{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lab06"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load libraries\n",
    "import pandas as pd\n",
    "#pd.set_option('display.max_rows', 500)\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>price2018</th>\n",
       "      <th>price2000</th>\n",
       "      <th>purchacingPower2017</th>\n",
       "      <th>crime2019</th>\n",
       "      <th>crime1993</th>\n",
       "      <th>wage2018</th>\n",
       "      <th>IMT2018percapita</th>\n",
       "      <th>IMI2018percapita</th>\n",
       "      <th>Waste2018</th>\n",
       "      <th>WasteSel2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>108016.0</td>\n",
       "      <td>53344</td>\n",
       "      <td>100.0</td>\n",
       "      <td>32.6</td>\n",
       "      <td>30.8</td>\n",
       "      <td>1166.9</td>\n",
       "      <td>97.6</td>\n",
       "      <td>146.8</td>\n",
       "      <td>0.512959</td>\n",
       "      <td>0.109483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Continente</td>\n",
       "      <td>109778.0</td>\n",
       "      <td>53935</td>\n",
       "      <td>100.7</td>\n",
       "      <td>32.1</td>\n",
       "      <td>30.9</td>\n",
       "      <td>1170.3</td>\n",
       "      <td>100.3</td>\n",
       "      <td>149.2</td>\n",
       "      <td>0.510957</td>\n",
       "      <td>0.107298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Norte</td>\n",
       "      <td>80153.0</td>\n",
       "      <td>47145</td>\n",
       "      <td>92.1</td>\n",
       "      <td>28.1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1056.6</td>\n",
       "      <td>55.0</td>\n",
       "      <td>120.2</td>\n",
       "      <td>0.469846</td>\n",
       "      <td>0.081176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alto Minho</td>\n",
       "      <td>50090.0</td>\n",
       "      <td>26874</td>\n",
       "      <td>79.7</td>\n",
       "      <td>28.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>978.1</td>\n",
       "      <td>36.6</td>\n",
       "      <td>115.1</td>\n",
       "      <td>0.469507</td>\n",
       "      <td>0.080048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arcos de Valdevez</td>\n",
       "      <td>30437.0</td>\n",
       "      <td>22576</td>\n",
       "      <td>67.8</td>\n",
       "      <td>26.5</td>\n",
       "      <td>16.2</td>\n",
       "      <td>882.1</td>\n",
       "      <td>20.2</td>\n",
       "      <td>96.9</td>\n",
       "      <td>0.396206</td>\n",
       "      <td>0.052423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Region  price2018  price2000  purchacingPower2017  crime2019  \\\n",
       "0           Portugal   108016.0      53344                100.0       32.6   \n",
       "1         Continente   109778.0      53935                100.7       32.1   \n",
       "2              Norte    80153.0      47145                 92.1       28.1   \n",
       "3         Alto Minho    50090.0      26874                 79.7       28.7   \n",
       "4  Arcos de Valdevez    30437.0      22576                 67.8       26.5   \n",
       "\n",
       "   crime1993  wage2018  IMT2018percapita  IMI2018percapita  Waste2018  \\\n",
       "0       30.8    1166.9              97.6             146.8   0.512959   \n",
       "1       30.9    1170.3             100.3             149.2   0.510957   \n",
       "2       23.0    1056.6              55.0             120.2   0.469846   \n",
       "3       18.0     978.1              36.6             115.1   0.469507   \n",
       "4       16.2     882.1              20.2              96.9   0.396206   \n",
       "\n",
       "   WasteSel2018  \n",
       "0      0.109483  \n",
       "1      0.107298  \n",
       "2      0.081176  \n",
       "3      0.080048  \n",
       "4      0.052423  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_excel('realEstate.xlsx')\n",
    "\n",
    "df=df.dropna()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>price2018</th>\n",
       "      <th>price2000</th>\n",
       "      <th>purchacingPower2017</th>\n",
       "      <th>crime2019</th>\n",
       "      <th>crime1993</th>\n",
       "      <th>wage2018</th>\n",
       "      <th>IMT2018percapita</th>\n",
       "      <th>IMI2018percapita</th>\n",
       "      <th>Waste2018</th>\n",
       "      <th>WasteSel2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>108016.0</td>\n",
       "      <td>53344</td>\n",
       "      <td>100.0</td>\n",
       "      <td>32.6</td>\n",
       "      <td>30.8</td>\n",
       "      <td>1166.9</td>\n",
       "      <td>97.6</td>\n",
       "      <td>146.8</td>\n",
       "      <td>0.512959</td>\n",
       "      <td>0.109483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Continente</td>\n",
       "      <td>109778.0</td>\n",
       "      <td>53935</td>\n",
       "      <td>100.7</td>\n",
       "      <td>32.1</td>\n",
       "      <td>30.9</td>\n",
