Description
Code Sample, a copy-pastable example if possible
import pandas as pd
import numpy as np
a = pd.Series([0, 1, np.nan, 3, 4, np.nan, np.nan, np.nan, np.nan])
a_int=a.interpolate(method='cubic', limit_area=None)Problem description
Some of the offered methods (it seems all of them that are provided by interp1d) are unable to extrapolate over np.nan. However, the limit_area switch for df.interpolate() indicates you can force extrapolation. A combination of limit_area=None and an incompatible method should raise a warning.
There used to be a similar issue where extrapolation over trailing NaN was done unintentionally, so maybe the fix for that overdid it. #8000
Expected Output
Extrapolation over the NaNs in the array is expected. Using a different method, such as pchip achieves this.
Output of pd.show_versions()
[paste the output of pd.show_versions() here below this line]
INSTALLED VERSIONS
commit : None
python : 3.7.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 0.25.3 (also tested with 1.0.0)
numpy : 1.15.4
pytz : 2018.9
dateutil : 2.7.5
pip : 20.0.2
setuptools : 41.0.1
Cython : 0.29.15
pytest : None
hypothesis : None
sphinx : 1.8.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.5.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.3
matplotlib : 3.0.3
numexpr : None
odfpy : None
openpyxl : 2.5.12
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None