EDIT I've rewritten this issue after Joris' comment.

Pandas' docstrings are somewhat inconsistent in using single or double back-ticks, e.g. isin has both

`values`

and

``values``

In keeping with the [numpydoc docstring guide](https://numpydoc.read

If you join Dask DataFrame on a categorical column, then the outputted Dask DataFrame column is still category dtype. However, the moment you .compute() the outputted Dask DataFrame, then the column is the wrong dtype, not categorical.

Tested on Dask 2.14.0 and Pandas 1.0.3
This example where the category type looks like a float, so after .compute(), the dtype is float.

import dask.d

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

Implement Series.reindex.

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.reindex.html

Support DataFrame.select_dtypes

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