Return the last row(s) without any NaNs before `where`.

        The last row (for each element in `where`, if list) without any
        NaN is taken.
        In case of a :class:`~pandas.DataFrame`, the last row without NaN
        considering only the subset of columns (if not `None`)

        .. versionadded:: 0.19.0 For DataFrame

        If there is no good value, NaN is returned for a Series or
        a Series of NaN values for a DataFrame

        Parameters
        ----------
        where : date or array-like of dates
            Date(s) before which the last row(s) are returned.
        subset : str or array-like of str, default `None`
            For DataFrame, if not `None`, only use these columns to
            check for NaNs.

        Returns
        -------
        scalar, Series, or DataFrame

            The return can be:

            * scalar : when `self` is a Series and `where` is a scalar
            * Series: when `self` is a Series and `where` is an array-like,
              or when `self` is a DataFrame and `where` is a scalar
            * DataFrame : when `self` is a DataFrame and `where` is an
              array-like

            Return scalar, Series, or DataFrame.

        See Also
        --------
        merge_asof : Perform an asof merge. Similar to left join.

        Notes
        -----
        Dates are assumed to be sorted. Raises if this is not the case.

        Examples
        --------
        A Series and a scalar `where`.

        >>> s = pd.Series([1, 2, np.nan, 4], index=[10, 20, 30, 40])
        >>> s
        10    1.0
        20    2.0
        30    NaN
        40    4.0
        dtype: float64

        >>> s.asof(20)
        2.0

        For a sequence `where`, a Series is returned. The first value is
        NaN, because the first element of `where` is before the first
        index value.

        >>> s.asof([5, 20])
        5     NaN
        20    2.0
        dtype: float64

        Missing values are not considered. The following is ``2.0``, not
        NaN, even though NaN is at the index location for ``30``.

        >>> s.asof(30)
        2.0

        Take all columns into consideration

        >>> df = pd.DataFrame({'a': [10, 20, 30, 40, 50],
        ...                    'b': [None, None, None, None, 500]},
        ...                   index=pd.DatetimeIndex(['2018-02-27 09:01:00',
        ...                                           '2018-02-27 09:02:00',
        ...                                           '2018-02-27 09:03:00',
        ...                                           '2018-02-27 09:04:00',
        ...                                           '2018-02-27 09:05:00']))
        >>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
        ...                           '2018-02-27 09:04:30']))
                              a   b
        2018-02-27 09:03:30 NaN NaN
        2018-02-27 09:04:30 NaN NaN

        Take a single column into consideration

        >>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
        ...                           '2018-02-27 09:04:30']),
        ...         subset=['a'])
                                 a   b
        2018-02-27 09:03:30   30.0 NaN
        2018-02-27 09:04:30   40.0 NaN

def asof(self, where, subset=None):
        """
        Return the last row(s) without any NaNs before `where`.

        The last row (for each element in `where`, if list) without any
        NaN is taken.
        In case of a :class:`~pandas.DataFrame`, the last row without NaN
        considering only the subset of columns (if not `None`)

        .. versionadded:: 0.19.0 For DataFrame

        If there is no good value, NaN is returned for a Series or
        a Series of NaN values for a DataFrame

        Parameters
        ----------
        where : date or array-like of dates
            Date(s) before which the last row(s) are returned.
        subset : str or array-like of str, default `None`
            For DataFrame, if not `None`, only use these columns to
            check for NaNs.

        Returns
        -------
        scalar, Series, or DataFrame

            The return can be:

            * scalar : when `self` is a Series and `where` is a scalar
            * Series: when `self` is a Series and `where` is an array-like,
              or when `self` is a DataFrame and `where` is a scalar
            * DataFrame : when `self` is a DataFrame and `where` is an
              array-like

            Return scalar, Series, or DataFrame.

        See Also
        --------
        merge_asof : Perform an asof merge. Similar to left join.

        Notes
        -----
        Dates are assumed to be sorted. Raises if this is not the case.

        Examples
        --------
        A Series and a scalar `where`.

        >>> s = pd.Series([1, 2, np.nan, 4], index=[10, 20, 30, 40])
        >>> s
        10    1.0
        20    2.0
        30    NaN
        40    4.0
        dtype: float64

        >>> s.asof(20)
        2.0

        For a sequence `where`, a Series is returned. The first value is
        NaN, because the first element of `where` is before the first
        index value.

        >>> s.asof([5, 20])
        5     NaN
        20    2.0
        dtype: float64

        Missing values are not considered. The following is ``2.0``, not
        NaN, even though NaN is at the index location for ``30``.

        >>> s.asof(30)
        2.0

        Take all columns into consideration

        >>> df = pd.DataFrame({'a': [10, 20, 30, 40, 50],
        ...                    'b': [None, None, None, None, 500]},
        ...                   index=pd.DatetimeIndex(['2018-02-27 09:01:00',
        ...                                           '2018-02-27 09:02:00',
        ...                                           '2018-02-27 09:03:00',
        ...                                           '2018-02-27 09:04:00',
        ...                                           '2018-02-27 09:05:00']))
        >>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
        ...                           '2018-02-27 09:04:30']))
                              a   b
        2018-02-27 09:03:30 NaN NaN
        2018-02-27 09:04:30 NaN NaN

        Take a single column into consideration

        >>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
        ...                           '2018-02-27 09:04:30']),
        ...         subset=['a'])
                                 a   b
        2018-02-27 09:03:30   30.0 NaN
        2018-02-27 09:04:30   40.0 NaN
        """
        if isinstance(where, str):
            from pandas import to_datetime
            where = to_datetime(where)

        if not self.index.is_monotonic:
            raise ValueError("asof requires a sorted index")

        is_series = isinstance(self, ABCSeries)
        if is_series:
            if subset is not None:
                raise ValueError("subset is not valid for Series")
        elif self.ndim > 2:
            raise NotImplementedError("asof is not implemented "
                                      "for {type}".format(type=type(self)))
        else:
            if subset is None:
                subset = self.columns
            if not is_list_like(subset):
                subset = [subset]

        is_list = is_list_like(where)
        if not is_list:
            start = self.index[0]
            if isinstance(self.index, PeriodIndex):
                where = Period(where, freq=self.index.freq).ordinal
                start = start.ordinal

            if where < start:
                if not is_series:
                    from pandas import Series
                    return Series(index=self.columns, name=where)
                return np.nan

            # It's always much faster to use a *while* loop here for
            # Series than pre-computing all the NAs. However a
            # *while* loop is extremely expensive for DataFrame
            # so we later pre-compute all the NAs and use the same
            # code path whether *where* is a scalar or list.
            # See PR: https://github.com/pandas-dev/pandas/pull/14476
            if is_series:
                loc = self.index.searchsorted(where, side='right')
                if loc > 0:
                    loc -= 1

                values = self._values
                while loc > 0 and isna(values[loc]):
                    loc -= 1
                return values[loc]

        if not isinstance(where, Index):
            where = Index(where) if is_list else Index([where])

        nulls = self.isna() if is_series else self[subset].isna().any(1)
        if nulls.all():
            if is_series:
                return self._constructor(np.nan, index=where, name=self.name)
            elif is_list:
                from pandas import DataFrame
                return DataFrame(np.nan, index=where, columns=self.columns)
            else:
                from pandas import Series
                return Series(np.nan, index=self.columns, name=where[0])

        locs = self.index.asof_locs(where, ~(nulls.values))

        # mask the missing
        missing = locs == -1
        data = self.take(locs, is_copy=False)
        data.index = where
        data.loc[missing] = np.nan
        return data if is_list else data.iloc[-1]