BUG: Unexpected output of `to_json` for `complex` data

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Code Sample, a copy-pastable example

import pandas as pd
s = pd.Series({0: 0+2j, 1: 3+4j, 2: 5+6j})
s.to_json()

Problem description

Calling to_json() on a DataFrame or Series with complex data gives unexpected results. Running the above Code Sample gives the following output:

'{"0":{"imag":2.0},"1":{"imag":4.0},"2":{"imag":6.0}}'

This is a problem both because (1) the output is not terribly useful (the real part is just dropped), and (2) according to the docs this should raise an error (see the following snippet copied from the linked docs page):

>>> DataFrame([1.0, 2.0, complex(1.0, 2.0)]).to_json()  # raises
RuntimeError: Unhandled numpy dtype 15

Expected Output

According to the docs, this should at least raise a RuntimeError. However, it would be nice for this to actually be supported.

Output of pd.show_versions()

INSTALLED VERSIONS

commit : 0158382ac8459a197d088790fd45f433c83c25c8 python : 3.8.5.final.0 python-bits : 64 OS : Darwin OS-release : 20.1.0 Version : Darwin Kernel Version 20.1.0: Sat Oct 31 00:07:11 PDT 2020; root:xnu-7195.50.7~2/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8

pandas : 1.3.0.dev0+1441.g0158382ac8 numpy : 1.20.2 pytz : 2021.1 dateutil : 2.8.1 pip : 20.1.1 setuptools : 47.1.0 Cython : 0.29.23 pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : 7.22.0 pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None

[mroeschke]

@lorenzophys yes that sounds like a good location

[lorenzophys]

@mroeschke I would like to write the tests for this behavior. Being my first issue with this codebase, tests related to this issue will go in pandas/tests/io/json/test_pandas.py? Or there’s a more appropriate place?