BUG: Inconsistent "is_year_start" from DatetimeIndex with freq "MS"

[pforero]

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

import pandas as pd
dr = pd.date_range("2017-12-01", periods=2, freq="MS")
dr_comp = [dt.is_year_start for dt in dr]
dr_attr = dr.is_year_start
assert dr[1].is_year_start
assert dr_comp[1]
assert dr_attr[0]  # Should raise error but doesn't
assert dr_attr[1]  # Raises error but shouldn't

Issue Description

When working with DatetimeIndex with frequency "MS" the attribute "is_year_start" does not obtain the same attribute if you obtain the attribute directly from the DatatimeIndex instead of obtaining directly from each individual value of the DatetimeIndex.

Expected Behavior

I would expect for the same array of attributes independently of if it is obtained from a comprehensive list or as an attibute as shown in the documentation.

https://pandas.pydata.org/docs/reference/api/pandas.DatetimeIndex.is_year_start.html

import pandas as pd
dr = pd.date_range("2017-12-01", periods=2, freq="MS")
dr_comp = [dt.is_year_start for dt in dr]
dr_attr = dr.is_year_start
assert not dr[1].is_year_start
assert dr[1].is_year_start
assert not dr_comp[1]
assert dr_comp[1]
assert not dr_attr[0] 
assert dr_attr[1] 

Installed Versions

INSTALLED VERSIONS

commit : f538741
python : 3.11.6.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1160.95.1.el7.x86_64
Version : #1 SMP Fri Jun 23 08:44:55 EDT 2023
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.2.0
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.8.2
setuptools : 68.2.2
pip : 23.2.1
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 : 8.16.1
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 2.0.21
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.4
qtpy : None
pyqt5 : None

[rhshadrach]

Thanks for the report. Confirmed on main, further investigations and PRs to fix are welcome!

[mattheeter]

take

[mattheeter]

Upon further inspection, it looks like is_quarter_start with the freq 'MS' is having the same issue

idx = pd.date_range('2017-01-01', periods=6, freq='MS')
idx[idx.is_quarter_start]

This gives the dates 2017-03-01 and 2017-06-01, which are of course not the actual start of quarters

[MarcoGorelli]

looks like this was already reported earlier: #49606