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Note: Please read this guide detailing how to provide the necessary information for us to reproduce your bug.

Code Sample, a copy-pastable example

import pandas as pd
data = {'A' : [1, 2, 2, pd.NA, 4, 8, 8, 8, 8, 9],
 'B': [pd.NA] * 10}
data = pd.DataFrame(data)
data = data.astype(pd.Int64Dtype()) # in my example I get this from data.convert_dtypes()
data_json = data.to_json(orient='table', indent=4)
pd.read_json(data_json, orient='table') #ValueError: Cannot convert non-finite values (NA or inf) to integer

Problem description

I am investigating different json-ouput formats for my use-case. I expect as an end-user that if I can write a json with pandas, I can also read it in without an error.

Background: I used pd.convert_dtypes on a larger DataFrame, where some columns are populated by identical values. convert_dtypes converted some of them having only missing to pd.Int64Dtype. I tried understand why this is failing und could replicate the issue using the above example having identical dtypes as in my use-case.

Expected Output

      A     B
0     1  <NA>
1     2  <NA>
2     2  <NA>
3  <NA>  <NA>
4     4  <NA>
5     8  <NA>
6     8  <NA>
7     8  <NA>
8     8  <NA>
9     9  <NA>

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit : f2c8480af2f25efdbd803218b9d87980f416563e python : 3.8.5.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19041 machine : AMD64 processor : Intel64 Family 6 Model 165 Stepping 2, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : Danish_Denmark.1252 pandas : 1.2.3 numpy : 1.18.5 pytz : 2020.1 dateutil : 2.8.1 pip : 20.2.4 setuptools : 50.3.1.post20201107 Cython : None pytest : None hypothesis : None sphinx : 3.5.1 blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 2.11.2 IPython : 7.19.0 pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.3.2 numexpr : None odfpy : None openpyxl : 3.0.5 pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : 1.5.2 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None

Comment From: attack68

can you provide more descriptive title

Comment From: lithomas1

xref #29752 cc @jorisvandenbossche