Пример #1
0
def _convert_listlike(arg, unit='ns', box=True, errors='raise', name=None):
    """Convert a list of objects to a timedelta index object."""

    if isinstance(arg, (list, tuple)) or not hasattr(arg, 'dtype'):
        arg = np.array(list(arg), dtype='O')

    # these are shortcut-able
    if is_timedelta64_dtype(arg):
        value = arg.astype('timedelta64[ns]')
    elif is_integer_dtype(arg):
        value = arg.astype('timedelta64[{0}]'.format(
            unit)).astype('timedelta64[ns]', copy=False)
    else:
        try:
            value = tslib.array_to_timedelta64(_ensure_object(arg),
                                               unit=unit, errors=errors)
            value = value.astype('timedelta64[ns]', copy=False)
        except ValueError:
            if errors == 'ignore':
                return arg
            else:
                # This else-block accounts for the cases when errors='raise'
                # and errors='coerce'. If errors == 'raise', these errors
                # should be raised. If errors == 'coerce', we shouldn't
                # expect any errors to be raised, since all parsing errors
                # cause coercion to pd.NaT. However, if an error / bug is
                # introduced that causes an Exception to be raised, we would
                # like to surface it.
                raise

    if box:
        from pandas import TimedeltaIndex
        value = TimedeltaIndex(value, unit='ns', name=name)
    return value
Пример #2
0
def _convert_listlike(arg, unit='ns', box=True, errors='raise', name=None):
    """Convert a list of objects to a timedelta index object."""

    if isinstance(arg, (list, tuple)) or not hasattr(arg, 'dtype'):
        arg = np.array(list(arg), dtype='O')

    # these are shortcut-able
    if is_timedelta64_dtype(arg):
        value = arg.astype('timedelta64[ns]')
    elif is_integer_dtype(arg):
        value = arg.astype('timedelta64[{unit}]'.format(unit=unit)).astype(
            'timedelta64[ns]', copy=False)
    else:
        try:
            value = tslib.array_to_timedelta64(_ensure_object(arg),
                                               unit=unit,
                                               errors=errors)
            value = value.astype('timedelta64[ns]', copy=False)
        except ValueError:
            if errors == 'ignore':
                return arg
            else:
                # This else-block accounts for the cases when errors='raise'
                # and errors='coerce'. If errors == 'raise', these errors
                # should be raised. If errors == 'coerce', we shouldn't
                # expect any errors to be raised, since all parsing errors
                # cause coercion to pd.NaT. However, if an error / bug is
                # introduced that causes an Exception to be raised, we would
                # like to surface it.
                raise

    if box:
        from pandas import TimedeltaIndex
        value = TimedeltaIndex(value, unit='ns', name=name)
    return value
Пример #3
0
    def _simple_new(cls, values, name=None, freq=None, **kwargs):
        values = np.array(values, copy=False)
        if values.dtype == np.object_:
            values = libts.array_to_timedelta64(values)
        if values.dtype != _TD_DTYPE:
            values = _ensure_int64(values).view(_TD_DTYPE)

        result = object.__new__(cls)
        result._data = values
        result.name = name
        result.freq = freq
        result._reset_identity()
        return result
Пример #4
0
    def _simple_new(cls, values, name=None, freq=None, **kwargs):
        values = np.array(values, copy=False)
        if values.dtype == np.object_:
            values = libts.array_to_timedelta64(values)
        if values.dtype != _TD_DTYPE:
            values = _ensure_int64(values).view(_TD_DTYPE)

        result = object.__new__(cls)
        result._data = values
        result.name = name
        result.freq = freq
        result._reset_identity()
        return result