BUG: read_csv raises ParserError in inconsistent ways when column names exceed number of parsed columns

[sjdemartini]

Pandas version checks

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Reproducible Example

import pandas as pd
from io import StringIO

# With single row of data, raises a ParserError:
pd.read_csv(StringIO("1,2\n"), names=['a', 'b', 'c'])

# With two rows of data, does NOT raise a ParseError ("c" column values are NaN):
pd.read_csv(StringIO("1,2\n4,5\n"), names=['a', 'b', 'c'])

Issue Description

In pandas 1.3.4, the following code would parse the dataframe as a single row without issue, with the value in column c as NaN:

import pandas as pd
from io import StringIO
pd.read_csv(StringIO("1,2\n"), names=['a', 'b', 'c'])

In pandas 1.5.1, this raises ParserError: Too many columns specified: expected 3 and found 2, which is an understandable exception. However, also in pandas 1.5.1, if multiple rows are being parsed, it does not raise a ParserError, and treats all values in column c as NaN (like in pandas 1.3.4):

pd.read_csv(StringIO("1,2\n4,5\n"), names=['a', 'b', 'c'])

Expected Behavior

read_csv should have consistent behavior when the length of names exceeds the number of parsed columns, regardless of the number of rows being parsed. i.e., either treat the missing columns as NaN/null consistently (like in pandas 1.3.4), or raise a ParserError consistently, regardless of the number of rows being parsed.

Installed Versions

INSTALLED VERSIONS

commit : 91111fd
python : 3.10.6.final.0
python-bits : 64
OS : Darwin
OS-release : 21.6.0
Version : Darwin Kernel Version 21.6.0: Mon Aug 22 20:17:10 PDT 2022; root:xnu-8020.140.49~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.5.1
numpy : 1.23.4
pytz : 2020.5
dateutil : 2.8.2
setuptools : 59.8.0
pip : 22.3
Cython : None
pytest : 7.1.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.9.3
jinja2 : 3.1.2
IPython : 8.5.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : 1.3.5
brotli :
fastparquet : 0.8.3
fsspec : 2022.10.0
gcsfs : None
matplotlib : 3.5.1
numba : 0.56.3
numexpr : 2.8.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.9.1
snappy : None
sqlalchemy : 1.4.32
tables : None
tabulate : 0.9.0
xarray : 2022.10.0
xlrd : None
xlwt : None
zstandard : None
tzdata : None

[vamsi-verma-s]

fixed in #49182