Add first example

Author: canyon289

EasierWayToGetVariableNames.ipynb

@@ -0,0 +1,150 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Getting all Variable names from xarray\n",
+    "\n",
+    "Question: Is there a short way to get number of unique coordinates in a DataSet object?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.11.3'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import arviz as az\n",
+    "import xarray as xr\n",
+    "xr.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "arviz.data.inference_data.InferenceData"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load NetCDF File from fileshare\n",
+    "radon_data = az.load_arviz_data(dataset=\"radon\")\n",
+    "\n",
+    "type(radon_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "xarray.core.dataset.Dataset"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "observed_dataset = radon_data.observed_data\n",
+    "type(observed_dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:          (observed_county: 919)\n",
+       "Coordinates:\n",
+       "  * observed_county  (observed_county) object 'AITKIN' ... 'YELLOW MEDICINE'\n",
+       "Data variables:\n",
+       "    y_like           (observed_county) float64 ...\n",
+       "Attributes:\n",
+       "    created_at:                 2018-10-05T15:29:14.882229\n",
+       "    inference_library:          pymc3\n",
+       "    inference_library_version:  3.5"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "observed_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(85,)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Can this be made shorter?\n",
+    "radon_data.observed_data.to_dataframe().index.unique().shape"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}