{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": {
       "Software versions": [
        {
         "module": "Python",
         "version": "3.6.2 64bit [GCC 4.2.1 Compatible Apple LLVM 8.0.0 (clang-800.0.38)]"
        },
        {
         "module": "IPython",
         "version": "6.2.1"
        },
        {
         "module": "OS",
         "version": "Darwin 17.4.0 x86_64 i386 64bit"
        },
        {
         "module": "networkx",
         "version": "2.1"
        },
        {
         "module": "cypher",
         "version": "The 'cypher' distribution was not found and is required by the application"
        },
        {
         "module": "pandas",
         "version": "0.22.0"
        },
        {
         "module": "matplotlib",
         "version": "2.2.2"
        }
       ]
      },
      "text/html": [
       "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>3.6.2 64bit [GCC 4.2.1 Compatible Apple LLVM 8.0.0 (clang-800.0.38)]</td></tr><tr><td>IPython</td><td>6.2.1</td></tr><tr><td>OS</td><td>Darwin 17.4.0 x86_64 i386 64bit</td></tr><tr><td>networkx</td><td>2.1</td></tr><tr><td>cypher</td><td>The 'cypher' distribution was not found and is required by the application</td></tr><tr><td>pandas</td><td>0.22.0</td></tr><tr><td>matplotlib</td><td>2.2.2</td></tr><tr><td colspan='2'>Sun Mar 25 23:33:21 2018 PDT</td></tr></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{|l|l|}\\hline\n",
       "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
       "Python & 3.6.2 64bit [GCC 4.2.1 Compatible Apple LLVM 8.0.0 (clang-800.0.38)] \\\\ \\hline\n",
       "IPython & 6.2.1 \\\\ \\hline\n",
       "OS & Darwin 17.4.0 x86\\_64 i386 64bit \\\\ \\hline\n",
       "networkx & 2.1 \\\\ \\hline\n",
       "cypher & The 'cypher' distribution was not found and is required by the application \\\\ \\hline\n",
       "pandas & 0.22.0 \\\\ \\hline\n",
       "matplotlib & 2.2.2 \\\\ \\hline\n",
       "\\hline \\multicolumn{2}{|l|}{Sun Mar 25 23:33:21 2018 PDT} \\\\ \\hline\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Software versions\n",
       "Python 3.6.2 64bit [GCC 4.2.1 Compatible Apple LLVM 8.0.0 (clang-800.0.38)]\n",
       "IPython 6.2.1\n",
       "OS Darwin 17.4.0 x86_64 i386 64bit\n",
       "networkx 2.1\n",
       "cypher The 'cypher' distribution was not found and is required by the application\n",
       "pandas 0.22.0\n",
       "matplotlib 2.2.2\n",
       "Sun Mar 25 23:33:21 2018 PDT"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# !pip install matplotlib pandas networkx version_information\n",
    "%load_ext version_information\n",
    "%version_information networkx, cypher, pandas, matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext cypher"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CypherMagic options\n",
      "-----------------\n",
      "CypherMagic.auto_html=<Bool>\n",
      "    Current: False\n",
      "    Return a D3 representation of the graph instead of regular result sets\n",
      "CypherMagic.auto_limit=<Int>\n",
      "    Current: 0\n",
      "    Automatically limit the size of the returned result sets\n",
      "CypherMagic.auto_networkx=<Bool>\n",
      "    Current: False\n",
      "    Return Networkx MultiDiGraph instead of regular result sets\n",
      "CypherMagic.auto_pandas=<Bool>\n",
      "    Current: False\n",
      "    Return Pandas DataFrame instead of regular result sets\n",
      "CypherMagic.data_contents=<Bool>\n",
      "    Current: True\n",
      "    Bring extra data to render the results as a graph\n",
      "CypherMagic.display_limit=<Int>\n",
      "    Current: 0\n",
      "    Automatically limit the number of rows displayed (full result set is still\n",
      "    stored)\n",
      "CypherMagic.feedback=<Bool>\n",
      "    Current: True\n",
      "    Print number of rows affected\n",
      "CypherMagic.rest=<Bool>\n",
      "    Current: False\n",
      "    Return full REST representations of objects inside the result sets\n",
      "CypherMagic.short_errors=<Bool>\n",
      "    Current: True\n",
      "    Don't display the full traceback on Neo4j errors\n",
      "CypherMagic.style=<Unicode>\n",
      "    Current: 'DEFAULT'\n",
      "    Set the table printing style to any of prettytable's defined styles\n",
      "    (currently DEFAULT, MSWORD_FRIENDLY, PLAIN_COLUMNS, RANDOM)\n",
      "CypherMagic.uri=<Unicode>\n",
      "    Current: 'http://localhost:7474/db/data/'\n",
      "    Default database URL if none is defined inline\n"
     ]
    }
   ],
   "source": [
    "%config CypherMagic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 nodes deleted.\n",
      "6 relationship deleted.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%cypher match (n)-[r]-() delete n, r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 nodes created.\n",
      "6 properties set.\n",
