sandbox.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "Looking at data here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>Type</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>visitorfi5kl7q7i</td>\n",
       "      <td>zpffh4wwhuu3ndxm</td>\n",
       "      <td>Directed</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>visitorfi5kl7q7i</td>\n",
       "      <td>toristfgqiroaded</td>\n",
       "      <td>Directed</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>visitorfi5kl7q7i</td>\n",
       "      <td>x3nelbld33llasqv</td>\n",
       "      <td>Directed</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>visitorfi5kl7q7i</td>\n",
       "      <td>bzreqoyjwop3dbtn</td>\n",
       "      <td>Directed</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>visitorfi5kl7q7i</td>\n",
       "      <td>deaddropinli5cme</td>\n",
       "      <td>Directed</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20327</td>\n",
       "      <td>mihl4p6zv57y2e53</td>\n",
       "      <td>3g2upl4pq6kufc4m</td>\n",
       "      <td>Directed</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20328</td>\n",
       "      <td>jjrbfzv3f2vff7fb</td>\n",
       "      <td>fhostingesps6bly</td>\n",
       "      <td>Directed</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20329</td>\n",
       "      <td>fbr3urtao5c4zecb</td>\n",
       "      <td>fhostingesps6bly</td>\n",
       "      <td>Directed</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20330</td>\n",
       "      <td>grams7hm4x3gb7wj</td>\n",
       "      <td>toradsc6vvmtugty</td>\n",
       "      <td>Directed</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20331</td>\n",
       "      <td>grams6qu7rkusupi</td>\n",
       "      <td>toradsc6vvmtugty</td>\n",
       "      <td>Directed</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20332 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Source            Target      Type  Weight\n",
       "0      visitorfi5kl7q7i  zpffh4wwhuu3ndxm  Directed       1\n",
       "1      visitorfi5kl7q7i  toristfgqiroaded  Directed       1\n",
       "2      visitorfi5kl7q7i  x3nelbld33llasqv  Directed       1\n",
       "3      visitorfi5kl7q7i  bzreqoyjwop3dbtn  Directed       1\n",
       "4      visitorfi5kl7q7i  deaddropinli5cme  Directed       1\n",
       "...                 ...               ...       ...     ...\n",
       "20327  mihl4p6zv57y2e53  3g2upl4pq6kufc4m  Directed       1\n",
       "20328  jjrbfzv3f2vff7fb  fhostingesps6bly  Directed       2\n",
       "20329  fbr3urtao5c4zecb  fhostingesps6bly  Directed       1\n",
       "20330  grams7hm4x3gb7wj  toradsc6vvmtugty  Directed       1\n",
       "20331  grams6qu7rkusupi  toradsc6vvmtugty  Directed       1\n",
       "\n",
       "[20332 rows x 4 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.facecolor'] = 'white'\n",
    "plt.rcParams['axes.linewidth'] = 1 #set the value globally\n",
    "%matplotlib inline\n",
    "\n",
    "data = pd.read_csv('data/edges-spokeless.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "\n",
    "temp = []\n",
    "for index, row in data.iterrows():\n",
    "    a,b,weight = row['Source'], row['Target'], row['Weight']\n",
    "    temp.append((a,b,weight))\n",
    "    \n",
    "G = nx.DiGraph()\n",
    "G.add_weighted_edges_from(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kins, kouts = [], []\n",
    "for node in G.nodes():\n",
    "    kin = G.in_degree(node)\n",
    "    kout = G.out_degree(node)\n",
    "    kins.append(kin)\n",
    "    kouts.append(kout)\n",
    "plt.plot(kins,kouts,'.')\n",
    "plt.xlabel('in degree',fontsize=12)\n",
    "plt.ylabel('out degree',fontsize=12)\n",
    "plt.xlim([0,60])\n",
    "plt.ylim([0,500])\n",
    "plt.savefig('scatter-plot-kin-kout.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show average\n",
    "d = {}\n",
    "for kin,kout in zip(kins,kouts):\n",
    "    if kin not in d:\n",
    "        d[kin] = (kout,1)\n",
    "    else:\n",
    "        t1,t2 = d[kin]\n",
    "        d[kin] = (t1+kout, t2+1)\n",
    "        \n",
    "d1 = {key:1.*(val)[0]/val[1] for key,val in d.items()}\n",
    "plt.plot(d1.keys(),d1.values(),'.')\n",
    "plt.xlabel('in degree',fontsize=12)\n",
    "plt.ylabel('mean out degree',fontsize=12)\n",
    "plt.savefig('scatter-plot-kin-mean-kout.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'out degree')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,3), linewidth=100)\n",
    "plt.subplot(121)\n",
    "\n",
    "#show average\n",
    "d = {}\n",
    "for kin,kout in zip(kins,kouts):\n",
    "    if kin not in d:\n",
    "        d[kin] = (kout,1)\n",
    "    else:\n",
    "        t1,t2 = d[kin]\n",
    "        d[kin] = (t1+kout, t2+1)\n",
    "        \n",
    "d1 = {key:1.*(val)[0]/val[1] for key,val in d.items()}\n",
    "plt.plot(d1.keys(),d1.values(),'.')\n",
    "plt.xlabel('in degree',fontsize=12)\n",
    "plt.ylabel('mean out degree',fontsize=12)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(kins,kouts,'.')\n",
    "plt.xlabel('in degree',fontsize=12)\n",
    "plt.ylabel('out degree',fontsize=12)\n",
    "#plt.savefig('scatter-plot-kin-kout.pdf')"
   ]
  }
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