BUG: IndexError with pandas.DataFrame.cumsum where dtype=timedelta64[ns] #57956
Description
Pandas version checks
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I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
import pandas as pd # Example DataFrame from pandas.DataFrame.cumsum documentation df = pd.DataFrame([[2.0, 1.0], [3.0, np.nan], [1.0, 0.0]], columns=list('AB')) # IndexError: too many indices for array: array is 1-dimensional, but 2 were indexed df.astype('timedelta64[ns]').cumsum() # These are fine df.cumsum().astype('timedelta64[ns]') df.apply(lambda s: s.astype('timedelta64[ns]').cumsum()) df.astype('timedelta64[ns]').apply(lambda s: s.cumsum())Issue Description
A cumulative sum along the columns of a DataFrame with timedelta64[ns] types results in an IndexError. Cumulative min and max work correctly, and product correctly results in a TypeError, since it's not supported for timedelta.
Cumulative sums work correctly for Series with timedelta64[ns] type, so this seems to be a bug in DataFrame.
This is perhaps related to #41720?
Expected Behavior
pandas.DataFrame.cumsum on a DataFrame with timedelta64[ns] columns should not erroneously raise IndexError and instead compute the cumulative sum, with the same effect as df.apply(lambda s: s.cumsum()).
Installed Versions
Installed Versions
INSTALLED VERSIONS ------------------ commit : bdc79c146c2e32f2cab629be240f01658cfb6cc2 python : 3.12.2.final.0 python-bits : 64 OS : Darwin OS-release : 23.4.0 Version : Darwin Kernel Version 23.4.0: Wed Feb 21 21:45:48 PST 2024; root:xnu-10063.101.15~2/RELEASE_ARM64_T8122 machine : arm64 processor : arm byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 2.2.1 numpy : 1.26.4 pytz : 2024.1 dateutil : 2.9.0.post0 setuptools : None pip : 24.0 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 : 8.22.2 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 : 3.8.3 numba : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyreadstat : None python-calamine : None pyxlsb : None s3fs : None scipy : 1.12.0 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None zstandard : None tzdata : 2024.1 qtpy : None pyqt5 : None