friendship_predictor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Social Network Analysis\n",
    "\n",
    "This notebook contains the code used to generate the visualizations and results of my Medium post \"Predicting Friendship\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing packages\n",
    "%matplotlib inline\n",
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating the graph\n",
    "G = nx.Graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [],
   "source": [
    "#adding edges\n",
    "G.add_edge('Jack', 'Mike')\n",
    "G.add_edge('Jack', 'Pete')\n",
    "G.add_edge('Jack', 'Cindy')\n",
    "G.add_edge('Cindy', 'Liz')\n",
    "G.add_edge('Mike', 'Liz')\n",
    "G.add_edge('Mike', 'Roger')\n",
    "G.add_edge('Roger', 'Liz')\n",
    "G.add_edge('Joana', 'Liz')\n",
    "G.add_edge('Elle', 'Pete')\n",
    "G.add_edge('Cindy', 'Mark')\n",
    "G.add_edge('Steve', 'Mark')\n",
    "G.add_edge('Elle', 'Steve')\n",
    "G.add_edge('Steve', 'Ronald')\n",
    "G.add_edge('Ronald', 'Mark')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py:611: MatplotlibDeprecationWarning: isinstance(..., numbers.Number)\n",
      "  if cb.is_numlike(alpha):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.89125346, -0.7, 'Ronald')"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#first visualization\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.title('Visualization of Social Network', fontsize=20) #adding a title\n",
    "pos = nx.circular_layout(G) #specifying a circular layout\n",
    "nx.draw_networkx(G, pos, alpha=0.7, with_labels=False, edge_color='.4')\n",
    "plt.axis('off') #turning off the axis\n",
    "\n",
    "#adding the name labels to the nodes\n",
    "plt.text(1.12,0.07,s='Jack', fontsize=14, bbox=dict(facecolor='red', alpha=0.5),horizontalalignment='center')\n",
    "plt.text(0.87,0.64,s='Mike', fontsize=14, bbox=dict(facecolor='red', alpha=0.5),horizontalalignment='center')\n",
    "plt.text(0.54, 0.9,s='Pete', fontsize=14, bbox=dict(facecolor='red', alpha=0.5),horizontalalignment='center')\n",
    "plt.text(0, 1,s='Cindy', fontsize=14, bbox=dict(facecolor='red', alpha=0.5),horizontalalignment='center')\n",
    "plt.text(-0.654, 0.85,s='Liz', fontsize=14, bbox=dict(facecolor='red', alpha=0.5),horizontalalignment='center')\n",
    "plt.text(-1.1, 0.28173262,s='Roger', fontsize=14, bbox=dict(facecolor='red', alpha=0.5),horizontalalignment='center')\n",
    "plt.text(-1.1, -0.3,s='Joana', fontsize=14, bbox=dict(facecolor='red', alpha=0.5),horizontalalignment='center')\n",
    "plt.text(-0.65486072, -0.89,s='Elle', fontsize=14, bbox=dict(facecolor='red', alpha=0.5),horizontalalignment='center')\n",
    "plt.text(-0.14231501, -1.13,s='Mark', fontsize=14, bbox=dict(facecolor='red', alpha=0.5),horizontalalignment='center')\n",
    "plt.text(0.41541511, -1.06,s='Steve', fontsize=14, bbox=dict(facecolor='red', alpha=0.5),horizontalalignment='center')\n",
    "plt.text(0.89125346, -0.7,s='Ronald', fontsize=14, bbox=dict(facecolor='red', alpha=0.5),horizontalalignment='center')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: Graph\n",
      "Number of nodes: 11\n",
      "Number of edges: 14\n",
      "Average degree:   2.5455\n"
     ]
    }
   ],
   "source": [
    "#printing some general info \n",
    "print(nx.info(G))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating df from graph\n",
    "df = pd.DataFrame(index=G.nodes())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jack</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mike</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pete</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cindy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liz</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roger</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Joana</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elle</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mark</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steve</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ronald</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [Jack, Mike, Pete, Cindy, Liz, Roger, Joana, Elle, Mark, Steve, Ronald]"
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#taking a look at empty df\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [],
   "source": [
    "#adding centrality characteristics of nodes\n",
    "df['clustering'] = pd.Series(nx.clustering(G))\n",
    "df['degree'] = pd.Series(dict(G.degree()).values())[0]\n",
    "df['degree_centrality'] = pd.Series(nx.degree_centrality(G))\n",
    "df['closeness'] = pd.Series(nx.closeness_centrality(G))\n",
    "df['betweeness'] = pd.Series(nx.betweenness_centrality(G, normalized=True))\n",
    "df['pr'] = pd.Series(nx.pagerank(G))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "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>clustering</th>\n",
       "      <th>degree</th>\n",
       "      <th>degree_centrality</th>\n",
       "      <th>closeness</th>\n",
       "      <th>betweeness</th>\n",
       "      <th>pr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jack</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.103966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mike</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>0.125926</td>\n",
       "      <td>0.103670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pete</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.125926</td>\n",
       "      <td>0.075299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cindy</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.102197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liz</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>0.329630</td>\n",
       "      <td>0.139678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roger</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.072691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Joana</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.043318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elle</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.370370</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.075776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mark</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>0.274074</td>\n",
       "      <td>0.103838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steve</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.103704</td>\n",
       "      <td>0.106371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ronald</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.370370</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.073195</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        clustering  degree  degree_centrality  closeness  betweeness        pr\n",
       "Jack      0.000000       3                0.3   0.500000    0.255556  0.103966\n",
       "Mike      0.333333       3                0.3   0.434783    0.125926  0.103670\n",
       "Pete      0.000000       2                0.2   0.416667    0.125926  0.075299\n",
       "Cindy     0.000000       3                0.3   0.555556    0.400000  0.102197\n",
       "Liz       0.166667       4                0.4   0.476190    0.329630  0.139678\n",
       "Roger     1.000000       2                0.2   0.384615    0.000000  0.072691\n",
       "Joana     0.000000       1                0.1   0.333333    0.000000  0.043318\n",
       "Elle      0.000000       2                0.2   0.370370    0.074074  0.075776\n",
       "Mark      0.333333       3                0.3   0.476190    0.274074  0.103838\n",
       "Steve     0.333333       3                0.3   0.400000    0.103704  0.106371\n",
       "Ronald    1.000000       2                0.2   0.370370    0.000000  0.073195"
      ]
     },
