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SORTING MULTIINDICES

Sorting multi-indices is important because many slicing operations only work on sorted data. Let’s create a multi-indexed Series object with indices that are not sorted and then try to slice it. Here’s the Series object with random data:

In [10]:

import numpy as np
import pandas as pd

index = pd.MultiIndex.from_product([['Columbus', 'Seattle', 'Denver', 'Dallas'], [2019, 2020]])
data = pd.Series(np.random.rand(8), index=index)
data.index.names = ['city', 'year']
data

Out[10]:

city      year
Columbus  2019    0.374872
          2020    0.996970
Seattle   2019    0.614238
          2020    0.597602
Denver    2019    0.997905
          2020    0.006256
Dallas    2019    0.660692
          2020    0.493068
dtype: float64

As you can see the cities are not sorted. Let’s see what happens if we try to slice the object:

In [11]:

data['Columbus':'Denver']

---------------------------------------------------------------------------
UnsortedIndexError                        Traceback (most recent call last)
<ipython-input-11-e98b33f60046> in <module>
----> 1 data['Columbus':'Denver']

~\Anaconda3\lib\site-packages\pandas\core\series.py in __getitem__(self, key)
   1108             key = check_bool_indexer(self.index, key)
   1109 
-> 1110         return self._get_with(key)
   1111 
   1112     def _get_with(self, key):

~\Anaconda3\lib\site-packages\pandas\core\series.py in _get_with(self, key)
   1113         # other: fancy integer or otherwise
   1114         if isinstance(key, slice):
-> 1115             indexer = self.index._convert_slice_indexer(key, kind="getitem")
   1116             return self._get_values(indexer)
   1117         elif isinstance(key, ABCDataFrame):

~\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in _convert_slice_indexer(self, key, kind)
   3214         else:
   3215             try:
-> 3216                 indexer = self.slice_indexer(start, stop, step, kind=kind)
   3217             except Exception:
   3218                 if is_index_slice:

~\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in slice_indexer(self, start, end, step, kind)
   5032         slice(1, 3)
   5033         """
-> 5034         start_slice, end_slice = self.slice_locs(start, end, step=step, kind=kind)
   5035 
   5036         # return a slice

~\Anaconda3\lib\site-packages\pandas\core\indexes\multi.py in slice_locs(self, start, end, step, kind)
   2579         # This function adds nothing to its parent implementation (the magic
   2580         # happens in get_slice_bound method), but it adds meaningful doc.
-> 2581         return super().slice_locs(start, end, step, kind=kind)
   2582 
   2583     def _partial_tup_index(self, tup, side="left"):

~\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in slice_locs(self, start, end, step, kind)
   5246         start_slice = None
   5247         if start is not None:
-> 5248             start_slice = self.get_slice_bound(start, "left", kind)
   5249         if start_slice is None:
   5250             start_slice = 0

~\Anaconda3\lib\site-packages\pandas\core\indexes\multi.py in get_slice_bound(self, label, side, kind)
   2523         if not isinstance(label, tuple):
   2524             label = (label,)
-> 2525         return self._partial_tup_index(label, side=side)
   2526 
   2527     def slice_locs(self, start=None, end=None, step=None, kind=None):

~\Anaconda3\lib\site-packages\pandas\core\indexes\multi.py in _partial_tup_index(self, tup, side)
   2585             raise UnsortedIndexError(
   2586                 "Key length (%d) was greater than MultiIndex"
-> 2587                 " lexsort depth (%d)" % (len(tup), self.lexsort_depth)
   2588             )
   2589 

UnsortedIndexError: 'Key length (1) was greater than MultiIndex lexsort depth (0)'

Yes, that’s right. You get an UnsortedIndexError. So, let’s sort the indices. To do that we’ll use the sort_index method:

In [12]:

data = data.sort_index()
data

Out[12]:

city      year
Columbus  2019    0.374872
          2020    0.996970
Dallas    2019    0.660692
          2020    0.493068
Denver    2019    0.997905
          2020    0.006256
Seattle   2019    0.614238
          2020    0.597602
dtype: float64

Now the indices are sorted and slicing will work:

In [13]:

data['Columbus':'Denver']

Out[13]:

city      year
Columbus  2019    0.374872
          2020    0.996970
Dallas    2019    0.660692
          2020    0.493068
Denver    2019    0.997905
          2020    0.006256
dtype: float64