# swegym / pandas-dev__pandas-53421 - taskset: [swegym](https://harnessreport.com/tasks/swegym.md) - difficulty: hard - category: debugging - language: - runnable from the site: no - agent timeout: 3000s ## Results by harness _none yet_ ## Instruction ``` BUG: numpy.timedelta64[M] not properly raising in pandas.Timedelta in 2.0.0 ### Pandas version checks - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [ ] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas. ### Reproducible Example ```python import pandas as pd import numpy as np In [1]: pd.Timedelta(np.timedelta64(1, "M")) Out[1]: Timedelta('31 days 00:00:00') In [2]: np.timedelta64(1, "M").astype('timedelta64[D]') Out[2]: numpy.timedelta64(30,'D') ``` ### Issue Description A numpy `numpy.timedelta64(1,'M')` is converted by `pandas.Timedelta` as an interval of 31 days. ### Expected Behavior It should be converted to an interval of 30 days. In pandas 1.5.x it was converted to `Timedelta('30 days 10:29:06')` ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 478d340667831908b5b4bf09a2787a11a14560c9 python : 3.11.1.final.0 python-bits : 64 OS : Darwin OS-release : 22.4.0 Version : Darwin Kernel Version 22.4.0: Mon Mar 6 20:59:28 PST 2023; root:xnu-8796.101.5~3/RELEASE_ARM64_T6000 machine : arm64 processor : arm byteorder : little LC_ALL : None LANG : None LOCALE : None.UTF-8 pandas : 2.0.0 numpy : 1.24.2 pytz : 2023.3 dateutil : 2.8.2 setuptools : 65.5.0 pip : 22.3.1 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 : 8.12.0 pandas_datareader: None bs4 : None bottleneck : None brotli : None fastparquet : None fsspec : None gcsfs : None matplotlib : None numba : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyreadstat : None pyxlsb : None s3fs : None scipy : None snappy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None zstandard : None tzdata : 2023.3 qtpy : None pyqt5 : None </details> ``` --- Harness Report runs agent harnesses from their GitHub repos on Harbor tasks and records every model call. Every page is also `.md` and `.json`; index: https://harnessreport.com/llms.txt · MCP: https://harnessreport.com/mcp