Series.reindex() does nothing

[dniku]

Code Sample, a copy-pastable example if possible

import numpy as np
import pandas as pd

np.random.seed(42)

nums = np.random.choice(range(0, 999), 1000)
cut = pd.cut(nums, 10)

discretization = cut.value_counts()
discretization.sort_index(inplace=True)

intervals = list(discretization.index)
mids = [i.mid for i in intervals]

print(discretization.reindex(index=mids))

Output:

(-0.998, 99.8]    100
(99.8, 199.6]     103
(199.6, 299.4]     97
(299.4, 399.2]     94
(399.2, 499.0]     97
(499.0, 598.8]     85
(598.8, 698.6]    118
(698.6, 798.4]    103
(798.4, 898.2]    108
(898.2, 998.0]     95
dtype: int64

Problem description

Series.reindex() returns the original Series even though the index is changed.

Expected Output

print(pd.Series(discretization.values, index=mids)) produces:

49.401     100
149.700    103
249.500     97
349.300     94
449.100     97
548.900     85
648.700    118
748.500    103
848.300    108
948.100     95
dtype: int64

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None
python: 3.6.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.39-1-MANJARO
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.20.3
pytest: 3.1.3
pip: 9.0.1
setuptools: 36.2.2
Cython: None
numpy: 1.13.1
scipy: 0.19.1
xarray: None
IPython: 5.3.0
sphinx: None
patsy: None
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: None
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: 0.4.0

[gfyoung]

@Pastafarianist : Thanks for the report! Unfortunately, we can't run this code because discretization_classes is not defined. Could you provide a reproducible code example?

In addition, if you could copy + paste the output (both actual and expected), that would be useful for anyone who's reading these issues.

[dniku]

Apologies; please set it to any integer like 10. I've updated the code in the issue description.

[gfyoung]

I've updated the code in the issue description.

Could you move whichever is your expected output under your sentence regarding the "second print" ?

[dniku]

Done.

[gfyoung]

Did you reverse the two? The expected output should be at the bottom, but I think you put it second-to-last if I understand the issue properly.

[dniku]

No, I did not. The issue description reads correctly right now.

[gfyoung]

Ah, actually, yes I see that now. One other thing: you're missing a definition for intervals in your code. Could you add a definition for that?

[dniku]

So sorry for that. I was copy-pasting from a Jupyter Notebook. I've updated the description and made sure that the code runs as-is.

[gfyoung]

No worries! Can confirm the code is runnable standalone.

That's odd...not sure why you can't reindex when the index are Interval. This re-indexing works if the index is a sequence of integers. Marking as a bug unless otherwise explained. PR is welcome!

[jreback]

@Pastafarianist not sure what you think this should do. .reindex matches up the existing with new. Reindexing with floats is meaningless with an IntervalIndex (which is the index backing a CategoricalIndex).

do you simply want

In [3]: discretization.index = mids

In [4]: discretization
Out[4]: 
49.401     100
149.700    103
249.500     97
349.300     94
449.100     97
548.900     85
648.700    118
748.500    103
848.300    108
948.100     95
dtype: int64

This is reindexing.

In [7]: discretization.reindex(discretization.index[[0, 2, 5]])
Out[7]: 
(-0.998, 99.8]    100
(199.6, 299.4]     97
(499.0, 598.8]     85
dtype: int64