I would like to interpolate a Series at new values (which itself doesn't appear to be supported), which can be accomplished by reindexing and interpolating. An example of this is

s = pd.Series(np.random.rand(6), index= np.arange(20,140,20))

idx = pd.Index(np.arange(20,140,10))

s.reindex(s.index + idx).interpolate()

But the operation s.index + idx results in ValueError: cannot evaluate a numeric op with unequal lengths.

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.5.1.final.0 python-bits: 64 OS: Windows OS-release: 7 machine: AMD64 processor: Intel64 Family 6 Model 30 Stepping 5, GenuineIntel byteorder: little LC_ALL: None LANG: en_US LOCALE: None.None pandas: 0.19.1 nose: 1.3.7 pip: 8.1.1 setuptools: 20.3 Cython: 0.23.4 numpy: 1.11.0 scipy: 0.17.0 statsmodels: 0.6.1 xarray: None IPython: 4.1.2 sphinx: 1.3.1 patsy: 0.4.0 dateutil: 2.5.1 pytz: 2016.2 blosc: None bottleneck: 1.0.0 tables: 3.2.2 numexpr: 2.5.2 matplotlib: 1.5.1 openpyxl: 2.3.2 xlrd: 0.9.4 xlwt: 1.0.0 xlsxwriter: 0.8.4 lxml: 3.6.0 bs4: 4.4.1 html5lib: None httplib2: None apiclient: None sqlalchemy: 1.0.12 pymysql: None psycopg2: None jinja2: 2.8 boto: 2.39.0 pandas_datareader: None

Comment From: sinhrks

+ operator no longer means union. See: - http://pandas.pydata.org/pandas-docs/version/0.19.0/whatsnew.html#whatsnew-0150-deprecations

you can use .union instead.

s.reindex(s.index.union(idx)).interpolate()