pandas (original) (raw)

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 master branch of pandas.

Reproducible Example

import pandas as pd import numpy as np

Series: Result 608202560.0

pd.to_numeric(pd.Series([608202549]), downcast='float')

#Series: Result 608202560.0 pd.to_numeric(pd.Series([608202549.0]), downcast='float')

Single: Result 608202549

pd.to_numeric(608202549, downcast='float')

List: Result array([608202560, 608202560])

pd.to_numeric([608202548, 608202549], downcast='float').astype(int)

Range: 1st issue at 16777217

arr = np.arange(0, 6e8, dtype='int64') a = pd.Series(arr) b = pd.to_numeric(a, downcast='float').astype(int) np.where(a != b)[0]

Issue Description

When attempting to use to_numeric() on a Pandas Series or list with values >= 16777217, the resulting float is not expected given the input number.

pd.to_numeric(pd.Series([608202549]), downcast='float') 0 608202560.0

pd.to_numeric(pd.Series([608202549.0]), downcast='float') 0 608202560.0

This behavior does not occur if the value is passed as a scalar, but does also appear if a List is passed. By comparing values within a range, it seems the issue starts from 16,777,217 onward. This seems to have roots in the discussion about 32 bit floating point in this Stack Overflow as well as another Pandas issue on float issues with rank #18271

Edit- I updated this issue to be clear that this is affecting both integers and floats larger than 16,777,217

Expected Behavior

to_numeric() should yield the same functional number in float form as the original value(s)

Installed Versions

pd.show_versions()

INSTALLED VERSIONS

commit : 73c6825
python : 3.9.4.final.0
python-bits : 64
OS : Darwin
OS-release : 20.6.0
Version : Darwin Kernel Version 20.6.0: Wed Jun 23 00:26:27 PDT 2021; root:xnu-7195.141.2~5/RELEASE_ARM64_T8101
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.3.3
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.0.1
setuptools : 54.2.0
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
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None