hello-world.ipynb

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   "source": [
    "# Hello World\n",
    "\n",
    "This notebook walks through basic code examples for integrating various packages with Neo4j, including `py2neo`, `ipython-cypher`, `pandas`, `networkx`, `igraph`, and `jgraph`."
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   "source": [
    "# py2neo\n",
    "\n",
    "`py2neo` is one of Neo4j's Python drivers. It offers a fully-featured interface for interacting with your data in Neo4j. Install `py2neo` with `pip install py2neo`."
   ]
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   "source": [
    "## Connect\n",
    "\n",
    "Connect to Neo4j with the `Graph` class."
   ]
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   "source": [
    "from py2neo import Graph\n",
    "\n",
    "graph = Graph()"
   ]
  },
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   "source": [
    "graph.delete_all()"
   ]
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   "source": [
    "## Nodes\n",
    "\n",
    "Create nodes with the `Node` class. The first argument is the node's label. The remaining arguments are an arbitrary amount of node properties or key-value pairs."
   ]
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   "source": [
    "from py2neo import Node\n",
    "\n",
    "nicole = Node(\"Person\", name=\"Nicole\", age=24)\n",
    "drew = Node(\"Person\", name=\"Drew\", age=20)\n",
    "\n",
    "mtdew = Node(\"Drink\", name=\"Mountain Dew\", calories=9000)\n",
    "cokezero = Node(\"Drink\", name=\"Coke Zero\", calories=0)\n",
    "\n",
    "coke = Node(\"Manufacturer\", name=\"Coca Cola\")\n",
    "pepsi = Node(\"Manufacturer\", name=\"Pepsi\")\n",
    "\n",
    "graph.create(nicole | drew | mtdew | cokezero | coke | pepsi)"
   ]
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   "source": [
    "from scripts.vis import draw\n",
    "\n",
    "options = {\"Person\": \"name\", \"Drink\": \"name\", \"Manufacturer\": \"name\"}\n",
    "draw(graph, options)"
   ]
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   "source": [
    "P.S. - If you want to check out what's going on behind the scenes for the `draw()` function used above, take a look at [`scripts/vis.py`](https://github.com/nicolewhite/neo4j-jupyter/blob/master/scripts/vis.py)."
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   "source": [
    "## Relationships\n",
    "\n",
    "Create relationships between nodes with the `Relationship` class."
   ]
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   "source": [
    "from py2neo import Relationship\n",
    "\n",
    "graph.create(Relationship(nicole, \"LIKES\", cokezero))\n",
    "graph.create(Relationship(nicole, \"LIKES\", mtdew))\n",
    "graph.create(Relationship(drew, \"LIKES\", mtdew))\n",
    "graph.create(Relationship(coke, \"MAKES\", cokezero))\n",
    "graph.create(Relationship(pepsi, \"MAKES\", mtdew))\n",
    "\n",
    "draw(graph, options)"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "## Cypher\n",
    "\n",
    "Retrieve Cypher query results with `Graph.cypher.execute`."
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [
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     "text": [
      "('name': 'Drew', 'drink': 'Mountain Dew')\n",
      "('name': 'Nicole', 'drink': 'Mountain Dew')\n",
      "('name': 'Nicole', 'drink': 'Coke Zero')\n"
     ]
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (person:Person)-[:LIKES]->(drink:Drink)\n",
    "RETURN person.name AS name, drink.name AS drink\n",
    "\"\"\"\n",
    "\n",
    "data = graph.run(query)\n",
    "\n",
    "for d in data:\n",
    "    print(d)"
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Parameterized Cypher\n",
    "\n",
    "Pass parameters to Cypher queries by passing additional key-value arguments to `Graph.cypher.execute.` Parameters in Cypher are named and are wrapped in curly braces."
   ]
  },
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     "text": [
      "('name': 'Nicole', 'avg_calories': 4500.0)\n"
     ]
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (p:Person)-[:LIKES]->(drink:Drink)\n",
    "WHERE p.name = {name}\n",
    "RETURN p.name AS name, AVG(drink.calories) AS avg_calories\n",
    "\"\"\"\n",
    "\n",
    "data = graph.run(query, name=\"Nicole\")\n",
    "\n",
    "for d in data:\n",
    "    print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "slide"
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   },
   "source": [
    "# ipython-cypher\n",
    "\n",
    "`ipython-cypher` exposes `%cypher` magic in Jupyter. Install `ipython-cypher` with `pip install ipython-cypher`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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   "outputs": [],
   "source": [
    "%load_ext cypher"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "subslide"
    }
   },
   "source": [
    "## Cypher\n",
    "\n",
    "`%cypher` is intended for one-line Cypher queries and `%%cypher` is intended for multi-line Cypher queries. Placing `%%cypher` above a Cypher query will display that query's results."
