diff --git a/.github/workflows/xskillscore_testing.yml b/.github/workflows/xskillscore_testing.yml
index d675843b..07a37860 100644
--- a/.github/workflows/xskillscore_testing.yml
+++ b/.github/workflows/xskillscore_testing.yml
@@ -51,7 +51,7 @@ jobs:
         run: |
           pytest -n 4 --cov=xskillscore --cov-report=xml --verbose
       - name: Upload coverage to codecov
-        uses: codecov/codecov-action@v1.4.1
+        uses: codecov/codecov-action@v1.5.2
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
           file: ./coverage.xml
diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index 13c71757..58eec925 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -12,6 +12,7 @@ Internal Changes
 Documentation
 ~~~~~~~~~~~~~
 - Added more info in ``quick-start.ipynb`` (:pr:`316`) `Ray Bell`_.
+- Created ``tabular-data.ipynb`` (:pr:`330`) `Ray Bell`_.
 
 Breaking changes
 ~~~~~~~~~~~~~~~~
diff --git a/ci/docs_notebooks.yml b/ci/docs_notebooks.yml
index 6625a3b8..9a5ec040 100644
--- a/ci/docs_notebooks.yml
+++ b/ci/docs_notebooks.yml
@@ -16,13 +16,15 @@ dependencies:
   # see https://github.com/xgcm/xhistogram/issues/48
   - xhistogram==0.1.2
   - importlib_metadata
+  - ipykernel
   - jupyterlab
   - matplotlib-base
   - nbsphinx
   - nbstripout
   - sphinx
-  - sphinxcontrib-napoleon
+  - sphinx-autosummary-accessors
   - sphinx_rtd_theme
+  - sphinxcontrib-napoleon
   - black
   - doc8
   - isort
@@ -30,6 +32,5 @@ dependencies:
   - pre-commit
   - pip
   - pip:
-      - sphinx_autosummary_accessors
       # Install latest version of xskillscore.
       - -e ..
diff --git a/docs/source/geophysical-data.ipynb b/docs/source/geophysical-data.ipynb
new file mode 100644
index 00000000..09d93658
--- /dev/null
+++ b/docs/source/geophysical-data.ipynb
@@ -0,0 +1,39 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Geophysical Data\n",
+    "\n",
+    "See the [Quick Start](https://xskillscore.readthedocs.io/en/stable/quick-start.html) section.\n",
+    "\n",
+    "[climpred](https://climpred.readthedocs.io/en/stable/) has examples on evaluating climate predictions which uses `xskillscore`."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ]
+}
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 03244e99..7c4cb6b0 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -51,6 +51,8 @@ You can also install the bleeding edge (pre-release versions) by running:
     :caption: Getting Started
 
     quick-start.ipynb
+    geophysical-data.ipynb
+    tabular-data.ipynb
 
 **Help & Reference**
 
diff --git a/docs/source/tabular-data.ipynb b/docs/source/tabular-data.ipynb
new file mode 100644
index 00000000..e4babdca
--- /dev/null
+++ b/docs/source/tabular-data.ipynb
@@ -0,0 +1,507 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.4"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.9.4 64-bit ('xskillscore-docs-notebooks': conda)"
+  },
+  "interpreter": {
+   "hash": "e5607b67897ceeb4cb8d1a6f5e8f77cf995244d75ab9ff3b133e23bb37c07f75"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Tabular Data\n",
+    "\n",
+    "`xskillscore` can be used on tabular data such as that stored in a `pandas.DataFrame`.\n",
+    "\n",
+    "It can be used most effectively when evaluating predictions over different fields."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import xskillscore as xs\n",
+    "from sklearn.datasets import load_boston\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "np.random.seed(seed=42)"
+   ]
+  },
+  {
+   "source": [
+    "## Boston house prices dataset"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "A small example is to take a dataset and evaluate the model according to a field (column).\n",
+    "\n",
+    "Load the Boston house prices dataset:"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
+       "0    0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
+       "1    0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
+       "2    0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
+       "3    0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
+       "4    0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
