BUG: Large number of PerformanceWarnings when attempting to read .xpt file

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Pandas version checks

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

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

• [ ] I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

import pandas as pd
diet = pd.read_sas('DR1TOT_J.XPT',index='SEQN')

Issue Description

Hi there,

When attempting to read in a .XPT file downloaded from the CDC website using pandas.read_sas, a large number of PerformanceWarnings is printed. The file that causes the warnings to appear is this one: https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/DR1TOT_J.XPT .

```:1: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling frame.insert many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()````

The above warning is repeated 68 times. A different file that doesn't produce any warnings is: https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/DEMO_J.XPT

I'm not familiar with the inner workings of read_sas but I can only assume that it's creating the DataFrame in a way that is causing another part of pandas to complain.

Thanks a lot!

Expected Behavior

The expected behavior is for the file to be read in without a large number of warnings being printed. :)

Installed Versions

INSTALLED VERSIONS

commit : ca60aab7340d9989d9428e11a51467658190bb6b python : 3.9.13.final.0 python-bits : 64 OS : Linux OS-release : 5.10.16.3-microsoft-standard-WSL2 Version : #1 SMP Fri Apr 2 22:23:49 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : C.UTF-8 LOCALE : en_US.UTF-8

pandas : 1.4.4 numpy : 1.23.3 pytz : 2022.2.1 dateutil : 2.8.2 setuptools : 65.3.0 pip : 22.2.2 Cython : None pytest : 7.1.3 hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : 4.9.1 html5lib : None pymysql : None psycopg2 : None jinja2 : 3.1.2 IPython : 8.5.0 pandas_datareader: None bs4 : 4.11.1 bottleneck : None brotli : fastparquet : None fsspec : 2022.8.2 gcsfs : None markupsafe : 2.1.1 matplotlib : 3.5.3 numba : None numexpr : 2.7.3 odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyreadstat : None pyxlsb : None s3fs : None scipy : 1.9.1 snappy : None sqlalchemy : 1.4.41 tables : 3.6.1 tabulate : 0.8.10 xarray : 2022.6.0 xlrd : None xlwt : None zstandard : None

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Thank you very much for looking at this. :)