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Reproducible Example

import numpy as np
import pandas as pd

pd.qcut([1,2,3,4,5,-np.inf, np.inf], q=3)


Results in:

ValueError: Bin edges must be unique: array([nan, 2., 4., nan]). You can drop duplicate edges by setting the 'duplicates' kwarg


```python
import numpy as np
import pandas as pd
pd.qcut([1,2,3,4,5,-np.inf, np.inf], q=3, duplicates="drop")

Results in:

ValueError: missing values must be missing in the same location both left and right sides

```

Issue Description

After upgrading from pandas 1.1.5 to the latest version 1.5.3 I am now receiving an error when the list contains np.inf that is given to pd.qcut. In the older version this was working. Using duplicates="drop" also doesn't help.

Expected Behavior

I was expecting the first and last bin to contain np.inf. This was working in pandas 1.1.5.

Installed Versions

INSTALLED VERSIONS ------------------ commit : 2e218d10984e9919f0296931d92ea851c6a6faf5 python : 3.9.16.final.0 python-bits : 64 OS : Darwin OS-release : 22.2.0 Version : Darwin Kernel Version 22.2.0: Fri Nov 11 02:04:44 PST 2022; root:xnu-8792.61.2~4/RELEASE_ARM64_T8103 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : None LOCALE : None.UTF-8 pandas : 1.5.3 numpy : 1.23.5 pytz : 2022.7.1 dateutil : 2.8.2 setuptools : 67.0.0 pip : 22.3.1 Cython : None pytest : 7.2.1 hypothesis : None sphinx : 6.1.3 blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : 2.9.5 jinja2 : 3.1.2 IPython : None pandas_datareader: None bs4 : None bottleneck : None brotli : None fastparquet : 2023.1.0 fsspec : 0.8.7 gcsfs : None matplotlib : None numba : 0.56.4 numexpr : None odfpy : None openpyxl : 3.0.10 pandas_gbq : None pyarrow : None pyreadstat : None pyxlsb : None s3fs : None scipy : 1.9.3 snappy : None sqlalchemy : 1.4.46 tables : None tabulate : 0.8.10 xarray : None xlrd : 1.2.0 xlwt : None zstandard : None tzdata : None

Comment From: HansBambel

Since this was working in 1.1.3 and not working in 1.5.3 it is likely something different than, but they might be related: https://github.com/pandas-dev/pandas/issues/11113 https://github.com/pandas-dev/pandas/issues/24314