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Fix broken links (#504)
* Remove Matlab link since it is refusing the link-checker * Update link to ENSO data description * Also remove offending links from References sections * Update ENSO data links
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core/pandas/pandas.ipynb

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"\n",
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"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",
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"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/)."
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"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."
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"In the next notebook, we will look more into using pandas for more in-depth data analysis.\n",
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"\n",
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"## Resources and References\n",
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"1. [NOAA NCDC ENSO Dataset Used in this Example](https://www.ncdc.noaa.gov/teleconnections/enso/indicators/sst/)\n",
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"1. [ENSO data used in this example](https://www.ncei.noaa.gov/access/monitoring/enso/sst)\n",
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"1. [Getting Started with Pandas](https://pandas.pydata.org/docs/getting_started/index.html#getting-started)\n",
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"1. [Pandas User Guide](https://pandas.pydata.org/docs/user_guide/index.html#user-guide)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a66d4939-70fc-4655-9075-e516e646999e",
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"metadata": {},
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"outputs": [],
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"source": []
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"metadata": {
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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"version": "3.11.11"
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}
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"nbformat": 4,

core/xarray/computation-masking.ipynb

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"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",
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"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",
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"![](https://www.ncdc.noaa.gov/monitoring-content/teleconnections/nino-regions.gif)\n",
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"\n"
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"![](https://www.ncei.noaa.gov/monitoring-content/teleconnections/nino-regions.gif)"
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"name": "python",
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"nbconvert_exporter": "python",
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"version": "3.10.9"
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"version": "3.11.11"
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foundations/how-to-run-python.md

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### Other IDEs
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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.
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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.
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_We recommend eventually learning how to develop and run Python code in each of these platforms._
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- [Linux commands](https://cheatography.com/davechild/cheat-sheets/linux-command-line/)
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- [Spyder](https://www.spyder-ide.org)
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- [Anaconda](https://www.anaconda.com/products/distribution)
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- [Matlab](https://www.mathworks.com/products/matlab.html)
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- [PyCharm](https://www.jetbrains.com/pycharm/)
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- [Visual Studio Code](https://code.visualstudio.com)

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