+       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...    ...   \n",
+       "501  0.06263   0.0  11.93   0.0  0.573  6.593  69.1  2.4786  1.0  273.0   \n",
+       "502  0.04527   0.0  11.93   0.0  0.573  6.120  76.7  2.2875  1.0  273.0   \n",
+       "503  0.06076   0.0  11.93   0.0  0.573  6.976  91.0  2.1675  1.0  273.0   \n",
+       "504  0.10959   0.0  11.93   0.0  0.573  6.794  89.3  2.3889  1.0  273.0   \n",
+       "505  0.04741   0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  273.0   \n",
+       "\n",
+       "     PTRATIO       B  LSTAT     y  \n",
+       "0       15.3  396.90   4.98  24.0  \n",
+       "1       17.8  396.90   9.14  21.6  \n",
+       "2       17.8  392.83   4.03  34.7  \n",
+       "3       18.7  394.63   2.94  33.4  \n",
+       "4       18.7  396.90   5.33  36.2  \n",
+       "..       ...     ...    ...   ...  \n",
+       "501     21.0  391.99   9.67  22.4  \n",
+       "502     21.0  396.90   9.08  20.6  \n",
+       "503     21.0  396.90   5.64  23.9  \n",
+       "504     21.0  393.45   6.48  22.0  \n",
+       "505     21.0  396.90   7.88  11.9  \n",
+       "\n",
+       "[506 rows x 14 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CRIM</th>\n      <th>ZN</th>\n      <th>INDUS</th>\n      <th>CHAS</th>\n      <th>NOX</th>\n      <th>RM</th>\n      <th>AGE</th>\n      <th>DIS</th>\n      <th>RAD</th>\n      <th>TAX</th>\n      <th>PTRATIO</th>\n      <th>B</th>\n      <th>LSTAT</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.00632</td>\n      <td>18.0</td>\n      <td>2.31</td>\n      <td>0.0</td>\n      <td>0.538</td>\n      <td>6.575</td>\n      <td>65.2</td>\n      <td>4.0900</td>\n      <td>1.0</td>\n      <td>296.0</td>\n      <td>15.3</td>\n      <td>396.90</td>\n      <td>4.98</td>\n      <td>24.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.02731</td>\n      <td>0.0</td>\n      <td>7.07</td>\n      <td>0.0</td>\n      <td>0.469</td>\n      <td>6.421</td>\n      <td>78.9</td>\n      <td>4.9671</td>\n      <td>2.0</td>\n      <td>242.0</td>\n      <td>17.8</td>\n      <td>396.90</td>\n      <td>9.14</td>\n      <td>21.6</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.02729</td>\n      <td>0.0</td>\n      <td>7.07</td>\n      <td>0.0</td>\n      <td>0.469</td>\n      <td>7.185</td>\n      <td>61.1</td>\n      <td>4.9671</td>\n      <td>2.0</td>\n      <td>242.0</td>\n      <td>17.8</td>\n      <td>392.83</td>\n      <td>4.03</td>\n      <td>34.7</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.03237</td>\n      <td>0.0</td>\n      <td>2.18</td>\n      <td>0.0</td>\n      <td>0.458</td>\n      <td>6.998</td>\n      <td>45.8</td>\n      <td>6.0622</td>\n      <td>3.0</td>\n      <td>222.0</td>\n      <td>18.7</td>\n      <td>394.63</td>\n      <td>2.94</td>\n      <td>33.4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.06905</td>\n      <td>0.0</td>\n      <td>2.18</td>\n      <td>0.0</td>\n      <td>0.458</td>\n      <td>7.147</td>\n      <td>54.2</td>\n      <td>6.0622</td>\n      <td>3.0</td>\n      <td>222.0</td>\n      <td>18.7</td>\n      <td>396.90</td>\n      <td>5.33</td>\n      <td>36.2</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>501</th>\n      <td>0.06263</td>\n      <td>0.0</td>\n      <td>11.93</td>\n      <td>0.0</td>\n      <td>0.573</td>\n      <td>6.593</td>\n      <td>69.1</td>\n      <td>2.4786</td>\n      <td>1.0</td>\n      <td>273.0</td>\n      <td>21.0</td>\n      <td>391.99</td>\n      <td>9.67</td>\n      <td>22.4</td>\n    </tr>\n    <tr>\n      <th>502</th>\n      <td>0.04527</td>\n      <td>0.0</td>\n      <td>11.93</td>\n      <td>0.0</td>\n      <td>0.573</td>\n      <td>6.120</td>\n      <td>76.7</td>\n      <td>2.2875</td>\n      <td>1.0</td>\n      <td>273.0</td>\n      <td>21.0</td>\n      <td>396.90</td>\n      <td>9.08</td>\n      <td>20.6</td>\n    </tr>\n    <tr>\n      <th>503</th>\n      <td>0.06076</td>\n      <td>0.0</td>\n      <td>11.93</td>\n      <td>0.0</td>\n      <td>0.573</td>\n      <td>6.976</td>\n      <td>91.0</td>\n      <td>2.1675</td>\n      <td>1.0</td>\n      <td>273.0</td>\n      <td>21.0</td>\n      <td>396.90</td>\n      <td>5.64</td>\n      <td>23.9</td>\n    </tr>\n    <tr>\n      <th>504</th>\n      <td>0.10959</td>\n      <td>0.0</td>\n      <td>11.93</td>\n      <td>0.0</td>\n      <td>0.573</td>\n      <td>6.794</td>\n      <td>89.3</td>\n      <td>2.3889</td>\n      <td>1.0</td>\n      <td>273.0</td>\n      <td>21.0</td>\n      <td>393.45</td>\n      <td>6.48</td>\n      <td>22.0</td>\n    </tr>\n    <tr>\n      <th>505</th>\n      <td>0.04741</td>\n      <td>0.0</td>\n      <td>11.93</td>\n      <td>0.0</td>\n      <td>0.573</td>\n      <td>6.030</td>\n      <td>80.8</td>\n      <td>2.5050</td>\n      <td>1.0</td>\n      <td>273.0</td>\n      <td>21.0</td>\n      <td>396.90</td>\n      <td>7.88</td>\n      <td>11.9</td>\n    </tr>\n  </tbody>\n</table>\n<p>506 rows × 14 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 2
+    }
+   ],
+   "source": [
+    "data = load_boston()\n",
+    "df = pd.DataFrame(data.data, columns=data.feature_names)\n",
+    "df['y'] = pd.Series(data.target)\n",
+    "df"
+   ]
+  },
+  {
+   "source": [
+    "Create a dummy prediction column by adding noise to `y`:"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "noise = np.random.uniform(-1, 1, size=len(df['y']))\n",
+    "df['yhat'] = (df['y'] + (df['y'] * noise)).clip(lower=df[\"y\"].min())"
+   ]
+  },
+  {
+   "source": [
+    "Evaluate the model over the field `RAD` using `pandas.groupby.apply` with `mean_squared_error` from `scikit-learn`:"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "RAD\n",
+       "1.0     161.237554\n",
+       "2.0     313.855750\n",
+       "3.0     307.220760\n",
+       "4.0     162.634430\n",
+       "5.0     221.852969\n",
+       "6.0     155.612978\n",
+       "7.0     214.375240\n",
+       "8.0     278.092560\n",
+       "24.0    148.840507\n",
+       "dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "df.groupby('RAD').apply(lambda x: mean_squared_error(x[\"y\"], x[\"yhat\"]))"
+   ]
+  },
+  {
+   "source": [
+    "You could also do the following using `xskillscore`.\n",
+    "\n",
+    "First, structure the `pandas.DataFrame` to keep the core fields when converting to an `xarray` object:"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "              y       yhat\n",
+       "index RAD                 \n",
+       "0     1.0  24.0  17.977926\n",
+       "1     2.0  21.6  41.070858\n",
+       "2     2.0  34.7  50.800380\n",
+       "3     3.0  33.4  39.990387\n",
+       "4     3.0  36.2  11.295750\n",
+       "...         ...        ...\n",
+       "501   1.0  22.4  24.017117\n",
+       "502   1.0  20.6  12.752538\n",
+       "503   1.0  23.9  38.899402\n",
+       "504   1.0  22.0  30.128172\n",
+       "505   1.0  11.9   5.000000\n",
+       "\n",
+       "[506 rows x 2 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>y</th>\n      <th>yhat</th>\n    </tr>\n    <tr>\n      <th>index</th>\n      <th>RAD</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <th>1.0</th>\n      <td>24.0</td>\n      <td>17.977926</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <th>2.0</th>\n      <td>21.6</td>\n      <td>41.070858</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <th>2.0</th>\n      <td>34.7</td>\n      <td>50.800380</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <th>3.0</th>\n      <td>33.4</td>\n      <td>39.990387</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <th>3.0</th>\n      <td>36.2</td>\n      <td>11.295750</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>501</th>\n      <th>1.0</th>\n      <td>22.4</td>\n      <td>24.017117</td>\n    </tr>\n    <tr>\n      <th>502</th>\n      <th>1.0</th>\n      <td>20.6</td>\n      <td>12.752538</td>\n    </tr>\n    <tr>\n      <th>503</th>\n      <th>1.0</th>\n      <td>23.9</td>\n      <td>38.899402</td>\n    </tr>\n    <tr>\n      <th>504</th>\n      <th>1.0</th>\n      <td>22.0</td>\n      <td>30.128172</td>\n    </tr>\n    <tr>\n      <th>505</th>\n      <th>1.0</th>\n      <td>11.9</td>\n      <td>5.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>506 rows × 2 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "source": [
+    "min_df = df.reset_index().set_index([\"index\", \"RAD\"])[[\"y\", \"yhat\"]]\n",
