This file is indexed.

/usr/share/pyshared/pandas/sparse/frame.py is in python-pandas 0.7.0-1.

This file is owned by root:root, with mode 0o644.

The actual contents of the file can be viewed below.

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
"""
Data structures for sparse float data. Life is made simpler by dealing only with
float64 data
"""

# pylint: disable=E1101,E1103,W0231,E0202

from numpy import nan
import numpy as np

from pandas.core.common import _pickle_array, _unpickle_array, _try_sort
from pandas.core.index import Index, MultiIndex, NULL_INDEX, _ensure_index
from pandas.core.series import Series
from pandas.core.frame import (DataFrame, extract_index, _prep_ndarray,
                               _default_index)
from pandas.util.decorators import cache_readonly
import pandas.core.common as com
import pandas.core.datetools as datetools

from pandas.sparse.series import SparseSeries
from pandas.util.decorators import Appender


class _SparseMockBlockManager(object):

    def __init__(self, sp_frame):
        self.sp_frame = sp_frame

    def get(self, item):
        return self.sp_frame[item].values

    @property
    def shape(self):
        x, y = self.sp_frame.shape
        return y, x

    @property
    def axes(self):
        return [self.sp_frame.columns, self.sp_frame.index]

class SparseDataFrame(DataFrame):
    """
    DataFrame containing sparse floating point data in the form of SparseSeries
    objects

    Parameters
    ----------
    data : same types as can be passed to DataFrame
    index : array-like, optional
    column : array-like, optional
    default_kind : {'block', 'integer'}, default 'block'
        Default sparse kind for converting Series to SparseSeries. Will not
        override SparseSeries passed into constructor
    default_fill_value : float
        Default fill_value for converting Series to SparseSeries. Will not
        override SparseSeries passed in
    """
    _verbose_info = False
    _columns = None
    _series = None
    _is_mixed_type = False
    ndim = 2

    def __init__(self, data=None, index=None, columns=None,
                 default_kind='block', default_fill_value=None):
        if default_fill_value is None:
            default_fill_value = np.nan

        self.default_kind = default_kind
        self.default_fill_value = default_fill_value

        if isinstance(data, dict):
            sdict, columns, index = self._init_dict(data, index, columns)
        elif isinstance(data, (np.ndarray, list)):
            sdict, columns, index = self._init_matrix(data, index, columns)
        elif isinstance(data, DataFrame):
            sdict, columns, index = self._init_dict(data, data.index,
                                                    data.columns)
        elif data is None:
            sdict = {}

            if index is None:
                index = NULL_INDEX

            if columns is None:
                columns = NULL_INDEX
            else:
                for c in columns:
                    sdict[c] = Series(np.nan, index=index)

        self._series = sdict
        self.columns = columns
        self.index = index

    def _from_axes(self, data, axes):
        columns, index = axes
        return self._constructor(data, index=index, columns=columns)

    @cache_readonly
    def _data(self):
        return _SparseMockBlockManager(self)

    def _consolidate_inplace(self):
        # do nothing when DataFrame calls this method
        pass

    def convert_objects(self):
        # XXX
        return self

    @property
    def _constructor(self):
        def wrapper(data, index=None, columns=None):
            return SparseDataFrame(data, index=index, columns=columns,
                                   default_fill_value=self.default_fill_value,
                                   default_kind=self.default_kind)
        return wrapper

    def _init_dict(self, data, index, columns, dtype=None):
        # pre-filter out columns if we passed it
        if columns is not None:
            columns = _ensure_index(columns)
            data = dict((k, v) for k, v in data.iteritems() if k in columns)
        else:
            columns = Index(_try_sort(data.keys()))

        if index is None:
            index = extract_index(data)

        sp_maker = lambda x: SparseSeries(x, index=index,
                                          kind=self.default_kind,
                                          fill_value=self.default_fill_value,
                                          copy=True)

        sdict = {}
        for k, v in data.iteritems():
            if isinstance(v, Series):
                # Force alignment, no copy necessary
                if not v.index.equals(index):
                    v = v.reindex(index)

                if not isinstance(v, SparseSeries):
                    v = sp_maker(v)
            else:
                if isinstance(v, dict):
                    v = [v.get(i, nan) for i in index]