       "      <td>1170.3</td>\n",
       "      <td>100.3</td>\n",
       "      <td>149.2</td>\n",
       "      <td>0.510957</td>\n",
       "      <td>0.107298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Norte</td>\n",
       "      <td>80153.0</td>\n",
       "      <td>47145</td>\n",
       "      <td>92.1</td>\n",
       "      <td>28.1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1056.6</td>\n",
       "      <td>55.0</td>\n",
       "      <td>120.2</td>\n",
       "      <td>0.469846</td>\n",
       "      <td>0.081176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alto Minho</td>\n",
       "      <td>50090.0</td>\n",
       "      <td>26874</td>\n",
       "      <td>79.7</td>\n",
       "      <td>28.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>978.1</td>\n",
       "      <td>36.6</td>\n",
       "      <td>115.1</td>\n",
       "      <td>0.469507</td>\n",
       "      <td>0.080048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arcos de Valdevez</td>\n",
       "      <td>30437.0</td>\n",
       "      <td>22576</td>\n",
       "      <td>67.8</td>\n",
       "      <td>26.5</td>\n",
       "      <td>16.2</td>\n",
       "      <td>882.1</td>\n",
       "      <td>20.2</td>\n",
       "      <td>96.9</td>\n",
       "      <td>0.396206</td>\n",
       "      <td>0.052423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>Ribeira Brava</td>\n",
       "      <td>34080.0</td>\n",
       "      <td>17867</td>\n",
       "      <td>68.4</td>\n",
       "      <td>25.9</td>\n",
       "      <td>22.4</td>\n",
       "      <td>895.3</td>\n",
       "      <td>29.6</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.426618</td>\n",
       "      <td>0.098271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>Santa Cruz</td>\n",
       "      <td>95380.0</td>\n",
       "      <td>68514</td>\n",
       "      <td>71.5</td>\n",
       "      <td>14.3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1084.3</td>\n",
       "      <td>46.7</td>\n",
       "      <td>104.6</td>\n",
       "      <td>0.398313</td>\n",
       "      <td>0.088227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>Santana</td>\n",
       "      <td>25299.0</td>\n",
       "      <td>15434</td>\n",
       "      <td>58.3</td>\n",
       "      <td>19.9</td>\n",
       "      <td>26.5</td>\n",
       "      <td>862.4</td>\n",
       "      <td>8.6</td>\n",
       "      <td>62.6</td>\n",
       "      <td>0.408434</td>\n",
       "      <td>0.087468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>São Vicente</td>\n",
       "      <td>45500.0</td>\n",
       "      <td>20604</td>\n",
       "      <td>61.0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>46.2</td>\n",
       "      <td>854.1</td>\n",
       "      <td>24.3</td>\n",
       "      <td>87.3</td>\n",
       "      <td>0.388295</td>\n",
       "      <td>0.086720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>Porto Santo</td>\n",
       "      <td>74163.0</td>\n",
       "      <td>49418</td>\n",
       "      <td>93.5</td>\n",
       "      <td>31.8</td>\n",
       "      <td>24.4</td>\n",
       "      <td>1142.9</td>\n",
       "      <td>57.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>1.165898</td>\n",
       "      <td>0.707036</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>341 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Region  price2018  price2000  purchacingPower2017  crime2019  \\\n",
       "0             Portugal   108016.0      53344                100.0       32.6   \n",
       "1           Continente   109778.0      53935                100.7       32.1   \n",
       "2                Norte    80153.0      47145                 92.1       28.1   \n",
       "3           Alto Minho    50090.0      26874                 79.7       28.7   \n",
       "4    Arcos de Valdevez    30437.0      22576                 67.8       26.5   \n",
       "..                 ...        ...        ...                  ...        ...   \n",
       "347      Ribeira Brava    34080.0      17867                 68.4       25.9   \n",
       "348         Santa Cruz    95380.0      68514                 71.5       14.3   \n",
       "349            Santana    25299.0      15434                 58.3       19.9   \n",
       "350        São Vicente    45500.0      20604                 61.0       17.3   \n",
       "352        Porto Santo    74163.0      49418                 93.5       31.8   \n",
       "\n",
       "     crime1993  wage2018  IMT2018percapita  IMI2018percapita  Waste2018  \\\n",
       "0         30.8    1166.9              97.6             146.8   0.512959   \n",
       "1         30.9    1170.3             100.3             149.2   0.510957   \n",
       "2         23.0    1056.6              55.0             120.2   0.469846   \n",
       "3         18.0     978.1              36.6             115.1   0.469507   \n",
