GoT_Network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network Visualization in Python\n",
    "\n",
    "- Helper notebook for article of same name published on Medium."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and process data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>Type</th>\n",
       "      <th>weight</th>\n",
       "      <th>book</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Aemon-Targaryen-(Maester-Aemon)</td>\n",
       "      <td>Jeor-Mormont</td>\n",
       "      <td>Undirected</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Aemon-Targaryen-(Maester-Aemon)</td>\n",
       "      <td>Jon-Snow</td>\n",
       "      <td>Undirected</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Aerys-II-Targaryen</td>\n",
       "      <td>Robert-Baratheon</td>\n",
       "      <td>Undirected</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Aggo</td>\n",
       "      <td>Daenerys-Targaryen</td>\n",
       "      <td>Undirected</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Alliser-Thorne</td>\n",
       "      <td>Jon-Snow</td>\n",
       "      <td>Undirected</td>\n",
       "      <td>32</td>\n",
       "      <td>1</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>658</th>\n",
       "      <td>Sandor-Clegane</td>\n",
       "      <td>Sansa-Stark</td>\n",
       "      <td>Undirected</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>Shae</td>\n",
       "      <td>Tyrion-Lannister</td>\n",
       "      <td>Undirected</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>Shagga</td>\n",
       "      <td>Tyrion-Lannister</td>\n",
       "      <td>Undirected</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>Tyrion-Lannister</td>\n",
       "      <td>Tywin-Lannister</td>\n",
       "      <td>Undirected</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>Waymar-Royce</td>\n",
       "      <td>Will-(prologue)</td>\n",
       "      <td>Undirected</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>175 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Source              Target        Type  weight  \\\n",
       "8    Aemon-Targaryen-(Maester-Aemon)        Jeor-Mormont  Undirected      13   \n",
       "9    Aemon-Targaryen-(Maester-Aemon)            Jon-Snow  Undirected      34   \n",
       "16                Aerys-II-Targaryen    Robert-Baratheon  Undirected      12   \n",
       "17                              Aggo  Daenerys-Targaryen  Undirected      11   \n",
       "30                    Alliser-Thorne            Jon-Snow  Undirected      32   \n",
       "..                               ...                 ...         ...     ...   \n",
       "658                   Sandor-Clegane         Sansa-Stark  Undirected      23   \n",
       "664                             Shae    Tyrion-Lannister  Undirected      12   \n",
       "666                           Shagga    Tyrion-Lannister  Undirected      17   \n",
       "676                 Tyrion-Lannister     Tywin-Lannister  Undirected      40   \n",
       "683                     Waymar-Royce     Will-(prologue)  Undirected      18   \n",
       "\n",
       "     book  \n",
       "8       1  \n",
       "9       1  \n",
       "16      1  \n",
       "17      1  \n",
       "30      1  \n",
       "..    ...  \n",
       "658     1  \n",
       "664     1  \n",
       "666     1  \n",
       "676     1  \n",
       "683     1  \n",
       "\n",
       "[175 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data\n",
    "df = pd.read_csv(\"data/book1.csv\")\n",
    "# pick only important weights (hard threshold)\n",
    "df = df.loc[df['weight']>10, :]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of unique characters: 80\n",
      "No of connections: 175\n"
     ]
    }
   ],
   "source": [
    "# import\n",
    "import networkx as nx\n",
    "# load pandas df as networkx graph\n",
    "G = nx.from_pandas_edgelist(df, \n",
    "                            source='Source', \n",
    "                            target='Target', \n",
    "                            edge_attr='weight')\n",
    "print(\"No of unique characters:\", len(G.nodes))\n",
    "print(\"No of connections:\", len(G.edges))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Option 1: NetworkX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all graph options\n",
    "graphs_viz_options = [nx.draw, nx.draw_networkx, nx.draw_circular, nx.draw_kamada_kawai, nx.draw_random, nx.draw_shell, nx.draw_spring]\n",
    "\n",
    "# plot graph option\n",
    "selected_graph_option = 0\n",
    "\n",
    "# plot\n",
    "plt.figure(figsize=(8,6), dpi=100) \n",
    "graphs_viz_options[selected_graph_option](G)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Option 2: PyVis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1000\"\n",
       "            height=\"600\"\n",
       "            src=\"example.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x2595117bc18>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import pyvis\n",
    "from pyvis.network import Network\n",
    "# create vis network\n",
    "net = Network(notebook=True, width=1000, height=600)\n",
    "# load the networkx graph\n",
    "net.from_nx(G)\n",
    "# show\n",
    "net.show(\"example.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Option 3: Visdcc in Dash\n",
    "\n",
    "- See `dash_app.py` file for the demo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extra - get all draw functions in NetworkX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('draw', <function draw at 0x0000023163903BF8>)\n",
      "('draw_circular', <function draw_circular at 0x0000023163903F28>)\n",
      "('draw_kamada_kawai', <function draw_kamada_kawai at 0x0000023163909048>)\n",
      "('draw_networkx', <function draw_networkx at 0x0000023163903C80>)\n",
      "('draw_networkx_edge_labels', <function draw_networkx_edge_labels at 0x0000023163903EA0>)\n",
      "('draw_networkx_edges', <function draw_networkx_edges at 0x0000023163903D90>)\n",
      "('draw_networkx_labels', <function draw_networkx_labels at 0x0000023163903E18>)\n",
      "('draw_networkx_nodes', <function draw_networkx_nodes at 0x0000023163903D08>)\n",
      "('draw_planar', <function draw_planar at 0x00000231639092F0>)\n",
      "('draw_random', <function draw_random at 0x00000231639090D0>)\n",
      "('draw_shell', <function draw_shell at 0x0000023163909268>)\n",
      "('draw_spectral', <function draw_spectral at 0x0000023163909158>)\n",
      "('draw_spring', <function draw_spring at 0x00000231639091E0>)\n",
      "('drawing', <module 'networkx.drawing' from 'C:\\\\Users\\\\Mohit\\\\Anaconda3\\\\lib\\\\site-packages\\\\networkx\\\\drawing\\\\__init__.py'>)\n",
      "('planar_drawing', <module 'networkx.algorithms.planar_drawing' from 'C:\\\\Users\\\\Mohit\\\\Anaconda3\\\\lib\\\\site-packages\\\\networkx\\\\algorithms\\\\planar_drawing.py'>)\n"
     ]
    }
   ],
   "source": [
    "from inspect import getmembers\n",
    "for x in getmembers(nx):\n",
    "    if 'draw' in x[0]:\n",
    "        print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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