Description
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
import pytest
t=pd.Timestamp("010120")
print(t.unit)
print(t.min, type(t.min))
print(t.max, type(t.max))
t2=pd.Timestamp(2020,1,1)
print(t2.unit)
print(t2.min, type(t2.min))
print(t2.max, type(t2.max))
with pytest.raises(ValueError):
pd.Timestamp(10000,1,1)Issue Description
While preparing our code for Pandas 2, we noticed the following:
1. Different ways of Timestamp creation ends up in a different timestamp unit
pd.Timestamp("010120") -> Timestamp('2020-01-01 00:00:00') with unit = 's'
pd.Timestamp(2020, 1, 1) -> Timestamp('2020-01-01 00:00:00') with `unit = 'us'
Moreover I could create Timestamps, which I cannot reproduce with using pd.Timestamp.
Let us take the first timestamp from above:
t = pd.Timestamp("010120")
tmax = t.maxWe get tmax = Timestamp('292277026596-12-04 15:30:07') where it is at least not possible to create a Timestamp with a year greater than 9999.
t_impossible = pd.Timestamp(10000,1,1)lead to
ValueError Traceback (most recent call last)
Cell In[29], line 1
----> 1 pd.Timestamp(10000,1,1)
File ~/venv/myvenv/lib/python3.10/site-packages/pandas/_libs/tslibs/timestamps.pyx:1645, in pandas._libs.tslibs.timestamps.Timestamp.__new__()
ValueError: year 10000 is out of range
I even cannot count on a timestamp that is outside the max values (pd.Timestamp.max = Timestamp('2262-04-11 23:47:16.854775807') and pd.Timestamp.min = Timestamp('1677-09-21 00:12:43.145224193'))
To what extent is the behaviour intentional or erroneous?
With pandas 1.5.3 it was not possible to create timestamps greater than pd.Timestamp.max due to its ns-representation.
Expected Behavior
I don't know if it is even a bug, but I would expect, that I can create timestamp (e.g. year > 10000) and that I can calculate with timestamps (e.g. 2300-01-01 + 100 days) [ not in every unit, but if the unit allows it, why not?
Installed Versions
INSTALLED VERSIONS
python : 3.10.2.final.0
python-bits : 64
OS : Darwin
OS-release : 22.1.0
Version : Darwin Kernel Version 22.1.0: Sun Oct 9 20:14:54 PDT 2022; root:xnu-8792.41.9~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : de_DE.UTF-8
pandas : 2.0.0rc1
numpy : 1.23.5
pytz : 2022.6
dateutil : 2.8.2
setuptools : 65.6.3
pip : 22.3.1
Cython : None
pytest : 7.2.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.1
html5lib : None
pymysql : None
psycopg2 : 2.9.5
jinja2 : 3.1.2
IPython : 8.7.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : None
brotli : None
fastparquet : None
fsspec : 2022.11.0
gcsfs : 2022.11.0
matplotlib : 3.6.2
numba : None
numexpr : 2.8.4
odfpy : None
openpyxl : 3.0.10
pandas_gbq : None
pyarrow : 10.0.1
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.9.3
snappy : None
sqlalchemy : 1.4.45
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
xlwt : None
zstandard : None
tzdata : None