The following seems to happen on all transform groupby kernels:

df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
df.groupby([1, 1], axis=1).transform("shift")

results in ValueError: Length mismatch: Expected axis has 2 elements, new values have 3 elements.

I think the issue is in pandas.core.groupby.generic._wrap_transformed_output; this method does not take into account `s

The documentation in the Joins section https://docs.dask.org/en/latest/dataframe-joins.html has code blocks but they aren't interactive and therefore not as useful.

I think the documentation would benefit from an interactive example with use of real data (or data from the demo API).

I had a quick rummage of the tutorial (h

There are (at least) two ways in which the reinterpret_cast is misused:

1. Used instead of static_cast:
   https://github.com/rapidsai/cudf/blob/f7fbc1160b17b969db2708e0cf6033d3db9fc1cf/cpp/src/io/avro/avro_gpu.cu#L95
   static cast should be used to cast from void*.
2. The use causes undefined behavior:
   https://github.com/rapidsai/cudf/blob/f7fbc1160b17b969db2708e0cf6033d3db9fc1cf/cpp/src/io

Support Series.median()

What happened:
xr.DataArray([1], coords=[('onecoord', [2])]).sel(onecoord=2).to_dataframe(name='name') raise an exception ValueError: no valid index for a 0-dimensional object

What you expected to happen:

the same behavior as: xr.DataArray([1], coords=[('onecoord', [2])]).to_dataframe(name='name')

Anything else we need to know?:

I see that the array after the select