     "execution_count": 424,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#taking a look at more specific node importance measures\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating Scores for Link Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {},
   "outputs": [],
   "source": [
    "cn = [(e[0], e[1], len(list(nx.common_neighbors(G,e[0], e[1])))) for e in nx.non_edges(G)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 426,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [],
   "source": [
    "jc = list(nx.jaccard_coefficient(G))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 428,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(jc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [],
   "source": [
    "ra = list(nx.resource_allocation_index(G))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 430,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ra)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [],
   "source": [
    "aa = list(nx.adamic_adar_index(G))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 432,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(aa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [],
   "source": [
    "pa = list(nx.preferential_attachment(G))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [],
   "source": [
    "#extracting index from score results\n",
    "index = pd.Series([(e[0], e[1]) for e in cn])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [],
   "source": [
    "#initializing new empty df\n",
    "df = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [],
   "source": [
    "#extracting score results and transforming them into a pandas series\n",
    "cn = pd.Series([(e[2]) for e in cn])\n",
    "jc = pd.Series([(e[2]) for e in jc])\n",
    "ra = pd.Series([(e[2]) for e in ra])\n",
    "aa = pd.Series([(e[2]) for e in aa])\n",
    "pa = pd.Series([(e[2]) for e in pa])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [],
   "source": [
    "#adding scores to df\n",
    "df['common_neigh'] = cn\n",
    "df['jaccard'] = jc\n",
    "df['resource_alloc'] = ra\n",
    "df['adamic_adar'] = aa\n",
    "df['pref_attachment'] = pa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaling scores so that mean is between 0 and 1\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [],
   "source": [
    "#adding index to df\n",
    "df.index = index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py:323: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "scaled_df = pd.DataFrame(scaler.fit_transform(df))\n",
    "scaled_df.columns = ['common_neigh', 'jaccard', 'resource_alloc', 'adamic_adar', 'pref_attachment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [],
   "source": [
    "#taking the mean and adding it as a new column\n",
    "scaled_df['mean_score'] = (scaled_df['common_neigh'] + scaled_df['jaccard'] + scaled_df['resource_alloc']\\\n",
    "+ scaled_df['adamic_adar'] + scaled_df['pref_attachment'])/5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_df.index = index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>common_neigh</th>\n",
       "      <th>jaccard</th>\n",
       "      <th>resource_alloc</th>\n",
       "      <th>adamic_adar</th>\n",
       "      <th>pref_attachment</th>\n",
       "      <th>mean_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(Liz, Jack)</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Mike, Cindy)</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.896241</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.894248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Steve, Pete)</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.792481</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.588496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Jack, Elle)</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.792481</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.588496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Liz, Mark)</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.566667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Jack, Mark)</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Cindy, Steve)</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Mark, Elle)</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Mike, Pete)</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Roger, Jack)</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Cindy, Pete)</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Cindy, Ronald)</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Ronald, Elle)</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.473333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Joana, Roger)</th>\n",
       "      <td>0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.396241</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.454248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Cindy, Roger)</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.396241</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.434248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Mike, Joana)</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.396241</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.407581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Joana, Cindy)</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.396241</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.407581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Liz, Steve)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Mike, Mark)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Mike, Steve)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Jack, Steve)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Liz, Ronald)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Liz, Pete)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Liz, Elle)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Cindy, Elle)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Roger, Steve)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Mike, Ronald)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Mark, Pete)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Jack, Ronald)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Mike, Elle)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Roger, Mark)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Roger, Pete)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Roger, Elle)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Ronald, Pete)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Roger, Ronald)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Joana, Steve)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Joana, Mark)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Joana, Jack)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Joana, Ronald)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Joana, Pete)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Joana, Elle)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 common_neigh   jaccard  resource_alloc  adamic_adar  \\\n",
       "(Liz, Jack)               1.0  0.800000           1.000     1.000000   \n",
       "(Mike, Cindy)             1.0  1.000000           0.875     0.896241   \n",
       "(Steve, Pete)             0.5  0.500000           0.750     0.792481   \n",
       "(Jack, Elle)              0.5  0.500000           0.750     0.792481   \n",
       "(Liz, Mark)               0.5  0.333333           0.500     0.500000   \n",
       "(Jack, Mark)              0.5  0.400000           0.500     0.500000   \n",
       "(Cindy, Steve)            0.5  0.400000           0.500     0.500000   \n",
       "(Mark, Elle)              0.5  0.500000           0.500     0.500000   \n",
       "(Mike, Pete)              0.5  0.500000           0.500     0.500000   \n",
       "(Roger, Jack)             0.5  0.500000           0.500     0.500000   \n",
       "(Cindy, Pete)             0.5  0.500000           0.500     0.500000   \n",
       "(Cindy, Ronald)           0.5  0.500000           0.500     0.500000   \n",
