Normaldistribution_SDGscores.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ef7790c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b2773d3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"D:\\IIT\\Projects\\DataScienceproject\\DSpro_sustainabledev_working\\Composite Score.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "063e37d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat-0.92, p-0.012286775745451450347900390625\n",
      "not a normal distribution\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import shapiro\n",
    "DataToTest=data[\"SDG 1\"]\n",
    "stat, p=shapiro(DataToTest)\n",
    "print('stat-%.2f, p-%.30f' % (stat,p))\n",
    "\n",
    "if p> 0.05:\n",
    "    print(\"yes normal distribution\")\n",
    "else:\n",
    "    print(\"not a normal distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "12592e4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat-0.99, p-0.967886567115783691406250000000\n",
      "yes normal distribution\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import shapiro\n",
    "DataToTest=data[\"SDG 2\"]\n",
    "stat, p=shapiro(DataToTest)\n",
    "print('stat-%.2f, p-%.30f' % (stat,p))\n",
    "\n",
    "if p> 0.05:\n",
    "    print(\"yes normal distribution\")\n",
    "else:\n",
    "    print(\"not a normal distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "80cd3444",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat-0.98, p-0.588117778301239013671875000000\n",
      "yes normal distribution\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import shapiro\n",
    "DataToTest=data[\"SDG 3\"]\n",
    "stat, p=shapiro(DataToTest)\n",
    "print('stat-%.2f, p-%.30f' % (stat,p))\n",
    "\n",
    "if p> 0.05:\n",
    "    print(\"yes normal distribution\")\n",
    "else:\n",
    "    print(\"not a normal distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "89b52f8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat-0.99, p-0.904253959655761718750000000000\n",
      "yes normal distribution\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import shapiro\n",
    "DataToTest=data[\"SDG 4\"]\n",
    "stat, p=shapiro(DataToTest)\n",
    "print('stat-%.2f, p-%.30f' % (stat,p))\n",
    "\n",
    "if p> 0.05:\n",
    "    print(\"yes normal distribution\")\n",
    "else:\n",
    "    print(\"not a normal distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9a4b3b63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "67.52777777777777\n",
      "11.434562886051294\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist=data.hist('SDG 10',bins=50)\n",
    "mean=np.mean(data['SDG 10'])\n",
    "print(mean)\n",
    "std=np.std(data['SDG 10'])\n",
    "print(std)\n",
    "import math\n",
    "def fx(x,mu,sigma):\n",
    "    return 1/(sigma*np.sqrt(2*np.pi))*np.exp(-np.power((x-mu)/sigma,2)/2)\n",
    "x=np.arange(-10,120,0.001)\n",
    "plt.plot(x,fx(x,mean,std))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a9874706",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat-0.98, p-0.885340034961700439453125000000\n",
      "yes normal distribution\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import shapiro\n",
    "DataToTest=data[\"SDG 5\"]\n",
    "stat, p=shapiro(DataToTest)\n",
    "print('stat-%.2f, p-%.30f' % (stat,p))\n",
    "\n",
    "if p> 0.05:\n",
    "    print(\"yes normal distribution\")\n",
    "else:\n",
    "    print(\"not a normal distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a2d11647",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat-0.88, p-0.001290484331548213958740234375\n",
      "not a normal distribution\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import shapiro\n",
    "DataToTest=data[\"SDG 6\"]\n",
    "stat, p=shapiro(DataToTest)\n",
    "print('stat-%.2f, p-%.30f' % (stat,p))\n",
    "\n",
    "if p> 0.05:\n",
    "    print(\"yes normal distribution\")\n",
    "else:\n",
    "    print(\"not a normal distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0e0aea37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat-0.69, p-0.000000227739832325823954306543\n",
      "not a normal distribution\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import shapiro\n",
    "DataToTest=data[\"SDG 7\"]\n",
    "stat, p=shapiro(DataToTest)\n",
    "print('stat-%.2f, p-%.30f' % (stat,p))\n",
    "\n",
    "if p> 0.05:\n",
    "    print(\"yes normal distribution\")\n",
    "else:\n",
    "    print(\"not a normal distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "16d6862e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat-0.98, p-0.887121379375457763671875000000\n",
      "yes normal distribution\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import shapiro\n",
    "DataToTest=data[\"SDG 8\"]\n",
    "stat, p=shapiro(DataToTest)\n",
    "print('stat-%.2f, p-%.30f' % (stat,p))\n",
    "\n",
    "if p> 0.05:\n",
    "    print(\"yes normal distribution\")\n",
    "else:\n",
    "    print(\"not a normal distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c998a76f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat-0.95, p-0.146500617265701293945312500000\n",
      "yes normal distribution\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import shapiro\n",
    "DataToTest=data[\"SDG 9\"]\n",
    "stat, p=shapiro(DataToTest)\n",
