Python lib.is_integer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 is_integer [as 别名]
def validate_periods(periods):
    """
    If a `periods` argument is passed to the Datetime/Timedelta Array/Index
    constructor, cast it to an integer.

    Parameters
    ----------
    periods : None, float, int

    Returns
    -------
    periods : None or int

    Raises
    ------
    TypeError
        if periods is None, float, or int
    """
    if periods is not None:
        if lib.is_float(periods):
            periods = int(periods)
        elif not lib.is_integer(periods):
            raise TypeError('periods must be a number, got {periods}'
                            .format(periods=periods))
    return periods 

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

        if is_integer(other) and other != 0:
            if (len(self) == 0 or
                    self._start % other == 0 and
                    self._step % other == 0):
                start = self._start // other
                step = self._step // other
                stop = start + len(self) * step
                return RangeIndex._simple_new(
                    start, stop, step, name=self.name)
            if len(self) == 1:
                start = self._start // other
                return RangeIndex._simple_new(
                    start, start + 1, 1, name=self.name)
        return self._int64index // other 

# 需要导入模块: from pandas._libs import lib [as 别名]
# 或者: from pandas._libs.lib import is_integer [as 别名]
def _simple_new(cls, start, stop=None, step=None, name=None,
                    dtype=None, **kwargs):
        result = object.__new__(cls)

        # handle passed None, non-integers
        if start is None and stop is None:
            # empty
            start, stop, step = 0, 0, 1

        if start is None or not is_integer(start):
            try:

                return RangeIndex(start, stop, step, name=name, **kwargs)
            except TypeError:
                return Index(start, stop, step, name=name, **kwargs)

        result._start = start
        result._stop = stop or 0
        result._step = step or 1
        result.name = name
        for k, v in compat.iteritems(kwargs):
            setattr(result, k, v)

        result._reset_identity()
        return result

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

# 需要导入模块: from pandas._libs import lib [as 别名]
# 或者: from pandas._libs.lib import is_integer [as 别名]
def is_number(obj):
    """
    Check if the object is a number.

    Returns True when the object is a number, and False if is not.

    Parameters
    ----------
    obj : any type
        The object to check if is a number.

    Returns
    -------
    is_number : bool
        Whether `obj` is a number or not.

    See Also
    --------
    pandas.api.types.is_integer: checks a subgroup of numbers

    Examples
    --------
    >>> pd.api.types.is_number(1)
    True
    >>> pd.api.types.is_number(7.15)
    True

    Booleans are valid because they are int subclass.

    >>> pd.api.types.is_number(False)
    True

    >>> pd.api.types.is_number("foo")
    False
    >>> pd.api.types.is_number("5")
    False
    """

    return isinstance(obj, (Number, np.number)) 

# 需要导入模块: from pandas._libs import lib [as 别名]
# 或者: from pandas._libs.lib import is_integer [as 别名]
def __getitem__(self, key):
        """
        This getitem defers to the underlying array, which by-definition can
        only handle list-likes, slices, and integer scalars
        """

        is_int = lib.is_integer(key)
        if lib.is_scalar(key) and not is_int:
            raise IndexError("only integers, slices (`:`), ellipsis (`...`), "
                             "numpy.newaxis (`None`) and integer or boolean "
                             "arrays are valid indices")

        getitem = self._data.__getitem__
        if is_int:
            val = getitem(key)
            return self._box_func(val)

        if com.is_bool_indexer(key):
            key = np.asarray(key, dtype=bool)
            if key.all():
                key = slice(0, None, None)
            else:
                key = lib.maybe_booleans_to_slice(key.view(np.uint8))

        is_period = is_period_dtype(self)
        if is_period:
            freq = self.freq
        else:
            freq = None
            if isinstance(key, slice):
                if self.freq is not None and key.step is not None:
                    freq = key.step * self.freq
                else:
                    freq = self.freq
            elif key is Ellipsis:
                # GH#21282 indexing with Ellipsis is similar to a full slice,
                #  should preserve `freq` attribute
                freq = self.freq

        result = getitem(key)
        if result.ndim > 1:
            # To support MPL which performs slicing with 2 dim
            # even though it only has 1 dim by definition
            if is_period:
                return self._simple_new(result, dtype=self.dtype, freq=freq)
            return result

        return self._simple_new(result, dtype=self.dtype, freq=freq) 

# 需要导入模块: from pandas._libs import lib [as 别名]
# 或者: from pandas._libs.lib import is_integer [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