       "(Ronald, Elle)            0.5  0.666667           0.500     0.500000   \n",
       "(Joana, Roger)            0.5  1.000000           0.375     0.396241   \n",
       "(Cindy, Roger)            0.5  0.500000           0.375     0.396241   \n",
       "(Mike, Joana)             0.5  0.666667           0.375     0.396241   \n",
       "(Joana, Cindy)            0.5  0.666667           0.375     0.396241   \n",
       "(Liz, Steve)              0.0  0.000000           0.000     0.000000   \n",
       "(Mike, Mark)              0.0  0.000000           0.000     0.000000   \n",
       "(Mike, Steve)             0.0  0.000000           0.000     0.000000   \n",
       "(Jack, Steve)             0.0  0.000000           0.000     0.000000   \n",
       "(Liz, Ronald)             0.0  0.000000           0.000     0.000000   \n",
       "(Liz, Pete)               0.0  0.000000           0.000     0.000000   \n",
       "(Liz, Elle)               0.0  0.000000           0.000     0.000000   \n",
       "(Cindy, Elle)             0.0  0.000000           0.000     0.000000   \n",
       "(Roger, Steve)            0.0  0.000000           0.000     0.000000   \n",
       "(Mike, Ronald)            0.0  0.000000           0.000     0.000000   \n",
       "(Mark, Pete)              0.0  0.000000           0.000     0.000000   \n",
       "(Jack, Ronald)            0.0  0.000000           0.000     0.000000   \n",
       "(Mike, Elle)              0.0  0.000000           0.000     0.000000   \n",
       "(Roger, Mark)             0.0  0.000000           0.000     0.000000   \n",
       "(Roger, Pete)             0.0  0.000000           0.000     0.000000   \n",
       "(Roger, Elle)             0.0  0.000000           0.000     0.000000   \n",
       "(Ronald, Pete)            0.0  0.000000           0.000     0.000000   \n",
       "(Roger, Ronald)           0.0  0.000000           0.000     0.000000   \n",
       "(Joana, Steve)            0.0  0.000000           0.000     0.000000   \n",
       "(Joana, Mark)             0.0  0.000000           0.000     0.000000   \n",
       "(Joana, Jack)             0.0  0.000000           0.000     0.000000   \n",
       "(Joana, Ronald)           0.0  0.000000           0.000     0.000000   \n",
       "(Joana, Pete)             0.0  0.000000           0.000     0.000000   \n",
       "(Joana, Elle)             0.0  0.000000           0.000     0.000000   \n",
       "\n",
       "                 pref_attachment  mean_score  \n",
       "(Liz, Jack)                  1.0    0.960000  \n",
       "(Mike, Cindy)                0.7    0.894248  \n",
       "(Steve, Pete)                0.4    0.588496  \n",
       "(Jack, Elle)                 0.4    0.588496  \n",
       "(Liz, Mark)                  1.0    0.566667  \n",
       "(Jack, Mark)                 0.7    0.520000  \n",
       "(Cindy, Steve)               0.7    0.520000  \n",
       "(Mark, Elle)                 0.4    0.480000  \n",
       "(Mike, Pete)                 0.4    0.480000  \n",
       "(Roger, Jack)                0.4    0.480000  \n",
       "(Cindy, Pete)                0.4    0.480000  \n",
       "(Cindy, Ronald)              0.4    0.480000  \n",
       "(Ronald, Elle)               0.2    0.473333  \n",
       "(Joana, Roger)               0.0    0.454248  \n",
       "(Cindy, Roger)               0.4    0.434248  \n",
       "(Mike, Joana)                0.1    0.407581  \n",
       "(Joana, Cindy)               0.1    0.407581  \n",
       "(Liz, Steve)                 1.0    0.200000  \n",
       "(Mike, Mark)                 0.7    0.140000  \n",
       "(Mike, Steve)                0.7    0.140000  \n",
       "(Jack, Steve)                0.7    0.140000  \n",
       "(Liz, Ronald)                0.6    0.120000  \n",
       "(Liz, Pete)                  0.6    0.120000  \n",
       "(Liz, Elle)                  0.6    0.120000  \n",
       "(Cindy, Elle)                0.4    0.080000  \n",
       "(Roger, Steve)               0.4    0.080000  \n",
       "(Mike, Ronald)               0.4    0.080000  \n",
       "(Mark, Pete)                 0.4    0.080000  \n",
       "(Jack, Ronald)               0.4    0.080000  \n",
       "(Mike, Elle)                 0.4    0.080000  \n",
       "(Roger, Mark)                0.4    0.080000  \n",
       "(Roger, Pete)                0.2    0.040000  \n",
       "(Roger, Elle)                0.2    0.040000  \n",
       "(Ronald, Pete)               0.2    0.040000  \n",
       "(Roger, Ronald)              0.2    0.040000  \n",
       "(Joana, Steve)               0.1    0.020000  \n",
       "(Joana, Mark)                0.1    0.020000  \n",
       "(Joana, Jack)                0.1    0.020000  \n",
       "(Joana, Ronald)              0.0    0.000000  \n",
       "(Joana, Pete)                0.0    0.000000  \n",
       "(Joana, Elle)                0.0    0.000000  "
      ]
     },
     "execution_count": 445,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displaying df sorted by mean score in descending order\n",
    "scaled_df.sort_values('mean_score', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "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>index</th>\n",
       "      <th>common_neigh</th>\n",
       "      <th>jaccard</th>\n",
       "      <th>resource_alloc</th>\n",
       "      <th>adamic_adar</th>\n",
       "      <th>pref_attachment</th>\n",
       "      <th>mean_score</th>\n",
       "      <th>first_p</th>\n",
       "      <th>second_p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(Mike, Joana)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.396241</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.407581</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Joana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(Mike, Cindy)</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.896241</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.894248</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Cindy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(Mike, Ronald)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Ronald</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(Mike, Mark)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Mark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(Mike, Elle)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Elle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(Mike, Steve)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Steve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(Mike, Pete)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Pete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(Joana, Cindy)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.396241</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.407581</td>\n",
       "      <td>Joana</td>\n",
       "      <td>Cindy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(Joana, Roger)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.396241</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.454248</td>\n",
       "      <td>Joana</td>\n",
       "      <td>Roger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(Joana, Jack)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>Joana</td>\n",
       "      <td>Jack</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(Joana, Ronald)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Joana</td>\n",
       "      <td>Ronald</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(Joana, Mark)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>Joana</td>\n",
       "      <td>Mark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(Joana, Elle)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Joana</td>\n",
       "      <td>Elle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(Joana, Steve)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>Joana</td>\n",
