Open
Description
Pandas version checks
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I have checked that this issue has not already been reported.
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I have confirmed this bug exists on the latest version of pandas.
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I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
import pandas as pd
import pytz
ts = pd.Timestamp('1858-11-17T00:00:00.0', tzinfo=pytz.utc)
ts
# Timestamp('1858-11-17 00:00:00+0000', tz='UTC')
ts.to_julian_date()
# 2400000.5
ts = pd.Timestamp('1858-11-17T00:00:00.0', tzinfo=pytz.timezone('US/Mountain'))
ts
# Timestamp('1858-11-17 00:00:00-0700', tz='US/Mountain')
ts.to_julian_date()
# 2400000.5
# it should be 2400000.7916666665
pd.__version__
# '2.0.3'
pytz.__version__
# '2022.1'Issue Description
Timestamp.to_julian_date() does not take into account the timezone information.
Expected Behavior
ts
# Timestamp('1858-11-17 00:00:00-0700', tz='US/Mountain')
ts.to_julian_date()
# 2400000.5
# it should be 2400000.7916666665Installed Versions
INSTALLED VERSIONS
------------------
commit : 0f437949513225922d851e9581723d82120684a6
python : 3.8.13.final.0
python-bits : 64
OS : Linux
OS-release : 4.18.0-372.32.1.el8_6.x86_64
Version : #1 SMP Fri Oct 7 12:35:10 EDT 2022
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.0.3
numpy : 1.23.1
pytz : 2022.1
dateutil : 2.8.2
setuptools : 68.0.0
pip : 23.2
Cython : 0.29.15
pytest : 7.3.1
hypothesis : None
sphinx : 4.3.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.1
html5lib : 1.1
pymysql : None
psycopg2 : 2.9.6
jinja2 : 3.1.2
IPython : 7.29.0
pandas_datareader: None
bs4 : 4.12.0
bottleneck : None
brotli : None
fastparquet : None
fsspec : 2023.3.0
gcsfs : None
matplotlib : 3.5.1
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.9.0
snappy : None
sqlalchemy : 2.0.19
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None