+    "min_df"
+   ]
+  },
+  {
+   "source": [
+    "Convert it to an `xarray.Dataset` using `pandas.DataFrame.to_xarray`. Note: This will create an array of `index` by `RAD` and pad the values that do not exist with `nan`."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:  (RAD: 9, index: 506)\n",
+       "Coordinates:\n",
+       "  * index    (index) int64 0 1 2 3 4 5 6 7 8 ... 498 499 500 501 502 503 504 505\n",
+       "  * RAD      (RAD) float64 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 24.0\n",
+       "Data variables:\n",
+       "    y        (index, RAD) float64 24.0 nan nan nan nan ... nan nan nan nan nan\n",
+       "    yhat     (index, RAD) float64 17.98 nan nan nan nan ... nan nan nan nan nan"
+      ],
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2;\n}\n\n.xr-var-dtype {\n  grid-column: 3;\n  text-align: right;\n  color: var(--xr-font-color2);\n}\n\n.xr-var-preview {\n  grid-column: 4;\n}\n\n.xr-var-name,\n.xr-var-dims,\n.xr-var-dtype,\n.xr-preview,\n.xr-attrs dt {\n  white-space: nowrap;\n  overflow: hidden;\n  text-overflow: ellipsis;\n  padding-right: 10px;\n}\n\n.xr-var-name:hover,\n.xr-var-dims:hover,\n.xr-var-dtype:hover,\n.xr-attrs dt:hover {\n  overflow: visible;\n  width: auto;\n  z-index: 1;\n}\n\n.xr-var-attrs,\n.xr-var-data {\n  display: none;\n  background-color: var(--xr-background-color) !important;\n  padding-bottom: 5px !important;\n}\n\n.xr-var-attrs-in:checked ~ .xr-var-attrs,\n.xr-var-data-in:checked ~ .xr-var-data {\n  display: block;\n}\n\n.xr-var-data > table {\n  float: right;\n}\n\n.xr-var-name span,\n.xr-var-data,\n.xr-attrs {\n  padding-left: 25px !important;\n}\n\n.xr-attrs,\n.xr-var-attrs,\n.xr-var-data {\n  grid-column: 1 / -1;\n}\n\ndl.xr-attrs {\n  padding: 0;\n  margin: 0;\n  display: grid;\n  grid-template-columns: 125px auto;\n}\n\n.xr-attrs dt,\n.xr-attrs dd {\n  padding: 0;\n  margin: 0;\n  float: left;\n  padding-right: 10px;\n  width: auto;\n}\n\n.xr-attrs dt {\n  font-weight: normal;\n  grid-column: 1;\n}\n\n.xr-attrs dt:hover span {\n  display: inline-block;\n  background: var(--xr-background-color);\n  padding-right: 10px;\n}\n\n.xr-attrs dd {\n  grid-column: 2;\n  white-space: pre-wrap;\n  word-break: break-all;\n}\n\n.xr-icon-database,\n.xr-icon-file-text2 {\n  display: inline-block;\n  vertical-align: middle;\n  width: 1em;\n  height: 1.5em !important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\nDimensions:  (RAD: 9, index: 506)\nCoordinates:\n  * index    (index) int64 0 1 2 3 4 5 6 7 8 ... 498 499 500 501 502 503 504 505\n  * RAD      (RAD) float64 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 24.0\nData variables:\n    y        (index, RAD) float64 24.0 nan nan nan nan ... nan nan nan nan nan\n    yhat     (index, RAD) float64 17.98 nan nan nan nan ... nan nan nan nan nan</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-145bafb0-fec2-480c-ba6e-c55b7823da0a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-145bafb0-fec2-480c-ba6e-c55b7823da0a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>RAD</span>: 9</li><li><span class='xr-has-index'>index</span>: 506</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6daa71ea-1876-4b36-a9e3-7317ee053b8d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6daa71ea-1876-4b36-a9e3-7317ee053b8d' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>index</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 501 502 503 504 505</div><input id='attrs-af46132f-8345-4679-b55d-663c96b28fdb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af46132f-8345-4679-b55d-663c96b28fdb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4e346e5-ea8e-44e1-b290-ed4121ac06d2' class='xr-var-data-in' type='checkbox'><label for='data-a4e346e5-ea8e-44e1-b290-ed4121ac06d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 503, 504, 505])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>RAD</span></div><div class='xr-var-dims'>(RAD)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 4.0 ... 