BUG: convert_dtypes() leaves int + pd.NA Series as object instead of converting to Int64 · Issue #48791 · pandas-dev/pandas (original) (raw)

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Reproducible Example

import numpy as np import pandas as pd from pandas.testing import assert_series_equal

Passes -- both dtypes are Int64

s1 = pd.Series([0, 1, 2, 3]) assert_series_equal(s1.astype("Int64"), s1.convert_dtypes())

Passes -- both dtypes are Int64

s2 = pd.Series([0, 1, 2, np.nan]) assert_series_equal(s2.astype("Int64"), s2.convert_dtypes())

Passes -- both dtypes are Int64

s3 = pd.Series([0, 1, 2, None]) assert_series_equal(s3.astype("Int64"), s3.convert_dtypes())

Fails -- dtypes are different (object vs. Int64)

s4 = pd.Series([0, 1, 2, pd.NA]) assert_series_equal(s4.astype("Int64"), s4.convert_dtypes())

Attribute "dtype" are different

[left]: Int64

[right]: object

Issue Description

With a Series containing python ints and values of np.nan or None, convert_dtypes() infers that the type should be the nullable pandas type Int64 and converts, as it does for a Series composed entirely of valid ints. However, a Series that contains int and pd.NA missing values unexpectedly retains its object dtype.

This behavior is present in at least pandas v1.4.4 and v1.5.0.

Expected Behavior

Given that pd.NA is already the appropriate missing value for an Int64 Series, I thought that convert_dtypes() would convert the type of the Series from object to Int64. This is also what I expected based on the documentation:

For object-dtyped columns, if infer_objects is True, use the inference rules as during normal Series/DataFrame construction. Then, if possible, convert to StringDtype, BooleanDtype or an appropriate integer or floating extension type, otherwise leave as object.

Clearly it's possible to convert to Int64 since the explicit astype("Int64") has no problem.

Installed Versions

INSTALLED VERSIONS

commit : 87cfe4e
python : 3.10.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.15.0-48-generic
Version : #54-Ubuntu SMP Fri Aug 26 13:26:29 UTC 2022
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.5.0
numpy : 1.23.3
pytz : 2022.2.1
dateutil : 2.8.2
setuptools : 65.4.0
pip : 22.2.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : None
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
zstandard : None
tzdata : None