this issue is present on the latest stable release (as well as latest master at the time of this writing). for frames with only int64 values, the following has strange behaviour
df = {"a": [1,3,1,4]}
df = pd.DataFrame(df, index=pd.date_range("2017-01-01", "2017-01-04"))
# these two are not the same
df.resample("H").mean()["a"].interpolate("cubic") # bad
df.resample("H")["a"].mean().interpolate("cubic") # good
# this works
df.astype("float64").resample("H").mean()["a"].interpolate("cubic")
my workaround is more than enough for me but i figured i'd report it anyway...
INSTALLED VERSIONS ------------------ commit: None python: 3.6.1.final.0 python-bits: 64 OS: Darwin OS-release: 16.5.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: en_IE.UTF-8 LANG: en_IE.UTF-8 LOCALE: en_IE.UTF-8pandas: 0.21.0.dev+31.g0ea0f25bf
pytest: 3.0.7
pip: 9.0.1
setuptools: 35.0.2
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.19.0
xarray: None
IPython: 6.0.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.0.0
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: None
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