7.0 8.0 24.0</div><input id='attrs-d8c29685-6ad8-4b83-b623-43975b4ab806' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d8c29685-6ad8-4b83-b623-43975b4ab806' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95031a48-1125-400d-bb98-975e87bddb65' class='xr-var-data-in' type='checkbox'><label for='data-95031a48-1125-400d-bb98-975e87bddb65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8., 24.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-52e6b5a5-7c83-440c-ae1c-529fe47cd58f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-52e6b5a5-7c83-440c-ae1c-529fe47cd58f' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(index, RAD)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>24.0 nan nan nan ... nan nan nan</div><input id='attrs-0f63230f-06d4-463a-b022-6d320d4d1bcf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0f63230f-06d4-463a-b022-6d320d4d1bcf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ce8017e-2167-4947-afc6-951a769437cd' class='xr-var-data-in' type='checkbox'><label for='data-1ce8017e-2167-4947-afc6-951a769437cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[24. ,  nan,  nan, ...,  nan,  nan,  nan],\n       [ nan, 21.6,  nan, ...,  nan,  nan,  nan],\n       [ nan, 34.7,  nan, ...,  nan,  nan,  nan],\n       ...,\n       [23.9,  nan,  nan, ...,  nan,  nan,  nan],\n       [22. ,  nan,  nan, ...,  nan,  nan,  nan],\n       [11.9,  nan,  nan, ...,  nan,  nan,  nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>yhat</span></div><div class='xr-var-dims'>(index, RAD)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>17.98 nan nan nan ... nan nan nan</div><input id='attrs-83d31733-6913-4be5-8875-84656aa341d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-83d31733-6913-4be5-8875-84656aa341d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-193ef302-f206-485f-b2b8-5d052695ef0e' class='xr-var-data-in' type='checkbox'><label for='data-193ef302-f206-485f-b2b8-5d052695ef0e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[17.9779257 ,         nan,         nan, ...,         nan,\n                nan,         nan],\n       [        nan, 41.07085804,         nan, ...,         nan,\n                nan,         nan],\n       [        nan, 50.80037956,         nan, ...,         nan,\n                nan,         nan],\n       ...,\n       [38.89940194,         nan,         nan, ...,         nan,\n                nan,         nan],\n       [30.12817159,         nan,         nan, ...,         nan,\n                nan,         nan],\n       [ 5.        ,         nan,         nan, ...,         nan,\n                nan,         nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-79636b22-b1cb-4a86-947d-1c316821be22' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-79636b22-b1cb-4a86-947d-1c316821be22' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "ds = min_df.to_xarray()\n",
+    "ds"
+   ]
+  },
+  {
+   "source": [
+    "You call now apply any metric from `xskillscore` using the accessor method. The input for the `dim` argument is `index` as we want to reduce this dimension and apply the metric over `RAD`. In addition, there are `nan`'s in the `xarray.Dataset` so you should use `skipna=True`:"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<xarray.DataArray (RAD: 9)>\n",
+       "array([161.23755363, 313.85575025, 307.22076012, 162.63442999,\n",
+       "       221.85296903, 155.6129776 , 214.37524005, 278.09256049,\n",
+       "       148.84050691])\n",
+       "Coordinates:\n",
+       "  * RAD      (RAD) float64 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 24.0"
+      ],
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grid-template-columns: 125px auto;\n}\n\n.xr-attrs dt,\n.xr-attrs dd {\n  padding: 0;\n  margin: 0;\n  float: left;\n  padding-right: 10px;\n  width: auto;\n}\n\n.xr-attrs dt {\n  font-weight: normal;\n  grid-column: 1;\n}\n\n.xr-attrs dt:hover span {\n  display: inline-block;\n  background: var(--xr-background-color);\n  padding-right: 10px;\n}\n\n.xr-attrs dd {\n  grid-column: 2;\n  white-space: pre-wrap;\n  word-break: break-all;\n}\n\n.xr-icon-database,\n.xr-icon-file-text2 {\n  display: inline-block;\n  vertical-align: middle;\n  width: 1em;\n  height: 1.5em !important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (RAD: 9)&gt;\narray([161.23755363, 313.85575025, 307.22076012, 162.63442999,\n       221.85296903, 155.6129776 , 214.37524005, 278.09256049,\n       148.84050691])\nCoordinates:\n  * RAD      (RAD) float64 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 24.