                v = sp_maker(v)
            sdict[k] = v

        # TODO: figure out how to handle this case, all nan's?
        # add in any other columns we want to have (completeness)
        nan_vec = np.empty(len(index))
        nan_vec.fill(nan)
        for c in columns:
            if c not in sdict:
                sdict[c] = sp_maker(nan_vec)

        return sdict, columns, index

    def _init_matrix(self, data, index, columns, dtype=None):
        data = _prep_ndarray(data, copy=False)
        N, K = data.shape
        if index is None:
            index = _default_index(N)
        if columns is None:
            columns = _default_index(K)

        if len(columns) != K:
            raise Exception('Column length mismatch: %d vs. %d' %
                            (len(columns), K))
        if len(index) != N:
            raise Exception('Index length mismatch: %d vs. %d' %
                            (len(index), N))

        data = dict([(idx, data[:, i]) for i, idx in enumerate(columns)])
        return self._init_dict(data, index, columns, dtype)

    def __array_wrap__(self, result):
        return SparseDataFrame(result, index=self.index, columns=self.columns,
                               default_kind=self.default_kind,
                               default_fill_value=self.default_fill_value)

    def __getstate__(self):
        series = dict((k, (v.sp_index, v.sp_values))
                      for k, v in self.iteritems())
        columns = _pickle_array(self.columns)
        index = _pickle_array(self.index)

        return (series, columns, index, self.default_fill_value,
                self.default_kind)

    def __setstate__(self, state):
        series, cols, idx, fv, kind = state
        columns = _unpickle_array(cols)
        index = _unpickle_array(idx)

        series_dict = {}
        for col, (sp_index, sp_values) in series.iteritems():
            series_dict[col] = SparseSeries(sp_values, sparse_index=sp_index,
                                            fill_value=fv)

        self._series = series_dict
        self.index = index
        self.columns = columns
        self.default_fill_value = fv
        self.default_kind = kind

    def to_dense(self):
        """
        Convert to dense DataFrame

        Returns
        -------
        df : DataFrame
        """
        data = dict((k, v.to_dense()) for k, v in self.iteritems())
        return DataFrame(data, index=self.index)

    def astype(self, dtype):
        raise NotImplementedError

    def copy(self, deep=True):
        """
        Make a copy of this SparseDataFrame
        """
        series = dict((k, v.copy()) for k, v in self.iteritems())
        return SparseDataFrame(series, index=self.index, columns=self.columns,
                               default_fill_value=self.default_fill_value,
                               default_kind=self.default_kind)

    @property
    def density(self):
        """
        Ratio of non-sparse points to total (dense) data points
        represented in the frame
        """
        tot_nonsparse = sum([ser.sp_index.npoints
                             for _, ser in self.iteritems()])
        tot = len(self.index) * len(self.columns)
        return tot_nonsparse / float(tot)

    #----------------------------------------------------------------------
    # Support different internal rep'n of SparseDataFrame

    def _set_item(self, key, value):
        sp_maker = lambda x: SparseSeries(x, index=self.index,
                                          fill_value=self.default_fill_value,
                                          kind=self.default_kind)
        if hasattr(value, '__iter__'):
            if isinstance(value, Series):
                clean_series = value.reindex(self.index)
                if not isinstance(value, SparseSeries):
                    clean_series = sp_maker(clean_series)
            else:
                clean_series = sp_maker(value)

            self._series[key] = clean_series
        # Scalar
        else:
            self._series[key] = sp_maker(value)

        if key not in self.columns:
            self._insert_column(key)

    def _insert_column(self, key):
        self.columns = Index(np.concatenate((self.columns, [key])))

    def __delitem__(self, key):
        """
        Delete column from DataFrame
        """
        loc = self.columns.get_loc(key)
        del self._series[key]
        self._delete_column_index(loc)

    def _delete_column_index(self, loc):
        if loc == len(self.columns) - 1:
            new_columns = self.columns[:loc]
        else:
            new_columns = Index(np.concatenate((self.columns[:loc],
                                               self.columns[loc+1:])))
        self.columns = new_columns

