Use more descriptive link texts (#7625)

* Use more descriptive link texts

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Fix link target name

* Fix typos

---------

Co-authored-by: remigathoni <marg.kahiga@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: 3 people

doc/contributing.rst

@@ -35,8 +35,8 @@ Bug reports and enhancement requests
 
 Bug reports are an important part of making *xarray* more stable. Having a complete bug
 report will allow others to reproduce the bug and provide insight into fixing. See
-`this stackoverflow article <https://stackoverflow.com/help/mcve>`_ for tips on
-writing a good bug report.
+this `stackoverflow article for tips on
+writing a good bug report <https://stackoverflow.com/help/mcve>`_ .
 
 Trying out the bug-producing code on the *main* branch is often a worthwhile exercise
 to confirm that the bug still exists. It is also worth searching existing bug reports and
@@ -102,7 +102,7 @@ Some great resources for learning Git:
 Getting started with Git
 ------------------------
 
-`GitHub has instructions <https://help.github.com/set-up-git-redirect>`__ for installing git,
+`GitHub has instructions for setting up Git <https://help.github.com/set-up-git-redirect>`__ including installing git,
 setting up your SSH key, and configuring git.  All these steps need to be completed before
 you can work seamlessly between your local repository and GitHub.
 
@@ -238,7 +238,7 @@ To return to your root environment::
 
       conda deactivate
 
-See the full conda docs `here <http://conda.pydata.org/docs>`__.
+See the full `conda docs here <http://conda.pydata.org/docs>`__.
 
 .. _contributing.documentation:
 
@@ -277,7 +277,7 @@ Some other important things to know about the docs:
 
 - The docstrings follow the **NumPy Docstring Standard**, which is used widely
   in the Scientific Python community. This standard specifies the format of
-  the different sections of the docstring. See `this document
+  the different sections of the docstring. Refer to the `documentation for the Numpy docstring format
   <https://numpydoc.readthedocs.io/en/latest/format.html#docstring-standard>`_
   for a detailed explanation, or look at some of the existing functions to
   extend it in a similar manner.
@@ -732,8 +732,8 @@ or, to use a specific Python interpreter,::
 This will display stderr from the benchmarks, and use your local
 ``python`` that comes from your ``$PATH``.
 
-Information on how to write a benchmark and how to use asv can be found in the
-`asv documentation <https://asv.readthedocs.io/en/latest/writing_benchmarks.html>`_.
+Learn `how to write a benchmark and how to use asv from the documentation <https://asv.readthedocs.io/en/latest/writing_benchmarks.html>`_ .
+
 
 ..
    TODO: uncomment once we have a working setup
@@ -752,8 +752,7 @@ GitHub issue number when adding your entry (using ``:issue:`1234```, where ``123
 issue/pull request number).
 
 If your code is an enhancement, it is most likely necessary to add usage
-examples to the existing documentation.  This can be done following the section
-regarding documentation :ref:`above <contributing.documentation>`.
+examples to the existing documentation.  This can be done by following the :ref:`guidelines for contributing to the documentation <contributing.documentation>`.
 
 .. _contributing.changes:
 

doc/developers-meeting.rst

@@ -5,9 +5,9 @@ Xarray developers meet bi-weekly every other Wednesday.
 
 The meeting occurs on `Zoom <https://us02web.zoom.us/j/88251613296?pwd=azZsSkU1UWJZTVFKNnhIUVdZcENUZz09>`__.
 
-Notes for the meeting are kept `here <https://hackmd.io/@U4W-olO3TX-hc-cvbjNe4A/xarray-dev-meeting/edit>`__.
+Find the `notes for the meeting here <https://hackmd.io/@U4W-olO3TX-hc-cvbjNe4A/xarray-dev-meeting/edit>`__.
 
-There is a :issue:`GitHub issue <4001>` for changes to the meeting.
+There is a :issue:`GitHub issue for changes to the meeting<4001>`.
 
 You can subscribe to this calendar to be notified of changes:
 

doc/user-guide/computation.rst

@@ -804,7 +804,7 @@ to set ``axis=-1``. As an example, here is how we would wrap
 Because ``apply_ufunc`` follows a standard convention for ufuncs, it plays
 nicely with tools for building vectorized functions, like
 :py:func:`numpy.broadcast_arrays` and :py:class:`numpy.vectorize`. For high performance
-needs, consider using Numba's :doc:`vectorize and guvectorize <numba:user/vectorize>`.
+needs, consider using :doc:`Numba's vectorize and guvectorize <numba:user/vectorize>`.
 
 In addition to wrapping functions, ``apply_ufunc`` can automatically parallelize
 many functions when using dask by setting ``dask='parallelized'``. See

doc/user-guide/dask.rst

@@ -39,7 +39,7 @@ The actual computation is controlled by a multi-processing or thread pool,
 which allows Dask to take full advantage of multiple processors available on
 most modern computers.
 