   ]
  },
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      "3 rows affected.\n"
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       "<table>\n",
       "    <tr>\n",
       "        <th>person.name</th>\n",
       "        <th>drink.name</th>\n",
       "        <th>drink.calories</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Drew</td>\n",
       "        <td>Mountain Dew</td>\n",
       "        <td>9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Nicole</td>\n",
       "        <td>Mountain Dew</td>\n",
       "        <td>9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Nicole</td>\n",
       "        <td>Coke Zero</td>\n",
       "        <td>0</td>\n",
       "    </tr>\n",
       "</table>"
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       "[['Drew', 'Mountain Dew', 9000],\n",
       " ['Nicole', 'Mountain Dew', 9000],\n",
       " ['Nicole', 'Coke Zero', 0]]"
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   "source": [
    "%%cypher\n",
    "MATCH (person:Person)-[:LIKES]->(drink:Drink)\n",
    "RETURN person.name, drink.name, drink.calories"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "## Pandas Data Frames\n",
    "\n",
    "Cypher query results can be coerced to `pandas` data frames with the `get_dataframe` method. To assign Cypher query results to a variable, you need to use `%cypher` and separate lines with \\\\. You'll first need to install `pandas` with `pip install pandas`."
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>drink</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Drew</td>\n",
       "      <td>Mountain Dew</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nicole</td>\n",
       "      <td>Mountain Dew</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nicole</td>\n",
       "      <td>Coke Zero</td>\n",
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       "  </tbody>\n",
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       "     name         drink\n",
       "0    Drew  Mountain Dew\n",
       "1  Nicole  Mountain Dew\n",
       "2  Nicole     Coke Zero"
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   "source": [
    "results = %cypher MATCH (person:Person)-[:LIKES]->(drink:Drink) \\\n",
    "                  RETURN person.name AS name, drink.name AS drink\n",
    "    \n",
    "df = results.get_dataframe()\n",
    "\n",
    "df"
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       "RangeIndex(start=0, stop=3, step=1)"
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   "source": [
    "df.index"
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>drink</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nicole</td>\n",
       "      <td>Coke Zero</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "     name      drink\n",
       "1  Nicole  Coke Zero"
      ]
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   "source": [
    "df.iloc[[1]]"
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    {
     "data": {
      "text/plain": [
       "0      Drew\n",
       "1    Nicole\n",
       "2    Nicole\n",
       "Name: name, dtype: object"
      ]
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     "execution_count": 12,
     "metadata": {},
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   "source": [
    "df[\"name\"]"
   ]
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   "source": [
    "## NetworkX Graphs\n",
    "\n",
    "Cypher query results can be coerced to `NetworkX` MultiDiGraphs, graphs that permit multiple edges between nodes, with the `get_graph` method. You'll first need to install `NetworkX` with `pip install networkx`."
   ]
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FJHcsA7gSHl9C0NhSXKz5Up8iLEkjJd0h6beSLvf/TidOnOh1Ryq2gATQV4QY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\nIEIMAIBBhBgAAIP+H2glV36FSYjAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c64b2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "%matplotlib inline\n",
    "\n",
    "results = %cypher MATCH p = (:Person)-[:LIKES]->(:Drink) RETURN p\n",
    "\n",
    "g = results.get_graph()\n",
    "\n",
    "nx.draw(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('4013', {'age': 24, 'labels': ['Person'], 'name': 'Nicole'}),\n",
       " ('4011', {'calories': 9000, 'labels': ['Drink'], 'name': 'Mountain Dew'}),\n",
       " ('4012', {'calories': 0, 'labels': ['Drink'], 'name': 'Coke Zero'}),\n",
       " ('4010', {'age': 20, 'labels': ['Person'], 'name': 'Drew'})]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.nodes(data=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'4010': 1, '4011': 2, '4012': 1, '4013': 2}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.degree(g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# igraph\n",
    "\n",
    "Cypher query results can be imported into `igraph` with `py2neo`. You'll need to install `igraph` with `pip install python-igraph`. Query results should be returned as edgelists, as `igraph` has a method for building an `igraph` object from a list of tuples representing edges between nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "from py2neo import Graph as PGraph\n",
    "from igraph import Graph as IGraph\n",
    "\n",
    "neo4j = PGraph()\n",
    "\n",
    "query = \"\"\"\n",
    "MATCH (person:Person)-[:LIKES]->(drink:Drink)\n",
    "RETURN person.name AS source, drink.name AS target\n",
    "\"\"\"\n",
    "\n",
    "data = neo4j.run(query)\n",
    "tups = []\n",
    "\n",
    "for d in data:\n",
    "    tups.append((d[\"source\"], d[\"target\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<igraph.Graph at 0x10c65ae58>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ig = IGraph.TupleList(tups)\n",
    "\n",
    "ig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Mountain Dew', 'Nicole']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best = ig.vs.select(_degree = ig.maxdegree())[\"name\"]\n",
