Pandas version checks

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Reproducible Example

import pandas as pd
df = pd.DataFrame([2,3])
df.index.name = 'a'
print(df.to_string(header=False))

Issue Description

The code results in

a  2
0  3
1

Expected Behavior

a   
0  2
1  3

Installed Versions

pd.show_versions() yields: INSTALLED VERSIONS ------------------ commit : 91111fd99898d9dcaa6bf6bedb662db4108da6e6 python : 3.9.2.final.0 python-bits : 64 OS : Linux OS-release : 5.10.0-17-amd64 Version : #1 SMP Debian 5.10.136-1 (2022-08-13) machine : x86_64 processor : byteorder : little LC_ALL : None LANG : en_DK.UTF-8 LOCALE : en_DK.UTF-8 pandas : 1.5.1 numpy : 1.23.3 pytz : 2022.4 dateutil : 2.8.2 setuptools : 44.1.1 pip : 22.2.2 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 : 7.34.0 pandas_datareader: None bs4 : None bottleneck : None brotli : None fastparquet : None fsspec : None gcsfs : None matplotlib : 3.6.1 numba : None numexpr : None odfpy : None openpyxl : 3.0.10 pandas_gbq : None pyarrow : None pyreadstat : None pyxlsb : None s3fs : None scipy : None snappy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None zstandard : None tzdata : None

Comment From: jondo

Workaround: reset df.index.name to None before calling df.to_string.

Comment From: vamsi-verma-s

use index_names=False 

print(df.to_string(header=False, index_names=False))
0  2
1  3

Comment From: jondo

@vamsi-verma-s, thank you for pointing out the index_names argument.

In my case df.to_string(header=False) evaluates to the string 'a 2\n0 3\n1 ', which nonsensically has the index name and the first value of the data column in the same row, and the rest of the index shifted w.r.t the data.

I now updated my report: I expect df.to_string(header=False) to give 'a \n0 2\n1 3', analogous to the ' 0\na \n0 2\n1 3' ofdf.to_string()`.

Comment From: andrewchen1216

take

Comment From: attack68

Note that Styler.to_string is also a workaround

>>> print(df.style.hide(axis="columns").to_string())
a 
0 2
1 3

>>> print(df.style.hide(axis="columns").hide(axis="index", names=True).to_string())
0 2
1 3