BUG: unable to insert 64-bit integers into dataframe.

[dominiquegarmier]

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Reproducible Example

import pandas as pd

df1 = pd.DataFrame(index=range(1), columns=['foo','bar'], dtype='Int64')
df2 = df1.copy()

# insert using a dict
df1.loc[0] = {'foo': 1729509695129311113}

# insert normally
df2.loc[0, 'foo'] = 1729509695129311113

assert df1.equals(df2), 'oops'

Issue Description

when trying to insert an int into a (nullable) Int64 column, depending on the method used, the integer is first coereced to a float64 and therefore loses some of its precision.

Expected Behavior

both methods should work without losing precsion, i.e., like df2.

Installed Versions

INSTALLED VERSIONS

commit : 0691c5c
python : 3.12.3
python-bits : 64
OS : Darwin
OS-release : 23.5.0
Version : Darwin Kernel Version 23.5.0: Wed May 1 20:12:58 PDT 2024; root:xnu-10063.121.3~5/RELEASE_ARM64_T6000
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.2.3
numpy : 2.0.2
pytz : 2024.2
dateutil : 2.9.0.post0
pip : 24.2
Cython : None
sphinx : None
IPython : 8.28.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : None
lxml.etree : None
matplotlib : None
numba : 0.60.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : 17.0.0
pyreadstat : None
pytest : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2024.2
qtpy : None
pyqt5 : None

[rhshadrach]

Thanks for the report.

df1.loc[0] = {'foo': 1729509695129311113}

In this, you are setting all of row 0 in df1, providing no values for other columns. pandas aligns with the remaining columns, filling in NA values as necessary. Because this is a dictionary and there is no type information (int64 vs Int64), pandas defaults to NumPy dtypes, and the alignment results in float64 with np.nan as the missing value.

Both of these produce the expected behavior:

df1 = pd.DataFrame(index=range(1), columns=['foo','bar'], dtype='Int64')
df1.loc[0] = {'foo': 1_729_509_695_129_311_113, 'bar': 0}

df1 = pd.DataFrame(index=range(1), columns=['foo','bar'], dtype='Int64')
df1.loc[0] = pd.Series({'foo': 1_729_509_695_129_311_113}, dtype='Int64')

When pandas switches to nullable dtypes by default, this issue would be automatically resolved. As such, I'd suggest keeping the current behavior as-is.

[dominiquegarmier]

Thanks for the response.

I assumed that something like this was happening. Sadly the solution you provided does not work in my case as it would be too costly to create a dict filled with default values.

I found another solution since then.