       "4         16.2     882.1              20.2              96.9   0.396206   \n",
       "..         ...       ...               ...               ...        ...   \n",
       "347       22.4     895.3              29.6              63.0   0.426618   \n",
       "348       25.0    1084.3              46.7             104.6   0.398313   \n",
       "349       26.5     862.4               8.6              62.6   0.408434   \n",
       "350       46.2     854.1              24.3              87.3   0.388295   \n",
       "352       24.4    1142.9              57.0             285.0   1.165898   \n",
       "\n",
       "     WasteSel2018  \n",
       "0        0.109483  \n",
       "1        0.107298  \n",
       "2        0.081176  \n",
       "3        0.080048  \n",
       "4        0.052423  \n",
       "..            ...  \n",
       "347      0.098271  \n",
       "348      0.088227  \n",
       "349      0.087468  \n",
       "350      0.086720  \n",
       "352      0.707036  \n",
       "\n",
       "[341 rows x 11 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price2018              float64\n",
       "price2000                int64\n",
       "purchacingPower2017    float64\n",
       "crime2019              float64\n",
       "crime1993              float64\n",
       "wage2018               float64\n",
       "IMT2018percapita       float64\n",
       "IMI2018percapita       float64\n",
       "Waste2018              float64\n",
       "WasteSel2018           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=df.drop(\"Region\",axis=1)\n",
    "X.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline([('scaling', StandardScaler()), ('pca', PCA(n_components=3))])\n",
    "\n",
    "principalComponents=pipeline.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>1.972826</td>\n",
       "      <td>-1.198204</td>\n",
       "      <td>0.136065</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.582760</td>\n",
       "      <td>-0.974156</td>\n",
       "      <td>-0.041264</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.432546</td>\n",
       "      <td>-0.046321</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.497081</td>\n",
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       "      <td>0.228310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>-1.267692</td>\n",
       "      <td>0.385313</td>\n",
       "      <td>0.391458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>0.053560</td>\n",
       "      <td>-1.485031</td>\n",
       "      <td>-1.389320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>-1.840092</td>\n",
       "      <td>0.506480</td>\n",
       "      <td>0.216991</td>\n",
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       "    <tr>\n",
       "      <th>339</th>\n",
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       "      <td>0.251504</td>\n",
       "      <td>0.456633</td>\n",
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       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>5.802427</td>\n",
       "      <td>3.826544</td>\n",
       "      <td>-1.314745</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>341 rows × 3 columns</p>\n",
       "</div>"
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       "            0         1         2\n",
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       "3   -0.432546 -0.046321  0.237438\n",
       "4   -1.497081  0.214608  0.228310\n",
       "..        ...       ...       ...\n",
       "336 -1.267692  0.385313  0.391458\n",
       "337  0.053560 -1.485031 -1.389320\n",
       "338 -1.840092  0.506480  0.216991\n",
       "339 -1.072990  0.251504  0.456633\n",
       "340  5.802427  3.826544 -1.314745\n",
       "\n",
       "[341 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(principalComponents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.59161215, 0.16106816, 0.06103431])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline[1].explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8137146211505711"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## View the ratio of explained variance\n",
    "sum(pipeline[1].explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "Comp=pd.DataFrame(pipeline[1].components_).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.363071</td>\n",
       "      <td>-0.145598</td>\n",
       "      <td>-0.376953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.314375</td>\n",
       "      <td>-0.360249</td>\n",
       "      <td>-0.312781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.311255</td>\n",
       "      <td>-0.436224</td>\n",
       "      <td>0.167600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.302549</td>\n",
       "      <td>0.205530</td>\n",
       "      <td>0.627603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.322923</td>\n",
       "      <td>-0.053272</td>\n",
       "      <td>0.481302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.210538</td>\n",
