BUG: Interpolate over time does not work with Int64 or Float64

[pspeter]

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• (optional) I have confirmed this bug exists on the master branch of pandas.

---

Code Sample, a copy-pastable example

pd.DataFrame({"a": [1, None, 2]}, index=pd.to_datetime([1,2,3], unit="d")).convert_dtypes().interpolate(method="time")

Traceback (most recent call last):
  File "C:\venv\lib\site-packages\IPython\core\interactiveshell.py", line 3437, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-7-4eba02001155>", line 1, in <module>
    pd.DataFrame({"a": [1, None, 2]}, index=pd.to_datetime([1,2,3], unit="d")).convert_dtypes().interpolate(method="time")
  File "C:\venv\lib\site-packages\pandas\core\generic.py", line 7222, in interpolate
    new_data = obj._mgr.interpolate(
  File "C:\venv\lib\site-packages\pandas\core\internals\managers.py", line 593, in interpolate
    return self.apply("interpolate", **kwargs)
  File "C:\venv\lib\site-packages\pandas\core\internals\managers.py", line 427, in apply
    applied = getattr(b, f)(**kwargs)
  File "C:\venv\lib\site-packages\pandas\core\internals\blocks.py", line 1931, in interpolate
    values=values.fillna(value=fill_value, method=method, limit=limit),
  File "C:\venv\lib\site-packages\pandas\core\arrays\base.py", line 655, in fillna
    value, method = validate_fillna_kwargs(value, method)
  File "C:\venv\lib\site-packages\pandas\util\_validators.py", line 367, in validate_fillna_kwargs
    method = clean_fill_method(method)
  File "C:\venv\lib\site-packages\pandas\core\missing.py", line 82, in clean_fill_method
    raise ValueError(f"Invalid fill method. Expecting {expecting}. Got {method}")

ValueError: Invalid fill method. Expecting pad (ffill) or backfill (bfill). Got time

Problem description

Without the convert_dtypes() this works without any problems.

Expected Output

              a
1970-01-02  1.0
1970-01-03  1.5
1970-01-04  2.0

Output of pd.show_versions()

INSTALLED VERSIONS

commit : f2c8480
python : 3.8.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English
pandas : 1.2.3
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.2.1
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None

[janlugt]

Ran into this today on pandas 1.4.1, so the bug still exists. My assumption would be that Float64 with pd.NA's works the same as a float64 with np.nan's, but I guess Float64 is still experimental, and that assumption is not always true.

[ba05]

Ran in to this issue with pandas 1.4.2 when plotting with matplotlib. Had to cast the Series with .astype('float'). Original values were Float64 with some NaNs mixed in.

[chukarsten]

We also ran into this multiple times today!

[tamargrey]

Still running into this in pandas 1.5.3 with both Int64 and boolean dtypes - ValueError: Invalid fill method. Expecting pad (ffill) or backfill (bfill). Got linear