Convert readme to markdown (#6495)

Still some improvements we can make, but it is nicer in markdown. Also
we were making some mistaken; e.g. the list of external files in the
license section wasn't actually formatted as a list.

Author: max-sixty

README.md

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+# xarray: N-D labeled arrays and datasets
+
+[![image](https://github.com/pydata/xarray/workflows/CI/badge.svg?branch=main)](https://github.com/pydata/xarray/actions?query=workflow%3ACI)
+[![image](https://codecov.io/gh/pydata/xarray/branch/main/graph/badge.svg)](https://codecov.io/gh/pydata/xarray)
+[![image](https://readthedocs.org/projects/xray/badge/?version=latest)](https://docs.xarray.dev/)
+[![image](https://img.shields.io/badge/benchmarked%20by-asv-green.svg?style=flat)](https://pandas.pydata.org/speed/xarray/)
+[![image](https://img.shields.io/pypi/v/xarray.svg)](https://pypi.python.org/pypi/xarray/)
+[![image](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/python/black)
+[![image](https://zenodo.org/badge/DOI/10.5281/zenodo.598201.svg)](https://doi.org/10.5281/zenodo.598201)
+[![image](https://img.shields.io/twitter/follow/xarray_dev?style=social)](https://twitter.com/xarray_dev)
+
+**xarray** (formerly **xray**) is an open source project and Python
+package that makes working with labelled multi-dimensional arrays
+simple, efficient, and fun!
+
+Xarray introduces labels in the form of dimensions, coordinates and
+attributes on top of raw [NumPy](https://www.numpy.org)-like arrays,
+which allows for a more intuitive, more concise, and less error-prone
+developer experience. The package includes a large and growing library
+of domain-agnostic functions for advanced analytics and visualization
+with these data structures.
+
+Xarray was inspired by and borrows heavily from
+[pandas](https://pandas.pydata.org), the popular data analysis package
+focused on labelled tabular data. It is particularly tailored to working
+with [netCDF](https://www.unidata.ucar.edu/software/netcdf) files, which
+were the source of xarray\'s data model, and integrates tightly with
+[dask](https://dask.org) for parallel computing.
+
+## Why xarray?
+
+Multi-dimensional (a.k.a. N-dimensional, ND) arrays (sometimes called
+"tensors") are an essential part of computational science. They are
+encountered in a wide range of fields, including physics, astronomy,
+geoscience, bioinformatics, engineering, finance, and deep learning. In
+Python, [NumPy](https://www.numpy.org) provides the fundamental data
+structure and API for working with raw ND arrays. However, real-world
+datasets are usually more than just raw numbers; they have labels which
+encode information about how the array values map to locations in space,
+time, etc.
+
+Xarray doesn\'t just keep track of labels on arrays \-- it uses them to
+provide a powerful and concise interface. For example:
+
+- Apply operations over dimensions by name: `x.sum('time')`.
+- Select values by label instead of integer location:
+    `x.loc['2014-01-01']` or `x.sel(time='2014-01-01')`.
+- Mathematical operations (e.g., `x - y`) vectorize across multiple
+    dimensions (array broadcasting) based on dimension names, not shape.
+- Flexible split-apply-combine operations with groupby:
+    `x.groupby('time.dayofyear').mean()`.
+- Database like alignment based on coordinate labels that smoothly
+    handles missing values: `x, y = xr.align(x, y, join='outer')`.
+- Keep track of arbitrary metadata in the form of a Python dictionary:
+    `x.attrs`.
+
+## Documentation
+
+Learn more about xarray in its official documentation at
+<https://docs.xarray.dev/>.
+
+Try out an [interactive Jupyter
+notebook](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/weather-data.ipynb).
+
+## Contributing
+
+You can find information about contributing to xarray at our
+[Contributing
+page](https://docs.xarray.dev/en/latest/contributing.html#).
+
+## Get in touch
+
+- Ask usage questions ("How do I?") on
+    [StackOverflow](https://stackoverflow.com/questions/tagged/python-xarray).
+- Report bugs, suggest features or view the source code [on
+    GitHub](https://github.com/pydata/xarray).
+- For less well defined questions or ideas, or to announce other
+    projects of interest to xarray users, use the [mailing
+    list](https://groups.google.com/forum/#!forum/xarray).
+
+## NumFOCUS
+
+[![image](https://numfocus.org/wp-content/uploads/2017/07/NumFocus_LRG.png)](https://numfocus.org/)
+
+Xarray is a fiscally sponsored project of
+[NumFOCUS](https://numfocus.org), a nonprofit dedicated to supporting
+the open source scientific computing community. If you like Xarray and
+want to support our mission, please consider making a
+[donation](https://numfocus.salsalabs.org/donate-to-xarray/) to support
+our efforts.
+
+## History
+
+Xarray is an evolution of an internal tool developed at [The Climate
+Corporation](http://climate.com/). It was originally written by Climate
+Corp researchers Stephan Hoyer, Alex Kleeman and Eugene Brevdo and was
+released as open source in May 2014. The project was renamed from
+"xray" in January 2016. Xarray became a fiscally sponsored project of
+[NumFOCUS](https://numfocus.org) in August 2018.
+
+## License
+
+Copyright 2014-2019, xarray Developers
+
+Licensed under the Apache License, Version 2.0 (the "License"); you
+may not use this file except in compliance with the License. You may
+obtain a copy of the License at
+
+ <https://www.apache.org/licenses/LICENSE-2.0>
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+Xarray bundles portions of pandas, NumPy and Seaborn, all of which are
+available under a "3-clause BSD" license:
+
+- pandas: setup.py, xarray/util/print_versions.py
+- NumPy: xarray/core/npcompat.py
+- Seaborn: _determine_cmap_params in xarray/core/plot/utils.py
+
+Xarray also bundles portions of CPython, which is available under the
+"Python Software Foundation License" in xarray/core/pycompat.py.
+
+Xarray uses icons from the icomoon package (free version), which is
+available under the "CC BY 4.0" license.
+
+The full text of these licenses are included in the licenses directory.