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>RAD</span>: 9</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e2c6666f-e9c5-46e9-b0ca-863b54bd5119' class='xr-array-in' type='checkbox' checked><label for='section-e2c6666f-e9c5-46e9-b0ca-863b54bd5119' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>161.2 313.9 307.2 162.6 221.9 155.6 214.4 278.1 148.8</span></div><div class='xr-array-data'><pre>array([161.23755363, 313.85575025, 307.22076012, 162.63442999,\n       221.85296903, 155.6129776 , 214.37524005, 278.09256049,\n       148.84050691])</pre></div></div></li><li class='xr-section-item'><input id='section-bdf711e8-aa8e-48c4-aaa1-d8d4a7e9fa6b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bdf711e8-aa8e-48c4-aaa1-d8d4a7e9fa6b' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>RAD</span></div><div class='xr-var-dims'>(RAD)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 4.0 ... 7.0 8.0 24.0</div><input id='attrs-ff476cbf-48c9-4894-9267-aef0869e7fc2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ff476cbf-48c9-4894-9267-aef0869e7fc2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad2d9e47-00ed-4108-9ff9-6bf6efaa9de6' class='xr-var-data-in' type='checkbox'><label for='data-ad2d9e47-00ed-4108-9ff9-6bf6efaa9de6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8., 24.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4e92da2a-2076-42d6-895a-48c38df9c70d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4e92da2a-2076-42d6-895a-48c38df9c70d' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ],
+   "source": [
+    "out = ds.xs.mse('y', 'yhat', dim=\"index\", skipna=True)\n",
+    "out"
+   ]
+  },
+  {
+   "source": [
+    "It makes sense to return the data in tabular form hence you can call `xarray.DataArray.to_series` to convert it to a `pandas.Series`:"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "RAD\n",
+       "1.0     161.237554\n",
+       "2.0     313.855750\n",
+       "3.0     307.220760\n",
+       "4.0     162.634430\n",
+       "5.0     221.852969\n",
+       "6.0     155.612978\n",
+       "7.0     214.375240\n",
+       "8.0     278.092560\n",
+       "24.0    148.840507\n",
+       "dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "out.to_series()"
+   ]
+  },
+  {
+   "source": [
+    "## Evaluating predictions over many columns"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "`xskillscore` is built upon `xarray.apply_ufunc` which offers speed-up by vectorizing operations. As a result `xskillscore` can be faster than `pandas.groupby.apply`. This is espicially true if there are many samples in the dataset and if the predictions have to be evaluated over many fields.\n",
+    "\n",
+    "For this exercise we will create fake data for which the predictions have to be evaluated over three fields:"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "            DATE  STORE  SKU  y       yhat\n",
+       "0     2020-01-01      0    0  3   4.617306\n",
+       "1     2020-01-01      0    1  6   1.000000\n",
+       "2     2020-01-01      0    2  2   3.039347\n",
+       "3     2020-01-01      0    3  3   5.102145\n",
+       "4     2020-01-01      0    4  5   3.563087\n",
+       "...          ...    ...  ... ..        ...\n",
+       "99995 2020-01-10     99   95  9  15.836256\n",
+       "99996 2020-01-10     99   96  5   7.515791\n",
+       "99997 2020-01-10     99   97  1   1.000000\n",
+       "99998 2020-01-10     99   98  6   6.676512\n",
+       "99999 2020-01-10     99   99  5   4.600985\n",
+       "\n",
