Python pandas.core.frame.DataFrame.sort_index() Examples

The following are 8 code examples of pandas.core.frame.DataFrame.sort_index(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module pandas.core.frame.DataFrame , or try the search function .
Example #1
Source Project: vnpy_crypto   Author: birforce   File: series.py    License: MIT License 5 votes vote down vote up
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) 
Example #2
Source Project: Splunking-Crime   Author: nccgroup   File: series.py    License: GNU Affero General Public License v3.0 5 votes vote down vote up
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) 
Example #3
Source Project: elasticintel   Author: securityclippy   File: series.py    License: GNU General Public License v3.0 5 votes vote down vote up
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) 
Example #4
Source Project: recruit   Author: Frank-qlu   File: series.py    License: Apache License 2.0 4 votes vote down vote up
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 
Example #5
Source Project: vnpy_crypto   Author: birforce   File: series.py    License: MIT License 4 votes vote down vote up
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 
Example #6
Source Project: predictive-maintenance-using-machine-learning   Author: awslabs   File: series.py    License: Apache License 2.0 4 votes vote down vote up
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 
Example #7
Source Project: Splunking-Crime   Author: nccgroup   File: series.py    License: GNU Affero General Public License v3.0 4 votes vote down vote up
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) 
Example #8
Source Project: elasticintel   Author: securityclippy   File: series.py    License: GNU General Public License v3.0 4 votes vote down vote up
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)