       "      <td>Steve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(Joana, Pete)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Joana</td>\n",
       "      <td>Pete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(Liz, Ronald)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>Liz</td>\n",
       "      <td>Ronald</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(Liz, Jack)</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>Liz</td>\n",
       "      <td>Jack</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(Liz, Mark)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>Liz</td>\n",
       "      <td>Mark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(Liz, Elle)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>Liz</td>\n",
       "      <td>Elle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(Liz, Steve)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>Liz</td>\n",
       "      <td>Steve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(Liz, Pete)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>Liz</td>\n",
       "      <td>Pete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(Cindy, Roger)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.396241</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.434248</td>\n",
       "      <td>Cindy</td>\n",
       "      <td>Roger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(Cindy, Ronald)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>Cindy</td>\n",
       "      <td>Ronald</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>(Cindy, Elle)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>Cindy</td>\n",
       "      <td>Elle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>(Cindy, Steve)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>Cindy</td>\n",
       "      <td>Steve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>(Cindy, Pete)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>Cindy</td>\n",
       "      <td>Pete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>(Roger, Ronald)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>Roger</td>\n",
       "      <td>Ronald</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>(Roger, Jack)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>Roger</td>\n",
       "      <td>Jack</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(Roger, Mark)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>Roger</td>\n",
       "      <td>Mark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>(Roger, Elle)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>Roger</td>\n",
       "      <td>Elle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>(Roger, Steve)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>Roger</td>\n",
       "      <td>Steve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>(Roger, Pete)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>Roger</td>\n",
       "      <td>Pete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>(Jack, Steve)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>Jack</td>\n",
       "      <td>Steve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>(Jack, Mark)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>Jack</td>\n",
       "      <td>Mark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>(Jack, Elle)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.792481</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.588496</td>\n",
       "      <td>Jack</td>\n",
       "      <td>Elle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>(Jack, Ronald)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>Jack</td>\n",
       "      <td>Ronald</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>(Ronald, Pete)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>Ronald</td>\n",
       "      <td>Pete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>(Ronald, Elle)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.473333</td>\n",
       "      <td>Ronald</td>\n",
       "      <td>Elle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>(Mark, Pete)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>Mark</td>\n",
       "      <td>Pete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>(Mark, Elle)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>Mark</td>\n",
       "      <td>Elle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>(Steve, Pete)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.792481</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.588496</td>\n",
       "      <td>Steve</td>\n",
       "      <td>Pete</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              index  common_neigh   jaccard  resource_alloc  adamic_adar  \\\n",
       "0     (Mike, Joana)           0.5  0.666667           0.375     0.396241   \n",
       "1     (Mike, Cindy)           1.0  1.000000           0.875     0.896241   \n",
       "2    (Mike, Ronald)           0.0  0.000000           0.000     0.000000   \n",
       "3      (Mike, Mark)           0.0  0.000000           0.000     0.000000   \n",
       "4      (Mike, Elle)           0.0  0.000000           0.000     0.000000   \n",
       "5     (Mike, Steve)           0.0  0.000000           0.000     0.000000   \n",
       "6      (Mike, Pete)           0.5  0.500000           0.500     0.500000   \n",
       "7    (Joana, Cindy)           0.5  0.666667           0.375     0.396241   \n",
       "8    (Joana, Roger)           0.5  1.000000           0.375     0.396241   \n",
       "9     (Joana, Jack)           0.0  0.000000           0.000     0.000000   \n",
       "10  (Joana, Ronald)           0.0  0.000000           0.000     0.000000   \n",
       "11    (Joana, Mark)           0.0  0.000000           0.000     0.000000   \n",
       "12    (Joana, Elle)           0.0  0.000000           0.000     0.000000   \n",
       "13   (Joana, Steve)           0.0  0.000000           0.000     0.000000   \n",
       "14    (Joana, Pete)           0.0  0.000000           0.000     0.000000   \n",
       "15    (Liz, Ronald)           0.0  0.000000           0.000     0.000000   \n",
       "16      (Liz, Jack)           1.0  0.800000           1.000     1.000000   \n",
       "17      (Liz, Mark)           0.5  0.333333           0.500     0.500000   \n",
       "18      (Liz, Elle)           0.0  0.000000           0.000     0.000000   \n",
       "19     (Liz, Steve)           0.0  0.000000           0.000     0.000000   \n",
       "20      (Liz, Pete)           0.0  0.000000           0.000     0.000000   \n",
       "21   (Cindy, Roger)           0.5  0.500000           0.375     0.396241   \n",
       "22  (Cindy, Ronald)           0.5  0.500000           0.500     0.500000   \n",
       "23    (Cindy, Elle)           0.0  0.000000           0.000     0.000000   \n",
       "24   (Cindy, Steve)           0.5  0.400000           0.500     0.500000   \n",
       "25    (Cindy, Pete)           0.5  0.500000           0.500     0.500000   \n",
       "26  (Roger, Ronald)           0.0  0.000000           0.000     0.000000   \n",
       "27    (Roger, Jack)           0.5  0.500000           0.500     0.500000   \n",
       "28    (Roger, Mark)           0.0  0.000000           0.000     0.000000   \n",
       "29    (Roger, Elle)           0.0  0.000000           0.000     0.000000   \n",
       "30   (Roger, Steve)           0.0  0.000000           0.000     0.000000   \n",
       "31    (Roger, Pete)           0.0  0.000000           0.000     0.000000   \n",
       "32    (Jack, Steve)           0.0  0.000000           0.000     0.000000   \n",
       "33     (Jack, Mark)           0.5  0.400000           0.500     0.500000   \n",
       "34     (Jack, Elle)           0.5  0.500000           0.750     0.792481   \n",
       "35   (Jack, Ronald)           0.0  0.000000           0.000     0.000000   \n",
       "36   (Ronald, Pete)           0.0  0.000000           0.000     0.000000   \n",
