Python cast.maybe_upcast_putmask方法代码示例

# 需要导入模块: from pandas.core.dtypes import cast [as 别名]
# 或者: from pandas.core.dtypes.cast import maybe_upcast_putmask [as 别名]
def _arith_method_PANEL(op, name, str_rep=None, fill_zeros=None,
                        default_axis=None, **eval_kwargs):
    # copied from Series na_op above, but without unnecessary branch for
    # non-scalar
    def na_op(x, y):
        import pandas.core.computation.expressions as expressions

        try:
            result = expressions.evaluate(op, str_rep, x, y, **eval_kwargs)
        except TypeError:

            # TODO: might need to find_common_type here?
            result = np.empty(len(x), dtype=x.dtype)
            mask = notna(x)
            result[mask] = op(x[mask], y)
            result, changed = maybe_upcast_putmask(result, ~mask, np.nan)

        result = missing.fill_zeros(result, x, y, name, fill_zeros)
        return result

    # work only for scalars
    def f(self, other):
        if not is_scalar(other):
            raise ValueError('Simple arithmetic with {name} can only be '
                             'done with scalar values'
                             .format(name=self._constructor.__name__))

        return self._combine(other, op)

    f.__name__ = name
    return f 

# 需要导入模块: from pandas.core.dtypes import cast [as 别名]
# 或者: from pandas.core.dtypes.cast import maybe_upcast_putmask [as 别名]
def _get_values(values, skipna, fill_value=None, fill_value_typ=None,
                isfinite=False, copy=True, mask=None):
    """ utility to get the values view, mask, dtype
    if necessary copy and mask using the specified fill_value
    copy = True will force the copy
    """

    if is_datetime64tz_dtype(values):
        # com.values_from_object returns M8[ns] dtype instead of tz-aware,
        #  so this case must be handled separately from the rest
        dtype = values.dtype
        values = getattr(values, "_values", values)
    else:
        values = com.values_from_object(values)
        dtype = values.dtype

    if mask is None:
        if isfinite:
            mask = _isfinite(values)
        else:
            mask = isna(values)

    if is_datetime_or_timedelta_dtype(values) or is_datetime64tz_dtype(values):
        # changing timedelta64/datetime64 to int64 needs to happen after
        #  finding `mask` above
        values = getattr(values, "asi8", values)
        values = values.view(np.int64)

    dtype_ok = _na_ok_dtype(dtype)

    # get our fill value (in case we need to provide an alternative
    # dtype for it)
    fill_value = _get_fill_value(dtype, fill_value=fill_value,
                                 fill_value_typ=fill_value_typ)

    if skipna:
        if copy:
            values = values.copy()
        if dtype_ok:
            np.putmask(values, mask, fill_value)

        # promote if needed
        else:
            values, changed = maybe_upcast_putmask(values, mask, fill_value)

    elif copy:
        values = values.copy()

    # return a platform independent precision dtype
    dtype_max = dtype
    if is_integer_dtype(dtype) or is_bool_dtype(dtype):
        dtype_max = np.int64
    elif is_float_dtype(dtype):
        dtype_max = np.float64

    return values, mask, dtype, dtype_max, fill_value 

# 需要导入模块: from pandas.core.dtypes import cast [as 别名]
# 或者: from pandas.core.dtypes.cast import maybe_upcast_putmask [as 别名]
def _get_values(values, skipna, fill_value=None, fill_value_typ=None,
                isfinite=False, copy=True):
    """ utility to get the values view, mask, dtype
    if necessary copy and mask using the specified fill_value
    copy = True will force the copy
    """
    values = com._values_from_object(values)
    if isfinite:
        mask = _isfinite(values)
    else:
        mask = isna(values)

    dtype = values.dtype
    dtype_ok = _na_ok_dtype(dtype)

    # get our fill value (in case we need to provide an alternative
    # dtype for it)
    fill_value = _get_fill_value(dtype, fill_value=fill_value,
                                 fill_value_typ=fill_value_typ)

    if skipna:
        if copy:
            values = values.copy()
        if dtype_ok:
            np.putmask(values, mask, fill_value)

        # promote if needed
        else:
            values, changed = maybe_upcast_putmask(values, mask, fill_value)

    elif copy:
        values = values.copy()

    values = _view_if_needed(values)

    # return a platform independent precision dtype
    dtype_max = dtype
    if is_integer_dtype(dtype) or is_bool_dtype(dtype):
        dtype_max = np.int64
    elif is_float_dtype(dtype):
        dtype_max = np.float64

    return values, mask, dtype, dtype_max 

# 需要导入模块: from pandas.core.dtypes import cast [as 别名]
# 或者: from pandas.core.dtypes.cast import maybe_upcast_putmask [as 别名]
def _get_values(values, skipna, fill_value=None, fill_value_typ=None,
                isfinite=False, copy=True):
    """ utility to get the values view, mask, dtype
    if necessary copy and mask using the specified fill_value
    copy = True will force the copy
    """
    values = _values_from_object(values)
    if isfinite:
        mask = _isfinite(values)
    else:
        mask = isna(values)

    dtype = values.dtype
    dtype_ok = _na_ok_dtype(dtype)

    # get our fill value (in case we need to provide an alternative
    # dtype for it)
    fill_value = _get_fill_value(dtype, fill_value=fill_value,
                                 fill_value_typ=fill_value_typ)

    if skipna:
        if copy:
            values = values.copy()
        if dtype_ok:
            np.putmask(values, mask, fill_value)

        # promote if needed
        else:
            values, changed = maybe_upcast_putmask(values, mask, fill_value)

    elif copy:
        values = values.copy()

    values = _view_if_needed(values)

    # return a platform independent precision dtype
    dtype_max = dtype
    if is_integer_dtype(dtype) or is_bool_dtype(dtype):
        dtype_max = np.int64
    elif is_float_dtype(dtype):
        dtype_max = np.float64

    return values, mask, dtype, dtype_max