DEPR: Remove sql.execute, *args/**kwargs in resample, unrecognized timezones

doc/source/whatsnew/v3.0.0.rst

@@ -107,8 +107,11 @@ Removal of prior version deprecations/changes
 - Removed ``DataFrameGroupBy.grouper`` and ``SeriesGroupBy.grouper`` (:issue:`56521`)
 - Removed ``axis`` argument from :meth:`DataFrame.groupby`, :meth:`Series.groupby`, :meth:`DataFrame.rolling`, :meth:`Series.rolling`, :meth:`DataFrame.resample`, and :meth:`Series.resample` (:issue:`51203`)
 - Removed ``axis`` argument from all groupby operations (:issue:`50405`)
+- Removed ``pandas.io.sql.execute`` (:issue:`50185`)
 - Removed deprecated argument ``obj`` in :meth:`.DataFrameGroupBy.get_group` and :meth:`.SeriesGroupBy.get_group` (:issue:`53545`)
 - Removed the ``ArrayManager`` (:issue:`55043`)
+- Removed unused arguments ``*args`` and ``**kwargs`` in :class:`Resampler` methods (:issue:`50977`)
+- Unrecognized timezones when parsing strings to datetimes now raises a ``ValueError`` (:issue:`51477`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_300.performance:

pandas/_libs/tslibs/parsing.pyx

@@ -733,15 +733,10 @@ cdef datetime dateutil_parse(
                 )
         elif res.tzname is not None:
             # e.g. "1994 Jan 15 05:16 FOO" where FOO is not recognized
-            # GH#18702
-            warnings.warn(
+            # GH#18702, # GH 50235 enforced in 3.0
+            raise ValueError(
                 f'Parsed string "{timestr}" included an un-recognized timezone '
-                f'"{res.tzname}". Dropping unrecognized timezones is deprecated; '
-                "in a future version this will raise. Instead pass the string "
-                "without the timezone, then use .tz_localize to convert to a "
-                "recognized timezone.",
-                FutureWarning,
-                stacklevel=find_stack_level()
+                f'"{res.tzname}".'
             )
 
     out_bestunit[0] = attrname_to_npy_unit[reso]

pandas/core/resample.py

@@ -27,7 +27,6 @@
 )
 from pandas._libs.tslibs.dtypes import freq_to_period_freqstr
 from pandas._typing import NDFrameT
-from pandas.compat.numpy import function as nv
 from pandas.errors import AbstractMethodError
 from pandas.util._decorators import (
     Appender,
@@ -1156,8 +1155,6 @@ def sum(
         self,
         numeric_only: bool = False,
         min_count: int = 0,
-        *args,
-        **kwargs,
     ):
         """
         Compute sum of group values.
@@ -1195,17 +1192,13 @@ def sum(
         2023-02-01    7
         Freq: MS, dtype: int64
         """
-        maybe_warn_args_and_kwargs(type(self), "sum", args, kwargs)
-        nv.validate_resampler_func("sum", args, kwargs)
         return self._downsample("sum", numeric_only=numeric_only, min_count=min_count)
 
     @final
     def prod(
         self,
         numeric_only: bool = False,
         min_count: int = 0,
-        *args,
-        **kwargs,
     ):
         """
         Compute prod of group values.
@@ -1243,17 +1236,13 @@ def prod(
         2023-02-01   12
         Freq: MS, dtype: int64
         """
-        maybe_warn_args_and_kwargs(type(self), "prod", args, kwargs)
-        nv.validate_resampler_func("prod", args, kwargs)
         return self._downsample("prod", numeric_only=numeric_only, min_count=min_count)
 
     @final
     def min(
         self,
         numeric_only: bool = False,
         min_count: int = 0,
-        *args,
-        **kwargs,
     ):
         """
         Compute min value of group.
@@ -1277,18 +1266,13 @@ def min(
         2023-02-01    3
         Freq: MS, dtype: int64
         """
-
-        maybe_warn_args_and_kwargs(type(self), "min", args, kwargs)
-        nv.validate_resampler_func("min", args, kwargs)
         return self._downsample("min", numeric_only=numeric_only, min_count=min_count)
 
     @final
     def max(
         self,
         numeric_only: bool = False,
         min_count: int = 0,
-        *args,
-        **kwargs,
     ):
         """
         Compute max value of group.
@@ -1312,8 +1296,6 @@ def max(
         2023-02-01    4
         Freq: MS, dtype: int64
         """
-        maybe_warn_args_and_kwargs(type(self), "max", args, kwargs)
-        nv.validate_resampler_func("max", args, kwargs)
         return self._downsample("max", numeric_only=numeric_only, min_count=min_count)
 
