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2 Basic xarray - subsetting data.ipynb

@@ -78,7 +78,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "xarray builds on top of numpy, and stores it's data internally as numpy arrays. It supports many numpy operations, so it is possible to find out the shape of the underlying data, and use numpy style indexing"
+    "xarray builds on top of numpy, and stores its data internally as numpy arrays. It supports many numpy operations, so it is possible to find out the shape of the underlying data, and use numpy style indexing"
    ]
   },
   {
@@ -139,14 +139,14 @@
     }
    ],
    "source": [
-    "tas[0,:]"
+    "tas[0,...]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "By selecting just the first time index it has created a DataArray with no time dimension, but time is still a coordinate not associated with any variable, as indicated by no longer having `*` beside it. The index selection above is equivalent to using `isel` like so"
+    "By selecting just the first time index it has created a DataArray with no time dimension, but time is still a coordinate not associated with any variable, as indicated by no longer having `*` beside it. It now only has one value: the value of the first time index. The index selection above is equivalent to using `isel` like so"
    ]
   },
   {
@@ -201,7 +201,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The power of xarray comes with the close association of data with coordinates. So it is possible to use the equivalent `.sel` operator but with coordinate values. For example, to select an area that includes the Indian Ocean and Australia use `slice` to indicate the range of latitude and longitude values required and pass as key/value pairs to `sel`"
+    "The power of xarray comes with the close association of data with coordinates. So it is possible to use the equivalent `.sel` operator but with coordinate values. For example, to select an area that includes the Indian Ocean and Australia use `slice` to indicate the range of latitude and longitude values required and pass as key/value pairs to `sel`. `slice` will include coordinate values inferior **or equal** to the upper bound, not like `range` in basic Python that excludes the upper bound."
    ]
   },
   {
@@ -335,6 +335,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "Note when using `sel` to select only 1 time index, the resulting array still has a time dimension of length 1. Depending on what you want to do with the array afterwards, it might be better to still have the time dimension or not. \n",
+    "\n",
     "It is also possible to use `slice` for the `time` dimension. To select Mar to November of 1871:"
    ]
   },
@@ -448,9 +450,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:analysis3-19.07]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-env-analysis3-19.07-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -462,7 +464,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.7.2"
   }
  },
  "nbformat": 4,

4 Basic xarray - calculating metrics.ipynb

@@ -230,7 +230,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Can now calculate the anomaly from the 30 year climatology simply by subtracting it from the original data. xarray checks which named coordinates match between the two data sets, and *broadcasts* the `tas_clim` data along the missing `time` dimension automatically."
+    "We can now calculate the anomaly from the 30 year climatology simply by subtracting it from the original data. xarray checks which named coordinates match between the two data sets, and *broadcasts* the `tas_clim` data along the missing `time` dimension automatically."
    ]
   },
   {