Python dtypes.DatetimeTZDtype方法代碼示例

# 需要導入模塊: from pandas.core.dtypes import dtypes [as 別名]
# 或者: from pandas.core.dtypes.dtypes import DatetimeTZDtype [as 別名]
def test_frame_no_datetime64_dtype(self, tz):
        # after GH#7822
        # these retain the timezones on dict construction
        dr = date_range('2011/1/1', '2012/1/1', freq='W-FRI')
        dr_tz = dr.tz_localize(tz)
        df = DataFrame({'A': 'foo', 'B': dr_tz}, index=dr)
        tz_expected = DatetimeTZDtype('ns', dr_tz.tzinfo)
        assert df['B'].dtype == tz_expected

        # GH#2810 (with timezones)
        datetimes_naive = [ts.to_pydatetime() for ts in dr]
        datetimes_with_tz = [ts.to_pydatetime() for ts in dr_tz]
        df = DataFrame({'dr': dr,
                        'dr_tz': dr_tz,
                        'datetimes_naive': datetimes_naive,
                        'datetimes_with_tz': datetimes_with_tz})
        result = df.get_dtype_counts().sort_index()
        expected = Series({'datetime64[ns]': 2,
                           str(tz_expected): 2}).sort_index()
        tm.assert_series_equal(result, expected) 

# 需要導入模塊: from pandas.core.dtypes import dtypes [as 別名]
# 或者: from pandas.core.dtypes.dtypes import DatetimeTZDtype [as 別名]
def _add_datetimelike_scalar(self, other):
        # adding a timedeltaindex to a datetimelike
        from pandas.core.arrays import DatetimeArray

        assert other is not NaT
        other = Timestamp(other)
        if other is NaT:
            # In this case we specifically interpret NaT as a datetime, not
            # the timedelta interpretation we would get by returning self + NaT
            result = self.asi8.view('m8[ms]') + NaT.to_datetime64()
            return DatetimeArray(result)

        i8 = self.asi8
        result = checked_add_with_arr(i8, other.value,
                                      arr_mask=self._isnan)
        result = self._maybe_mask_results(result)
        dtype = DatetimeTZDtype(tz=other.tz) if other.tz else _NS_DTYPE
        return DatetimeArray(result, dtype=dtype, freq=self.freq) 

# 需要導入模塊: from pandas.core.dtypes import dtypes [as 別名]
# 或者: from pandas.core.dtypes.dtypes import DatetimeTZDtype [as 別名]
def _get_dtype(self, sqltype):
        from sqlalchemy.types import (Integer, Float, Boolean, DateTime,
                                      Date, TIMESTAMP)

        if isinstance(sqltype, Float):
            return float
        elif isinstance(sqltype, Integer):
            # TODO: Refine integer size.
            return np.dtype('int64')
        elif isinstance(sqltype, TIMESTAMP):
            # we have a timezone capable type
            if not sqltype.timezone:
                return datetime
            return DatetimeTZDtype
        elif isinstance(sqltype, DateTime):
            # Caution: np.datetime64 is also a subclass of np.number.
            return datetime
        elif isinstance(sqltype, Date):
            return date
        elif isinstance(sqltype, Boolean):
            return bool
        return object 

# 需要導入模塊: from pandas.core.dtypes import dtypes [as 別名]
# 或者: from pandas.core.dtypes.dtypes import DatetimeTZDtype [as 別名]
def test_frame_no_datetime64_dtype(self):

        # after 7822
        # these retain the timezones on dict construction

        dr = date_range('2011/1/1', '2012/1/1', freq='W-FRI')
        dr_tz = dr.tz_localize(self.tzstr('US/Eastern'))
        e = DataFrame({'A': 'foo', 'B': dr_tz}, index=dr)
        tz_expected = DatetimeTZDtype('ns', dr_tz.tzinfo)
        assert e['B'].dtype == tz_expected

        # GH 2810 (with timezones)
        datetimes_naive = [ts.to_pydatetime() for ts in dr]
        datetimes_with_tz = [ts.to_pydatetime() for ts in dr_tz]
        df = DataFrame({'dr': dr,
                        'dr_tz': dr_tz,
                        'datetimes_naive': datetimes_naive,
                        'datetimes_with_tz': datetimes_with_tz})
        result = df.get_dtype_counts().sort_index()
        expected = Series({'datetime64[ns]': 2,
                           str(tz_expected): 2}).sort_index()
        assert_series_equal(result, expected) 

# 需要導入模塊: from pandas.core.dtypes import dtypes [as 別名]
# 或者: from pandas.core.dtypes.dtypes import DatetimeTZDtype [as 別名]
def dtype(request):
    return DatetimeTZDtype(unit="ns", tz=request.param) 

# 需要導入模塊: from pandas.core.dtypes import dtypes [as 別名]
# 或者: from pandas.core.dtypes.dtypes import DatetimeTZDtype [as 別名]
def test_mismatched_timezone_raises(self):
        arr = DatetimeArray(np.array(['2000-01-01T06:00:00'], dtype='M8[ns]'),
                            dtype=DatetimeTZDtype(tz='US/Central'))
        dtype = DatetimeTZDtype(tz='US/Eastern')
        with pytest.raises(TypeError, match='Timezone of the array'):
            DatetimeArray(arr, dtype=dtype) 

