BUG: csv reader not parsing dates when parse_dates & dayfirst are used in pandas-2.x

[galipremsagar]

Pandas version checks

• 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 main branch of pandas.

Reproducible Example

In [1]: import pandas as pd

In [2]: df = pd.DataFrame({"col": [
   ...:             "18/04/1995",
   ...:             "14 / 07 / 1994",
   ...:             "07/06/2006",
   ...:             "16/09/2005",
   ...:             "2/2/1970",
   ...:             "2007-4-30 1:6:40.000PM",
   ...:             "2038-01-19 03:14:08",
   ...:         ]})

In [3]: df.to_csv("sample.csv", index=False, header=False)

In [4]: pd.read_csv("sample.csv", names=["col"], parse_dates=[0], dayfirst=True)
Out[4]: 
                      col
0              18/04/1995
1          14 / 07 / 1994
2              07/06/2006
3              16/09/2005
4                2/2/1970
5  2007-4-30 1:6:40.000PM
6     2038-01-19 03:14:08

Issue Description

Starting from pandas 2.0, it seems like the csv reader isn't parsing dates when parse_dates and dayfirst=True is passed. Until 1.5.3, the above data was parsed correctly to datetime64[ns] dtype, now it is being read as a raw string(object dtype).

Expected Behavior

In [14]: pd.read_csv("sample.csv", names=["col"], parse_dates=[0], dayfirst=True)
Out[14]: 
                  col
0 1995-04-18 00:00:00
1 1994-07-14 00:00:00
2 2006-06-07 00:00:00
3 2005-09-16 00:00:00
4 1970-02-02 00:00:00
5 2007-04-30 13:06:40
6 2038-01-19 03:14:08
In [15]: df = pd.read_csv("sample.csv", names=["col"], parse_dates=[0], dayfirst=True)
In [16]: df.dtypes
Out[16]: 
col    datetime64[ns]
dtype: object

Installed Versions

INSTALLED VERSIONS

commit : 37ea63d
python : 3.10.11.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
Version : #86-Ubuntu SMP Fri Jan 17 17:24:28 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.0.1
numpy : 1.23.5
pytz : 2023.3
dateutil : 2.8.2
setuptools : 67.7.2
pip : 23.1.2
Cython : 0.29.34
pytest : 7.3.1
hypothesis : 6.75.3
sphinx : 5.3.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.13.2
pandas_datareader: None
bs4 : 4.12.2
bottleneck : None
brotli :
fastparquet : None
fsspec : 2023.5.0
gcsfs : None
matplotlib : None
numba : 0.56.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 11.0.0
pyreadstat : None
pyxlsb : None
s3fs : 2023.5.0
scipy : 1.10.1
snappy :
sqlalchemy : 2.0.15
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None

[mroeschke]

Thanks but this is intended behavior according to https://pandas.pydata.org/docs/dev/whatsnew/v2.0.0.html#datetimes-are-now-parsed-with-a-consistent-format. date_format="mixed" needs to be passed now

In [2]: pd.read_csv("sample.csv", names=["col"], parse_dates=[0], dayfirst=True, date_format="mixed")
Out[2]: 
                  col
0 1995-04-18 00:00:00
1 1994-07-14 00:00:00
2 2006-06-07 00:00:00
3 2005-09-16 00:00:00
4 1970-02-02 00:00:00
5 2007-04-30 13:06:40
6 2038-01-19 03:14:08