BUG: qcut fails with Float64Dtype · Issue #40730 · pandas-dev/pandas (original) (raw)

Code Sample, a copy-pastable example

series = pd.Series([1.0, 2.0, 3.0, 4.4], dtype=pd.Float64Dtype()) pd.qcut(series, 2)

Problem description

pd.qcut currently accepts the nullable Int64Dtype as well as 'float64', so I would expect it to work with the Float64Dtype as well. Instead the following error is produced:

IndexError Traceback (most recent call last) in 1 series = pd.Series([1.0,2.0,3.0,4.0], dtype=pd.Float64Dtype()) ----> 2 pd.qcut(series, 2)

~/.pyenv/versions/3.8.2/envs/woodwork/lib/python3.8/site-packages/pandas/core/reshape/tile.py in qcut(x, q, labels, retbins, precision, duplicates) 356 quantiles = q 357 bins = algos.quantile(x, quantiles) --> 358 fac, bins = _bins_to_cuts( 359 x, 360 bins,

~/.pyenv/versions/3.8.2/envs/woodwork/lib/python3.8/site-packages/pandas/core/reshape/tile.py in _bins_to_cuts(x, bins, right, labels, precision, include_lowest, dtype, duplicates, ordered) 408 409 if include_lowest: --> 410 ids[x == bins[0]] = 1 411 412 na_mask = isna(x) | (ids == len(bins)) | (ids == 0)

IndexError: only integers, slices (:), ellipsis (...), numpy.newaxis (None) and integer or boolean arrays are valid indices

Expected Output

Should match that of float64

0    (0.999, 2.5]
1    (0.999, 2.5]
2      (2.5, 4.0]
3      (2.5, 4.0]
dtype: category
Categories (2, interval[float64]): [(0.999, 2.5] < (2.5, 4.0]]

Output of pd.show_versions()

INSTALLED VERSIONS

commit : f2c8480
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Sun Jul 5 00:43:10 PDT 2020; root:xnu-6153.141.1~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.2.3
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 41.2.0
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.7
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None