-For more details on Dask, read `its documentation <https://docs.dask.org/>`__.
+For more details, read the `Dask documentation <https://docs.dask.org/>`__.
 Note that xarray only makes use of ``dask.array`` and ``dask.delayed``.
 
 .. _dask.io:
@@ -234,7 +234,7 @@ disk.
 .. note::
 
    For more on the differences between :py:meth:`~xarray.Dataset.persist` and
-   :py:meth:`~xarray.Dataset.compute` see this `Stack Overflow answer <https://stackoverflow.com/questions/41806850/dask-difference-between-client-persist-and-client-compute>`_ and the `Dask documentation <https://distributed.dask.org/en/latest/manage-computation.html#dask-collections-to-futures>`_.
+   :py:meth:`~xarray.Dataset.compute` see this `Stack Overflow answer on the differences between client persist and client compute <https://stackoverflow.com/questions/41806850/dask-difference-between-client-persist-and-client-compute>`_ and the `Dask documentation <https://distributed.dask.org/en/latest/manage-computation.html#dask-collections-to-futures>`_.
 
 For performance you may wish to consider chunk sizes.  The correct choice of
 chunk size depends both on your data and on the operations you want to perform.
@@ -549,7 +549,7 @@ larger chunksizes.
 
 .. tip::
 
-   Check out the dask documentation on `chunks <https://docs.dask.org/en/latest/array-chunks.html>`_.
+   Check out the `dask documentation on chunks <https://docs.dask.org/en/latest/array-chunks.html>`_.
 
 
 Optimization Tips
@@ -562,7 +562,7 @@ through experience:
 1. Do your spatial and temporal indexing (e.g. ``.sel()`` or ``.isel()``) early in the pipeline, especially before calling ``resample()`` or ``groupby()``. Grouping and resampling triggers some computation on all the blocks, which in theory should commute with indexing, but this optimization hasn't been implemented in Dask yet. (See `Dask issue #746 <https://github.com/dask/dask/issues/746>`_).
 
 2. More generally, ``groupby()`` is a costly operation and will perform a lot better if the ``flox`` package is installed.
-   See the `flox documentation <flox.readthedocs.io/>`_ for more. By default Xarray will use ``flox`` if installed.
+   See the `flox documentation <https://flox.readthedocs.io>`_ for more. By default Xarray will use ``flox`` if installed.
 
 3. Save intermediate results to disk as a netCDF files (using ``to_netcdf()``) and then load them again with ``open_dataset()`` for further computations. For example, if subtracting temporal mean from a dataset, save the temporal mean to disk before subtracting. Again, in theory, Dask should be able to do the computation in a streaming fashion, but in practice this is a fail case for the Dask scheduler, because it tries to keep every chunk of an array that it computes in memory. (See `Dask issue #874 <https://github.com/dask/dask/issues/874>`_)
 
@@ -572,6 +572,6 @@ through experience:
 
 6. Using the h5netcdf package by passing ``engine='h5netcdf'`` to :py:meth:`~xarray.open_mfdataset` can be quicker than the default ``engine='netcdf4'`` that uses the netCDF4 package.
 
-7. Some dask-specific tips may be found `here <https://docs.dask.org/en/latest/array-best-practices.html>`_.
+7. Find `best practices specific to Dask arrays in the documentation <https://docs.dask.org/en/latest/array-best-practices.html>`_.
 
-8. The dask `diagnostics <https://docs.dask.org/en/latest/understanding-performance.html>`_ can be useful in identifying performance bottlenecks.
+8. The `dask diagnostics <https://docs.dask.org/en/latest/understanding-performance.html>`_ can be useful in identifying performance bottlenecks.

doc/user-guide/groupby.rst

@@ -25,12 +25,9 @@ the same pipeline.
 .. tip::
 
    To substantially improve the performance of GroupBy operations, particularly
-   with dask install the `flox <https://flox.readthedocs.io>`_ package. flox also
-   `extends <https://flox.readthedocs.io/en/latest/xarray.html>`_
-   Xarray's in-built GroupBy capabilities by allowing grouping by multiple variables,
-   and lazy grouping by dask arrays. Xarray will automatically use flox by default
-   if it is installed.
-
+   with dask `install the flox package <https://flox.readthedocs.io>`_. flox
+   `extends Xarray's in-built GroupBy capabilities <https://flox.readthedocs.io/en/latest/xarray.html>`_
+   by allowing grouping by multiple variables, and lazy grouping by dask arrays. If installed, Xarray will automatically use flox by default.
 
