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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 io import json import pandas as pd d = [{"col1": 1, "col2": 1.0}, {"col1": 2, "col2": 2.0}] df = pd.read_json(io.StringIO(json.dumps(d))) assert df["col1"].dtype == df["col2"].dtype
Issue Description
Although the explicit JSON values are of float type, the corresponding column dtype is of int dtype.
Expected Behavior
If the JSON contains float values, we would expect the corresponding column dtype is float as well.
At least, we should be able to avoid this casting if needed.
Installed Versions
INSTALLED VERSIONS
commit : d9cdd2e
python : 3.9.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.15.0-107-generic
Version : #117~20.04.1-Ubuntu SMP Tue Apr 30 10:35:57 UTC 2024
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.2
numpy : 1.22.4
pytz : 2021.3
dateutil : 2.8.2
setuptools : 57.0.0
pip : 24.0
Cython : 0.29.24
pytest : None
hypothesis : None
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.4
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.30.1
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 202450
gcsfs : None
matplotlib : None
numba : 0.58.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 14.0.2
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : 202450
scipy : 1.11.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None