    _index = None
    def _set_index(self, index):
        self._index = _ensure_index(index)
        for v in self._series.values():
            v.index = self._index

    def _get_index(self):
        return self._index

    def _get_columns(self):
        return self._columns

    def _set_columns(self, cols):
        if len(cols) != len(self._series):
            raise Exception('Columns length %d did not match data %d!' %
                            (len(cols), len(self._series)))
        self._columns = _ensure_index(cols)

    index = property(fget=_get_index, fset=_set_index)
    columns = property(fget=_get_columns, fset=_set_columns)

    def __getitem__(self, item):
        """
        Retrieve column or slice from DataFrame
        """
        try:
            # unsure about how kludgy this is
            s = self._series[item]
            s.name = item
            return s
        except (TypeError, KeyError):
            if isinstance(item, slice):
                dateRange = self.index[item]
                return self.reindex(dateRange)

            elif isinstance(item, np.ndarray):
                if len(item) != len(self.index):
                    raise Exception('Item wrong length %d instead of %d!' %
                                    (len(item), len(self.index)))
                newIndex = self.index[item]
                return self.reindex(newIndex)
            else: # pragma: no cover
                raise

    @Appender(DataFrame.get_value.__doc__, indents=0)
    def get_value(self, index, col):
        s = self._series[col]
        return s.get_value(index)

    def set_value(self, index, col, value):
        """
        Put single value at passed column and index

        Parameters
        ----------
        index : row label
        col : column label
        value : scalar value

        Notes
        -----
        This method *always* returns a new object. It is currently not
        particularly efficient (and potentially very expensive) but is provided
        for API compatibility with DataFrame

        Returns
        -------
        frame : DataFrame
        """
        dense = self.to_dense().set_value(index, col, value)
        return dense.to_sparse(kind=self.default_kind,
                               fill_value=self.default_fill_value)

    def _slice(self, slobj, axis=0):
        if axis == 0:
            new_index = self.index[slobj]
            new_columns = self.columns
        else:
            new_index = self.index
            new_columns = self.columns[slobj]

        return self.reindex(index=new_index, columns=new_columns)

    def as_matrix(self, columns=None):
        """
        Convert the frame to its Numpy-array matrix representation

        Columns are presented in sorted order unless a specific list
        of columns is provided.
        """
        if columns is None:
            columns = self.columns

        if len(columns) == 0:
            return np.zeros((len(self.index), 0), dtype=float)

        return np.array([self[col].values for col in columns]).T

    values = property(as_matrix)

    def xs(self, key, axis=0, copy=False):
        """
        Returns a row (cross-section) from the SparseDataFrame as a Series
        object.

        Parameters
        ----------
        key : some index contained in the index

        Returns
        -------
        xs : Series
        """
        if axis == 1:
            data = self[key]
            return data

        i = self.index.get_loc(key)
        series = self._series
        values = [series[k][i] for k in self.columns]
        return Series(values, index=self.columns)

    #----------------------------------------------------------------------
    # Arithmetic-related methods

    def _combine_frame(self, other, func, fill_value=None, level=None):
        this, other = self.align(other, join='outer', level=level,
                                 copy=False)
        new_index, new_columns = this.index, this.columns

        if fill_value is not None or level is not None:
            raise NotImplementedError

        if not self and not other:
            return SparseDataFrame(index=new_index)

        new_data = {}
        for col in new_columns:
            if col in this and col in other:
                new_data[col] = func(this[col], other[col])

        return self._constructor(data=new_data, index=new_index,
                                 columns=new_columns)

    def _combine_match_index(self, other, func, fill_value=None):
        new_data = {}

        if fill_value is not None:
            raise NotImplementedError

        new_index = self.index.union(other.index)
        this = self
        if self.index is not new_index:
            this = self.reindex(new_index)

        if other.index is not new_index:
            other = other.reindex(new_index)

        for col, series in this.iteritems():
            new_data[col] = func(series.values, other.values)

        return self._constructor(new_data, index=new_index,
                                 columns=self.columns)

    def _combine_match_columns(self, other, func, fill_value):
        # patched version of DataFrame._combine_match_columns to account for
        # NumPy circumventing __rsub__ with float64 types, e.g.: 3.0 - series,
        # where 3.0 is numpy.float64 and series is a SparseSeries. Still
        # possible for this to happen, which is bothersome

        if fill_value is not None:
            raise NotImplementedError

        new_data = {}

        union = intersection = self.columns

        if not union.equals(other.index):
            union = other.index.union(self.columns)
            intersection = other.index.intersection(self.columns)

        for col in intersection:
            new_data[col] = func(self[col], float(other[col]))

        return self._constructor(new_data, index=self.index,
                                 columns=union)

    def _combine_const(self, other, func):
        new_data = {}
        for col, series in self.iteritems():
            new_data[col] = func(series, other)

