Fix broken links

core/pandas/pandas.ipynb

@@ -78,7 +78,7 @@
     "\n",
     "The `columns` that make up our `DataFrame` can be lists, dictionaries, NumPy arrays, pandas `Series`, or many other data types not mentioned here. Within these `columns`, you can have data values of many different data types used in Python and NumPy, including text, numbers, and dates/times. The first column of a `DataFrame`, shown in the image above in dark gray, is uniquely referred to as an `index`; this column contains information characterizing each row of our `DataFrame`. Similar to any other `column`, the `index` can label rows by text, numbers, datetime objects, and many other data types. Datetime objects are a quite popular way to label rows.\n",
     "\n",
-    "For our first example using Pandas DataFrames, we start by reading in some data in comma-separated value (`.csv`) format. We retrieve this dataset from the Pythia DATASETS class (imported at the top of this page); however, the dataset was originally contained within the NCDC teleconnections database. This dataset contains many types of geoscientific data, including El Nino/Southern Oscillation indices. For more information on this dataset, review the description [here](https://www.ncdc.noaa.gov/teleconnections/enso/indicators/sst/)."
+    "For our first example using Pandas DataFrames, we start by reading in some data in comma-separated value (`.csv`) format. We retrieve this dataset from the Pythia `DATASETS` class (imported at the top of this page); however, the dataset was originally contained within the NCDC teleconnections database. This dataset contains many types of geoscientific data, including El Nino/Southern Oscillation (ENSO) indices. See [here](https://www.ncei.noaa.gov/access/monitoring/enso/sst) for more information on these indices and the underlying data."
    ]
   },
   {
@@ -1219,10 +1219,18 @@
     "In the next notebook, we will look more into using pandas for more in-depth data analysis.\n",
     "\n",
     "## Resources and References\n",
-    "1. [NOAA NCDC ENSO Dataset Used in this Example](https://www.ncdc.noaa.gov/teleconnections/enso/indicators/sst/)\n",
+    "1. [ENSO data used in this example](https://www.ncei.noaa.gov/access/monitoring/enso/sst)\n",
     "1. [Getting Started with Pandas](https://pandas.pydata.org/docs/getting_started/index.html#getting-started)\n",
     "1. [Pandas User Guide](https://pandas.pydata.org/docs/user_guide/index.html#user-guide)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a66d4939-70fc-4655-9075-e516e646999e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -1241,7 +1249,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,

core/xarray/computation-masking.ipynb

@@ -790,10 +790,9 @@
    "id": "d1796fc8-039b-4c40-a6f4-b3a00c130770",
    "metadata": {},
    "source": [
-    "In addition to using `DataArrays` and `Datasets` in Boolean conditions provided to `.where()`, we can also use coordinate variables. In the following example, we make use of Boolean conditions containing `latitude` and `longitude` coordinates. This greatly simplifies the masking of regions outside of the [Niño 3.4 region](https://www.ncdc.noaa.gov/teleconnections/enso/indicators/sst/):\n",
+    "In addition to using `DataArrays` and `Datasets` in Boolean conditions provided to `.where()`, we can also use coordinate variables. In the following example, we make use of Boolean conditions containing `latitude` and `longitude` coordinates. This greatly simplifies the masking of regions outside of the [Niño 3.4 region](https://www.ncei.noaa.gov/access/monitoring/enso/sst/):\n",
     "\n",
-    "![](https://www.ncdc.noaa.gov/monitoring-content/teleconnections/nino-regions.gif)\n",
-    "\n"
+    "![](https://www.ncei.noaa.gov/monitoring-content/teleconnections/nino-regions.gif)"
    ]
   },
   {
@@ -896,7 +895,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,

foundations/how-to-run-python.md

@@ -48,7 +48,7 @@ We highly encourage the use of Jupyter notebooks: a free, open-source, interacti
 
 ### Other IDEs
 
-If you code in other languages, you might already have a favorite IDE that will work just as well in Python. [Spyder](https://www.spyder-ide.org) is a Python specific IDE that comes with the [Anaconda download](https://www.anaconda.com/products/distribution). It is perhaps the most familiar IDE if you are coming from languages such as [Matlab](https://www.mathworks.com/products/matlab.html) that have a language specific platform and display a list of variables. [PyCharm](https://www.jetbrains.com/pycharm/) and [Visual Studio Code](https://code.visualstudio.com) are also popular IDEs. Many IDEs offer support for terminal execution, scripts, and Jupyter display. To learn about your specific IDE, visit its official documentation.
+If you code in other languages, you might already have a favorite IDE that will work just as well in Python. [Spyder](https://www.spyder-ide.org) is a Python specific IDE that comes with the [Anaconda download](https://www.anaconda.com/products/distribution). It is perhaps the most familiar IDE if you are coming from languages such as Matlab that have a language specific platform and display a list of variables. [PyCharm](https://www.jetbrains.com/pycharm/) and [Visual Studio Code](https://code.visualstudio.com) are also popular IDEs. Many IDEs offer support for terminal execution, scripts, and Jupyter display. To learn about your specific IDE, visit its official documentation.
 
 _We recommend eventually learning how to develop and run Python code in each of these platforms._
 
@@ -75,6 +75,5 @@ Python can be run on many different platforms. You may choose where to run Pytho
 - [Linux commands](https://cheatography.com/davechild/cheat-sheets/linux-command-line/)
 - [Spyder](https://www.spyder-ide.org)
 - [Anaconda](https://www.anaconda.com/products/distribution)
-- [Matlab](https://www.mathworks.com/products/matlab.html)
 - [PyCharm](https://www.jetbrains.com/pycharm/)
 - [Visual Studio Code](https://code.visualstudio.com)