ranking_table.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>NDR</th>\n",
       "      <th>TR</th>\n",
       "      <th>BC</th>\n",
       "      <th>CNC</th>\n",
       "      <th>DC</th>\n",
       "      <th>EC</th>\n",
       "      <th>PR</th>\n",
       "      <th>TC</th>\n",
       "      <th>PI</th>\n",
       "      <th>CC</th>\n",
       "      <th>TPN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   NDR  TR  BC  CNC  DC  EC  PR  TC  PI  CC   TPN\n",
       "0    0   0   0    0  33  33  33   0   0   0   0.0\n",
       "1   33  33  33    2   0   0   0  33  33  33  33.0\n",
       "2   32  32  32   33  32   2  32  32  32   2  32.0\n",
       "3    1   1   2   31   2  32   1   1   1  31   2.0\n",
       "4    2   2  31    8   1   1   2   2   2  32   NaN"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#concatinate all the measures together.\n",
    "cent = pd.read_csv('result/kc--/cent/Top_cent_kc.csv')\n",
    "ds = pd.read_csv('result/kc--/ds_Ranking_top_kc.csv')\n",
    "Top_three = pd.read_csv('result/kc--/propose/top_rank_table_kc.csv')\n",
    "all_in = pd.concat([ds,cent,Top_three], axis=1, sort=False)\n",
    "\n",
    "Top_Five_all_in = pd.DataFrame(all_in.loc[0:4])\n",
    "ranking_table_all_in = pd.concat([Top_Five_all_in['NDR'],Top_Five_all_in['TR'], Top_Five_all_in['BC'], Top_Five_all_in['CNC'],\n",
    "                                  Top_Five_all_in['DC'], Top_Five_all_in['EC'],Top_Five_all_in['PR'],\n",
    "                                  Top_Five_all_in['TC'], \n",
    "                                  Top_Five_all_in['PI'], Top_Five_all_in['CC'],Top_Five_all_in['TPN']], \n",
    "                                 axis=1, sort=False)\n",
    "\n",
    "#convert column nodes_TPN to int\n",
    "#ranking_table_all_in= ranking_table_all_in.astype(int)\n",
    "#print(ranking_table_all_in)\n",
    "ranking_table_all_in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ranking_table_all_in.to_csv('result/kc--/ranking_table_kc.csv')#save top three ranking of all the 11 measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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