Description
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I have checked that this issue has not already been reported.
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I have confirmed this bug exists on the latest version of pandas.
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(optional) I have confirmed this bug exists on the master branch of pandas.
Code Sample, a copy-pastable example
import pandas as pd
import numpy as np
def create(M, shift=0):
lst=[x<<shift for x in range(M)]
return np.array(lst, dtype=np.object)
#linear running time:
%timeit pd.unique(create(10**3, 0))
%timeit pd.unique(create(10**4, 0))
# quadratic running time:
# for shift=32, the lower 32bits returned
# by Py_Hash are 0 for 64bit builds
%timeit pd.unique(create(10**3, 32))
%timeit pd.unique(create(10**4, 32))Problem description
The times for small integers are: 222 µs vs 1.6 ms, i.e. about factor 10
The times for big integers are: 16.5 ms vs 1.68 s, i.e. about factor 100
Expected Output
Also for big integers, the factor should be about 10 (i.e. linear) and not quadratic.
Output of pd.show_versions()
INSTALLED VERSIONS
commit : 824869f
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-53-generic
Version : #74-Ubuntu SMP Fri Dec 2 15:59:10 UTC 2016
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0.dev0+1033.g824869f
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : 0.29.21
pytest : 5.4.3
hypothesis : 5.36.0
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.50.1