BUG: read_json returns wrong value for big integer

[mle-els]

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

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Code Sample, a copy-pastable example

$ cat first-line.json
{"col":"72057594038135102"}

>>> pd.read_json("first-line.json", lines=True)
                 col
0  72057594038135104
>>> pd.read_json("first-line.json", lines=True, dtype=False)
                 col
0  72057594038135102

Problem description

pd.read_json() silently returns a wrong value when reading a big integer. This creates a problem when the integers are used as unique IDs.

Expected Output

Output of pd.show_versions()

INSTALLED VERSIONS

commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.0.3
numpy : 1.18.3
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0
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 : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None

[jschendel]

This is a duplicate of #20608 and is caused by an intermediate conversion to float:

In [1]: int(float(72057594038135102))
Out[1]: 72057594038135104