BUG: using 2 character seperator in pd.json_normalize

[pustetorte]

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

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

• I have confirmed this bug exists on the master branch of pandas.

Reproducible Example

import pandas as pd
d = {'a': [1, 2], 'b': {'b_1': 2, 'b_2': (3, 4)}}
r = pd.json_normalize(d, sep='__')

Issue Description

When using a separator with more then 1 character, for example '__', the new column names get the separator as prefix, too. If you use a separator with only 1 character, for example '_', everything works fine.

I get the following output from the code provided:

    a      __b__b_1      __b__b_2
 0  [1, 2]      2         (3, 4)

Expected Behavior

    a      b__b_1      b__b_2
 0  [1, 2]     2       (3, 4)

Installed Versions

INSTALLED VERSIONS

commit : 73c6825
python : 3.9.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18363
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : de_DE.cp1252
pandas : 1.3.3
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 49.6.0.post20210108
Cython : 0.29.24
pytest : 6.2.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.26.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pyxlsb : None
s3fs : 0.4.2
scipy : 1.7.1
sqlalchemy : 1.3.23
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None

[pustetorte]

A workaround is to set record_prefix='', but I don't think, that this should be the solution.

[Vijay-Arora]

Hey @pustetorte, I am new to open source contribution; Could you please suggest me if I could help you with any of the things related to this issue? Would love to hear from you!
Thanks!