Fix mdash

Author: tomvothecoder

docs/examples/parallel-computing-with-dask.ipynb

@@ -129,8 +129,8 @@
     "> on datasets that don’t fit into memory. Currently, Dask is an entirely optional feature\n",
     "> for xarray. However, the benefits of using Dask are sufficiently strong that Dask may\n",
     "> become a required dependency in a future version of xarray.\n",
-    ">\n",
-    "> &mdash; <cite>https://docs.xarray.dev/en/stable/use\n",
+    "\n",
+    "&mdash; <cite>https://docs.xarray.dev/en/stable/use\n",
     "\n",
     "**Which Xarray features support Dask?**\n",
     "\n",
@@ -139,16 +139,16 @@
     "> Dask arrays. When you load data as a Dask array in an xarray data structure, almost\n",
     "> all xarray operations will keep it as a Dask array; when this is not possible, they\n",
     "> will raise an exception rather than unexpectedly loading data into memory.\n",
-    ">\n",
-    "> &mdash; <cite>https://docs.xarray.dev/en/stable/user-guide/dask.html#using-dask-with-xarray</cite>\n",
+    "\n",
+    "&mdash; <cite>https://docs.xarray.dev/en/stable/user-guide/dask.html#using-dask-with-xarray</cite>\n",
     "\n",
     "**What is the default Dask behavior for distributing work on compute hardware?**\n",
     "\n",
     "> By default, dask uses its multi-threaded scheduler, which distributes work across\n",
     "> multiple cores and allows for processing some datasets that do not fit into memory.\n",
     "> For running across a cluster, [setup the distributed scheduler](https://docs.dask.org/en/latest/setup.html).\n",
-    ">\n",
-    "> &mdash; <cite>https://docs.xarray.dev/en/stable/user-guide/dask.html#using-dask-with-xarray</cite>\n",
+    "\n",
+    "&mdash; <cite>https://docs.xarray.dev/en/stable/user-guide/dask.html#using-dask-with-xarray</cite>\n",
     "\n",
     "**How do I use Dask arrays in an `xarray.Dataset`?**\n",
     "\n",
@@ -161,8 +161,8 @@
     "> `open_mfdataset()` called without `chunks` argument will return dask arrays with\n",
     "> chunk sizes equal to the individual files. Re-chunking the dataset after creation\n",
     "> with `ds.chunk()` will lead to an ineffective use of memory and is not recommended.\n",
-    ">\n",
-    "> &mdash; <cite>https://docs.xarray.dev/en/stable/user-guide/dask.html#reading-and-writing-data</cite>\n"
+    "\n",
+    "&mdash; <cite>https://docs.xarray.dev/en/stable/user-guide/dask.html#reading-and-writing-data</cite>\n"
    ]
   },
   {
@@ -196,8 +196,8 @@
     ">    - Array data formats are often chunked as well. When loading or saving data,\n",
     ">      if is useful to have Dask array chunks that are aligned with the chunking\n",
     ">      of your storage, often an even multiple times larger in each direction\n",
-    ">\n",
-    "> &mdash; <cite>https://docs.dask.org/en/latest/array-chunks.html</cite>\n"
+    "\n",
+    "&mdash; <cite>https://docs.dask.org/en/latest/array-chunks.html</cite>\n"
    ]
   },
   {
@@ -267,8 +267,8 @@
     ">\n",
     ">    - **Single-machine scheduler**: This scheduler provides basic features on a local process or thread pool. This scheduler was made first and is the default. It is simple and cheap to use, although it can only be used on a single machine and does not scale\n",
     ">    - **Distributed scheduler**: This scheduler is more sophisticated, offers more features, but also requires a bit more effort to set up. It can run locally or distributed across a cluster\n",
-    ">\n",
-    "> &mdash; <cite>https://docs.dask.org/en/stable/scheduling.html</cite>\n"
+    "\n",
+    "&mdash; <cite>https://docs.dask.org/en/stable/scheduling.html</cite>\n"
    ]
   },
   {