     @final
@@ -1323,11 +1305,7 @@ def first(
         numeric_only: bool = False,
         min_count: int = 0,
         skipna: bool = True,
-        *args,
-        **kwargs,
     ):
-        maybe_warn_args_and_kwargs(type(self), "first", args, kwargs)
-        nv.validate_resampler_func("first", args, kwargs)
         return self._downsample(
             "first", numeric_only=numeric_only, min_count=min_count, skipna=skipna
         )
@@ -1339,28 +1317,20 @@ def last(
         numeric_only: bool = False,
         min_count: int = 0,
         skipna: bool = True,
-        *args,
-        **kwargs,
     ):
-        maybe_warn_args_and_kwargs(type(self), "last", args, kwargs)
-        nv.validate_resampler_func("last", args, kwargs)
         return self._downsample(
             "last", numeric_only=numeric_only, min_count=min_count, skipna=skipna
         )
 
     @final
     @doc(GroupBy.median)
-    def median(self, numeric_only: bool = False, *args, **kwargs):
-        maybe_warn_args_and_kwargs(type(self), "median", args, kwargs)
-        nv.validate_resampler_func("median", args, kwargs)
+    def median(self, numeric_only: bool = False):
         return self._downsample("median", numeric_only=numeric_only)
 
     @final
     def mean(
         self,
         numeric_only: bool = False,
-        *args,
-        **kwargs,
     ):
         """
         Compute mean of groups, excluding missing values.
@@ -1395,17 +1365,13 @@ def mean(
         2023-02-01    3.5
         Freq: MS, dtype: float64
         """
-        maybe_warn_args_and_kwargs(type(self), "mean", args, kwargs)
-        nv.validate_resampler_func("mean", args, kwargs)
         return self._downsample("mean", numeric_only=numeric_only)
 
     @final
     def std(
         self,
         ddof: int = 1,
         numeric_only: bool = False,
-        *args,
-        **kwargs,
     ):
         """
         Compute standard deviation of groups, excluding missing values.
@@ -1443,17 +1409,13 @@ def std(
         2023-02-01    2.645751
         Freq: MS, dtype: float64
         """
-        maybe_warn_args_and_kwargs(type(self), "std", args, kwargs)
-        nv.validate_resampler_func("std", args, kwargs)
         return self._downsample("std", ddof=ddof, numeric_only=numeric_only)
 
     @final
     def var(
         self,
         ddof: int = 1,
         numeric_only: bool = False,
-        *args,
-        **kwargs,
     ):
         """
         Compute variance of groups, excluding missing values.
@@ -1497,8 +1459,6 @@ def var(
         2023-02-01    4.666667
         Freq: MS, dtype: float64
         """
-        maybe_warn_args_and_kwargs(type(self), "var", args, kwargs)
-        nv.validate_resampler_func("var", args, kwargs)
         return self._downsample("var", ddof=ddof, numeric_only=numeric_only)
 
     @final
@@ -1507,23 +1467,12 @@ def sem(
         self,
         ddof: int = 1,
         numeric_only: bool = False,
-        *args,
-        **kwargs,
     ):
-        maybe_warn_args_and_kwargs(type(self), "sem", args, kwargs)
-        nv.validate_resampler_func("sem", args, kwargs)
         return self._downsample("sem", ddof=ddof, numeric_only=numeric_only)
 
     @final
     @doc(GroupBy.ohlc)
-    def ohlc(
-        self,
-        *args,
-        **kwargs,
-    ):
-        maybe_warn_args_and_kwargs(type(self), "ohlc", args, kwargs)
-        nv.validate_resampler_func("ohlc", args, kwargs)
-
+    def ohlc(self):
         ax = self.ax
         obj = self._obj_with_exclusions
         if len(ax) == 0:
@@ -1544,13 +1493,7 @@ def ohlc(
 
     @final
     @doc(SeriesGroupBy.nunique)
-    def nunique(
-        self,
-        *args,
-        **kwargs,
-    ):
-        maybe_warn_args_and_kwargs(type(self), "nunique", args, kwargs)
-        nv.validate_resampler_func("nunique", args, kwargs)
+    def nunique(self):
         return self._downsample("nunique")
 
     @final
@@ -2874,40 +2817,6 @@ def _asfreq_compat(index: DatetimeIndex | PeriodIndex | TimedeltaIndex, freq):
     return new_index
 
