BUG: `series.reindex(mi)` behaves different for series with Index and MultiIndex (original) (raw)

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Reproducible Example

Create a series with Index and a MultiIndex to use for reindexing later

series = pd.Series( ... [26.7300, 24.2550], ... index=pd.Index([81, 82], name='a') ... ) series a 81 26.730 82 24.255 dtype: float64 series.index Index([81, 82], dtype='int64', name='a') other_index = pd.MultiIndex( ... levels=[ ... pd.Index([81, 82], name='a'), ... pd.Index([np.nan], name='b'), ... pd.Index([ ... '2018-06-01', '2018-07-01' ... ], name='c') ... ], ... codes=[ ... [0, 0, 1, 1], ... [0, 0, 0, 0], ... [0, 1, 0, 1] ... ], ... names=['a', 'b', 'c'] ... ) other_index MultiIndex([(81, nan, '2018-06-01'), (81, nan, '2018-07-01'), (82, nan, '2018-06-01'), (82, nan, '2018-07-01')], names=['a', 'b', 'c'])

reindex to MultiIndex (other_index) which expands series.index by two more levels.
unfortunately the reindex sets all values of the original series to NaN which can be fixed by turning series.index into a 1-level MultiIndex first

series.reindex(other_index) # this removes all values of the series a b c
81 NaN 2018-06-01 NaN 2018-07-01 NaN 82 NaN 2018-06-01 NaN 2018-07-01 NaN dtype: float64

apply to_mi(...) to turn the series.index into a 1-level MultiIndex
rerun reindex on the new series with MultiIndex and the values are maintained/filled as expected

def to_mi(series): ... if isinstance(series.index, pd.MultiIndex): ... series_mi = series.index ... else: ... level_names = [series.index.name] ... level_values = [series.index] ... series_mi = pd.MultiIndex.from_arrays(level_values, names=level_names) ... series_with_mi = pd.Series(series.values, index=series_mi, name=series.name) ... return series_with_mi ... series_mi = to_mi(series) series_mi a 81 26.730 82 24.255 dtype: float64 series_mi.index MultiIndex([(81,), (82,)], names=['a']) series_mi.reindex(other_index) a b c
81 NaN 2018-06-01 26.730 2018-07-01 26.730 82 NaN 2018-06-01 24.255 2018-07-01 24.255 dtype: float64

Issue Description

In the above case, series.reindex(multi_index) will turn the series values to NaN when the series has a single Index. However when the series index is converted to a 1-level MultiIndex prior to the reindex, the values are maintained and filled as expected.

In my opinion it shouldn't matter if a 1-level MultiIndex or an Index is used for a reindex - the outcomes should be the same.

As a further discussion point (here or elsewhere), this issue (and others) also begs the question why a distinction between Index and MultiIndex is necessary (I suspect there are historic reasons). I would imagine that many issues (and code) would go away if MultiIndex was used exclusively (even for 1-dimensional indices).

Expected Behavior

The missing levels in series_mi (compared to other_index) are added and the values of the partial index from the original series are used to fill the places of the added indices.

>>> series_mi.reindex(other_index)
a   b    c         
81  NaN  2018-06-01    26.730 # from index <81> of `series` (`series_mi`)
         2018-07-01    26.730 # from index <81> of `series` (`series_mi`)
82  NaN  2018-06-01    24.255 # from index <82> of `series` (`series_mi`)
         2018-07-01    24.255 # from index <82> of `series` (`series_mi`)
dtype: float64

Installed Versions

INSTALLED VERSIONS

commit : 3979e95
python : 3.11.11
python-bits : 64
OS : Linux
OS-release : 6.12.11-200.fc41.x86_64
Version : #1 SMP PREEMPT_DYNAMIC Fri Jan 24 04:59:58 UTC 2025
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8

pandas : 3.0.0.dev0+1909.g3979e954a3.dirty
numpy : 1.26.4
dateutil : 2.9.0.post0
pip : 24.2
Cython : 3.0.11
sphinx : 8.1.3
IPython : 8.32.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.13.3
blosc : None
bottleneck : 1.4.2
fastparquet : 2024.11.0
fsspec : 2025.2.0
html5lib : 1.1
hypothesis : 6.125.2
gcsfs : 2025.2.0
jinja2 : 3.1.5
lxml.etree : 5.3.0
matplotlib : 3.10.0
numba : 0.61.0
numexpr : 2.10.2
odfpy : None
openpyxl : 3.1.5
psycopg2 : 2.9.10
pymysql : 1.4.6
pyarrow : 19.0.0
pyreadstat : 1.2.8
pytest : 8.3.4
python-calamine : None
pytz : 2025.1
pyxlsb : 1.0.10
s3fs : 2025.2.0
scipy : 1.15.1
sqlalchemy : 2.0.38
tables : 3.10.2
tabulate : 0.9.0
xarray : 2024.9.0
xlrd : 2.0.1
xlsxwriter : 3.2.2
zstandard : 0.23.0
tzdata : 2025.1
qtpy : None
pyqt5 : None