      "6 relationships created.\n",
      "7 labels added.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%cypher\n",
    "create\n",
    "    // Nodes\n",
    "    (Neo:Crew {name:'Neo'}),\n",
    "    (Morpheus:Crew {name: 'Morpheus'}),\n",
    "    (Trinity:Crew {name: 'Trinity'}),\n",
    "    (Cypher:Crew:Matrix {name: 'Cypher'}),\n",
    "    (Smith:Matrix {name: 'Agent Smith'}),\n",
    "    (Architect:Matrix {name:'The Architect'}),\n",
    "    // Relationships\n",
    "    (Neo)-[:KNOWS]->(Morpheus),\n",
    "    (Neo)-[:LOVES]->(Trinity),\n",
    "    (Morpheus)-[:KNOWS]->(Trinity),\n",
    "    (Morpheus)-[:KNOWS]->(Cypher),\n",
    "    (Cypher)-[:KNOWS]->(Smith),\n",
    "    (Smith)-[:CODED_BY]->(Architect);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>n</th>\n",
       "        <th>degree</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Morpheus&#x27;}</td>\n",
       "        <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Cypher&#x27;}</td>\n",
       "        <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Trinity&#x27;}</td>\n",
       "        <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Agent Smith&#x27;}</td>\n",
       "        <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Neo&#x27;}</td>\n",
       "        <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;The Architect&#x27;}</td>\n",
       "        <td>1</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[[{'name': 'Morpheus'}, 3],\n",
       " [{'name': 'Cypher'}, 2],\n",
       " [{'name': 'Trinity'}, 2],\n",
       " [{'name': 'Agent Smith'}, 2],\n",
       " [{'name': 'Neo'}, 2],\n",
       " [{'name': 'The Architect'}, 1]]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%cypher match (n)-[r]-() return n, count(r) as degree order by degree desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 rows affected.\n"
     ]
    }
   ],
   "source": [
    "results = %cypher match (n)-[r]-() return n.name as name, type(r) as rel, count(r) as degree order by degree desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>name</th>\n",
       "      <th>rel</th>\n",
       "      <th>degree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Morpheus</td>\n",
       "      <td>KNOWS</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cypher</td>\n",
       "      <td>KNOWS</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Architect</td>\n",
       "      <td>CODED_BY</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Agent Smith</td>\n",
       "      <td>CODED_BY</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Agent Smith</td>\n",
       "      <td>KNOWS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Neo</td>\n",
       "      <td>LOVES</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Trinity</td>\n",
       "      <td>LOVES</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Neo</td>\n",
       "      <td>KNOWS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Trinity</td>\n",
       "      <td>KNOWS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            name       rel  degree\n",
       "0       Morpheus     KNOWS       3\n",
       "1         Cypher     KNOWS       2\n",
       "2  The Architect  CODED_BY       1\n",
       "3    Agent Smith  CODED_BY       1\n",
       "4    Agent Smith     KNOWS       1\n",
       "5            Neo     LOVES       1\n",
       "6        Trinity     LOVES       1\n",
       "7            Neo     KNOWS       1\n",
       "8        Trinity     KNOWS       1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.get_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10a040fd0>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 9 artists>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pFTmgO7IsEhGjJnbSiE5I0ioRMQ4Y1632Jd0eEat2q/0qvSJLr8gBlqWX5YDekaVX5IDekqU/dZqP/gksVNlesOwb8BxJI4BZgWdrlMkYY8wEqFMp3AYsIWlRSdMCOwGX9jvnUuBj5f0OwLVRlz3LGGPMRKnNfBQRb0r6NHA1MBw4PSLul/RN4PaIuBQ4DThH0sPAc6Ti6EW6ZroagF6RpVfkAMsyEL0iB/SOLL0iB/SWLO+gNkezMcaYoYczmo0xxjSxUjBNnCNijLFSMEiaXdL0U5qTv1eUXKflaLW9Uo2g55A0TbdlmBqwUughJC0laQ9JIzvY5jLAT8hs8inieZA0WtKM3VRykhaQdA5AJ+SQNEbSx1ptT9L7gA9JmrltwrUBSUsBx0lasIsyLF/k6DkkLSFpk3Z81hTRCUwJlM75fGAU0JHOTNLSZJmRiyLiioh4u3JsKD8bBwO7Q1dnCwF0RLmX+3gxMIekOSr7J+m7S9oc+DHw8uR+Rh2Ujvh84CHg1bKvo8+npC3I38qSkqbvZNsTo/QdFwKrSpqu1c8bEhnNUzqS5gZOB46KiPMr+1XzKPNTwE8j4rTy458bWAZ4ICKekjSsqiiGEE8D80JnRunj4VVgUUkbRMTv6mpE0gLAucAxEXFO9dikfPdSrPJYYM+IuLFyaCSlI+4GkmYhFdXJpbJBg7mApzokw8bAUcCnI+KGTrQ5WMrM6WzgyGrf0QpWCr3BMOCZxk2VtBOwITBc0s0RcVo7G5M0d0Q8BbxV2X0kWVdqOWBaSVtGxAPtbLdOisltuoj4Dzmq3L/YoCMi3uyQDAvSVwjyXknnVo7VpeBnAm5tKIRSc2xVYDRwWEQ8OMi2ZwXui4gbJc1KFqvcmHwGz4uIy2uQfaJExIuSHgR+UgYu+wErAZtI+m5EnNABMVYCjouIG8pMbHlgTeAf7eqIW2Bu8nlr9B1bAhsBL5LJwJOsxKwUukiZIewKXAk8LulY8gf9DPAE8DfS1n9XRNzZpjaHAb+SdDZwMvB7SXsB/wbOjIgPSToMOEbSNhHx1oQ+rxeQNCfwPWC0pEtIE9xiwJwR8e8OzLgabA2sCHxV0j3AdsBvJd0RES+1Uw5JY4B1gMuB9SXtDuxJlol5CvgfcLak9SPitQl8zirl7UPA4pLGASsD9wEvAfcA35L0YEQ80g7ZB4OkJYEtIuJ7ZImc08kByyPALcAvgXGS/hoRl9Ukw3rAHcBrwN6SbgG+A7xNJuQeIGlMRBxZR/sTkW0+sq/4a9n+BPBBclb3P2A2YDdJD0XE05Py2VYK3WVFYFPgKjLDcTPgXuAE4JGICEkXALO0q8GIeFvSQcA+pB3yPcDoiLi9Et1xCzAPHfJttIKkERHxrKTPk+t2LEGawEYBn5J0Qjlem2KQND95vc6KiFclLQK8AdxPmjkOkHRcRLzcRpPcPMCp5Cj2s8A2ZPXgY4G/RsRbkq4CVgBuHY/cmwHfBXaNiOeLo3pv4ALSrNioXrw22dF0hOJDOIf8fgBbATsC15NBEW+W67wiMGeNoowFZiwDpbWAHwF/As6IiNskrQB8V9LxE1K8NbElaf5dm1SQ65JVIY6NiLvLrOq35ExikpQCEeFXh1/ATJX33wbuBqYf4LwVy7GV29DmYsAC5f0swDHAgQOctxpwJ7BVt6/TIL7TsmTncQGweL9ja5N24COAUTXKsDTwYPlhPg3MWjk2nJzKH1tkmbkN7Q2nrxLBfuQIevYBzluLHO0vOZ7P2RK4EfhA2Z4NmGOA8z5C1jGbr0P3dCngdtK3ATAdsPEA561drvu6NcqyIFmKZ/OyPVe/43uX+z5tJ65NaXPayvufkgM7DXDee8lZzoD3f4JtdOrL+NW8WfMDv6h2yOQs4XhgRNmek7TpPgBs3YY2hwFXAI+Ro4vRpF30QXL9CoAZSifzALBN2feuh61XXqXzuI00mRxZlOc0/c5ZHziRVLzT1CDDkkWGPcr2GWRhx6rSH0bOAE8AlmixvUWBX5fPm6k8S6c1nhFAZNXhrchZyoCKnZxlvAV8rmwvRg4E3l85Z17gEHLmulyH7ukI4FpyxtX4PlcAh1TOWah0xg8BW9Ygw2bkDGH2sv1Zcsaiyjlzk9FttwPLd/CZX7TIskPZngE4E/hE5ZzZSevDfY3f8SS306kv5Ffzpi1fOt7Hga+W7S3JkeT65Zxlga/QN0JpqXMuP673kyPqj5PmqveSFWrvoG8GsSBlVtLjCmEBchrf6NSGA9eUTmx9YO7Kue8Dlq5BhmmAi4ALK/v+jwwNvRPYBZilcv3nbEObo0g/04WlM5gD2A34AzBDOWcZ4IekPf5d97Hc95VI8+H1pNnp18D+/c4bXZ7JTimEJcszunpRDLsUhfDdAc7dDdioJjkOA14gBxo7kYrqBuCIyjkfLfs6phAqz/JzpcP/MjAzOcg7ijIbJv1MP6AMCCbnd+yCeB1C0ozA2xHxWrHRfpx0YD1E/gAXB26OiKPL+SOjRTulpMWB+SPiekkzkKPmPwLXkfbR80lb7VXASRHx31ba6xTFZv8z4BSy4/gJ+UO+h4wKuSgizhr/J7RNjtVI89Tl5OjsLxGxv6Q9ydHsPhFxXxvaGR7F4V8cikE6Oz8CfImcZT5GmlzekjRzDODYLhFah5FRMz+Q9DlyFnVKRBzQOL+EYAbw++hc5NbmpH9jVbLz+ybwZERsXTlnfWDtxm+kJjmWIa/pz8nAgcfJmf25wJcj4lflvDkjoiNrvxRf39vl3n6IHDQ2fEVvAHuRVai/X86fLSL+M9l+tE5quqn1RZqDjgO+RZ9GH0uO0pYiR0V/Jn/on2pju7sCr5BT4EVIu/E15Gh6gbL/frJDeZdduhdf9NnTlyVHuPcCJ1aOf5QcAb/LR1OTHKsBvwF+3e/4mcBurbZBzg7uBDYhTQMLktFqywJrkLPNH5MjyA9WZRvPZ+5Nzg4bM4v9SDPIKmV7d9IUN7qT95Sc9YwDNi37NiNH4x8u22sWuVs2pw7Q/nrADyvbnwXOLe9PJEfi15LJa3O1u/2JyDYf6ZPaiTQXLUQqrWXImdVe5Kz5bdpkTuvYl5uaX+QUdDtyRHcf6ZzcsWxPV87ZovzYN2xDe9NUOq2Vy4/tzPIALQ98vfwIRxTlUJuzrqbrOaz8HVOu2WcpTlzSNHI1bTDXDEKOxjVesXQa+5bt1Uklv3qb2vkSOVI9vtzP5UjFtwgZNLARuYrhoO4j6fv4BjC8bO8H3EzOeq6ncyaj/j6gLwF30edb27pc18OLohrQJNYGOeYvCucMYNuy7zDgS+X9RkUhPMsAzviar9EM5V6dTM6OZyJNf+Mq5+xGWhza87x18gtOba/yo924KIQZyBHR58kR0F7lB3hy5fymDbqFNpclI1J+Xh6mDcr+zUjT0e/K8c27fX1avLYNxdCYMewDbE6Oqj/YQTmqM4ZfkyPLu2lx1EaOEDcoCmCGsv2Zomz2Bj4JHESJRqFvcNHfh7AaOeBYrLJvG3J2MaKy7yAyP6YjdnLSWXsCFed22f9T0rTauK5bF0WxRU0yLFTZ/mS5LueRtvkDgWUqx2uLYhvP/X8vRUGTM8QTyJnx+uRg6NDK+fO0re1Ofcmp7UWOyO8Bvk/a8C8Hdi/HNiIdRReSmYc7lv2tOpSXKQ/N3uRMZG9yBDm2HJ+WjFZ5qrymb7XNDl7PgcLuGophGVLR/oOaRpODkY2cIfyOFhUuqegeIG3Zl5IJSuuVY6uSeSTXkI7txQb6vhWZDiF9LreQ5sRZyBni74Cv9fuf2Tp4zZYvv4FHycHLLmX/WOD4fuc2IoHadk9JxXgzcBPpW/teoy3S1PsvctD27U5dk37X5q6inG4sCqBh3htLOpLPLb/1xv72XZtOf+Gp4UWOQG5vdPZl38dJx+jeZXse0qH2KLBOm9o9CfhMv31rkdnRH208POSsYe1uX6dBfJ+ZSyc2qt/+anhg1ZS0av/jbZBhMXJ6fhxpex49nvManfBM/WWbxPYWIGcau1b27Ukq8c3K9vykyeyvlIi1AT5nfrJu0bDKPb+ONCV+ljRD/RyYt8P3dB4y0mjhsr0I8LnSAZ9ZOusnKL6EmmT4ACUcm0wuXIr0C11dOWdn0nT1bFEUHRlkkDOCeyhhp2Xfd0nFvmbZbkSP/bfxzLdVhk4+EFPLixy5/rK8ryab7EEmu8xR2Td9+dvqLGEEOfP4WNkeRp/NeAfSHt2xJJs2XcPfkKPlv5GjyQGV5+R0voOUYWnSB/RN0qxxPqnY1xzfPWhVHnKWcHLlHjaU3sdKZzmmbA+nJMr1f3bIEOebS6d/NzljnIX0I61HzqpuJp2THTMjlnv6QLmOd5A28q0r3+f7pF/j9XJsZE1yHExlkFTZfzXvNOcuQiW8uUPXaF2y3AwUk2B5fyTwm37n1jKz69iXnRpelU5haeC3/feX91dREtfIEVxLHRo5slywvN+XjHCarfH55e/G5YEfEkqhXL/byNnVjGS455FkNcj1Bji/ofxmoE05CeTo8R5g+8q+lYtyGke/RLSKDLOROSYzTma7GwF/odiIeWcG87kUH9EE/n8DUoluSJoLP0yaDA8DFqmct23Zv8zkyDmZz+lD9GUqL06aOG+nmFXL/jlJx/emNcryI+D7le3G73YV0qFcizIapGxbANf0l628v55KQlrluWjrLGYo18zvKUqBqkMkjY6Ih4BhpbgYEfFmyVOAtGE+WvZHtFAHpxS3+yhwfimhfDM50nyfpFmjPDFkPsQrZCfR00ial2JLj4gfR8QrEXE1qRAeBTaXNE2p7dKM4Zc0G1nrpeVnusSFf570UVzZ2B9ZlPCXZOmFxSvnN2SYtRy/KSJemYT25pV0YWnjWnKGtF3JN3iLjCaDrD80YB2sxvUgBwAnRMR1EfFGRPycDCwYTY5CG9/ll2Qm7IODlbNFFgZujIgzSvsPkzOZo4APlueXiHg2In4QEVdXvlPLSFqtFA2ENMVEyeMh+nIx/kWGfM7QrnYHKdsikg4sm78FRkr6QkM2STOVYw8Bzzf+r/H7rvzO24KVQvt4k7RR7itpLtIOPErSyQAR8Yqk5Uln37/b0WBRKGeQncgp5AjxXNJMta+k7SRtSkZUnBMRL4/vs3oB5TKQ05Ojx+ckLdE4VhTt1aQpbN6IiH4K4edkCGFL5b6V5a+nJTuOB4DPSFq0Isdd5CI0e5Tzh1VkuAj4Sgxy/YRKp/cUMJ2khgK6mowa+oikOSLijVLNdF0ymepdVDqGVymFDFUWXImIm8jIqC9Imr7RbisDksngLWAFSaP6yXwzObuarf8/tKuzK4On+YD9JH2YfL7mJq/vkpVTNyD7xDfa0e6kiEg+Z1+NiNfJ2f7ykg4AiCykuCx5/wc92JhsujVNmlJe5CiuERY4L9mZHEM6Ppcg7bfXkXbU+6mYI1pocxFgk8r2tOVBupT8ca0FfKG0fTZDo5bR0pQEIvLHeSyZmLVEVXZyJL5I5f9mJUdX7zIrTYYMjWJs7y/bHyDDAL9IxcEMfIJSYqNsT0d25JMkA++O07+AnCEBbE+aqe4lTWcPMohQW9IB+RB9eRsjK9fpJ/3b7OD9XYD0ea1d2de4p2cyHod5G9uflnRi/550us9H+olOJWdSXyD9R+/p0vVZggynPpgMsNiKjDy6ivQHPtSOvmNQsnTjAkwpr9I530PWmlmPjPaYlrRLHkapLElWdFwTWKFst5KHMJLMN3ibtI2OI00Z7yHj2H9JX9b0SPoclb2sEJYiQyarhb1WISN+vgIsVfatT5rfFqictzNtiN4qMtxKsXmXfcPKfT2u/FhHkuUF/ky/JEMmMQOYNOfcTtauadSpmYZMUrqict6HyCi1CUZW8U7b88lkDaYZK/v2LB3iLB26p8Oqf8v7g8uzuyZ9OTnrkD6UFWqQYcPSoS5ByUQm8x5+QzrjR5bn7DukH6Mj/pUixxhy9rZvpV+YrzyDny/bM5X7tjV9hStr/x279tFkUqbmS5Dmm4VJx+izZF7AreSI7SbgtIj4V5vaXJSc2q5OdpZnkxEdIrNqLyVHQ08Dn4yIJ9rRbp0UE9H5ZO2l04s9fyzZsa1HOkT/StY2OhD4RkRc2mYZ5idDCQuxAAAgAElEQVRH+qdFxPeKeeU8stTBZaUW0AdIJ+i65I/28vK/zbpEk9DecLJsxenkDGAUOSp8ruz7PvBsROw2kc9ZlhwIfCbS9jxNRPyvfJ+vkZ3iT0gb+Y5ktm7LtZgmhqR5yIHRRyPimbLmxZvl2JfJmew05Mz5g2QxvrYulFOu8YXk8/NzssLoSWRe0Ivk9fl2RFzVznYHKdsIUimNA/5DhpZeTZp/7ySv3YkRcXKnZQMvsjNZSBpNpt5/juzAdibD/b4CHECOPpYnf/jLS9o90lbYSpvTljZ/EREXF6fmh0nTwv3kTGFTUmm8jzRl9bRSKJ3vQcDzRSEMI2c6d0eOVq4v5+xKZivvGxGXT3ahr/HzNunAe73YvE8ni9tdBhARv5H0FpnxekC1I5kMhbAQaV78KGkeW4X0B71F3sPjSZ/FrpKmj4gPT+DjhpGj3eMkHRgR/ysy/QvYpzhWZyb9XVtExJ8nRdbJJSKelPQscLGkbSPiuYbCiogjJS1GzpRmAs6PiFvbeU8lbUCayz5TdjXWuxhDDjSeJa/baZL2jAxk6AjFub0PGXr7OTJnY2ZyEHIYmV8yI3CSpAUi4iudkq1Jp6ZLU9KLNBs18xDIH/bZpJOxcc6G5E3frI3tXgDsVNkeS2a2btHvvKW6fY0m4TvNS5o6jinfZaBSyWOAFcv7dte9acyWR5NZ5/dTiVUvx5Yjo35GTq4MlXbWpvgNyvYBpFJoxOvPTyYnjWOAxWX6fyYZbXYKmeXaCK1s+LgW7cL9rJqLTiUdyXOU7UZOzgq0YeGogdou1+QE+pJEFyfNNPuRimIEOTs5jByVL9ZuOSYi41pk0AdkUtxHyVnxdmXfdGT123HUsF7EoGTsRqND9UVGxowgTTV/oK/ezDAyhv0s4OgB/q8VH8L89CXanEEp2FU5/nFy6rkVXYyvnsTvNEf5Xo21G2Yt1+4PVKqbkjOeC6mh4mnpLA4iV27bkwxFnJUMhz2EPpv3mqQPoSW/BcW+Xz6vf0XVz5LO1i0r7TbyHib47JROcLmiGE6mOJLJUfIDdKAw4AAyVf0bDcXQ8HO9nzQHtiWLv1+7DT/GScB+lf1LAL8iTUbV52uycklalG1r3pk5PQs5E/4h7/RnNe5jx32BDkkdJJJmJzuu9ckfYnPtgcjQvgfIiJlFJJ1Q/d8od3cyeQ9pCtiZDEd8od9n/5jsyD5PTjt7mmIHv4x07l0l6SSyns8nyTjxbyhZm0zvPzvavM6DpKWBS0gl/3eyozqCVOyfJJ2fH5e0BdnRHhAZ1jm57c0E/KqsGfAUMGf5jsMAIuJE0tG+G7BhsYe/XY6N99mpmFweIDtCAV+XtA85Mt41OlTzv0qkf2NEeb8PGYzxM0nbkiPgL7RyPQdCuQ5Cwwb/H9I01VjD+y+kklwdOFRSY13nV9spwwRkm4d81uck/X3NUOCIeJGMMLoOWF/SvmV/wxTYeadvp7XQUH4Bh5Kx6NuSDryNyNopc5OjzHnIDmXZNrY5IzkLuIB0RN5Ihs99pcjzMcr6CN2+PoP4LkuTnd/HyvZyZLnk88iOeRZyZvAzMjyw7cXtyFHjA1Rq65CBAvuTs4YxZftaMnmtPTXqM6/h9vJ9TynfdT76SlXMSJqSBozCGd81oBJdRpqSLiQ7uxW7cH+XBfaqbA+vvB9H+k0aZpJ2mwGXL7/JY8nw7A/3l6M8bxfQndnTuZTQaXJho0VIZ3vDJDl/eUY6FgE1vpejjwZBSVB6u7z/AmlumIeMHX6NtA2+Qc4ePhVtzhItI82NSMfkLOQ0+D1k7PdLpDni8na22W5KQt+vgOsi4guV/fOR0+cxEbFvGVWdAJwRbXYAlsimr5MzgrER8XhjtK1cze3LwL2Rq5ItSNa9ubNdTlBJO5Aj+rlJxbcGWa76v6SzcdOIeHqA/2u2X0bbIlfiurR6vDjlFwdei4gBk9zqoLQrcoCydER8qXKsumrc4hHxcA2BAg0ZliXNqZ8mR+PnktfjLTLo4gHgmGgx6GMS5ar2HaeQHf8bZA7RHGQZkxfI4IL9I+LRTsk2XrqtlYbKi3c60PYgb+qGZJji3OQNrk3Lk9ESW5JRFO9akJsezkMo8o0mO/sv06+gHDk6/zt9a1Q3HKZt/06kP+gIcjS5Sr9jB5IzhNoSvMiQ4QfJ/INpyHDU+RnEOgakaetecrbxKPDFgZ7PLtzbhmN7DLkK2Db9jg/vt13Ls0oqpmVIP8YfyZnp6mQi4CbUsFb3IOWq+li+Rs5AFyNniiuTEUhrdOv+vUvebgswlF79FMMXSVPShtWb3sa21qZfWVzSxNBIvtmr3W3WdM2qP4iFSof8bfrKADdmq2cMpmOcTBnmIf0WY4pynZf0aXyLShQMOWM5vAPX5CNk5Mt2gzxfZfDxW+C9Zd+i5HoKbVu+dTK/y2jgaPoSsLYrin94f2VQsxzN4nBkdNMpZERbVzK4B5Cv+jsYV37DHa3AOtiXHc3joVEfRtKyxZRARLxdcQ5+h7QRH0JxarWx7UY27zscYZFF1q4lTRD3trPNOihO5ZMk/bQkLS1E5lW8SRZBWzsiQtKapDms7dP6IsPvyPt0Khn2uiRZiG1aYHtJC0pakRzF3dBuGYoczZygiDifvL9HShrVeNb6na/K+RHpMH4UmKGYJP5GmklWrUPe8SFpRknrlPcbkElxzwFnSfocOUhamfSVTFIOxyTI8K7rVZ6jYZG97n1kAuB85H0e8H/qpvQdexX53iwBBETEWDJo5Irqc9EzdFsr9fKLNNf8H7BSv/3TVN63NRacdJidDhwxgXO6ZiqYhO+xDGlK+ARpa/4UadfdjpzxHEGOKD9N1tbftgYZFiTzDj5WtuckEw3/Q4a7zl3kOIcMk2x1Cc2Z6YvF35z0XxxQOT6i3/nzjedzqjX+VyfzYIaTZqOL6CurvTfpm+jIiLzIMJLMnL+IzOJvlF9YjYz//wXp5zqmDrn6XZttS5sDlpMmzUcdXUSo0vYw0vf47X7Hqs73jix9Osnyd1uAXn2RI9f76FsjdSEqUUV1/RBLu78oP7olu30dWvge46jEipd9HyBDMjcgneTHFqXbSNxqd0TKysAple1GpM5OpPlm9nK9T6BSYHAy25qR9PfsSjqQHyTzIC4CLqmcN2hTY1GYN5HrPv+JDKH9EZns95PSKXdqTeX5gT3K+w3J0Mqf9L9vZPLVHmTcfdvzSyrt9KR/pbQ/ST6WXnt1XYBeevV7uBctncX+ZNRPYyWrnepokxwRrl5GN7ORORGfp8MZl236TsNKx7VD2W7al8lZwzHl/eL0FbtrZ9hpo/Nfq3TO8zbaKK/pinwrlf0ztkMG0qH5GzJefreyb1oyTPTiQfz/zJX365DRWtORoao3VY4tT4Y2LtLBe7okGdI5F7lmwxrkqm5HVc6prij4B/oVDWyTHD3rXymyjKYHfCytvOxTqBARIen9ksZG2mz/QZbZvY9MR7+FNBG0u83NyVWwViKjJkaTNXCWAXaRNKadbdaFpPkkLRoZgvdbYDn1LRTTSNh5tuwfEREPR6nHE+UX1AYZFgcOl7QSaRK6Hlhd0sjSxrDIkMRXSQc05f1kyaBcn2CBsnkp6StYEVhJ0owR8QY5M5lO0niLr5V7fKik1cquf5GK62DSFLVhOe9DwJ8j4oaI+Mekyju5RMT/kdfzBFJh3Vrk2lLSoZI2BC6SNE8J752HjChrmV71rxTZuu5jaTdWCu/mNeBUSTtFxHGkaeMSUhnsQlYybBvKAmyfJ6OKngYeAZ6MiLtJxbA0ZdGUXqZkCf8M2EFZ8OxvpFJbR9JMlQ73BdLmPF0NMixLJsI9D7wUEU+SJSp2ATaSNGfkgjhrkiPdf0HLCmklcpW0LwJ3kaP7w8iOYBtJM0RWCN2GTDgcH7OSinO7otBeJ8tfbBsRm0QutPNRsvN714I0ddGvQ36NjNpalPwuL5AmwZXJhMoTI+LJoqxWK511y+037o+k1SWtUhy2D5G/m8aiPQuRSmJ4q21OgmyNzPMvSbqI9KP8PjII5ZPkoHIBslDlIZ2UrSW6PVXplRc5JZ2pvF+bfOA/WbbXIkecE13kZBLaa5iNZiSnm3uT9uMly/4dikwzdPvaDOK7LEk6dHfqt/9AMoP0QDJO/AMMcrGYybx/t9Jn966aAr9IzsRuIaOf/o8WHdtkZMtG5MDqQnJFrE9Vjm9FmpL2mNA9pLL4OmmeOZzsXBYgFc7TpPnhOFLpdNw5We7daeWZnI20lZ9DRnTNRppGZi/n1mLPp4f8K0WenvKxtPW7dVuAXniRI5/Tyg+5YV9eg8w0/VjZXqz/DZ/Mthq29ar99SfkiKNRDG11Mty17QuP1HT9vgB8uXF9eGc+x/akM/AP5LoJtawCR44Yz6jI8A4bbunINi2v1VuRoSiCrUonPj1ZouPHpUNflz5H43ZkiOv84/mcjUkFdQIZvTMfmUPxDTKUcl7Sf7AP6bReooP3tOGXWbl8hyNJP9fhpUNcjFSGX6UUhmxz+z3rXynt9oSPpZbv1m0BuvbF+3UI5Uf349JpNOrR/JhcLH2eNrQ3mrIUYelQ7gC+R46eR5JF4i4nR4V3UUOIZo3X8gfAoeX9sH5/G6vPTU+9mcqLkjbvFSv7Go7leWlzqWayWu5sZJTVzmXf0eWeLkvWo9qQCSQoAe8lBx4vkiPhv5BlGr5LKtLvdqGzm7vyflmymF2jBtXaZAjv4WS4b7OkeZtlGEOaqVar3NtPkiG+v6ZP6X6ILianld/tuUUukUr9XrIm2YZkfsw8ZJ2jR+hCKfPJeU2VPoVKrZhNJO0vab+IOJbsjLcDtpC0JZlktVqkbbpV1gIukPQxMmzxMOBJ0pewDRlz/UvgMXL93192I+FmMrkSWErSYtG3GHzj2dpc0pIR8d8oq29F+UW1i3I//0b+QDdXrjwGOVMIsoPeWblQUcttQSYjkR3BbcDGkrYiO4PXyYi1i0hz5FPj+6xIv9HK5X9eJM00b5N5CR8hzW77S5q2E89CScw8sRLY8BxZN6ixgPwfSGf6jKQSezwi7qlBlJ70r0D3fSwdodtaqYtafnNytL4x6Yz8Vtn/KXI6fx8DJMW02OZHSZv68WV7VmAv0k66G0MgKW0832tpcrbwOSqjITIa5HY6VLGTTDb8EemAXLrsW5sshNZSHkK/dtYFtqAvJHJ3cj2Exoh6MfryWyb63JCmoxfos1EPJzuWg+lQ1Uz6fFwjyFDhY8v2KHIWe1rl3LWoYSEnhoB/pcjWdR9Lna+ptkqqpB+QD9dypCNy16iE+EmaOyKearWiY///lzSWfNC3i4hrJc1IKov3kAv0PDa5bXWTMlLekvxB3E/Ogg4ja+e3dU3lAdquVqLchrRBb0sGB6xJmrZ+2aa2ViNnRj8hO8fvRcTPJO1Gfv9LI+Knk/m5vyZX7+vK2rxFjveSFTuvIZfKPFhZufYHwP8iYuea2t2YzO+4iry2j5NRd/uQvoQTSPv9uuRM5dLIdRI6QuMZk7RykeUGUlk9StZZmp6cNdxNhyuxtp1ua6UOafb+/oNhZP2gH5IRIo2Inw8DHxnof1ppl+w8xpIj5xnJWOZ7KY4nsnbSgt2+ToP4PjNUrtVS9CvTQNrIP0IuYHMUZSnSdlzLQcg2bb/7uzw5g2k5Oa5yH+clR4mN+/Z+0ubeeGb2AN7TQjurkGaTjhU7JP0GO5X3q5Kz5tnJDvhe4Dvl2HzkjGGyv99E5Og5/0qRq+s+lo5/524L0OEbvDQwV3n/ftJW+eGyvRYZ+7xum9vciIwwGUcmdB1KRi7sRDpGN+r2dZmE77IkOUpqrKe8aNnfkbLdTLy2UNuXMKwohC3IkNY/kdFBM1fu7yPkTLMd7a1Eh9bYLvfzrqLMFiJNm7tXjs9NlgM5sWxPW7M8y5Imot1JO/1e5IpkdxZleTyZId6p520YmXszpmzPW2S5pnLOGqTF4WhqiMLqxqvrAtR8Uxegz36/HplQdRUZRjc/6VT+B1k98y5gqza3vzg5E9mgbK9Dlmveq2x/HFiv29dpEr6PyBnAC8A3qvs70PZgawvVUYRtDdJmvBbpr/gxaSpqhC9vPJTuY5F5KXI0flTZno2swHtHv/PmIX0yHVmLgB7wr5R2u+5j6dZrivYpFFvoRWS6/ctkOd2Z6bN9f5c03YwkO5P7W/Uh9Gt7Y1IB/SYiPlP2f5hcP3fzyOiFd/kdeo1KtNZs5Mh4GXJEe2FE/KycM320eS3lAeTYHtiXnHndHBHnlIiin5L3b7s2tbM4WS/pRuWqd1eRQVPrl+MHkKPaK8lV714p+3v6PjYomd9nkdVirwWuj4ibin/rNPK3sWPju0iaJsqawR2Sryf8K0WWrvhYuskUHZIaGUq6PbnC1foRcV9E3AxcQYabfpU0AzwUEfeX/2mHQhhDhiVeTFlvQdKny+HGUp0zVuTs6Y6kKIStgavJJJzvksUBd5W0WelE95M0fbvbbldtoUlobxnyvq1TFN3L5OxgYeWaEETE8aTJ6IMMofsIIGkk2aF9j4yeGUmW41i3KLexZCfYXN61kwqhtHcbOaA6SWU9gk6hXANhp/J+VTLh8llyBrOFpO+UfuUz5O/6PZ2UryN0e6pSx4u+qV8jyWVesjM7s3LOuqQppI7QullJh9TupOL9SGn/GtJG2pbF4Dt4PVcjoyrWq+ybiVS415MRGG0L+ezX9lqk4/GLZJjwMNLZ+ztybYQZynkjaDFBjbSr30ZxHPc7thJZZuGQyr4hkYw0wHeZt/J+KeCbpE18nbJvVnL21daEv8mQs2P+ldJeT/lYuvWaYs1HkjYlwxL/Tabjv0A6SZ+PiE+Uc2aLiP+02M605DSyYV5RRDwvaSOywuqXyA50MzJb+m8RcUT536FibtgA+EBEfKWMNF+PDM8bQdpYR0VEW1eCkzQfaab6HVk/aXMyvPXkcnwrcjb2E+CCiHh1PB81KW2+j6x3tUvZ3oQMSHiGNLP8l3Q8/qxxD4cylTDLJcg8meHA1RFxvaThMUSqerYDSUuRs6MLI+KQ8lu+iKxusErlvHnIgd2HIuKh7khbL1Ok+UhZyvZocnS+FBnFsAY55Rsj6Yxy6gsttrMkORXftNhBTySXWNyYzExekXSOvUTaSH9Nlo3eA3rX3DBA9uww4COSZomI10pHsj4543miBoUwjAzPfJKMNjmFrH46n6R1JU0bEZeT135v2pfV+jzwP0l7SbqEDARYklROe5Mzoj1JRTXkiZLbERnvfzY529qqDJamJoWwLDm4+DvwoqR1ymBxa+Avkn5eyWR/kgw7nSIVAjDlzRRKiYMfko7IIyXNSpoZViITYeYBFoq0W7bSzrLkD+kMcqT6dEnJb6S8n0qGTP6WXIHsZUmzkLbSP0TEv1tpvy4qTuVNSBPbs+QPZl/SXPQJ8hoeC+wfEb+qSY4R5AzrO8B1EXGepKPJJKFxZBz9cOD+mEApiUlscxg5cFgGmIWMOLmjOJsvBcZGxMPtaKsXKTOGhpKYKigz3yvJiLLLyYi26YDLIgMNZiEtDHNHxJbdk7RzTFFKoUz/30v+qFchw9ruLz/2m4F9I+LONrQzC5mg9ZOIOL0yDW/8XZws5fwZYA7SLvlM+d+eNxlJ2oLMRj6c/A4PR8S+kg4jM8CnI5e5vLKGtqv182cnnaFrks7fq8mQ3tnJYmh7RsRl7ZZhAJlWJDuNnadkpTC1ImnexiCtmJF2JWeol0VGZc1Kzla/247+o9eZYpRCMd8cT45oXyWXfRwFnE6Odi8nS0v8uQ1tTUN2Ep+NiBf621+Vq429VN7/lFwp67BW2+0Uko4gZ1srkrOeHSPi0crx6SPiv3UpOEnrkiP1f0XE3ZJ2J0NhL4iIK5WL+IyMFkKIJU1HOgpfkjQ38Ez0FfNrnDML6ej+LhkeWWu5DtNd7GNJpgifQiMkErg3Iv4UEY+QI/m3yNDJU0kTzp8HsJdPDjOSCTXrAkSu5jWsyDILsJb6KnLeAMyjHl11SdIMxXmGpOXK7hnJkdFBZAmERyVtLWnXYtZ5HerxiRTlfjFZbbKxAt7ZpBlud0m7RMRfo4UQ4nIvVi2f9zEy+mb2AU5dgTSZfTkiLm3Ts2N6FPtYkhHdFqBNiEzEWUHS+hFxfUTcKem/wFPkjOGBdjUWEf+R9H1ge0n/jCyB3Ogw1iNrG91cOtAZSVNLrz5US5Kd49+BzysLfp1GxmefFRF/L07lY4FPRCl/3U4qfox5yc55x4i4TtL7gePK4XNKZ35fq+0VJf5vsnTFKuSA4VlVCuuV826S9ECJJut5s59pHxHxsKQfl/ctRSgONYak+ajSiaxM2v5eJpOJvkp2zpdH1n6nOH93IWunHBqZ7NQOGUaRIZFzkiGT15EFsn4EHBQRV5XzenLaWWYHK0fEVZJOJSO09o2I08qIeGMywekeMnv3KxFxRQ1yNO7lFqRjfgbgF6ST9yVlaO+PgK9FxLntaq+8H0f6fO4DTq+ayIyZWhlySqHRySrzEL5P+gp2Ik0d15JmpJnJeOObyv+sADzRcPa2UZZ5yFnBp8ikljFk+etL+o86ew1J25K1bx4jF6FZl5T/GODOco3nIDNepy8mubpkWYNMUDuZrA+1NGlC+l1EvKIM8X09Im5osZ2GAlqSnFk+QyY2HkSWaf4SWQ10dETc2EpbxgxVhoxSkDRHRDzXeE+aN44rI91VSN/BQcCN5EpRZ7XDqTxI2eYhZyLTRcTjQ8XUUK7jocB9ZYbwRVJBHEiald4TEUfW0G7XagtJ2pycedxEhrduTyrDT5A5LcuTUUZ/bKUdY4YqQ8LRLGk0cIekowCKcngEeKWMyO8gV/3aPSKeBo7slEIo8jwZEU9HxONluycVggqVXS+QqfyrFIfud0il+k0y4qbt8erqYm0hSUuT62PvSIYdPksuCP8Iabr6KVnB1grBTLUMFUfzm6QC20DScRFxIPAEmYx2B/AKGYb6VnHuvtI1SXsUSdNFWQ2qOI7nAx6IiHGSdiGv7dsRcbQyAXC6iPhbO2c9khYiozq+FRHnN/ZHxC2StiMLoEVEHFXkWDTakJhWHNQzk0lvbwFPFof5XpJOI2cN60XEeeX8ITHTM6YOhpL56PNk3PAo4M3IErankY7Cx8ion687lvzdKOu43EaWDH+DdIrfRHaQNxXFsBNZn+k64Oyawk07Wluo4kNo+KGWI53nFwHnlFkKks4EfhhZQdeYqZqenSkoE5RWj1Kvn4yC+RZZinp7Sd+MiL2VdY7mJTuSP3iU925KCO0ZpGnoEmD7ErK7JXktKYphBHBXjdevWVsI2IZUUNOQin5R0tG7J5kx3RIVhbAZsIukv5G1p/Ynl2J9S9J5EfFSROzRanvGTCn05ExBmfj1f8DCZGG7W8kiZFuRxc9uIH0IL0fEZ7sk5pCgGhJbErXOIBPSLlCm769Dlvi+Pmpe0EQdri0k6QPk83MQ6UieOSK2kbQWWc/mTHKG0HMhw8Z0i56cKUTEGyVk8hKy07qNDD29h6yff5Kk7wEHSFo2ItqWmDYlUUbLb0nakFyb+qyiCH4k6aGIuFfSTaS/pvYY/RKie8IAh8aQNv92M4YsdzIfuaTijmX/H8kw4p7MITGmm/SkUgCIiHuKYriO7DB2I6NU5lXWJrmPrD30UhfF7GmK+WQTsszHnmXfiSUC6deStiwj9ava3Tlq8moLtWWWoL51MkaSocrPAttExJMlJHUF4BibGY15Nz2rFADKSHYz4Ddktu1+xWn6culgrBAmQOmY9wM+HRG/V1lrNyJOKKac35VIo7ZGa6mvttB7Jb1MdvxfITvnKtXaQpe14g+q+BBWAj5afCg/IvMu/lMUwoZk0cTPWSEYMzA9rRQg12stGa1XSJorIk7stkxDAWUJkH8BfyOjjKrHloyI4yX9oo6ZVnShtlBRCJuTjuTlgMVIf8KXgaMk/Yr0YxwUEVe38PWMmaIZEslrkQvibA0cIWnhMso146E4Uk8ii8v9D9hOuVrZ/8pI+gRJC0ap9dMvoa3VthsrVD0C/JOMeFpB0sL9zUflvOfL31YT05YglcB+ZFby4+TiSsMi4oPkOtkfjIgr2vl9jZnS6Mnoo/GhXA7yxW7L0cso6zx9mlyR7MQSyfULMrnvJXLk/s2IuLiGtrtWW0jSGNJctEtE/Ls41C8jw2C/HGWxpYEUkzGmj6E24m4sXOORXqH4DaosWV4rlNnAG2S5iB+T5SXGRsTFdVzDignnWrJY4cVk6fJx5ZRLyHDilspvS1pK0k4lQ7rBc8D9ZGb2vBHxAulgn52sAIsVgjETZ0jNFMw7KZ3iaaSp6I+NkhCStiYrx/4KuCY6tB60srbQZ4BzydyScWSuyWZkQto2wL8j4roW2hAZrfRZ4ELg38AhkSvBbQlsRzrOHyHDUQ8ho9Y+FhFPTG67xkwt9Lyj2UyQVcl1D2YArlfWNzqoRPK8TdrUp5N0aTtqCI0PdbC2UJmN/ApYg1w/41jg25JeAI4EniSjmtYkE9amA2YlzWfGmIkw1MxHpkLxCxxNJvddRi77eaWkjwPXk8XnNiBLSbSdqgmq5AXsS5qGNilZykTE3pRKrJVzW5qeRsQ1pL9il+JEvgP4ApnTsi5wT0R8EliANCF9opiTjDETweajIYr6Fhn/ELBuRByoXA3uUdJ2vz5l8Z+IeKyG9t9RW4gMff016WA+CTgPOK/dIa+VdlcjzVEXkuaq48iIo/WAS0pS3grAG9HBMurGDHWsFIY4kmYnlcCtZIf4i4g4RtJHgEejxsqf3awtVLKkzyGV3/4R8cOyf2REvOZII2MmDyuFIUxltrA2WVDuexHxrQ62vw+ZhzAfWcF2x4j4RzErrULWFrqlxvZXA04EPhQRT1gRGNM6djQPUfp1gA8D15B+BCSNKI7eutruldpCd5NhqI6orbQAAATeSURBVOtJutAKwZjWsaN5CNBw6EpaTNI8kmYuM4ThACWy6FbgDEnTk+tF1yXDSsChkpYnk8UeAv7Sr7bQPZ2oLRQR/wN+CDxuhWBMe7D5aIhQRuBHkVFGWwPvi4gXquGdkpaJiAdrlqFRW+g20p/wYpFrerK20BERcUVdMhhj6sVKYQhQksLOJqN81iGTsd4XEc+V47Xb0kttoQvJqqbPAIeTOQk/KiUkZiXXdX6q1eJ2xpjuYfPR0OAVcsW05cmCbx+MiOckfaAkrHXCdPI26Tt4ufgTvgqsDBwpabmSB/AMtJ6HYIzpHlYKPYikacrf4WXXW+Ts4BRgjYh4pEQcHURG/rS7fdcWMmYqxUqhhyhO5GGlxPWWwHmS9iaVwofJ0fq+JWP5ZOCUiPh7m2UQMJbMAfiOpOMlTV9KXP+KLKtxiKTPkkrpW2TxvbYrJ2NM57FS6BFKNM+fgIuVC+QcANwJrEbOEl4FNidNSEsCX4yIX7a72mkx/fwKuIU0ES1K1hb6JvBbcnZwN+ls/gRZuda1hYyZQrCjuQco6w/8lCzXsDCwCXB4RPxM0opktNGswFkRcV+HZLoEuCMiDpe0Oxn6eTdwPnB9RNxZqrF+C9gtIu7thFzGmHqxUugykpYCrgB+HhGHlEJyPwUWiYgVyznLk6WwZySjfp6vy5nr2kLGTN1YKXQRScsCZ5FF5K4FboiIG0sC2ulkSeztSie9AvBaRDzcIdlcW8iYqRArhS4haSRwJbki2uWk03Y64LKiGGYgncnzA5t2I8zTtYWMmfqwo7lLRMRrwM4RcW4J7zwXeB3YStI6EfEqWfr6GTIfoBtUawtZIRgzFeCZQg9QqXa6BLAbMBz4dUT8vtudcZktTBMRf+iWDMaYzmGl0GNIWpwM9RRwVMkPMMaYjmCl0IOUGQMR8Zduy2KMmbqwUjDGGNPEjmZjjDFNrBSMMcY0sVIwxhjTxErBGGNMEysFY4wxTawUjDHGNLFSMKbHkbSBpMu7LYeZOrBSMKYHkDSi2zIYA1YKZipD0mhJD0r6kaT7Jf26VKxF0ick3SbpHkm/KJVqkXSmpFMk/VHSX8vI/fTyOWdWPnsTSTdLulPSz8vaGBOS5UxJp0q6hVz6dMbyubdKukvStnVeC2MGwkrBTI0sAfwgIpYj17LYvuy/KCJWK4sbPQjsXfmf2YG1yGVSLwWOJ5ckXUHSeyXNRS5funFErAzcDhw4CFkWBNaOiAOBrwDXRsTqwIbAMZJmbPG7GjNJeMpqpkb+FhF3l/d3AKPL++UlfQuYDZgJuLryP5eVxY7+BDwZEX8CkHR/+f8FgWWBm8qy2dMCNw9Clp9HxFvl/SbANpI+X7anJ5dnNaZjWCmYqZHXK+/fAkaW92cCH4yIeyTtAWwwwP+83e//3yZ/R28B10TEzpMoyyuV9wK277+8qaR5JvEzjZlsbD4ypo+ZgSckTQPsOon/+0dgnVL6nOIfWLK8P0rSdoP4jKuBz6hMNSStNIkyGNMyVgrG9HEocAtwE/DQpPxjRDwN7AGcJ+le0nS0dDm8AvDvQXzM4cA0wL3FLHX4pMhgTDtw6WxjakbS1RGxabflMGYwWCkYY4xpYvORMcaYJlYKxhhjmlgpGGOMaWKlYIwxpomVgjHGmCZWCsYYY5r8P8HSS5329KKRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x10a250518>,\n",
       "  <matplotlib.patches.Wedge at 0x10a2509e8>,\n",
       "  <matplotlib.patches.Wedge at 0x10a250f28>,\n",
       "  <matplotlib.patches.Wedge at 0x10a2584a8>,\n",
       "  <matplotlib.patches.Wedge at 0x10a2589e8>,\n",
       "  <matplotlib.patches.Wedge at 0x10a258f28>,\n",
       "  <matplotlib.patches.Wedge at 0x10a2624a8>,\n",
       "  <matplotlib.patches.Wedge at 0x10a2629e8>,\n",
       "  <matplotlib.patches.Wedge at 0x10a262f28>],\n",
       " [Text(0.777817,0.777817,'Morpheus KNOWS'),\n",
       "  Text(-0.55,0.952628,'Cypher KNOWS'),\n",
       "  Text(-1.06252,0.284701,'The Architect CODED_BY'),\n",
       "  Text(-1.06252,-0.284701,'Agent Smith CODED_BY'),\n",
       "  Text(-0.777817,-0.777818,'Agent Smith KNOWS'),\n",
       "  Text(-0.284701,-1.06252,'Neo LOVES'),\n",
       "  Text(0.284701,-1.06252,'Trinity LOVES'),\n",
       "  Text(0.777818,-0.777817,'Neo KNOWS'),\n",
       "  Text(1.06252,-0.284701,'Trinity KNOWS')])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.pie()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 rows affected.\n",
      "2 rows affected.\n",
      "3 rows affected.\n",
      "4 rows affected.\n"
     ]
    }
   ],
   "source": [
    "for i in range(1, 5):\n",
    "    %cypher match (n) return n, n.name limit {i}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>name</th>\n",
       "      <th>rel</th>\n",
       "      <th>degree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Morpheus</td>\n",
       "      <td>KNOWS</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cypher</td>\n",
       "      <td>KNOWS</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Architect</td>\n",
       "      <td>CODED_BY</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Agent Smith</td>\n",
       "      <td>CODED_BY</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Agent Smith</td>\n",