       "37   (Ronald, Elle)           0.5  0.666667           0.500     0.500000   \n",
       "38     (Mark, Pete)           0.0  0.000000           0.000     0.000000   \n",
       "39     (Mark, Elle)           0.5  0.500000           0.500     0.500000   \n",
       "40    (Steve, Pete)           0.5  0.500000           0.750     0.792481   \n",
       "\n",
       "    pref_attachment  mean_score first_p second_p  \n",
       "0               0.1    0.407581    Mike    Joana  \n",
       "1               0.7    0.894248    Mike    Cindy  \n",
       "2               0.4    0.080000    Mike   Ronald  \n",
       "3               0.7    0.140000    Mike     Mark  \n",
       "4               0.4    0.080000    Mike     Elle  \n",
       "5               0.7    0.140000    Mike    Steve  \n",
       "6               0.4    0.480000    Mike     Pete  \n",
       "7               0.1    0.407581   Joana    Cindy  \n",
       "8               0.0    0.454248   Joana    Roger  \n",
       "9               0.1    0.020000   Joana     Jack  \n",
       "10              0.0    0.000000   Joana   Ronald  \n",
       "11              0.1    0.020000   Joana     Mark  \n",
       "12              0.0    0.000000   Joana     Elle  \n",
       "13              0.1    0.020000   Joana    Steve  \n",
       "14              0.0    0.000000   Joana     Pete  \n",
       "15              0.6    0.120000     Liz   Ronald  \n",
       "16              1.0    0.960000     Liz     Jack  \n",
       "17              1.0    0.566667     Liz     Mark  \n",
       "18              0.6    0.120000     Liz     Elle  \n",
       "19              1.0    0.200000     Liz    Steve  \n",
       "20              0.6    0.120000     Liz     Pete  \n",
       "21              0.4    0.434248   Cindy    Roger  \n",
       "22              0.4    0.480000   Cindy   Ronald  \n",
       "23              0.4    0.080000   Cindy     Elle  \n",
       "24              0.7    0.520000   Cindy    Steve  \n",
       "25              0.4    0.480000   Cindy     Pete  \n",
       "26              0.2    0.040000   Roger   Ronald  \n",
       "27              0.4    0.480000   Roger     Jack  \n",
       "28              0.4    0.080000   Roger     Mark  \n",
       "29              0.2    0.040000   Roger     Elle  \n",
       "30              0.4    0.080000   Roger    Steve  \n",
       "31              0.2    0.040000   Roger     Pete  \n",
       "32              0.7    0.140000    Jack    Steve  \n",
       "33              0.7    0.520000    Jack     Mark  \n",
       "34              0.4    0.588496    Jack     Elle  \n",
       "35              0.4    0.080000    Jack   Ronald  \n",
       "36              0.2    0.040000  Ronald     Pete  \n",
       "37              0.2    0.473333  Ronald     Elle  \n",
       "38              0.4    0.080000    Mark     Pete  \n",
       "39              0.4    0.480000    Mark     Elle  \n",
       "40              0.4    0.588496   Steve     Pete  "
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#extracting first and second name from the tuple index and turning them into separate columns\n",
    "first_person = []\n",
    "second_person = []\n",
    "for el in scaled_df.index:\n",
    "    first_person.append(el[0])\n",
    "    second_person.append(el[1])\n",
    "    \n",
    "first_person = pd.Series(first_person)\n",
    "second_person = pd.Series(second_person)\n",
    "\n",
    "scaled_df.reset_index(inplace=True)\n",
    "scaled_df = pd.concat([scaled_df, first_person.rename('first_p'), second_person.rename('second_p')], axis=1)\n",
    "scaled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [],
   "source": [
    "#defining a function to easily look up most likely connection for every individual\n",
    "def most_likely_friend(name):\n",
    "    \"\"\"\n",
    "    Input: String of name of individual\n",
    "    Output: Data Frame containing three columns: Person 1, Person 2, and the mean connection probability score \n",
    "    \"\"\"\n",
    "    result_df = scaled_df[(scaled_df.first_p == name) | (scaled_df.second_p == name)].sort_values('mean_score', ascending=False)\n",
    "    result_df.reset_index(drop=True, inplace=True)\n",
    "        \n",
    "       \n",
    "    result_df = result_df[['first_p','second_p', 'mean_score']]\n",
    "    result_df.columns = ['Person 1', 'Person 2', 'Probability of Connection']\n",
    "\n",
    "    return result_df\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Person 1</th>\n",
       "      <th>Person 2</th>\n",
       "      <th>Probability of Connection</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Liz</td>\n",
       "      <td>Mark</td>\n",
       "      <td>0.566667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jack</td>\n",
       "      <td>Mark</td>\n",
       "      <td>0.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mark</td>\n",
       "      <td>Elle</td>\n",
       "      <td>0.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Mark</td>\n",
       "      <td>0.140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Roger</td>\n",
       "      <td>Mark</td>\n",
       "      <td>0.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mark</td>\n",
       "      <td>Pete</td>\n",
       "      <td>0.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Joana</td>\n",
       "      <td>Mark</td>\n",
       "      <td>0.020000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Person 1 Person 2  Probability of Connection\n",
       "0      Liz     Mark                   0.566667\n",
       "1     Jack     Mark                   0.520000\n",
       "2     Mark     Elle                   0.480000\n",
       "3     Mike     Mark                   0.140000\n",
       "4    Roger     Mark                   0.080000\n",
       "5     Mark     Pete                   0.080000\n",
       "6    Joana     Mark                   0.020000"
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_likely_friend('Mark')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {},
   "outputs": [],
   "source": [
    "#taking the community structure into account\n",
    "#assigning each node to a community\n",
    "G.node['Jack']['community'] = 0\n",
    "G.node['Mike']['community'] = 0\n",
    "G.node['Pete']['community'] = 0\n",
    "G.node['Cindy']['community'] = 0\n",
    "G.node['Liz']['community'] = 0\n",
    "G.node['Roger']['community'] = 0\n",
    "G.node['Joana']['community'] = 0\n",
    "G.node['Elle']['community'] = 1\n",
    "G.node['Mark']['community'] = 1\n",
    "G.node['Steve']['community'] = 1\n",
    "G.node['Ronald']['community'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculating the common neighbors and resource allocation score according to Soundarajan Hopcroft \n",
    "sh = list(nx.cn_soundarajan_hopcroft(G))\n",
    "cra = list(nx.ra_index_soundarajan_hopcroft(G))\n",
    "index2 = pd.Series([(e[0], e[1]) for e in sh])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>(Joana, Mark)</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Joana, Elle)</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Joana, Steve)</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Joana, Pete)</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Liz, Ronald)</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Liz, Jack)</th>\n",
       "      <td>4</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Liz, Mark)</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Liz, Elle)</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Liz, Steve)</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Liz, Pete)</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Cindy, Roger)</th>\n",
       "      <td>2</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Cindy, Ronald)</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Cindy, Elle)</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Cindy, Steve)</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Cindy, Pete)</th>\n",
