fixed a few typos (#74)

* fixed a few typos

Author: e-marshall

fundamentals/01_datastructures_and_io.ipynb

@@ -8,7 +8,7 @@
     "\n",
     "# Data structures for multi-dimensional data\n",
     "\n",
-    "In this lesson, we discuss cover the basics of Xarray data structures. Our\n",
+    "In this lesson, we cover the basics of Xarray data structures. Our\n",
     "learning goals are as follows. By the end of the lesson, we will be able to:\n",
     "\n",
     "- Understand the basic data structures in Xarray\n",

fundamentals/02.1_working_with_labeled_data.ipynb

@@ -137,7 +137,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "see also `tail` and `thin`.\n"
+    "see also [tail()](https://docs.xarray.dev/en/latest/generated/xarray.Dataset.tail.html) and [thin()](https://xarray.pydata.org/en/stable/generated/xarray.Dataset.thin.html).\n"
    ]
   },
   {

fundamentals/03.1_computation_with_xarray.ipynb

@@ -56,11 +56,13 @@
     "###       things might not go so well! Recommended to use the Google Cloud copy instead.\n",
     "\n",
     "# url = \"http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/noaa.ersst.v5/sst.mnmean.nc\"\n",
+    "# url emma added:\n",
+    "url = \"http://psl.noaa.gov/thredds/dodsC/Datasets/noaa.ersst.v5/sst.mnmean.nc\"\n",
     "# # drop an unnecessary variable which complicates some operations\n",
-    "# ds = xr.open_dataset(url, drop_variables=[\"time_bnds\"])\n",
+    "ds = xr.open_dataset(url, drop_variables=[\"time_bnds\"])\n",
     "# # will take a minute or two to complete\n",
-    "# ds = ds.sel(time=slice(\"1960\", \"2018\")).load()\n",
-    "# ds"
+    "ds = ds.sel(time=slice(\"1960\", \"2018\")).load()\n",
+    "ds"
    ]
   },
   {
@@ -403,7 +405,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.10.4"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {

fundamentals/03.2_groupby_with_xarray.ipynb

@@ -125,7 +125,7 @@
     "(called the \"climatology\") in order to better see the long-term variaitions in\n",
     "temperature. We can accomplish this using **groupby**.\n",
     "\n",
-    "Before moving forward, w note that xarray correctly parsed the time index,\n",
+    "Before moving forward, we note that xarray correctly parsed the time index,\n",
     "resulting in a Pandas datetime index on the time dimension.\n"
    ]
   },
@@ -162,7 +162,7 @@
     "\n",
     "The most important argument is `group`: this defines the unique values we will\n",
     "us to \"split\" the data for grouped analysis. We can pass either a DataArray or a\n",
-    "name of a variable in the dataset. Lets first use a DataArray. Just like with\n",
+    "name of a variable in the dataset. Let's first use a DataArray. Just like with\n",
     "Pandas, we can use the time index to extract specific components of dates and\n",
     "times. Xarray uses a special syntax for this `.dt`, called the\n",
     "`DatetimeAccessor`.\n"
@@ -187,10 +187,12 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "ds.time.dt.year\n"
+    "ds.time.dt.year"
    ]
   },
   {
@@ -327,7 +329,7 @@
     "So we did what we wanted to do: calculate the climatology at every point in the\n",
     "dataset. Let's look at the data a bit.\n",
     "\n",
-    "_Climatlogy at a specific point in the North Atlantic_\n"
+    "_Climatology at a specific point in the North Atlantic_\n"
    ]
   },
   {
@@ -343,7 +345,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "_Zonal Mean Climatolgoy_\n"
+    "_Zonal Mean Climatology_\n"
    ]
   },
   {

fundamentals/04_plotting_and_visualization.ipynb

@@ -64,7 +64,7 @@
    "source": [
     "## Load data\n",
     "\n",
-    "First lets load up a tutorial dataset to visualize.\n"
+    "First let's load up a tutorial dataset to visualize.\n"
    ]
   },
   {