Description
Pandas version checks
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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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I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
import pandas
df = pandas.DataFrame(data=[[1234567890123456789]], columns=['test'])
df.style.to_html()
df.style.to_latex()Issue Description
If the DataFrame contains integers with more digits than can be represented by floating point double precision, The default Styler (DataFrame.style) will display the wrong value:
html:
<style type="text/css">\n</style>\n<table id="T_2d9c5">\n <thead>\n <tr>\n <th class="blank level0" > </th>\n <th id="T_2d9c5_level0_col0" class="col_heading level0 col0" >test</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th id="T_2d9c5_level0_row0" class="row_heading level0 row0" >0</th>\n <td id="T_2d9c5_row0_col0" class="data row0 col0" >1234567890123456768</td>\n </tr>\n </tbody>\n</table>\n
latex:
\\begin{tabular}{lr}\n & test \\\\\n0 & 12345678901234567168 \\\\\n\\end{tabular}\n
In both cases, the displayed value is 12345678901234567168 instead of 1234567890123456789. I suspect that there is an internal lossy conversion to floating point.
It does not seem to matter, whether the dtype is int64, uint64, Int64, UInt64, or object
Expected Behavior
The integers should be displayed correctly. In pandas 1.1.4, this does work as expected:
<style type="text/css" >\n</style><table id="T_100b2b29_ce31_11ed_9d71_00505692da1d" ><thead> <tr> <th class="blank level0" ></th> <th class="col_heading level0 col0" >test</th> </tr></thead><tbody>\n <tr>\n <th id="T_100b2b29_ce31_11ed_9d71_00505692da1dlevel0_row0" class="row_heading level0 row0" >0</th>\n <td id="T_100b2b29_ce31_11ed_9d71_00505692da1drow0_col0" class="data row0 col0" >1234567890123456789</td>\n </tr>\n </tbody></table>
Installed Versions
tested versions (broken):
pandas 1.5.3
pandas 2.1.0.dev0+337.g8c7b8a4f3e
tested versions (fine):
pandas 1.1.4
pandas : 1.5.2
numpy : 1.24.1
pytz : 2022.7.1
dateutil : 2.8.2
setuptools : 66.0.0
pip : 22.3.1
Cython : None
pytest : 7.2.1
hypothesis : 6.62.1
sphinx : 6.1.3
blosc : None
feather : None
xlsxwriter : 3.0.7
lxml.etree : 4.9.2
html5lib : 1.1
pymysql : None
psycopg2 : 2.9.3
jinja2 : 3.1.2
IPython : 8.8.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : None
brotli :
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.6.2
numba : None
numexpr : None
odfpy : None
openpyxl : 3.0.10
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.10.0
snappy : None
sqlalchemy : 1.4.46
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
zstandard : None
tzdata : 2022.7