Python lib.item_from_zerodim方法代码示例

# 需要导入模块: from pandas._libs import lib [as 别名]
# 或者: from pandas._libs.lib import item_from_zerodim [as 别名]
def _wrap_result(name, data, sparse_index, fill_value, dtype=None):
    """
    wrap op result to have correct dtype
    """
    if name.startswith('__'):
        # e.g. __eq__ --> eq
        name = name[2:-2]

    if name in ('eq', 'ne', 'lt', 'gt', 'le', 'ge'):
        dtype = np.bool

    fill_value = lib.item_from_zerodim(fill_value)

    if is_bool_dtype(dtype):
        # fill_value may be np.bool_
        fill_value = bool(fill_value)
    return SparseArray(data,
                       sparse_index=sparse_index,
                       fill_value=fill_value,
                       dtype=dtype) 

# 需要导入模块: from pandas._libs import lib [as 别名]
# 或者: from pandas._libs.lib import item_from_zerodim [as 别名]
def test_is_scalar_numpy_zerodim_arrays(self):
        for zerodim in [np.array(1), np.array('foobar'),
                        np.array(np.datetime64('2014-01-01')),
                        np.array(np.timedelta64(1, 'h')),
                        np.array(np.datetime64('NaT'))]:
            assert not is_scalar(zerodim)
            assert is_scalar(lib.item_from_zerodim(zerodim)) 

# 需要导入模块: from pandas._libs import lib [as 别名]
# 或者: from pandas._libs.lib import item_from_zerodim [as 别名]
def __mul__(self, other):
        other = lib.item_from_zerodim(other)

        if isinstance(other, (ABCDataFrame, ABCSeries, ABCIndexClass)):
            return NotImplemented

        if is_scalar(other):
            # numpy will accept float and int, raise TypeError for others
            result = self._data * other
            freq = None
            if self.freq is not None and not isna(other):
                freq = self.freq * other
            return type(self)(result, freq=freq)

        if not hasattr(other, "dtype"):
            # list, tuple
            other = np.array(other)
        if len(other) != len(self) and not is_timedelta64_dtype(other):
            # Exclude timedelta64 here so we correctly raise TypeError
            #  for that instead of ValueError
            raise ValueError("Cannot multiply with unequal lengths")

        if is_object_dtype(other):
            # this multiplication will succeed only if all elements of other
            #  are int or float scalars, so we will end up with
            #  timedelta64[ns]-dtyped result
            result = [self[n] * other[n] for n in range(len(self))]
            result = np.array(result)
            return type(self)(result)

        # numpy will accept float or int dtype, raise TypeError for others
        result = self._data * other
        return type(self)(result) 

# 需要导入模块: from pandas._libs import lib [as 别名]
# 或者: from pandas._libs.lib import item_from_zerodim [as 别名]
def __mod__(self, other):
        # Note: This is a naive implementation, can likely be optimized
        if isinstance(other, (ABCSeries, ABCDataFrame, ABCIndexClass)):
            return NotImplemented

        other = lib.item_from_zerodim(other)
        if isinstance(other, (timedelta, np.timedelta64, Tick)):
            other = Timedelta(other)
        return self - (self // other) * other 

# 需要导入模块: from pandas._libs import lib [as 别名]
# 或者: from pandas._libs.lib import item_from_zerodim [as 别名]
def __rmod__(self, other):
        # Note: This is a naive implementation, can likely be optimized
        if isinstance(other, (ABCSeries, ABCDataFrame, ABCIndexClass)):
            return NotImplemented

        other = lib.item_from_zerodim(other)
        if isinstance(other, (timedelta, np.timedelta64, Tick)):
            other = Timedelta(other)
        return other - (other // self) * self 

# 需要导入模块: from pandas._libs import lib [as 别名]
# 或者: from pandas._libs.lib import item_from_zerodim [as 别名]
def __divmod__(self, other):
        # Note: This is a naive implementation, can likely be optimized
        if isinstance(other, (ABCSeries, ABCDataFrame, ABCIndexClass)):
            return NotImplemented

        other = lib.item_from_zerodim(other)
        if isinstance(other, (timedelta, np.timedelta64, Tick)):
            other = Timedelta(other)

        res1 = self // other
        res2 = self - res1 * other
        return res1, res2 

