Fix typos in example notebooks (#4908)

pydata · Feb 15, 2021 · 735a359 · 735a359
1 parent aa5d918
commit 735a359
Show file tree

Hide file tree

Showing 3 changed files with 5 additions and 5 deletions.
diff --git a/doc/examples/ERA5-GRIB-example.ipynb b/doc/examples/ERA5-GRIB-example.ipynb
@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "GRIB format is commonly used to disemminate atmospheric model data. With Xarray and the cfgrib engine, GRIB data can easily be analyzed and visualized."
+    "GRIB format is commonly used to disseminate atmospheric model data. With Xarray and the cfgrib engine, GRIB data can easily be analyzed and visualized."
    ]
   },
   {

diff --git a/doc/examples/ROMS_ocean_model.ipynb b/doc/examples/ROMS_ocean_model.ipynb
@@ -120,7 +120,7 @@
    "source": [
     "### A naive vertical slice\n",
     "\n",
-    "Create a slice using the s-coordinate as the vertical dimension is typically not very informative."
+    "Creating a slice using the s-coordinate as the vertical dimension is typically not very informative."
    ]
   },
   {

diff --git a/doc/examples/monthly-means.ipynb b/doc/examples/monthly-means.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Calculating Seasonal Averages from Timeseries of Monthly Means \n",
+    "Calculating Seasonal Averages from Time Series of Monthly Means \n",
     "=====\n",
     "\n",
     "Author: [Joe Hamman](https://github.com/jhamman/)\n",
@@ -60,10 +60,10 @@
    "source": [
     "#### Now for the heavy lifting:\n",
     "We first have to come up with the weights,\n",
-    "- calculate the month lengths for each monthly data record\n",
+    "- calculate the month length for each monthly data record\n",
     "- calculate weights using `groupby('time.season')`\n",
     "\n",
-    "Finally, we just need to multiply our weights by the `Dataset` and sum allong the time dimension.  Creating a `DataArray` for the month length is as easy as using the `days_in_month` accessor on the time coordinate.  The calendar type, in this case `'noleap'`, is automatically considered in this operation."
+    "Finally, we just need to multiply our weights by the `Dataset` and sum along the time dimension.  Creating a `DataArray` for the month length is as easy as using the `days_in_month` accessor on the time coordinate.  The calendar type, in this case `'noleap'`, is automatically considered in this operation."
    ]
   },
   {