       "      <td>2</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Roger, Ronald)</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Roger, Jack)</th>\n",
       "      <td>2</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Roger, Mark)</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Roger, Elle)</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Roger, Steve)</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>(Roger, Pete)</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>(Jack, Steve)</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>(Jack, Elle)</th>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>(Jack, Ronald)</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>(Ronald, Pete)</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>(Ronald, Elle)</th>\n",
       "      <td>2</td>\n",
       "      <td>0.333333</td>\n",
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       "    <tr>\n",
       "      <th>(Mark, Pete)</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>(Mark, Elle)</th>\n",
       "      <td>2</td>\n",
       "      <td>0.333333</td>\n",
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       "    <tr>\n",
       "      <th>(Steve, Pete)</th>\n",
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      ],
      "text/plain": [
       "                 sh       cra\n",
       "(Mike, Joana)     2  0.250000\n",
       "(Mike, Cindy)     4  0.583333\n",
       "(Mike, Ronald)    0  0.000000\n",
       "(Mike, Mark)      0  0.000000\n",
       "(Mike, Elle)      0  0.000000\n",
       "(Mike, Steve)     0  0.000000\n",
       "(Mike, Pete)      2  0.333333\n",
       "(Joana, Cindy)    2  0.250000\n",
       "(Joana, Roger)    2  0.250000\n",
       "(Joana, Jack)     0  0.000000\n",
       "(Joana, Ronald)   0  0.000000\n",
       "(Joana, Mark)     0  0.000000\n",
       "(Joana, Elle)     0  0.000000\n",
       "(Joana, Steve)    0  0.000000\n",
       "(Joana, Pete)     0  0.000000\n",
       "(Liz, Ronald)     0  0.000000\n",
       "(Liz, Jack)       4  0.666667\n",
       "(Liz, Mark)       1  0.000000\n",
       "(Liz, Elle)       0  0.000000\n",
       "(Liz, Steve)      0  0.000000\n",
       "(Liz, Pete)       0  0.000000\n",
       "(Cindy, Roger)    2  0.250000\n",
       "(Cindy, Ronald)   1  0.000000\n",
       "(Cindy, Elle)     0  0.000000\n",
       "(Cindy, Steve)    1  0.000000\n",
       "(Cindy, Pete)     2  0.333333\n",
       "(Roger, Ronald)   0  0.000000\n",
       "(Roger, Jack)     2  0.333333\n",
       "(Roger, Mark)     0  0.000000\n",
       "(Roger, Elle)     0  0.000000\n",
       "(Roger, Steve)    0  0.000000\n",
       "(Roger, Pete)     0  0.000000\n",
       "(Jack, Steve)     0  0.000000\n",
       "(Jack, Mark)      1  0.000000\n",
       "(Jack, Elle)      1  0.000000\n",
       "(Jack, Ronald)    0  0.000000\n",
       "(Ronald, Pete)    0  0.000000\n",
       "(Ronald, Elle)    2  0.333333\n",
       "(Mark, Pete)      0  0.000000\n",
       "(Mark, Elle)      2  0.333333\n",
       "(Steve, Pete)     1  0.000000"
      ]
     },
     "execution_count": 451,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a data frame\n",
    "df2 = pd.DataFrame() #initializing empty df\n",
    "\n",
    "sh = pd.Series([(e[2]) for e in sh]) #extracting score from list\n",
    "cra = pd.Series([(e[2]) for e in cra])\n",
    "\n",
    "df2['sh'] = sh #adding score columns to df\n",
    "df2['cra'] = cra\n",
    "\n",
    "df2.index = index2 #adding index to df\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py:323: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "#normalizing the scores and adding an index\n",
    "scaled_df2 = pd.DataFrame(scaler.fit_transform(df2))\n",
    "scaled_df2.columns = ['common_neigh', 'resource_alloc']\n",
    "scaled_df2.index = index2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {},
   "outputs": [],
   "source": [
    "#taking the mean\n",
    "scaled_df2['mean_score'] = (scaled_df2['common_neigh'] + scaled_df2['resource_alloc'])/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Joana, Roger)</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.4375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Cindy, Roger)</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.4375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Jack, Elle)</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Jack, Mark)</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Cindy, Steve)</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.1250</td>\n",
       "    </tr>\n",
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       "      <th>(Cindy, Ronald)</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Liz, Mark)</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Steve, Pete)</th>\n",
       "      <td>0.25</td>\n",
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       "      <td>0.1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Liz, Ronald)</th>\n",
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       "      <td>0.0000</td>\n",
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       "    <tr>\n",
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       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>(Mark, Pete)</th>\n",
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       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>(Mike, Mark)</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Ronald, Pete)</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Jack, Ronald)</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Mike, Elle)</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Mike, Steve)</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Jack, Steve)</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Roger, Pete)</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Roger, Steve)</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Roger, Elle)</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Roger, Mark)</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Joana, Jack)</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Roger, Ronald)</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Joana, Ronald)</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Joana, Mark)</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Cindy, Elle)</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Joana, Elle)</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Joana, Steve)</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Joana, Pete)</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Liz, Steve)</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(Liz, Pete)</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 common_neigh  resource_alloc  mean_score\n",
       "(Liz, Jack)              1.00           1.000      1.0000\n",
       "(Mike, Cindy)            1.00           0.875      0.9375\n",
       "(Mark, Elle)             0.50           0.500      0.5000\n",
       "(Ronald, Elle)           0.50           0.500      0.5000\n",
       "(Mike, Pete)             0.50           0.500      0.5000\n",
       "(Roger, Jack)            0.50           0.500      0.5000\n",
       "(Cindy, Pete)            0.50           0.500      0.5000\n",
       "(Mike, Joana)            0.50           0.375      0.4375\n",
       "(Joana, Cindy)           0.50           0.375      0.4375\n",
       "(Joana, Roger)           0.50           0.375      0.4375\n",
       "(Cindy, Roger)           0.50           0.375      0.4375\n",
       "(Jack, Elle)             0.25           0.000      0.1250\n",
       "(Jack, Mark)             0.25           0.000      0.1250\n",
       "(Cindy, Steve)           0.25           0.000      0.1250\n",
       "(Cindy, Ronald)          0.25           0.000      0.1250\n",
       "(Liz, Mark)              0.25           0.000      0.1250\n",
