Location of the documentation

https://pandas.pydata.org/docs/dev/reference/api/pandas.to_numeric.html?highlight=to_numeric#pandas.to_numeric

Documentation problem

The documentation declares that the downcast argument of pd.to_numeric can use the value int. However, only integer is working in the real code:

In [8]: pd.to_numeric(["42"], downcast="int", errors="coe

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

Depends on #6000

Current cuIO benchmarks only use random data with low repetition. Encode/decode of some formats (ORC, Parquet...) varies significantly depending on the data profile.
Also, newly supported data types (like lists) are not covered.
As of now, the missing cases are:

• Reading RLE encoded ORC files.
• Reading RLE encoded Parquet files.
• Writing RLE-friendly data t

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