# 需要导入模块: from pandas._libs import lib [as 别名]
# 或者: from pandas._libs.lib import item_from_zerodim [as 别名]
def __rdivmod__(self, other):
        # Note: This is a naive implementation, can likely be optimized
        if isinstance(other, (ABCSeries, ABCDataFrame, ABCIndexClass)):
            return NotImplemented

        other = lib.item_from_zerodim(other)
        if isinstance(other, (timedelta, np.timedelta64, Tick)):
            other = Timedelta(other)

        res1 = other // self
        res2 = other - res1 * self
        return res1, res2

    # Note: TimedeltaIndex overrides this in call to cls._add_numeric_methods 

# 需要导入模块: from pandas._libs import lib [as 别名]
# 或者: from pandas._libs.lib import item_from_zerodim [as 别名]
def _evaluate_compare(self, other, op):
        """
        We have been called because a comparison between
        8 aware arrays. numpy >= 1.11 will
        now warn about NaT comparisons
        """

        # coerce to a similar object
        if not isinstance(other, type(self)):
            if not is_list_like(other):
                # scalar
                other = [other]
            elif is_scalar(lib.item_from_zerodim(other)):
                # ndarray scalar
                other = [other.item()]
            other = type(self)(other)

        # compare
        result = op(self.asi8, other.asi8)

        # technically we could support bool dtyped Index
        # for now just return the indexing array directly
        mask = (self._isnan) | (other._isnan)
        if is_bool_dtype(result):
            result[mask] = False
            return result

        result[mask] = iNaT
        try:
            return Index(result)
        except TypeError:
            return result 

# 需要导入模块: from pandas._libs import lib [as 别名]
# 或者: from pandas._libs.lib import item_from_zerodim [as 别名]
def test_isscalar_numpy_zerodim_arrays(self):
        for zerodim in [np.array(1), np.array('foobar'),
                        np.array(np.datetime64('2014-01-01')),
                        np.array(np.timedelta64(1, 'h')),
                        np.array(np.datetime64('NaT'))]:
            assert not is_scalar(zerodim)
            assert is_scalar(lib.item_from_zerodim(zerodim)) 

# 需要导入模块: from pandas._libs import lib [as 别名]
# 或者: from pandas._libs.lib import item_from_zerodim [as 别名]
def __add__(self, other):
        other = lib.item_from_zerodim(other)
        if isinstance(other, (ABCSeries, ABCDataFrame)):
            return NotImplemented

        # scalar others
        elif other is NaT:
            result = self._add_nat()
        elif isinstance(other, (Tick, timedelta, np.timedelta64)):
            result = self._add_delta(other)
        elif isinstance(other, DateOffset):
            # specifically _not_ a Tick
            result = self._add_offset(other)
        elif isinstance(other, (datetime, np.datetime64)):
            result = self._add_datetimelike_scalar(other)
        elif lib.is_integer(other):
            # This check must come after the check for np.timedelta64
            # as is_integer returns True for these
            if not is_period_dtype(self):
                maybe_integer_op_deprecated(self)
            result = self._time_shift(other)

        # array-like others
        elif is_timedelta64_dtype(other):
            # TimedeltaIndex, ndarray[timedelta64]
            result = self._add_delta(other)
        elif is_offsetlike(other):
            # Array/Index of DateOffset objects
            result = self._addsub_offset_array(other, operator.add)
        elif is_datetime64_dtype(other) or is_datetime64tz_dtype(other):
            # DatetimeIndex, ndarray[datetime64]
            return self._add_datetime_arraylike(other)
        elif is_integer_dtype(other):
            if not is_period_dtype(self):
                maybe_integer_op_deprecated(self)
            result = self._addsub_int_array(other, operator.add)
        elif is_float_dtype(other):
            # Explicitly catch invalid dtypes
            raise TypeError("cannot add {dtype}-dtype to {cls}"
                            .format(dtype=other.dtype,
                                    cls=type(self).__name__))
        elif is_period_dtype(other):
            # if self is a TimedeltaArray and other is a PeriodArray with
            #  a timedelta-like (i.e. Tick) freq, this operation is valid.
            #  Defer to the PeriodArray implementation.
            # In remaining cases, this will end up raising TypeError.
            return NotImplemented
        elif is_extension_array_dtype(other):
            # Categorical op will raise; defer explicitly
            return NotImplemented
        else:  # pragma: no cover
            return NotImplemented

