Merge pull request #188 from RichardScottOZ/patch-1

Update xarray.ipynb

xarray.ipynb

@@ -72,7 +72,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Quickly inspecting the `Dataset` above, we'll note that this `Dataset` has three _dimensions_ akin to axes in NumPy (`lat`, `lon`, and `time`), three _coordinate variables_ akin to `pandas.Index` objects (also named `lat`, `lon`, and `time`), and one data variable (`air`). Xarray also holds Dataset specific metadata in as _attributes_."
+    "Quickly inspecting the `Dataset` above, we'll note that this `Dataset` has three _dimensions_ akin to axes in NumPy (`lat`, `lon`, and `time`), three _coordinate variables_ akin to `pandas.Index` objects (also named `lat`, `lon`, and `time`), and one data variable (`air`). Xarray also holds Dataset specific metadata as _attributes_."
    ]
   },
   {
@@ -89,7 +89,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Each data variable in xarray is called a `DataArray`. These are the fundemental labeled array object in xarray. Much like the `Dataset`, `DataArrays` also have _dimensions_ and _coordinates_ that support many of its label-based opperations."
+    "Each data variable in xarray is called a `DataArray`. These are the fundamental labeled array objects in xarray. Much like the `Dataset`, `DataArrays` also have _dimensions_ and _coordinates_ that support many of its label-based opperations."
    ]
   },
   {
@@ -258,7 +258,7 @@
    "source": [
     "Almost all of xarray’s built-in operations work on Dask arrays. If you want to use a function that isn’t wrapped by xarray, one option is to extract Dask arrays from xarray objects (.data) and use Dask directly.\n",
     "\n",
-    "Another option is to use xarray’s `apply_ufunc()` function, which can automate embarrassingly parallel “map” type operations where a functions written for processing NumPy arrays should be repeatedly applied to xarray objects containing Dask arrays. It works similarly to `dask.array.map_blocks()` and `dask.array.atop()`, but without requiring an intermediate layer of abstraction.\n",
+    "Another option is to use xarray’s `apply_ufunc()` function, which can automate embarrassingly parallel “map” type operations where a function written for processing NumPy arrays should be repeatedly applied to xarray objects containing Dask arrays. It works similarly to `dask.array.map_blocks()` and `dask.array.atop()`, but without requiring an intermediate layer of abstraction.\n",
     "\n",
     "Here we show an example using NumPy operations and a fast function from `bottleneck`, which we use to calculate Spearman’s rank-correlation coefficient:"
    ]