BUG: pandas.read_json casts float column to int

[albertvillanova]

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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 : 2024.5.0
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 : 2024.5.0
scipy : 1.11.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None

[albertvillanova]

Note that the behavior described above is different if the format is JSON-Lines and the "pyarrow" engine is used:

json_lines = b'{"col1": 1, "col2": 1.0}\n{"col1": 2, "col2": 2.0}'

df = pd.read_json(io.BytesIO(json_lines), lines=True, engine="pyarrow")
assert not (df["col1"].dtype == df["col2"].dtype)

On the other hand, the downcasting appears again if the "ujson" engine (the default one) is used:

json_lines = b'{"col1": 1, "col2": 1.0}\n{"col1": 2, "col2": 2.0}'

df = pd.read_json(io.BytesIO(json_lines), lines=True)
assert df["col1"].dtype == df["col2"].dtype

[PushpitSB]

Thats a good point

[albertvillanova]

Also note that this downcasting is not performed by pandas.read_csv:

csv_content = "col1,col2\n1,1.0\n2,2.0"

df = pd.read_csv(io.StringIO(csv_content))
assert not (df["col1"].dtype == df["col2"].dtype)

[albertvillanova]

Additionally, str column is also cast to int:

d = [{"col1": 1, "col2": 1.0, "col3": "1"}, {"col1": 2, "col2": 2.0, "col3": "2"}]

df = pd.read_json(io.StringIO(json.dumps(d)))
assert df["col1"].dtype == df["col2"].dtype == df["col3"].dtype

[rhshadrach]

Passing dtype=False, I get the expected behavior of the OP. But the docstring doesn't seem clear to me:

If True, infer dtypes; if a dict of column to dtype, then use those; if False, then don’t infer dtypes at all, applies only to the data.

Perhaps the language can be improved.

@albertvillanova - can you confirm if dtype=False satisfies your use-case? Labeling this as just a docs issue for now.

[albertvillanova]

@rhshadrach thanks for your reply.

Unfortunately, passing dtype=False does not satisfy my use-case, because indeed I was passing dtype_backend="pyarrow" as well (I did not mention it in the description to make things simpler).

Therefore the float-to-int downcasting persists even if passing dtype=False when passing dtype_backend="pyarrow":

d = [{"col1": 1, "col2": 1.0}, {"col1": 2, "col2": 2.0}]

df = pd.read_json(io.StringIO(json.dumps(d)), dtype_backend="pyarrow")
assert df["col1"].dtype == df["col2"].dtype

df = pd.read_json(io.StringIO(json.dumps(d)), dtype_backend="pyarrow", dtype=False)
assert df["col1"].dtype == df["col2"].dtype

Additionally, I would like to ask if in the former case (when no passing dtype_backend="pyarrow"), there would be other side effects when passing dtype=False. Would other dtypes be treated differently?