        return self._constructor(data=new_data, index=self.index,
                                 columns=self.columns)

    def _reindex_index(self, index, method, copy, level):
        if level is not None:
            raise Exception('Reindex by level not supported for sparse')

        if self.index.equals(index):
            if copy:
                return self.copy()
            else:
                return self

        if len(self.index) == 0:
            return SparseDataFrame(index=index, columns=self.columns)

        indexer = self.index.get_indexer(index, method)
        mask = indexer == -1
        need_mask = mask.any()

        new_series = {}
        for col, series in self.iteritems():
            values = series.values
            new = values.take(indexer)

            if need_mask:
                np.putmask(new, mask, nan)

            new_series[col] = new

        return SparseDataFrame(new_series, index=index, columns=self.columns,
                               default_fill_value=self.default_fill_value)

    def _reindex_columns(self, columns, copy, level):
        if level is not None:
            raise Exception('Reindex by level not supported for sparse')

        # TODO: fill value handling
        sdict = dict((k, v) for k, v in self.iteritems() if k in columns)
        return SparseDataFrame(sdict, index=self.index, columns=columns,
                               default_fill_value=self.default_fill_value)

    def _reindex_with_indexers(self, index, row_indexer, columns, col_indexer,
                               copy):
        if columns is None:
            columns = self.columns

        new_arrays = {}
        for col in columns:
            if col not in self:
                continue
            if row_indexer is not None:
                new_arrays[col] = com.take_1d(self[col].values, row_indexer)
            else:
                new_arrays[col] = self[col]

        return self._constructor(new_arrays, index=index, columns=columns)

    def _rename_index_inplace(self, mapper):
        self.index = [mapper(x) for x in self.index]

    def _rename_columns_inplace(self, mapper):
        new_series = {}
        new_columns = []

        for col in self.columns:
            new_col = mapper(col)
            if new_col in new_series: # pragma: no cover
                raise Exception('Non-unique mapping!')
            new_series[new_col] = self[col]
            new_columns.append(new_col)

        self.columns = new_columns
        self._series = new_series

    def take(self, indices, axis=0):
        """
        Analogous to ndarray.take, return SparseDataFrame corresponding to
        requested indices along an axis

        Parameters
        ----------
        indices : list / array of ints
        axis : {0, 1}

        Returns
        -------
        taken : SparseDataFrame
        """
        new_values = self.values.take(indices, axis=axis)
        if axis == 0:
            new_columns = self.columns
            new_index = self.index.take(indices)
        else:
            new_columns = self.columns.take(indices)
            new_index = self.index
        return self._constructor(new_values, index=new_index,
                                 columns=new_columns)

    def add_prefix(self, prefix):
        f = (('%s' % prefix) + '%s').__mod__
        return self.rename(columns=f)

    def add_suffix(self, suffix):
        f = ('%s' + ('%s' % suffix)).__mod__
        return self.rename(columns=f)

    def _join_compat(self, other, on=None, how='left', lsuffix='', rsuffix='',
                     sort=False):
        if on is not None:
            raise NotImplementedError
        else:
            return self._join_index(other, how, lsuffix, rsuffix)

    def _join_index(self, other, how, lsuffix, rsuffix):
        if isinstance(other, Series):
            assert(other.name is not None)
            other = SparseDataFrame({other.name : other},
                                    default_fill_value=self.default_fill_value)

        join_index = self.index.join(other.index, how=how)

        this = self.reindex(join_index)
        other = other.reindex(join_index)

        this, other = this._maybe_rename_join(other, lsuffix, rsuffix)

        result_series = this._series
        other_series = other._series
        result_series.update(other_series)

        return self._constructor(result_series, index=join_index)

    def _maybe_rename_join(self, other, lsuffix, rsuffix):
        intersection = self.columns.intersection(other.columns)

        if len(intersection) > 0:
            if not lsuffix and not rsuffix:
                raise Exception('columns overlap: %s' % intersection)

            def lrenamer(x):
                if x in intersection:
                    return '%s%s' % (x, lsuffix)
                return x

            def rrenamer(x):
                if x in intersection:
                    return '%s%s' % (x, rsuffix)
                return x

            this = self.rename(columns=lrenamer)
            other = other.rename(columns=rrenamer)
        else:
            this = self

        return this, other

    def transpose(self):
        """
        Returns a DataFrame with the rows/columns switched.
        """
        return SparseDataFrame(self.values.T, index=self.columns,
                               columns=self.index,
                               default_fill_value=self.default_fill_value,
                               default_kind=self.default_kind)
    T = property(transpose)

    @Appender(DataFrame.count.__doc__)
    def count(self, axis=0, **kwds):
        return self.apply(lambda x: x.count(), axis=axis)

    def cumsum(self, axis=0):
        """
        Return SparseDataFrame of cumulative sums over requested axis.

