BUG: The indexes of DataFrame.describe(percentiles=[0.29, 0.57, 0.58]) are incorrect

[idanhazan]

Pandas version checks

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

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

• I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

import pandas as pd

pd.DataFrame(data=[1, 2, 3], columns=['value']).describe(percentiles=[0.29, 0.57, 0.58])

 	value
count 	3.00
mean 	2.00
std 	1.00
min 	1.00
29.0% 	1.58
50% 	2.00
57.0% 	2.14
58.0% 	2.16
max 	3.00

Issue Description

The indexes ['29.0%', '57.0%', '58.0%'] should be named as ['29%', '57%', '58%'].

Expected Behavior

pd.DataFrame(data=[1, 2, 3], columns=['value']).describe(percentiles=[0.29, 0.57, 0.58]).index

# Output:
Index(['count', 'mean', 'std', 'min', '29.0%', '50%', '57.0%', '58.0%', 'max'], dtype='object')

# Expected:
Index(['count', 'mean', 'std', 'min', '29%', '50%', '57%', '58%', 'max'], dtype='object')

Installed Versions

INSTALLED VERSIONS

commit : 06d2301
python : 3.9.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.13.0-30-generic
Version : #33-Ubuntu SMP Fri Feb 4 17:03:31 UTC 2022
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_IL
LOCALE : en_IL.UTF-8

pandas : 1.4.1
numpy : 1.22.2
pytz : 2021.3
dateutil : 2.8.2
pip : 22.0.3
setuptools : 59.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : 4.4.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.8.0
html5lib : None
pymysql : None
psycopg2 : 2.9.3
jinja2 : 3.0.3
IPython : 7.32.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
fsspec : 2022.02.0
gcsfs : None
matplotlib : 3.5.1
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : 2022.02.0
scipy : 1.8.0
sqlalchemy : 1.4.31
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
zstandard : None

[ShamithaReddy]

Internally in python a number is converted from decimal to base 2 floating-point number, so any number that is not a prime factor of 2 will have numbers repeated in its binary format, and when it is precise to 53 bits we may get a number that is slightly lesser than or greater than the number we expect.

Here we are multiplying 0.29*100 which results in 28.999999999999996 and so it is pushed into the floating numbers category and is getting printed as 29.0%

this can be avoided by rounding the floating numbers using ndarray.round( )

I would like to contribute, can I work on this issue @idanhazan ?

[idanhazan]

Sure you can, thank you ;)

[ShamithaReddy]

take