       "      <td>KNOWS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Neo</td>\n",
       "      <td>LOVES</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Trinity</td>\n",
       "      <td>LOVES</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Neo</td>\n",
       "      <td>KNOWS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Trinity</td>\n",
       "      <td>KNOWS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            name       rel  degree\n",
       "0       Morpheus     KNOWS       3\n",
       "1         Cypher     KNOWS       2\n",
       "2  The Architect  CODED_BY       1\n",
       "3    Agent Smith  CODED_BY       1\n",
       "4    Agent Smith     KNOWS       1\n",
       "5            Neo     LOVES       1\n",
       "6        Trinity     LOVES       1\n",
       "7            Neo     KNOWS       1\n",
       "8        Trinity     KNOWS       1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name,rel,degree\r\n",
      "\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000Morpheus,KNOWS,3\r\n",
      "\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000Cypher,KNOWS,2\r\n",
      "\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000The Architect,CODED_BY,1\r\n",
      "\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000Agent Smith,CODED_BY,1\r\n",
      "\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000Agent Smith,KNOWS,1\r\n",
      "\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000Neo,LOVES,1\r\n",
      "\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000Trinity,LOVES,1\r\n",
      "\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000Neo,KNOWS,1\r\n",
      "\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000\u0000Trinity,KNOWS,1\r\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(results.csv())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>name</th>\n",
       "        <th>rel</th>\n",
       "        <th>degree</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Morpheus</td>\n",
       "        <td>KNOWS</td>\n",
       "        <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Cypher</td>\n",
       "        <td>KNOWS</td>\n",
       "        <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>The Architect</td>\n",
       "        <td>CODED_BY</td>\n",
       "        <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Agent Smith</td>\n",
       "        <td>CODED_BY</td>\n",
       "        <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Agent Smith</td>\n",
       "        <td>KNOWS</td>\n",
       "        <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Neo</td>\n",
       "        <td>LOVES</td>\n",
       "        <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Trinity</td>\n",
       "        <td>LOVES</td>\n",
       "        <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Neo</td>\n",
       "        <td>KNOWS</td>\n",
       "        <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Trinity</td>\n",
       "        <td>KNOWS</td>\n",
       "        <td>1</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[['Morpheus', 'KNOWS', 3],\n",
       " ['Cypher', 'KNOWS', 2],\n",
       " ['The Architect', 'CODED_BY', 1],\n",
       " ['Agent Smith', 'CODED_BY', 1],\n",
       " ['Agent Smith', 'KNOWS', 1],\n",
       " ['Neo', 'LOVES', 1],\n",
       " ['Trinity', 'LOVES', 1],\n",
       " ['Neo', 'KNOWS', 1],\n",
       " ['Trinity', 'KNOWS', 1]]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%cypher match (n)-[r]-() return n.name as name, type(r) as rel, count(r) as degree order by degree desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>n</th>\n",
       "        <th>type(r)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Trinity&#x27;}</td>\n",
       "        <td>LOVES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Morpheus&#x27;}</td>\n",
       "        <td>KNOWS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Cypher&#x27;}</td>\n",
       "        <td>KNOWS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Trinity&#x27;}</td>\n",
       "        <td>KNOWS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Neo&#x27;}</td>\n",
       "        <td>KNOWS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Morpheus&#x27;}</td>\n",
       "        <td>KNOWS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Neo&#x27;}</td>\n",
       "        <td>LOVES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Agent Smith&#x27;}</td>\n",
       "        <td>KNOWS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Morpheus&#x27;}</td>\n",
       "        <td>KNOWS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;The Architect&#x27;}</td>\n",
       "        <td>CODED_BY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Cypher&#x27;}</td>\n",
       "        <td>KNOWS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Agent Smith&#x27;}</td>\n",