        Parameters
        ----------
        axis : {0, 1}
            0 for row-wise, 1 for column-wise

        Returns
        -------
        y : SparseDataFrame
        """
        return self.apply(lambda x: x.cumsum(), axis=axis)

    def shift(self, periods, offset=None, timeRule=None):
        """
        Analogous to DataFrame.shift
        """
        if timeRule is not None and offset is None:
            offset = datetools.getOffset(timeRule)

        new_series = {}
        if offset is None:
            new_index = self.index
            for col, s in self.iteritems():
                new_series[col] = s.shift(periods)
        else:
            new_index = self.index.shift(periods, offset)
            for col, s in self.iteritems():
                new_series[col] = SparseSeries(s.sp_values, index=new_index,
                                               sparse_index=s.sp_index,
                                               fill_value=s.fill_value)

        return SparseDataFrame(new_series, index=new_index,
                               columns=self.columns,
                               default_fill_value=self.default_fill_value,
                               default_kind=self.default_kind)

    def apply(self, func, axis=0, broadcast=False):
        """
        Analogous to DataFrame.apply, for SparseDataFrame

        Parameters
        ----------
        func : function
            Function to apply to each column
        axis : {0, 1}
        broadcast : bool, default False
            For aggregation functions, return object of same size with values
            propagated

        Returns
        -------
        applied : Series or SparseDataFrame
        """
        if not len(self.columns):
            return self

        if isinstance(func, np.ufunc):
            new_series = {}
            for k, v in self.iteritems():
                applied = func(v)
                applied.fill_value = func(applied.fill_value)
                new_series[k] = applied
            return SparseDataFrame(new_series, index=self.index,
                                   columns=self.columns,
                                   default_fill_value=self.default_fill_value,
                                   default_kind=self.default_kind)
        else:
            if not broadcast:
                return self._apply_standard(func, axis)
            else:
                return self._apply_broadcast(func, axis)

    def fillna(self, *args, **kwargs):
        raise NotImplementedError

def stack_sparse_frame(frame):
    """
    Only makes sense when fill_value is NaN
    """
    lengths = [s.sp_index.npoints for _, s in frame.iteritems()]
    nobs = sum(lengths)

    # this is pretty fast
    minor_labels = np.repeat(np.arange(len(frame.columns)), lengths)

    inds_to_concat = []
    vals_to_concat = []
    for _, series in frame.iteritems():
        if not np.isnan(series.fill_value):
            raise Exception('This routine assumes NaN fill value')

        int_index = series.sp_index.to_int_index()
        inds_to_concat.append(int_index.indices)
        vals_to_concat.append(series.sp_values)

    major_labels = np.concatenate(inds_to_concat)
    stacked_values = np.concatenate(vals_to_concat)
    index = MultiIndex(levels=[frame.index, frame.columns],
                       labels=[major_labels, minor_labels])

    lp = DataFrame(stacked_values.reshape((nobs, 1)), index=index,
                   columns=['foo'])
    return lp.sortlevel(level=0)


def homogenize(series_dict):
    """
    Conform a set of SparseSeries (with NaN fill_value) to a common SparseIndex
    corresponding to the locations where they all have data

    Parameters
    ----------
    series_dict : dict or DataFrame

    Notes
    -----
    Using the dumbest algorithm I could think of. Should put some more thought
    into this

    Returns
    -------
    homogenized : dict of SparseSeries
    """
    index = None

    need_reindex = False

    for _, series in series_dict.iteritems():
        if not np.isnan(series.fill_value):
            raise Exception('this method is only valid with NaN fill values')

        if index is None:
            index = series.sp_index
        elif not series.sp_index.equals(index):
            need_reindex = True
            index = index.intersect(series.sp_index)

    if need_reindex:
        output = {}
        for name, series in series_dict.iteritems():
            if not series.sp_index.equals(index):
                series = series.sparse_reindex(index)

            output[name] = series
    else:
        output = series_dict

    return output