+       "[100000 rows x 5 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>DATE</th>\n      <th>STORE</th>\n      <th>SKU</th>\n      <th>y</th>\n      <th>yhat</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2020-01-01</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>4.617306</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2020-01-01</td>\n      <td>0</td>\n      <td>1</td>\n      <td>6</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2020-01-01</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>3.039347</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2020-01-01</td>\n      <td>0</td>\n      <td>3</td>\n      <td>3</td>\n      <td>5.102145</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2020-01-01</td>\n      <td>0</td>\n      <td>4</td>\n      <td>5</td>\n      <td>3.563087</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>99995</th>\n      <td>2020-01-10</td>\n      <td>99</td>\n      <td>95</td>\n      <td>9</td>\n      <td>15.836256</td>\n    </tr>\n    <tr>\n      <th>99996</th>\n      <td>2020-01-10</td>\n      <td>99</td>\n      <td>96</td>\n      <td>5</td>\n      <td>7.515791</td>\n    </tr>\n    <tr>\n      <th>99997</th>\n      <td>2020-01-10</td>\n      <td>99</td>\n      <td>97</td>\n      <td>1</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>99998</th>\n      <td>2020-01-10</td>\n      <td>99</td>\n      <td>98</td>\n      <td>6</td>\n      <td>6.676512</td>\n    </tr>\n    <tr>\n      <th>99999</th>\n      <td>2020-01-10</td>\n      <td>99</td>\n      <td>99</td>\n      <td>5</td>\n      <td>4.600985</td>\n    </tr>\n  </tbody>\n</table>\n<p>100000 rows × 5 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "source": [
+    "stores = np.arange(100)\n",
+    "skus = np.arange(100)\n",
+    "dates = pd.date_range(\"1/1/2020\", \"1/10/2020\", freq=\"D\")\n",
+    "\n",
+    "rows = []\n",
+    "for _, date in enumerate(dates):\n",
+    "    for _, store in enumerate(stores):\n",
+    "        for _, sku in enumerate(skus):\n",
+    "            rows.append(\n",
+    "                dict(\n",
+    "                    {\n",
+    "                        \"DATE\": date,\n",
+    "                        \"STORE\": store,\n",
+    "                        \"SKU\": sku,\n",
+    "                        \"y\": np.random.randint(9) + 1,\n",
+    "                    }\n",
+    "                )\n",
+    "            )\n",
+    "df = pd.DataFrame(rows)\n",
+    "\n",
+    "noise = np.random.uniform(-1, 1, size=len(df['y']))\n",
+    "df['yhat'] = (df['y'] + (df['y'] * noise)).clip(lower=df[\"y\"].min())\n",
+    "df"
+   ]
+  },
+  {
+   "source": [
+    "Time the `pandas.groupby.apply` method:"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "CPU times: user 2.57 s, sys: 10.3 ms, total: 2.58 s\nWall time: 2.58 s\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "STORE  SKU\n",
+       "0      0       8.384360\n",
+       "       1       7.071648\n",
+       "       2      14.677462\n",
+       "       3      13.391239\n",
+       "       4      12.131033\n",
+       "                ...    \n",
+       "99     95     18.473114\n",
+       "       96     10.154608\n",
+       "       97     11.743513\n",
+       "       98      8.406069\n",
+       "       99      7.098808\n",
+       "Length: 10000, dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "df.groupby(['STORE', 'SKU']).apply(lambda x: mean_squared_error(x[\"y\"], x[\"yhat\"]))"
+   ]
+  },
+  {
+   "source": [
+    "Time it using `xskillscore`:"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "CPU times: user 21.8 ms, sys: 4.02 ms, total: 25.8 ms\nWall time: 24.4 ms\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "STORE  SKU\n",
+       "0      0       8.384360\n",
+       "       1       7.071648\n",
+       "       2      14.677462\n",
+       "       3      13.391239\n",
+       "       4      12.131033\n",
+       "                ...    \n",
+       "99     95     18.473114\n",
+       "       96     10.154608\n",
+       "       97     11.743513\n",
+       "       98      8.406069\n",
+       "       99      7.098808\n",
+       "Length: 10000, dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "df.set_index([\"DATE\", \"STORE\", \"SKU\"]).to_xarray().xs.mse(\n",
+    "    \"y\", \"yhat\", dim=\"DATE\"\n",
+    ").to_series()"
+   ]
+  },
+  {
+   "source": [
+    "See [xskillscore-tutorial](https://github.com/raybellwaves/xskillscore-tutorial) for further reading."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ]
+}