        if is_timedelta64_dtype(result) and isinstance(result, np.ndarray):
            from pandas.core.arrays import TimedeltaArray
            # TODO: infer freq?
            return TimedeltaArray(result)
        return result 

# 需要导入模块: from pandas._libs import lib [as 别名]
# 或者: from pandas._libs.lib import item_from_zerodim [as 别名]
def __truediv__(self, other):
        # timedelta / X is well-defined for timedelta-like or numeric X
        other = lib.item_from_zerodim(other)

        if isinstance(other, (ABCSeries, ABCDataFrame, ABCIndexClass)):
            return NotImplemented

        if isinstance(other, (timedelta, np.timedelta64, Tick)):
            other = Timedelta(other)
            if other is NaT:
                # specifically timedelta64-NaT
                result = np.empty(self.shape, dtype=np.float64)
                result.fill(np.nan)
                return result

            # otherwise, dispatch to Timedelta implementation
            return self._data / other

        elif lib.is_scalar(other):
            # assume it is numeric
            result = self._data / other
            freq = None
            if self.freq is not None:
                # Tick division is not implemented, so operate on Timedelta
                freq = self.freq.delta / other
            return type(self)(result, freq=freq)

        if not hasattr(other, "dtype"):
            # e.g. list, tuple
            other = np.array(other)

        if len(other) != len(self):
            raise ValueError("Cannot divide vectors with unequal lengths")

        elif is_timedelta64_dtype(other):
            # let numpy handle it
            return self._data / other

        elif is_object_dtype(other):
            # Note: we do not do type inference on the result, so either
            #  an object array or numeric-dtyped (if numpy does inference)
            #  will be returned.  GH#23829
            result = [self[n] / other[n] for n in range(len(self))]
            result = np.array(result)
            return result

        else:
            result = self._data / other
            return type(self)(result) 

# 需要导入模块: from pandas._libs import lib [as 别名]
# 或者: from pandas._libs.lib import item_from_zerodim [as 别名]
def __rfloordiv__(self, other):
        if isinstance(other, (ABCSeries, ABCDataFrame, ABCIndexClass)):
            return NotImplemented

        other = lib.item_from_zerodim(other)
        if is_scalar(other):
            if isinstance(other, (timedelta, np.timedelta64, Tick)):
                other = Timedelta(other)
                if other is NaT:
                    # treat this specifically as timedelta-NaT
                    result = np.empty(self.shape, dtype=np.float64)
                    result.fill(np.nan)
                    return result

                # dispatch to Timedelta implementation
                result = other.__floordiv__(self._data)
                return result

            raise TypeError("Cannot divide {typ} by {cls}"
                            .format(typ=type(other).__name__,
                                    cls=type(self).__name__))

        if not hasattr(other, "dtype"):
            # list, tuple
            other = np.array(other)
        if len(other) != len(self):
            raise ValueError("Cannot divide with unequal lengths")

        elif is_timedelta64_dtype(other):
            other = type(self)(other)

            # numpy timedelta64 does not natively support floordiv, so operate
            #  on the i8 values
            result = other.asi8 // self.asi8
            mask = self._isnan | other._isnan
            if mask.any():
                result = result.astype(np.int64)
                result[mask] = np.nan
            return result

        elif is_object_dtype(other):
            result = [other[n] // self[n] for n in range(len(self))]
            result = np.array(result)
            return result

        else:
            dtype = getattr(other, "dtype", type(other).__name__)
            raise TypeError("Cannot divide {typ} by {cls}"
                            .format(typ=dtype, cls=type(self).__name__))