Backport PR #52195 on branch 2.0.x (WARN: Only warn about inconsistent parsing if there are multiple non-null elements)

pandas/core/tools/datetimes.py

@@ -139,13 +139,16 @@ def _guess_datetime_format_for_array(arr, dayfirst: bool | None = False) -> str
             )
             if guessed_format is not None:
                 return guessed_format
-            warnings.warn(
-                "Could not infer format, so each element will be parsed "
-                "individually, falling back to `dateutil`. To ensure parsing is "
-                "consistent and as-expected, please specify a format.",
-                UserWarning,
-                stacklevel=find_stack_level(),
-            )
+            # If there are multiple non-null elements, warn about
+            # how parsing might not be consistent
+            if tslib.first_non_null(arr[first_non_null + 1 :]) != -1:
+                warnings.warn(
+                    "Could not infer format, so each element will be parsed "
+                    "individually, falling back to `dateutil`. To ensure parsing is "
+                    "consistent and as-expected, please specify a format.",
+                    UserWarning,
+                    stacklevel=find_stack_level(),
+                )
     return None
 
 

pandas/tests/io/parser/test_parse_dates.py

@@ -1252,13 +1252,15 @@ def test_bad_date_parse(all_parsers, cache_dates, value):
     parser = all_parsers
     s = StringIO((f"{value},\n") * 50000)
 
-    if parser.engine == "pyarrow":
+    if parser.engine == "pyarrow" and not cache_dates:
         # None in input gets converted to 'None', for which
         # pandas tries to guess the datetime format, triggering
         # the warning. TODO: parse dates directly in pyarrow, see
         # https://github.com/pandas-dev/pandas/issues/48017
         warn = UserWarning
     else:
+        # Note: warning is not raised if 'cache_dates', because here there is only a
+        # single unique date and hence no risk of inconsistent parsing.
         warn = None
     parser.read_csv_check_warnings(
         warn,
@@ -1285,6 +1287,10 @@ def test_bad_date_parse_with_warning(all_parsers, cache_dates, value):
         # TODO: parse dates directly in pyarrow, see
         # https://github.com/pandas-dev/pandas/issues/48017
         warn = None
+    elif cache_dates:
+        # Note: warning is not raised if 'cache_dates', because here there is only a
+        # single unique date and hence no risk of inconsistent parsing.
+        warn = None
     else:
         warn = UserWarning
     parser.read_csv_check_warnings(
@@ -1737,9 +1743,7 @@ def test_parse_timezone(all_parsers):
 def test_invalid_parse_delimited_date(all_parsers, date_string):
     parser = all_parsers
     expected = DataFrame({0: [date_string]}, dtype="object")
-    result = parser.read_csv_check_warnings(
-        UserWarning,
-        "Could not infer format",
+    result = parser.read_csv(
         StringIO(date_string),
         header=None,
         parse_dates=[0],
@@ -2063,9 +2067,7 @@ def test_infer_first_column_as_index(all_parsers):
     # GH#11019
     parser = all_parsers
     data = "a,b,c\n1970-01-01,2,3,4"
-    result = parser.read_csv_check_warnings(
-        UserWarning,
-        "Could not infer format",
+    result = parser.read_csv(
         StringIO(data),
         parse_dates=["a"],
     )

pandas/tests/io/parser/usecols/test_parse_dates.py

@@ -124,9 +124,7 @@ def test_usecols_with_parse_dates4(all_parsers):
     }
     expected = DataFrame(cols, columns=["a_b"] + list("cdefghij"))
 
-    result = parser.read_csv_check_warnings(
-        UserWarning,
-        "Could not infer format",
+    result = parser.read_csv(
         StringIO(data),
         usecols=usecols,
         parse_dates=parse_dates,

pandas/tests/test_algos.py

@@ -1231,8 +1231,7 @@ def test_value_counts_datetime_outofbounds(self):
         tm.assert_series_equal(res, exp)
 
         # GH 12424
-        with tm.assert_produces_warning(UserWarning, match="Could not infer format"):
-            res = to_datetime(Series(["2362-01-01", np.nan]), errors="ignore")
+        res = to_datetime(Series(["2362-01-01", np.nan]), errors="ignore")
         exp = Series(["2362-01-01", np.nan], dtype=object)
         tm.assert_series_equal(res, exp)