 
-def maybe_warn_args_and_kwargs(cls, kernel: str, args, kwargs) -> None:
-    """
-    Warn for deprecation of args and kwargs in resample functions.
-
-    Parameters
-    ----------
-    cls : type
-        Class to warn about.
-    kernel : str
-        Operation name.
-    args : tuple or None
-        args passed by user. Will be None if and only if kernel does not have args.
-    kwargs : dict or None
-        kwargs passed by user. Will be None if and only if kernel does not have kwargs.
-    """
-    warn_args = args is not None and len(args) > 0
-    warn_kwargs = kwargs is not None and len(kwargs) > 0
-    if warn_args and warn_kwargs:
-        msg = "args and kwargs"
-    elif warn_args:
-        msg = "args"
-    elif warn_kwargs:
-        msg = "kwargs"
-    else:
-        return
-    warnings.warn(
-        f"Passing additional {msg} to {cls.__name__}.{kernel} has "
-        "no impact on the result and is deprecated. This will "
-        "raise a TypeError in a future version of pandas.",
-        category=FutureWarning,
-        stacklevel=find_stack_level(),
-    )
-
-
 def _apply(
     grouped: GroupBy, how: Callable, *args, include_groups: bool, **kwargs
 ) -> DataFrame:

pandas/io/sql.py

@@ -235,37 +235,6 @@ def _wrap_result_adbc(
     return df
 
 
-def execute(sql, con, params=None):
-    """
-    Execute the given SQL query using the provided connection object.
-
-    Parameters
-    ----------
-    sql : string
-        SQL query to be executed.
-    con : SQLAlchemy connection or sqlite3 connection
-        If a DBAPI2 object, only sqlite3 is supported.
-    params : list or tuple, optional, default: None
-        List of parameters to pass to execute method.
-
-    Returns
-    -------
-    Results Iterable
-    """
-    warnings.warn(
-        "`pandas.io.sql.execute` is deprecated and "
-        "will be removed in the future version.",
-        FutureWarning,
-        stacklevel=find_stack_level(),
-    )  # GH50185
-    sqlalchemy = import_optional_dependency("sqlalchemy", errors="ignore")
-
-    if sqlalchemy is not None and isinstance(con, (str, sqlalchemy.engine.Engine)):
-        raise TypeError("pandas.io.sql.execute requires a connection")  # GH50185
-    with pandasSQL_builder(con, need_transaction=True) as pandas_sql:
-        return pandas_sql.execute(sql, params)
-
-
 # -----------------------------------------------------------------------------
 # -- Read and write to DataFrames
 

pandas/tests/io/test_sql.py

@@ -1487,26 +1487,6 @@ def test_read_view_sqlite(sqlite_buildin):
     tm.assert_frame_equal(result, expected)
 
 
-def test_execute_typeerror(sqlite_engine_iris):
-    with pytest.raises(TypeError, match="pandas.io.sql.execute requires a connection"):
-        with tm.assert_produces_warning(
-            FutureWarning,
-            match="`pandas.io.sql.execute` is deprecated and "
-            "will be removed in the future version.",
-        ):
-            sql.execute("select * from iris", sqlite_engine_iris)
-
-
-def test_execute_deprecated(sqlite_conn_iris):
-    # GH50185
-    with tm.assert_produces_warning(
-        FutureWarning,
-        match="`pandas.io.sql.execute` is deprecated and "
-        "will be removed in the future version.",
-    ):
-        sql.execute("select * from iris", sqlite_conn_iris)
-
-
 def flavor(conn_name):
     if "postgresql" in conn_name:
         return "postgresql"

pandas/tests/resample/test_resample_api.py

@@ -5,7 +5,6 @@
 import pytest
 
 from pandas._libs import lib
-from pandas.errors import UnsupportedFunctionCall
 
 import pandas as pd
 from pandas import (
@@ -987,46 +986,6 @@ def test_series_downsample_method(method, numeric_only, expected_data):
         tm.assert_series_equal(result, expected)
 
 
-@pytest.mark.parametrize(
-    "method, raises",
-    [
-        ("sum", True),
-        ("prod", True),
-        ("min", True),
-        ("max", True),
-        ("first", False),
-        ("last", False),
-        ("median", False),
-        ("mean", True),
-        ("std", True),
-        ("var", True),
-        ("sem", False),
-        ("ohlc", False),
-        ("nunique", False),
-    ],
-)
-def test_args_kwargs_depr(method, raises):
-    index = date_range("20180101", periods=3, freq="h")
-    df = Series([2, 4, 6], index=index)
-    resampled = df.resample("30min")
-    args = ()
-
-    func = getattr(resampled, method)
-
-    error_msg = "numpy operations are not valid with resample."
-    error_msg_type = "too many arguments passed in"
-    warn_msg = f"Passing additional args to DatetimeIndexResampler.{method}"
-
-    if raises:
-        with tm.assert_produces_warning(FutureWarning, match=warn_msg):
-            with pytest.raises(UnsupportedFunctionCall, match=error_msg):
-                func(*args, 1, 2, 3, 4)
-    else:
-        with tm.assert_produces_warning(FutureWarning, match=warn_msg):
-            with pytest.raises(TypeError, match=error_msg_type):
-                func(*args, 1, 2, 3, 4)
-
-
 def test_resample_empty():
     # GH#52484
     df = DataFrame(