       "        <td>CODED_BY</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[[{'name': 'Trinity'}, 'LOVES'],\n",
       " [{'name': 'Morpheus'}, 'KNOWS'],\n",
       " [{'name': 'Cypher'}, 'KNOWS'],\n",
       " [{'name': 'Trinity'}, 'KNOWS'],\n",
       " [{'name': 'Neo'}, 'KNOWS'],\n",
       " [{'name': 'Morpheus'}, 'KNOWS'],\n",
       " [{'name': 'Neo'}, 'LOVES'],\n",
       " [{'name': 'Agent Smith'}, 'KNOWS'],\n",
       " [{'name': 'Morpheus'}, 'KNOWS'],\n",
       " [{'name': 'The Architect'}, 'CODED_BY'],\n",
       " [{'name': 'Cypher'}, 'KNOWS'],\n",
       " [{'name': 'Agent Smith'}, 'CODED_BY']]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%cypher match (n)-[r]-() return n, type(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 rows affected.\n"
     ]
    }
   ],
   "source": [
    "results = %cypher match (n)-[r]-() return n, r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>n</th>\n",
       "        <th>r</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Trinity&#x27;}</td>\n",
       "        <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Morpheus&#x27;}</td>\n",
       "        <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Cypher&#x27;}</td>\n",
       "        <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Trinity&#x27;}</td>\n",
       "        <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Neo&#x27;}</td>\n",
       "        <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Morpheus&#x27;}</td>\n",
       "        <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Neo&#x27;}</td>\n",
       "        <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Agent Smith&#x27;}</td>\n",
       "        <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Morpheus&#x27;}</td>\n",
       "        <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;The Architect&#x27;}</td>\n",
       "        <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Cypher&#x27;}</td>\n",
       "        <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>{&#x27;name&#x27;: &#x27;Agent Smith&#x27;}</td>\n",
       "        <td>{}</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[[{'name': 'Trinity'}, {}],\n",
       " [{'name': 'Morpheus'}, {}],\n",
       " [{'name': 'Cypher'}, {}],\n",
       " [{'name': 'Trinity'}, {}],\n",
       " [{'name': 'Neo'}, {}],\n",
       " [{'name': 'Morpheus'}, {}],\n",
       " [{'name': 'Neo'}, {}],\n",
       " [{'name': 'Agent Smith'}, {}],\n",
       " [{'name': 'Morpheus'}, {}],\n",
       " [{'name': 'The Architect'}, {}],\n",
       " [{'name': 'Cypher'}, {}],\n",
       " [{'name': 'Agent Smith'}, {}]]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "nx.draw(results.graph, with_labels=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<networkx.classes.multidigraph.MultiDiGraph at 0x10a276a20>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x10a38d518>,\n",
       " <matplotlib.collections.PathCollection at 0x10a466320>)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>name</th>\n",
       "        <th>rel</th>\n",
       "        <th>degree</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Morpheus</td>\n",
       "        <td>KNOWS</td>\n",
       "        <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Cypher</td>\n",
       "        <td>KNOWS</td>\n",
       "        <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>The Architect</td>\n",
       "        <td>CODED_BY</td>\n",
       "        <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Agent Smith</td>\n",
       "        <td>CODED_BY</td>\n",
       "        <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Agent Smith</td>\n",
       "        <td>KNOWS</td>\n",
       "        <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Neo</td>\n",
       "        <td>LOVES</td>\n",
       "        <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Trinity</td>\n",
       "        <td>LOVES</td>\n",
       "        <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Neo</td>\n",
       "        <td>KNOWS</td>\n",
       "        <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>Trinity</td>\n",
       "        <td>KNOWS</td>\n",
       "        <td>1</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[['Morpheus', 'KNOWS', 3],\n",
       " ['Cypher', 'KNOWS', 2],\n",
       " ['The Architect', 'CODED_BY', 1],\n",
       " ['Agent Smith', 'CODED_BY', 1],\n",
       " ['Agent Smith', 'KNOWS', 1],\n",
       " ['Neo', 'LOVES', 1],\n",
       " ['Trinity', 'LOVES', 1],\n",
       " ['Neo', 'KNOWS', 1],\n",
       " ['Trinity', 'KNOWS', 1]]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from cypher import run\n",
    "run(\"match (n)-[r]-() return n.name as name, type(r) as rel, count(r) as degree order by degree desc\")"
   ]
  }
 ],
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