       "(Steve, Pete)            0.25           0.000      0.1250\n",
       "(Liz, Ronald)            0.00           0.000      0.0000\n",
       "(Liz, Elle)              0.00           0.000      0.0000\n",
       "(Mike, Ronald)           0.00           0.000      0.0000\n",
       "(Mark, Pete)             0.00           0.000      0.0000\n",
       "(Mike, Mark)             0.00           0.000      0.0000\n",
       "(Ronald, Pete)           0.00           0.000      0.0000\n",
       "(Jack, Ronald)           0.00           0.000      0.0000\n",
       "(Mike, Elle)             0.00           0.000      0.0000\n",
       "(Mike, Steve)            0.00           0.000      0.0000\n",
       "(Jack, Steve)            0.00           0.000      0.0000\n",
       "(Roger, Pete)            0.00           0.000      0.0000\n",
       "(Roger, Steve)           0.00           0.000      0.0000\n",
       "(Roger, Elle)            0.00           0.000      0.0000\n",
       "(Roger, Mark)            0.00           0.000      0.0000\n",
       "(Joana, Jack)            0.00           0.000      0.0000\n",
       "(Roger, Ronald)          0.00           0.000      0.0000\n",
       "(Joana, Ronald)          0.00           0.000      0.0000\n",
       "(Joana, Mark)            0.00           0.000      0.0000\n",
       "(Cindy, Elle)            0.00           0.000      0.0000\n",
       "(Joana, Elle)            0.00           0.000      0.0000\n",
       "(Joana, Steve)           0.00           0.000      0.0000\n",
       "(Joana, Pete)            0.00           0.000      0.0000\n",
       "(Liz, Steve)             0.00           0.000      0.0000\n",
       "(Liz, Pete)              0.00           0.000      0.0000"
      ]
     },
     "execution_count": 454,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displaying df sorted my mean score\n",
    "scaled_df2.sort_values('mean_score', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "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>index</th>\n",
       "      <th>common_neigh</th>\n",
       "      <th>resource_alloc</th>\n",
       "      <th>mean_score</th>\n",
       "      <th>first_p</th>\n",
       "      <th>second_p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(Mike, Joana)</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Joana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(Mike, Cindy)</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Cindy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(Mike, Ronald)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Ronald</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(Mike, Mark)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Mark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(Mike, Elle)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Elle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(Mike, Steve)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Steve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(Mike, Pete)</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Pete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(Joana, Cindy)</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>Joana</td>\n",
       "      <td>Cindy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(Joana, Roger)</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>Joana</td>\n",
       "      <td>Roger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(Joana, Jack)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Joana</td>\n",
       "      <td>Jack</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(Joana, Ronald)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Joana</td>\n",
       "      <td>Ronald</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(Joana, Mark)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Joana</td>\n",
       "      <td>Mark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(Joana, Elle)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Joana</td>\n",
       "      <td>Elle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(Joana, Steve)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Joana</td>\n",
       "      <td>Steve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(Joana, Pete)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Joana</td>\n",
       "      <td>Pete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(Liz, Ronald)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Liz</td>\n",
       "      <td>Ronald</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(Liz, Jack)</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Liz</td>\n",
       "      <td>Jack</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(Liz, Mark)</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>Liz</td>\n",
       "      <td>Mark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(Liz, Elle)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Liz</td>\n",
       "      <td>Elle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(Liz, Steve)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Liz</td>\n",
       "      <td>Steve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(Liz, Pete)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Liz</td>\n",
       "      <td>Pete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(Cindy, Roger)</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>Cindy</td>\n",
       "      <td>Roger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(Cindy, Ronald)</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>Cindy</td>\n",
       "      <td>Ronald</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>(Cindy, Elle)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Cindy</td>\n",
       "      <td>Elle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>(Cindy, Steve)</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>Cindy</td>\n",
       "      <td>Steve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>(Cindy, Pete)</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>Cindy</td>\n",
       "      <td>Pete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>(Roger, Ronald)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Roger</td>\n",
       "      <td>Ronald</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>(Roger, Jack)</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>Roger</td>\n",
       "      <td>Jack</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(Roger, Mark)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Roger</td>\n",
       "      <td>Mark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>(Roger, Elle)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Roger</td>\n",
       "      <td>Elle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>(Roger, Steve)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Roger</td>\n",
       "      <td>Steve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>(Roger, Pete)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Roger</td>\n",
       "      <td>Pete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>(Jack, Steve)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Jack</td>\n",
       "      <td>Steve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>(Jack, Mark)</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>Jack</td>\n",
       "      <td>Mark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>(Jack, Elle)</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>Jack</td>\n",
       "      <td>Elle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>(Jack, Ronald)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Jack</td>\n",
       "      <td>Ronald</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>(Ronald, Pete)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Ronald</td>\n",
       "      <td>Pete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>(Ronald, Elle)</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>Ronald</td>\n",