       "      <td>-0.522779</td>\n",
       "      <td>0.103148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.340475</td>\n",
       "      <td>0.208544</td>\n",
       "      <td>-0.254828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.343871</td>\n",
       "      <td>0.212630</td>\n",
       "      <td>-0.101260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.325084</td>\n",
       "      <td>0.387359</td>\n",
       "      <td>-0.081231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.304099</td>\n",
       "      <td>0.318753</td>\n",
       "      <td>-0.118401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  0.363071 -0.145598 -0.376953\n",
       "1  0.314375 -0.360249 -0.312781\n",
       "2  0.311255 -0.436224  0.167600\n",
       "3  0.302549  0.205530  0.627603\n",
       "4  0.322923 -0.053272  0.481302\n",
       "5  0.210538 -0.522779  0.103148\n",
       "6  0.340475  0.208544 -0.254828\n",
       "7  0.343871  0.212630 -0.101260\n",
       "8  0.325084  0.387359 -0.081231\n",
       "9  0.304099  0.318753 -0.118401"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "Comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Region', 'price2018', 'price2000', 'purchacingPower2017', 'crime2019',\n",
       "       'crime1993', 'wage2018', 'IMT2018percapita', 'IMI2018percapita',\n",
       "       'Waste2018', 'WasteSel2018'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns[0:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "Comp['variables']=df.columns[1:]\n",
    "Comp=Comp.set_index('variables')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    float64\n",
       "1    float64\n",
       "2    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Comp.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "#T_cf7156a0_924a_11eb_9bef_eef48295f015row0_col0,#T_cf7156a0_924a_11eb_9bef_eef48295f015row1_col0,#T_cf7156a0_924a_11eb_9bef_eef48295f015row1_col1,#T_cf7156a0_924a_11eb_9bef_eef48295f015row2_col0,#T_cf7156a0_924a_11eb_9bef_eef48295f015row2_col1,#T_cf7156a0_924a_11eb_9bef_eef48295f015row3_col0,#T_cf7156a0_924a_11eb_9bef_eef48295f015row3_col1,#T_cf7156a0_924a_11eb_9bef_eef48295f015row4_col0,#T_cf7156a0_924a_11eb_9bef_eef48295f015row4_col2,#T_cf7156a0_924a_11eb_9bef_eef48295f015row5_col0,#T_cf7156a0_924a_11eb_9bef_eef48295f015row6_col0,#T_cf7156a0_924a_11eb_9bef_eef48295f015row6_col2,#T_cf7156a0_924a_11eb_9bef_eef48295f015row7_col0,#T_cf7156a0_924a_11eb_9bef_eef48295f015row7_col1,#T_cf7156a0_924a_11eb_9bef_eef48295f015row8_col0,#T_cf7156a0_924a_11eb_9bef_eef48295f015row8_col1,#T_cf7156a0_924a_11eb_9bef_eef48295f015row8_col2,#T_cf7156a0_924a_11eb_9bef_eef48295f015row9_col0,#T_cf7156a0_924a_11eb_9bef_eef48295f015row9_col2{\n",
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       "            color:  black;\n",
       "        }</style><table id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>    </tr>    <tr>        <th class=\"index_name level0\" >variables</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015level0_row0\" class=\"row_heading level0 row0\" >price2018</th>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row0_col0\" class=\"data row0 col0\" >0.363071</td>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row0_col1\" class=\"data row0 col1\" >-0.145598</td>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row0_col2\" class=\"data row0 col2\" >-0.376953</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015level0_row1\" class=\"row_heading level0 row1\" >IMI2018percapita</th>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row1_col0\" class=\"data row1 col0\" >0.343871</td>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row1_col1\" class=\"data row1 col1\" >0.212630</td>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row1_col2\" class=\"data row1 col2\" >-0.101260</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015level0_row2\" class=\"row_heading level0 row2\" >IMT2018percapita</th>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row2_col0\" class=\"data row2 col0\" >0.340475</td>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row2_col1\" class=\"data row2 col1\" >0.208544</td>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row2_col2\" class=\"data row2 col2\" >-0.254828</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015level0_row3\" class=\"row_heading level0 row3\" >Waste2018</th>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row3_col0\" class=\"data row3 col0\" >0.325084</td>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row3_col1\" class=\"data row3 col1\" >0.387359</td>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row3_col2\" class=\"data row3 col2\" >-0.081231</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015level0_row4\" class=\"row_heading level0 row4\" >crime1993</th>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row4_col0\" class=\"data row4 