

pandas/tests/io/xml/test_xml_dtypes.py

@@ -457,7 +457,7 @@ def test_day_first_parse_dates(parser):
     )
 
     with tm.assert_produces_warning(
-        UserWarning, match="Parsing '31/12/2020' in DD/MM/YYYY format"
+        UserWarning, match="Parsing dates DD/MM/YYYY format"
     ):
         df_result = read_xml(xml, parse_dates=["date"], parser=parser)
         df_iter = read_xml_iterparse(

pandas/tests/tools/test_to_datetime.py

@@ -1964,8 +1964,9 @@ def test_dayfirst(self, cache):
     def test_dayfirst_warnings_valid_input(self):
         # GH 12585
         warning_msg_day_first = (
-            "Parsing '31/12/2014' in DD/MM/YYYY format. Provide "
-            "format or specify infer_datetime_format=True for consistent parsing."
+            r"Parsing dates in DD/MM/YYYY format when dayfirst=False \(the default\) "
+            "was specified. This may lead to inconsistently parsed dates! Specify a "
+            "format to ensure consistent parsing."
         )
 
         # CASE 1: valid input