# 需要導入模塊: from pandas.core.dtypes import dtypes [as 別名]
# 或者: from pandas.core.dtypes.dtypes import DatetimeTZDtype [as 別名]
def test_astype_to_same(self):
        arr = DatetimeArray._from_sequence(['2000'], tz='US/Central')
        result = arr.astype(DatetimeTZDtype(tz="US/Central"), copy=False)
        assert result is arr 

# 需要導入模塊: from pandas.core.dtypes import dtypes [as 別名]
# 或者: from pandas.core.dtypes.dtypes import DatetimeTZDtype [as 別名]
def test_setitem_different_tz_raises(self):
        data = np.array([1, 2, 3], dtype='M8[ns]')
        arr = DatetimeArray(data, copy=False,
                            dtype=DatetimeTZDtype(tz="US/Central"))
        with pytest.raises(ValueError, match="None"):
            arr[0] = pd.Timestamp('2000')

        with pytest.raises(ValueError, match="US/Central"):
            arr[0] = pd.Timestamp('2000', tz="US/Eastern") 

# 需要導入模塊: from pandas.core.dtypes import dtypes [as 別名]
# 或者: from pandas.core.dtypes.dtypes import DatetimeTZDtype [as 別名]
def test_tz_dtype_mismatch_raises(self):
        arr = DatetimeArray._from_sequence(['2000'], tz='US/Central')
        with pytest.raises(TypeError, match='data is already tz-aware'):
            sequence_to_dt64ns(arr, dtype=DatetimeTZDtype(tz="UTC")) 

# 需要導入模塊: from pandas.core.dtypes import dtypes [as 別名]
# 或者: from pandas.core.dtypes.dtypes import DatetimeTZDtype [as 別名]
def _simple_new(cls, values, name=None, freq=None, tz=None, dtype=None):
        """
        we require the we have a dtype compat for the values
        if we are passed a non-dtype compat, then coerce using the constructor
        """
        if isinstance(values, DatetimeArray):
            if tz:
                tz = validate_tz_from_dtype(dtype, tz)
                dtype = DatetimeTZDtype(tz=tz)
            elif dtype is None:
                dtype = _NS_DTYPE

            values = DatetimeArray(values, freq=freq, dtype=dtype)
            tz = values.tz
            freq = values.freq
            values = values._data

        # DatetimeArray._simple_new will accept either i8 or M8[ns] dtypes
        if isinstance(values, DatetimeIndex):
            values = values._data

        dtype = tz_to_dtype(tz)
        dtarr = DatetimeArray._simple_new(values, freq=freq, dtype=dtype)
        assert isinstance(dtarr, DatetimeArray)

        result = object.__new__(cls)
        result._data = dtarr
        result.name = name
        # For groupby perf. See note in indexes/base about _index_data
        result._index_data = dtarr._data
        result._reset_identity()
        return result

    # -------------------------------------------------------------------- 

# 需要導入模塊: from pandas.core.dtypes import dtypes [as 別名]
# 或者: from pandas.core.dtypes.dtypes import DatetimeTZDtype [as 別名]
def _harmonize_columns(self, parse_dates=None):
        """
        Make the DataFrame's column types align with the SQL table
        column types.
        Need to work around limited NA value support. Floats are always
        fine, ints must always be floats if there are Null values.
        Booleans are hard because converting bool column with None replaces
        all Nones with false. Therefore only convert bool if there are no
        NA values.
        Datetimes should already be converted to np.datetime64 if supported,
        but here we also force conversion if required.
        """
        parse_dates = _process_parse_dates_argument(parse_dates)

        for sql_col in self.table.columns:
            col_name = sql_col.name
            try:
                df_col = self.frame[col_name]

                # Handle date parsing upfront; don't try to convert columns
                # twice
                if col_name in parse_dates:
                    try:
                        fmt = parse_dates[col_name]
                    except TypeError:
                        fmt = None
                    self.frame[col_name] = _handle_date_column(
                        df_col, format=fmt)
                    continue

                # the type the dataframe column should have
                col_type = self._get_dtype(sql_col.type)

                if (col_type is datetime or col_type is date or
                        col_type is DatetimeTZDtype):
                    # Convert tz-aware Datetime SQL columns to UTC
                    utc = col_type is DatetimeTZDtype
                    self.frame[col_name] = _handle_date_column(df_col, utc=utc)
                elif col_type is float:
                    # floats support NA, can always convert!
                    self.frame[col_name] = df_col.astype(col_type, copy=False)

                elif len(df_col) == df_col.count():
                    # No NA values, can convert ints and bools
                    if col_type is np.dtype('int64') or col_type is bool:
                        self.frame[col_name] = df_col.astype(
                            col_type, copy=False)
            except KeyError:
                pass  # this column not in results