 Split
 ~~~~~

doc/user-guide/indexing.rst

@@ -95,7 +95,7 @@ In this example, the selected is a subpart of the array
 in the range '2000-01-01':'2000-01-02' along the first coordinate `time`
 and with 'IA' value from the second coordinate `space`.
 
-You can perform any of the label indexing operations `supported by pandas`__,
+You can perform any of the `label indexing operations supported by pandas`__,
 including indexing with individual, slices and lists/arrays of labels, as well as
 indexing with boolean arrays. Like pandas, label based indexing in xarray is
 *inclusive* of both the start and stop bounds.
@@ -140,14 +140,14 @@ use them explicitly to slice data. There are two ways to do this:
 
 The arguments to these methods can be any objects that could index the array
 along the dimension given by the keyword, e.g., labels for an individual value,
-Python :py:class:`slice` objects or 1-dimensional arrays.
+:py:class:`Python slice` objects or 1-dimensional arrays.
 
 
 .. note::
 
     We would love to be able to do indexing with labeled dimension names inside
-    brackets, but unfortunately, Python `does yet not support`__ indexing with
-    keyword arguments like ``da[space=0]``
+    brackets, but unfortunately, `Python does not yet support indexing with
+    keyword arguments`__ like ``da[space=0]``
 
 __ https://legacy.python.org/dev/peps/pep-0472/
 
@@ -372,12 +372,12 @@ indexers' dimension:
     ind = xr.DataArray([[0, 1], [0, 1]], dims=["a", "b"])
     da[ind]
 
-Similar to how NumPy's `advanced indexing`_ works, vectorized
+Similar to how `NumPy's advanced indexing`_ works, vectorized
 indexing for xarray is based on our
 :ref:`broadcasting rules <compute.broadcasting>`.
 See :ref:`indexing.rules` for the complete specification.
 
-.. _advanced indexing: https://numpy.org/doc/stable/reference/arrays.indexing.html
+.. _NumPy's advanced indexing: https://numpy.org/doc/stable/reference/arrays.indexing.html
 
 Vectorized indexing also works with ``isel``, ``loc``, and ``sel``:
 

doc/user-guide/io.rst

@@ -684,9 +684,9 @@ instance and pass this, as follows:
 Zarr Compressors and Filters
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-There are many different options for compression and filtering possible with
-zarr. These are described in the
-`zarr documentation <https://zarr.readthedocs.io/en/stable/tutorial.html#compressors>`_.
+There are many different `options for compression and filtering possible with
+zarr <https://zarr.readthedocs.io/en/stable/tutorial.html#compressors>`_.
+
 These options can be passed to the ``to_zarr`` method as variable encoding.
 For example:
 
@@ -720,7 +720,7 @@ As of xarray version 0.18, xarray by default uses a feature called
 *consolidated metadata*, storing all metadata for the entire dataset with a
 single key (by default called ``.zmetadata``). This typically drastically speeds
 up opening the store. (For more information on this feature, consult the
-`zarr docs <https://zarr.readthedocs.io/en/latest/tutorial.html#consolidating-metadata>`_.)
+`zarr docs on consolidating metadata <https://zarr.readthedocs.io/en/latest/tutorial.html#consolidating-metadata>`_.)
 
 By default, xarray writes consolidated metadata and attempts to read stores
 with consolidated metadata, falling back to use non-consolidated metadata for
@@ -1165,7 +1165,7 @@ rasterio is installed. Here is an example of how to use
 
         Deprecated in favor of rioxarray.
         For information about transitioning, see:
-        https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html
+        `rioxarray getting started docs<https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html>``
 
 .. ipython::
     :verbatim:
@@ -1277,7 +1277,7 @@ Formats supported by PyNIO
 
 .. warning::
 
-    The PyNIO backend is deprecated_. PyNIO is no longer maintained_.
+    The `PyNIO backend is deprecated`_. `PyNIO is no longer maintained`_.
 
 Xarray can also read GRIB, HDF4 and other file formats supported by PyNIO_,
 if PyNIO is installed. To use PyNIO to read such files, supply
@@ -1288,8 +1288,8 @@ We recommend installing PyNIO via conda::
     conda install -c conda-forge pynio
 
 .. _PyNIO: https://www.pyngl.ucar.edu/Nio.shtml
-.. _deprecated: https://github.com/pydata/xarray/issues/4491
-.. _maintained: https://github.com/NCAR/pynio/issues/53
+.. _PyNIO backend is deprecated: https://github.com/pydata/xarray/issues/4491
+.. _PyNIO is no longer maintained: https://github.com/NCAR/pynio/issues/53
 
 .. _io.PseudoNetCDF:
 

doc/user-guide/pandas.rst

@@ -8,7 +8,7 @@ Working with pandas
 One of the most important features of xarray is the ability to convert to and