    "best"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# jgraph\n",
    "\n",
    "`jgraph` will plot tuple lists as 3D graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div id=\"graph-be94aa82-45c2-46d8-b718-c149477901c0\"></div>\n",
       "           <script type=\"text/javascript\">\n",
       "           require.config({baseUrl: '/',\n",
       "                             paths: {jgraph: ['nbextensions/jgraph.min', 'https://rawgit.com/patrickfuller/jgraph/master/js/build/jgraph.min']}});\n",
       "           require(['jgraph'], function () {\n",
       "               var $d = $('#graph-be94aa82-45c2-46d8-b718-c149477901c0');\n",
       "               $d.width(600); $d.height(400);\n",
       "               $d.jgraph = jQuery.extend({}, jgraph);\n",
       "               $d.jgraph.create($d, {nodeSize: 2.000000,\n",
       "                                     edgeSize: 0.250000,\n",
       "                                     defaultNodeColor: '0x5bc0de',\n",
       "                                     defaultEdgeColor: '0xaaaaaa',\n",
       "                                     shader: 'basic',\n",
       "                                     z: 100,\n",
       "                                     runOptimization: true,\n",
       "                                     directed: true,\n",
       "                                     showSave: false});\n",
       "               $d.jgraph.draw({\n",
       "    \"edges\": [\n",
       "        { \"source\": 1, \"target\": 2 },\n",
       "        { \"source\": 2, \"target\": 3 },\n",
       "        { \"source\": 3, \"target\": 4 },\n",
       "        { \"source\": 4, \"target\": 1 },\n",
       "        { \"source\": 4, \"target\": 5 },\n",
       "        { \"source\": 5, \"target\": 2 }\n",
       "    ],\n",
       "    \"nodes\": {\n",
       "        \"1\": { \"location\": [ -2.000, -2.000, -2.000 ] },\n",
       "        \"2\": { \"location\": [ -0.734, -1.093, -0.864 ] },\n",
       "        \"3\": { \"location\": [ 0.071, -0.301, -0.151 ] },\n",
       "        \"4\": { \"location\": [ 1.018, 0.904, 1.360 ] },\n",
       "        \"5\": { \"location\": [ 2.000, 2.000, 2.000 ] }\n",
       "    }\n",
       "});\n",
       "\n",
       "               $d.resizable({\n",
       "                   aspectRatio: 600 / 400,\n",
       "                   resize: function (evt, ui) {\n",
       "                       $d.jgraph.renderer.setSize(ui.size.width,\n",
       "                                                  ui.size.height);\n",
       "                   }\n",
       "               });\n",
       "           });\n",
       "           </script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import jgraph\n",
    "\n",
    "jgraph.draw([(1, 2), (2, 3), (3, 4), (4, 1), (4, 5), (5, 2)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div id=\"graph-800ba9f2-587c-4705-b84f-50dc9732c53b\"></div>\n",
       "           <script type=\"text/javascript\">\n",
       "           require.config({baseUrl: '/',\n",
       "                             paths: {jgraph: ['nbextensions/jgraph.min', 'https://rawgit.com/patrickfuller/jgraph/master/js/build/jgraph.min']}});\n",
       "           require(['jgraph'], function () {\n",
       "               var $d = $('#graph-800ba9f2-587c-4705-b84f-50dc9732c53b');\n",
       "               $d.width(600); $d.height(400);\n",
       "               $d.jgraph = jQuery.extend({}, jgraph);\n",
       "               $d.jgraph.create($d, {nodeSize: 2.000000,\n",
       "                                     edgeSize: 0.250000,\n",
       "                                     defaultNodeColor: '0x5bc0de',\n",
       "                                     defaultEdgeColor: '0xaaaaaa',\n",
       "                                     shader: 'basic',\n",
       "                                     z: 100,\n",
       "                                     runOptimization: true,\n",
       "                                     directed: true,\n",
       "                                     showSave: false});\n",
       "               $d.jgraph.draw({\n",
       "    \"edges\": [\n",
       "        { \"source\": 4010, \"target\": 4011 },\n",
       "        { \"source\": 4013, \"target\": 4011 },\n",
       "        { \"source\": 4013, \"target\": 4012 },\n",
       "        { \"source\": 4014, \"target\": 4012 },\n",
       "        { \"source\": 4015, \"target\": 4011 }\n",
       "    ],\n",
       "    \"nodes\": {\n",
       "        \"4010\": { \"location\": [ -2.000, -2.000, -2.000 ] },\n",
       "        \"4011\": { \"location\": [ -1.523, -1.512, -1.419 ] },\n",
       "        \"4012\": { \"location\": [ -0.185, -0.630, -0.443 ] },\n",
       "        \"4013\": { \"location\": [ 0.327, 0.510, 0.468 ] },\n",
       "        \"4014\": { \"location\": [ 1.460, 1.429, 1.237 ] },\n",
       "        \"4015\": { \"location\": [ 2.000, 2.000, 2.000 ] }\n",
       "    }\n",
       "});\n",
       "\n",
       "               $d.resizable({\n",
       "                   aspectRatio: 600 / 400,\n",
       "                   resize: function (evt, ui) {\n",
       "                       $d.jgraph.renderer.setSize(ui.size.width,\n",
       "                                                  ui.size.height);\n",
       "                   }\n",
       "               });\n",
       "           });\n",
       "           </script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = graph.run(\"MATCH (n)-->(m) RETURN ID(n), ID(m)\")\n",
    "data = [tuple(x) for x in data]\n",
    "\n",
    "jgraph.draw(data)"
   ]
  }
 ],
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  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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