       "      <td>Elle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>(Mark, Pete)</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Mark</td>\n",
       "      <td>Pete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>(Mark, Elle)</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>Mark</td>\n",
       "      <td>Elle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>(Steve, Pete)</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>Steve</td>\n",
       "      <td>Pete</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              index  common_neigh  resource_alloc  mean_score first_p second_p\n",
       "0     (Mike, Joana)          0.50           0.375      0.4375    Mike    Joana\n",
       "1     (Mike, Cindy)          1.00           0.875      0.9375    Mike    Cindy\n",
       "2    (Mike, Ronald)          0.00           0.000      0.0000    Mike   Ronald\n",
       "3      (Mike, Mark)          0.00           0.000      0.0000    Mike     Mark\n",
       "4      (Mike, Elle)          0.00           0.000      0.0000    Mike     Elle\n",
       "5     (Mike, Steve)          0.00           0.000      0.0000    Mike    Steve\n",
       "6      (Mike, Pete)          0.50           0.500      0.5000    Mike     Pete\n",
       "7    (Joana, Cindy)          0.50           0.375      0.4375   Joana    Cindy\n",
       "8    (Joana, Roger)          0.50           0.375      0.4375   Joana    Roger\n",
       "9     (Joana, Jack)          0.00           0.000      0.0000   Joana     Jack\n",
       "10  (Joana, Ronald)          0.00           0.000      0.0000   Joana   Ronald\n",
       "11    (Joana, Mark)          0.00           0.000      0.0000   Joana     Mark\n",
       "12    (Joana, Elle)          0.00           0.000      0.0000   Joana     Elle\n",
       "13   (Joana, Steve)          0.00           0.000      0.0000   Joana    Steve\n",
       "14    (Joana, Pete)          0.00           0.000      0.0000   Joana     Pete\n",
       "15    (Liz, Ronald)          0.00           0.000      0.0000     Liz   Ronald\n",
       "16      (Liz, Jack)          1.00           1.000      1.0000     Liz     Jack\n",
       "17      (Liz, Mark)          0.25           0.000      0.1250     Liz     Mark\n",
       "18      (Liz, Elle)          0.00           0.000      0.0000     Liz     Elle\n",
       "19     (Liz, Steve)          0.00           0.000      0.0000     Liz    Steve\n",
       "20      (Liz, Pete)          0.00           0.000      0.0000     Liz     Pete\n",
       "21   (Cindy, Roger)          0.50           0.375      0.4375   Cindy    Roger\n",
       "22  (Cindy, Ronald)          0.25           0.000      0.1250   Cindy   Ronald\n",
       "23    (Cindy, Elle)          0.00           0.000      0.0000   Cindy     Elle\n",
       "24   (Cindy, Steve)          0.25           0.000      0.1250   Cindy    Steve\n",
       "25    (Cindy, Pete)          0.50           0.500      0.5000   Cindy     Pete\n",
       "26  (Roger, Ronald)          0.00           0.000      0.0000   Roger   Ronald\n",
       "27    (Roger, Jack)          0.50           0.500      0.5000   Roger     Jack\n",
       "28    (Roger, Mark)          0.00           0.000      0.0000   Roger     Mark\n",
       "29    (Roger, Elle)          0.00           0.000      0.0000   Roger     Elle\n",
       "30   (Roger, Steve)          0.00           0.000      0.0000   Roger    Steve\n",
       "31    (Roger, Pete)          0.00           0.000      0.0000   Roger     Pete\n",
       "32    (Jack, Steve)          0.00           0.000      0.0000    Jack    Steve\n",
       "33     (Jack, Mark)          0.25           0.000      0.1250    Jack     Mark\n",
       "34     (Jack, Elle)          0.25           0.000      0.1250    Jack     Elle\n",
       "35   (Jack, Ronald)          0.00           0.000      0.0000    Jack   Ronald\n",
       "36   (Ronald, Pete)          0.00           0.000      0.0000  Ronald     Pete\n",
       "37   (Ronald, Elle)          0.50           0.500      0.5000  Ronald     Elle\n",
       "38     (Mark, Pete)          0.00           0.000      0.0000    Mark     Pete\n",
       "39     (Mark, Elle)          0.50           0.500      0.5000    Mark     Elle\n",
       "40    (Steve, Pete)          0.25           0.000      0.1250   Steve     Pete"
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#taking the names out of the index into separate columns\n",
    "first_person = []\n",
    "second_person = []\n",
    "for el in scaled_df2.index:\n",
    "    first_person.append(el[0])\n",
    "    second_person.append(el[1])\n",
    "    \n",
    "first_person = pd.Series(first_person)\n",
    "second_person = pd.Series(second_person)\n",
    "\n",
    "scaled_df2.reset_index(inplace=True)\n",
    "scaled_df2 = pd.concat([scaled_df2, first_person.rename('first_p'), second_person.rename('second_p')], axis=1)\n",
    "scaled_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "unindent does not match any outer indentation level (<ipython-input-460-cfb33f5ea8c1>, line 7)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-460-cfb33f5ea8c1>\"\u001b[0;36m, line \u001b[0;32m7\u001b[0m\n\u001b[0;31m    result_df = scaled_df2[(scaled_df2.first_p == name) | (scaled_df2.second_p == name)].sort_values('mean_score', ascending=False)\u001b[0m\n\u001b[0m                                                                                                                                   ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unindent does not match any outer indentation level\n"
     ]
    }
   ],
   "source": [
    "#adapting function for second df\n",
    "def most_likely_friend2(name):\n",
    "     \"\"\"\n",
    "    Input: String of name of individual\n",
    "    Output: Data Frame containing three columns: Person 1, Person 2, and the mean connection probability score \n",
    "    \"\"\"\n",
    "    result_df = scaled_df2[(scaled_df2.first_p == name) | (scaled_df2.second_p == name)].sort_values('mean_score', ascending=False)\n",
    "    result_df.reset_index(drop=True, inplace=True)\n",
    "        \n",
    "       \n",
    "    result_df = result_df[['first_p','second_p', 'mean_score']]\n",
    "    result_df.columns = ['Person 1', 'Person 2', 'Probability of Connection']\n",
    "\n",
    "    return result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "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>Person 1</th>\n",
       "      <th>Person 2</th>\n",
       "      <th>Probability of Connection</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mark</td>\n",
       "      <td>Elle</td>\n",
       "      <td>0.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Liz</td>\n",
       "      <td>Mark</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jack</td>\n",
       "      <td>Mark</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mike</td>\n",
       "      <td>Mark</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Joana</td>\n",
       "      <td>Mark</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Roger</td>\n",
       "      <td>Mark</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mark</td>\n",
       "      <td>Pete</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Person 1 Person 2  Probability of Connection\n",
       "0     Mark     Elle                      0.500\n",
       "1      Liz     Mark                      0.125\n",
       "2     Jack     Mark                      0.125\n",
       "3     Mike     Mark                      0.000\n",
       "4    Joana     Mark                      0.000\n",
       "5    Roger     Mark                      0.000\n",
       "6     Mark     Pete                      0.000"
      ]
     },
     "execution_count": 457,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_likely_friend2('Mark')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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