 from :py:mod:`pandas` objects to interact with the rest of the PyData
 ecosystem. For example, for plotting labeled data, we highly recommend
-using the visualization `built in to pandas itself`__ or provided by the pandas
+using the `visualization built in to pandas itself`__ or provided by the pandas
 aware libraries such as `Seaborn`__.
 
 __ https://pandas.pydata.org/pandas-docs/stable/visualization.html
@@ -168,7 +168,7 @@ multi-dimensional arrays to xarray.
 Xarray has most of ``Panel``'s features, a more explicit API (particularly around
 indexing), and the ability to scale to >3 dimensions with the same interface.
 
-As discussed :ref:`elsewhere <data structures>` in the docs, there are two primary data structures in
+As discussed in the :ref:`data structures section of the docs <data structures>`, there are two primary data structures in
 xarray: ``DataArray`` and ``Dataset``. You can imagine a ``DataArray`` as a
 n-dimensional pandas ``Series`` (i.e. a single typed array), and a ``Dataset``
 as the ``DataFrame`` equivalent (i.e. a dict of aligned ``DataArray`` objects).
@@ -240,6 +240,6 @@ While the xarray docs are relatively complete, a few items stand out for Panel u
   the Person dimension of a Dataset of Person x Score x Time.
 
 While xarray may take some getting used to, it's worth it! If anything is unclear,
-please post an issue on `GitHub <https://github.com/pydata/xarray>`__ or
+please `post an issue on GitHub <https://github.com/pydata/xarray>`__ or
 `StackOverflow <https://stackoverflow.com/questions/tagged/python-xarray>`__,
 and we'll endeavor to respond to the specific case or improve the general docs.

doc/user-guide/time-series.rst

@@ -108,10 +108,10 @@ For more details, read the pandas documentation and the section on :ref:`datetim
 Datetime components
 -------------------
 
-Similar `to pandas`_, the components of datetime objects contained in a
+Similar to `pandas accessors`_, the components of datetime objects contained in a
 given ``DataArray`` can be quickly computed using a special ``.dt`` accessor.
 
-.. _to pandas: https://pandas.pydata.org/pandas-docs/stable/basics.html#basics-dt-accessors
+.. _pandas accessors: https://pandas.pydata.org/pandas-docs/stable/basics.html#basics-dt-accessors
 
 .. ipython:: python
 

doc/user-guide/weather-climate.rst

@@ -10,7 +10,7 @@ Weather and climate data
 
     import xarray as xr
 
-Xarray can leverage metadata that follows the `Climate and Forecast (CF) conventions`_ if present. Examples include automatic labelling of plots with descriptive names and units if proper metadata is present (see :ref:`plotting`) and support for non-standard calendars used in climate science through the ``cftime`` module (see :ref:`CFTimeIndex`). There are also a number of geosciences-focused projects that build on xarray (see :ref:`ecosystem`).
+Xarray can leverage metadata that follows the `Climate and Forecast (CF) conventions`_ if present. Examples include :ref:`automatic labelling of plots<plotting>` with descriptive names and units if proper metadata is present and support for non-standard calendars used in climate science through the ``cftime`` module(Explained in the :ref:`CFTimeIndex` section). There are also a number of :ref:`geosciences-focused projects that build on xarray<ecosystem>`.
 
 .. _Climate and Forecast (CF) conventions: https://cfconventions.org
 
@@ -49,10 +49,10 @@ variable with the attribute, rather than with the dimensions.
 CF-compliant coordinate variables
 ---------------------------------
 
-`MetPy`_ adds a	``metpy`` accessor that allows accessing coordinates with appropriate CF metadata using generic names ``x``, ``y``, ``vertical`` and ``time``. There is also a `cartopy_crs` attribute that provides projection information, parsed from the appropriate CF metadata, as a `Cartopy`_ projection object. See `their documentation`_ for more information.
+`MetPy`_ adds a	``metpy`` accessor that allows accessing coordinates with appropriate CF metadata using generic names ``x``, ``y``, ``vertical`` and ``time``. There is also a `cartopy_crs` attribute that provides projection information, parsed from the appropriate CF metadata, as a `Cartopy`_ projection object. See the `metpy documentation`_ for more information.
 
 .. _`MetPy`: https://unidata.github.io/MetPy/dev/index.html
-.. _`their documentation`:	https://unidata.github.io/MetPy/dev/tutorials/xarray_tutorial.html#coordinates
+.. _`metpy documentation`:	https://unidata.github.io/MetPy/dev/tutorials/xarray_tutorial.html#coordinates
 .. _`Cartopy`: https://scitools.org.uk/cartopy/docs/latest/crs/projections.html
 
 .. _CFTimeIndex: