Python DataFrame.sort_index方法代码示例

# 需要导入模块: from pandas.core.frame import DataFrame [as 别名]
# 或者: from pandas.core.frame.DataFrame import sort_index [as 别名]
def sortlevel(self, level=0, ascending=True, sort_remaining=True):
        """Sort Series with MultiIndex by chosen level. Data will be
        lexicographically sorted by the chosen level followed by the other
        levels (in order),

        .. deprecated:: 0.20.0
            Use :meth:`Series.sort_index`

        Parameters
        ----------
        level : int or level name, default None
        ascending : bool, default True

        Returns
        -------
        sorted : Series

        See Also
        --------
        Series.sort_index(level=...)

        """
        warnings.warn("sortlevel is deprecated, use sort_index(level=...)",
                      FutureWarning, stacklevel=2)
        return self.sort_index(level=level, ascending=ascending,
                               sort_remaining=sort_remaining) 

# 需要导入模块: from pandas.core.frame import DataFrame [as 别名]
# 或者: from pandas.core.frame.DataFrame import sort_index [as 别名]
def sortlevel(self, level=0, ascending=True, sort_remaining=True):
        """
        DEPRECATED: use :meth:`Series.sort_index`

        Sort Series with MultiIndex by chosen level. Data will be
        lexicographically sorted by the chosen level followed by the other
        levels (in order)

        Parameters
        ----------
        level : int or level name, default None
        ascending : bool, default True

        Returns
        -------
        sorted : Series

        See Also
        --------
        Series.sort_index(level=...)

        """
        warnings.warn("sortlevel is deprecated, use sort_index(level=...)",
                      FutureWarning, stacklevel=2)
        return self.sort_index(level=level, ascending=ascending,
                               sort_remaining=sort_remaining) 

# 需要导入模块: from pandas.core.frame import DataFrame [as 别名]
# 或者: from pandas.core.frame.DataFrame import sort_index [as 别名]
def _init_dict(self, data, index=None, dtype=None):
        """
        Derive the "_data" and "index" attributes of a new Series from a
        dictionary input.

        Parameters
        ----------
        data : dict or dict-like
            Data used to populate the new Series
        index : Index or index-like, default None
            index for the new Series: if None, use dict keys
        dtype : dtype, default None
            dtype for the new Series: if None, infer from data

        Returns
        -------
        _data : BlockManager for the new Series
        index : index for the new Series
        """
        # Looking for NaN in dict doesn't work ({np.nan : 1}[float('nan')]
        # raises KeyError), so we iterate the entire dict, and align
        if data:
            keys, values = zip(*compat.iteritems(data))
            values = list(values)
        elif index is not None:
            # fastpath for Series(data=None). Just use broadcasting a scalar
            # instead of reindexing.
            values = na_value_for_dtype(dtype)
            keys = index
        else:
            keys, values = [], []

        # Input is now list-like, so rely on "standard" construction:
        s = Series(values, index=keys, dtype=dtype)

        # Now we just make sure the order is respected, if any
        if data and index is not None:
            s = s.reindex(index, copy=False)
        elif not PY36 and not isinstance(data, OrderedDict) and data:
            # Need the `and data` to avoid sorting Series(None, index=[...])
            # since that isn't really dict-like
            try:
                s = s.sort_index()
            except TypeError:
                pass
        return s._data, s.index 

# 需要导入模块: from pandas.core.frame import DataFrame [as 别名]
# 或者: from pandas.core.frame.DataFrame import sort_index [as 别名]
def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
                   kind='quicksort', na_position='last', sort_remaining=True):

        # TODO: this can be combined with DataFrame.sort_index impl as
        # almost identical
        inplace = validate_bool_kwarg(inplace, 'inplace')
        axis = self._get_axis_number(axis)
        index = self.index

        if level:
            new_index, indexer = index.sortlevel(level, ascending=ascending,
                                                 sort_remaining=sort_remaining)
        elif isinstance(index, MultiIndex):
            from pandas.core.sorting import lexsort_indexer
            labels = index._sort_levels_monotonic()
            indexer = lexsort_indexer(labels._get_labels_for_sorting(),
                                      orders=ascending,
                                      na_position=na_position)
        else:
            from pandas.core.sorting import nargsort

            # Check monotonic-ness before sort an index
            # GH11080
            if ((ascending and index.is_monotonic_increasing) or
                    (not ascending and index.is_monotonic_decreasing)):
                if inplace:
                    return
                else:
                    return self.copy()

            indexer = nargsort(index, kind=kind, ascending=ascending,
                               na_position=na_position)

        indexer = _ensure_platform_int(indexer)
        new_index = index.take(indexer)
        new_index = new_index._sort_levels_monotonic()

        new_values = self._values.take(indexer)
        result = self._constructor(new_values, index=new_index)

        if inplace:
            self._update_inplace(result)
        else:
            return result.__finalize__(self)