argmax(), use Series.idxmin() and Series.idxmax() for the old behavior (GH16955) • Passing a tz-aware datetime.datetime or Timestamp into the Timestamp constructor with the tz argument now raises a ValueError from read_gbq() (GH30200) • Calling np.array and np.asarray on tz-aware Series and DatetimeIndex will now return an object array of tz-aware Timestamp (GH24596) • 1.8 Performance improvements • Performance timezone-awareness of new data (GH30238) • Bug in Series.cummin() and Series.cummax() with timezone-aware dtype incorrectly dropping its timezone (GH15553) • Bug in DatetimeArray, TimedeltaArray, and PeriodArray

• Series.to_excel() and DataFrame.to_excel() will now raise a ValueError when saving time- zone aware data. (GH27008, GH7056) • ExtensionArray.argsort() places NA values at the end of the sorted array from the set {None, numpy.nan, pandas.NaT} (GH22305) • Bug in DataFrame and Series where timezone aware data with dtype='datetime64[ns] was not cast to naive (GH25843) • Improved Timestamp type checking would incorrect raise TypeError (GH26916) • Bug in to_datetime() which would raise ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True when called with cache=True, with

constructor. Passing a list of dataclasses is equivalent to passing a list of dictionaries. Please be aware, that that all values in the list should be dataclasses, mixing types in the list would result in For example, consider date- times with timezones. NumPy doesn’t have a dtype to represent timezone-aware datetimes, so there are two possibly useful representations: 1. An object-dtype numpy.ndarray with Function application To apply your own or another library’s functions to pandas objects, you should be aware of the three methods below. The appropriate method to use depends on whether your function expects

constructor. Passing a list of dataclasses is equivalent to passing a list of dictionaries. Please be aware, that that all values in the list should be dataclasses, mixing types in the list would result in For example, consider date- times with timezones. NumPy doesn’t have a dtype to represent timezone-aware datetimes, so there are two possibly useful representations: 1. An object-dtype numpy.ndarray with Function application To apply your own or another library’s functions to pandas objects, you should be aware of the three methods below. The appropriate method to use depends on whether your function expects

• Series.to_excel() and DataFrame.to_excel() will now raise a ValueError when saving time- zone aware data. (GH27008, GH7056) • ExtensionArray.argsort() places NA values at the end of the sorted array from the set {None, numpy.nan, pandas.NaT} (GH22305) • Bug in DataFrame and Series where timezone aware data with dtype='datetime64[ns] was not cast to naive (GH25843) • Improved Timestamp type checking would incorrect raise TypeError (GH26916) • Bug in to_datetime() which would raise ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True when called with cache=True, with

constructor. Passing a list of dataclasses is equivalent to passing a list of dictionaries. Please be aware, that all values in the list should be dataclasses, mixing types in the list would result in a TypeError For example, consider datetimes with timezones. NumPy doesn’t have a dtype to represent timezone-aware datetimes, so there are two possibly useful representations: 1. An object-dtype numpy.ndarray with Function application To apply your own or another library’s functions to pandas objects, you should be aware of the three methods below. The appropriate method to use depends on whether your function expects

constructor. Passing a list of dataclasses is equivalent to passing a list of dictionaries. Please be aware, that all values in the list should be dataclasses, mixing types in the list would result in a TypeError For example, consider datetimes with timezones. NumPy doesn’t have a dtype to represent timezone-aware datetimes, so there are two possibly useful representations: 1. An object-dtype numpy.ndarray with Function application To apply your own or another library’s functions to pandas objects, you should be aware of the three methods below. The appropriate method to use depends on whether your function expects

constructor. Passing a list of dataclasses is equivalent to passing a list of dictionaries. Please be aware, that all values in the list should be dataclasses, mixing types in the list would result in a TypeError For example, consider date- times with timezones. NumPy doesn’t have a dtype to represent timezone-aware datetimes, so there are two possibly useful representations: 1. An object-dtype numpy.ndarray with Function application To apply your own or another library’s functions to pandas objects, you should be aware of the three methods below. The appropriate method to use depends on whether your function expects

constructor. Passing a list of dataclasses is equivalent to passing a list of dictionaries. Please be aware, that all values in the list should be dataclasses, mixing types in the list would result in a TypeError For example, consider datetimes with timezones. NumPy doesn’t have a dtype to represent timezone-aware datetimes, so there are two possibly useful representations: 1. An object-dtype numpy.ndarray with Function application To apply your own or another library’s functions to pandas objects, you should be aware of the three methods below. The appropriate method to use depends on whether your function expects

constructor. Passing a list of dataclasses is equivalent to passing a list of dictionaries. Please be aware, that all values in the list should be dataclasses, mixing types in the list would result in a TypeError For example, consider datetimes with timezones. NumPy doesn’t have a dtype to represent timezone-aware datetimes, so there are two possibly useful representations: 1. An object-dtype numpy.ndarray with Function application To apply your own or another library’s functions to pandas objects, you should be aware of the three methods below. The appropriate method to use depends on whether your function expects