    "print('stat-%.2f, p-%.30f' % (stat,p))\n",
    "\n",
    "if p> 0.05:\n",
    "    print(\"yes normal distribution\")\n",
    "else:\n",
    "    print(\"not a normal distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3de2dc7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat-0.93, p-0.021405844017863273620605468750\n",
      "not a normal distribution\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import shapiro\n",
    "DataToTest=data[\"SDG 10\"]\n",
    "stat, p=shapiro(DataToTest)\n",
    "print('stat-%.2f, p-%.30f' % (stat,p))\n",
    "\n",
    "if p> 0.05:\n",
    "    print(\"yes normal distribution\")\n",
    "else:\n",
    "    print(\"not a normal distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4957b3e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat-0.96, p-0.197570458054542541503906250000\n",
      "yes normal distribution\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import shapiro\n",
    "DataToTest=data[\"SDG 11\"]\n",
    "stat, p=shapiro(DataToTest)\n",
    "print('stat-%.2f, p-%.30f' % (stat,p))\n",
    "\n",
    "if p> 0.05:\n",
    "    print(\"yes normal distribution\")\n",
    "else:\n",
    "    print(\"not a normal distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e16ed342",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat-0.97, p-0.560765802860260009765625000000\n",
      "yes normal distribution\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import shapiro\n",
    "DataToTest=data[\"SDG 12\"]\n",
    "stat, p=shapiro(DataToTest)\n",
    "print('stat-%.2f, p-%.30f' % (stat,p))\n",
    "\n",
    "if p> 0.05:\n",
    "    print(\"yes normal distribution\")\n",
    "else:\n",
    "    print(\"not a normal distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4b1d216b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat-0.91, p-0.005640229675918817520141601562\n",
      "not a normal distribution\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import shapiro\n",
    "DataToTest=data[\"SDG 13\"]\n",
    "stat, p=shapiro(DataToTest)\n",
    "print('stat-%.2f, p-%.30f' % (stat,p))\n",
    "\n",
    "if p> 0.05:\n",
    "    print(\"yes normal distribution\")\n",
    "else:\n",
    "    print(\"not a normal distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "86b1f7ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat-0.98, p-0.743561863899230957031250000000\n",
      "yes normal distribution\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import shapiro\n",
    "DataToTest=data[\"SDG 15\"]\n",
    "stat, p=shapiro(DataToTest)\n",
    "print('stat-%.2f, p-%.30f' % (stat,p))\n",
    "\n",
    "if p> 0.05:\n",
    "    print(\"yes normal distribution\")\n",
    "else:\n",
    "    print(\"not a normal distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0bca0304",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat-0.94, p-0.043810945004224777221679687500\n",
      "not a normal distribution\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import shapiro\n",
    "DataToTest=data[\"SDG 16\"]\n",
    "stat, p=shapiro(DataToTest)\n",
    "print('stat-%.2f, p-%.30f' % (stat,p))\n",
    "\n",
    "if p> 0.05:\n",
    "    print(\"yes normal distribution\")\n",
    "else:\n",
    "    print(\"not a normal distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2ee73ce",
   "metadata": {},
   "source": [
    "# SDG 2,3,4,5,8,9,11,12,15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1de536b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55.47222222222222\n",
      "16.306927824140523\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#normalised graphs for SDGs:\n",
    "hist=data.hist('SDG 10',bins=50)\n",
    "mean=np.mean(data['SDG 2'])\n",
    "print(mean)\n",
    "std=np.std(data['SDG 2'])\n",
    "print(std)\n",
    "import math\n",
    "def fx(x,mu,sigma):\n",
    "    return 1/(sigma*np.sqrt(2*np.pi))*np.exp(-np.power((x-mu)/sigma,2)/2)\n",
    "x=np.arange(-10,120,0.001)\n",
    "plt.plot(x,fx(x,mean,std))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "03168712",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55.47222222222222\n",
      "16.306927824140523\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist=data.hist('SDG 2',bins=30)\n",
    "import seaborn as sns\n",
    "sns.displot(data['SDG 2'],color='blue')\n",
    "mean=np.mean(data['SDG 2'])\n",
    "print(mean)\n",
    "std=np.std(data['SDG 2'])\n",
    "print(std)\n",
    "import math\n",
    "def fx(x,mu,sigma):\n",
    "    return 1/(sigma*np.sqrt(2*np.pi))*np.exp(-np.power((x-mu)/sigma,2)/2)\n",
    "x=np.arange(-10,120,0.001)\n",
    "plt.plot(x,fx(x,mean,std))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "bc93efd3",
   "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>SDG 1</th>\n",
       "      <th>SDG 2</th>\n",
       "      <th>SDG 3</th>\n",
       "      <th>SDG 4</th>\n",
       "      <th>SDG 5</th>\n",
       "      <th>SDG 6</th>\n",
       "      <th>SDG 7</th>\n",
       "      <th>SDG 8</th>\n",
       "      <th>SDG 9</th>\n",
       "      <th>SDG 10</th>\n",
       "      <th>SDG 11</th>\n",
       "      <th>SDG 12</th>\n",
       "      <th>SDG 13</th>\n",
       "      <th>SDG 15</th>\n",
       "      <th>SDG 16</th>\n",
       "      <th>Composite Score</th>\n",
       "      <th>Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66.500000</td>\n",
       "      <td>55.472222</td>\n",
       "      <td>71.805556</td>\n",
       "      <td>57.111111</td>\n",
       "      <td>50.111111</td>\n",
       "      <td>84.555556</td>\n",
       "      <td>92.333333</td>\n",
       "      <td>59.833333</td>\n",
       "      <td>48.138889</td>\n",
       "      <td>67.527778</td>\n",
       "      <td>72.222222</td>\n",
       "      <td>74.333333</td>\n",
       "      <td>53.277778</td>\n",
       "      <td>64.527778</td>\n",
       "      <td>72.583333</td>\n",
       "      <td>66.055556</td>\n",
       "      <td>11.638889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.456832</td>\n",
       "      <td>16.538243</td>\n",
       "      <td>7.645052</td>\n",
       "      <td>12.101423</td>\n",
       "      <td>9.662232</td>\n",
       "      <td>10.656841</td>\n",
       "      <td>12.125534</td>\n",
       "      <td>9.060117</td>\n",
       "      <td>14.552668</td>\n",
       "      <td>11.596763</td>\n",
       "      <td>14.178678</td>\n",
       "      <td>12.897397</td>\n",
       "      <td>15.331780</td>\n",
       "      <td>13.635010</td>\n",
       "      <td>7.969137</td>\n",
       "      <td>5.980657</td>\n",
       "      <td>8.394395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>59.750000</td>\n",
       "      <td>43.750000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>48.750000</td>\n",
       "      <td>44.500000</td>\n",
       "      <td>82.750000</td>\n",
       "      <td>84.500000</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>36.750000</td>\n",
       "      <td>64.750000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>65.750000</td>\n",
       "      <td>43.750000</td>\n",
       "      <td>57.500000</td>\n",
       "      <td>69.750000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>69.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>57.500000</td>\n",
       "      <td>50.500000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>59.500000</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>67.500000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>76.500000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>79.250000</td>\n",
       "      <td>66.750000</td>\n",
       "      <td>77.250000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>90.250000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>65.250000</td>\n",
       "      <td>60.250000</td>\n",
       "      <td>72.500000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>63.500000</td>\n",
       "      <td>72.250000</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>70.250000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>86.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           SDG 1      SDG 2      SDG 3      SDG 4      SDG 5       SDG 6  \\\n",
       "count  36.000000  36.000000  36.000000  36.000000  36.000000   36.000000   \n",
       "mean   66.500000  55.472222  71.805556  57.111111  50.111111   84.555556   \n",
       "std    14.456832  16.538243   7.645052  12.101423   9.662232   10.656841   \n",
       "min    32.000000  19.000000  59.000000  29.000000  25.000000   54.000000   \n",
       "25%    59.750000  43.750000  67.000000  48.750000  44.500000   82.750000   \n",
       "50%    69.000000  53.000000  71.000000  57.500000  50.500000   87.000000   \n",
       "75%    79.250000  66.750000  77.250000  64.000000  58.000000   90.250000   \n",
       "max    86.000000  97.000000  90.000000  80.000000  68.000000  100.000000   \n",
       "\n",
       "            SDG 7      SDG 8      SDG 9      SDG 10     SDG 11     SDG 12  \\\n",
       "count   36.000000  36.000000  36.000000   36.000000  36.000000  36.000000   \n",
       "mean    92.333333  59.833333  48.138889   67.527778  72.222222  74.333333   \n",
       "std     12.125534   9.060117  14.552668   11.596763  14.178678  12.897397   \n",
       "min     50.000000  36.000000  23.000000   41.000000  39.000000  47.000000   \n",
       "25%     84.500000  53.750000  36.750000   64.750000  64.000000  65.750000   \n",
       "50%    100.000000  59.500000  46.500000   67.500000  76.000000  76.500000   \n",
       "75%    100.000000  65.250000  60.250000   72.500000  81.000000  82.000000   \n",
       "max    100.000000  78.000000  72.000000  100.000000  98.000000  99.000000   \n",
       "\n",
       "          SDG 13     SDG 15     SDG 16  Composite Score       Rank  \n",
       "count  36.000000  36.000000  36.000000        36.000000  36.000000  \n",
       "mean   53.277778  64.527778  72.583333        66.055556  11.638889  \n",
       "std    15.331780  13.635010   7.969137         5.980657   8.394395  \n",
       "min    16.000000  27.000000  46.000000        52.000000   1.000000  \n",
       "25%    43.750000  57.500000  69.750000        61.000000   4.000000  \n",
       "50%    58.000000  64.000000  73.000000        67.000000  10.000000  \n",
       "75%    63.500000  72.250000  77.500000        70.250000  19.000000  \n",
       "max    77.000000  93.000000  86.000000        79.000000  28.000000  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "801f0c1d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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