col0\" >0.322923</td>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row4_col1\" class=\"data row4 col1\" >-0.053272</td>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row4_col2\" class=\"data row4 col2\" >0.481302</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015level0_row5\" class=\"row_heading level0 row5\" >price2000</th>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row5_col0\" class=\"data row5 col0\" >0.314375</td>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row5_col1\" class=\"data row5 col1\" >-0.360249</td>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row5_col2\" class=\"data row5 col2\" >-0.312781</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015level0_row6\" class=\"row_heading level0 row6\" >purchacingPower2017</th>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row6_col0\" class=\"data row6 col0\" >0.311255</td>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row6_col1\" class=\"data row6 col1\" >-0.436224</td>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row6_col2\" class=\"data row6 col2\" >0.167600</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015level0_row7\" class=\"row_heading level0 row7\" >WasteSel2018</th>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row7_col0\" class=\"data row7 col0\" >0.304099</td>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row7_col1\" class=\"data row7 col1\" >0.318753</td>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row7_col2\" class=\"data row7 col2\" >-0.118401</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015level0_row8\" class=\"row_heading level0 row8\" >crime2019</th>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row8_col0\" class=\"data row8 col0\" >0.302549</td>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row8_col1\" class=\"data row8 col1\" >0.205530</td>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row8_col2\" class=\"data row8 col2\" >0.627603</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015level0_row9\" class=\"row_heading level0 row9\" >wage2018</th>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row9_col0\" class=\"data row9 col0\" >0.210538</td>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row9_col1\" class=\"data row9 col1\" >-0.522779</td>\n",
       "                        <td id=\"T_cf7156a0_924a_11eb_9bef_eef48295f015row9_col2\" class=\"data row9 col2\" >0.103148</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x244e68f2760>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "def moreThan01(val):\n",
    "    color = 'red' if val > 0.1 else 'black'\n",
    "    return 'color: %s' % color\n",
    "\n",
    "Comp1=Comp.sort_values(by=[0, 1, 2], ascending=False)\n",
    "\n",
    "s = Comp1.style.applymap(moreThan01)\n",
    "s\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "from numpy.linalg import svd\n",
    "def varimax(Phi, gamma = 1.0, q = 20, tol = 1e-6):\n",
    "    from scipy.linalg import svd\n",
    "    p,k = Phi.shape\n",
    "    R = np.eye(k)\n",
    "    d=0\n",
    "    for i in range(q):\n",
    "        d_old = d\n",
    "        Lambda = np.dot(Phi, R)\n",
    "        u,s,vh = svd(np.dot(Phi.T,np.asarray(Lambda)**3 - (gamma/p) * np.dot(Lambda, np.diag(np.diag(np.dot(Lambda.T,Lambda))))))\n",
    "        R = np.dot(u,vh)\n",
    "        d = np.sum(s)\n",
    "        if d_old!=0 and d/d_old < 1 + tol: break\n",
    "    return np.dot(Phi, R)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "rot_comps = varimax(Comp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rot_comps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "Comp1=pd.DataFrame(rot_comps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.357865</td>\n",
       "      <td>-0.347737</td>\n",
       "      <td>-0.214767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.182294</td>\n",
       "      <td>-0.495110</td>\n",
       "      <td>-0.219267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.074974</td>\n",
       "      <td>-0.522058</td>\n",
       "      <td>0.192601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.045108</td>\n",
       "      <td>0.050224</td>\n",
       "      <td>0.723261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.008642</td>\n",
       "      <td>-0.186997</td>\n",
       "      <td>0.551114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.164236</td>\n",
       "      <td>-0.543472</td>\n",
       "      <td>0.076999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.470298</td>\n",
       "      <td>-0.031651</td>\n",
       "      <td>-0.046571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.405968</td>\n",
       "      <td>-0.019052</td>\n",
       "      <td>0.092405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.473936</td>\n",
       "      <td>0.138197</td>\n",
       "      <td>0.136421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.439762</td>\n",
       "      <td>0.089916</td>\n",
       "      <td>0.081377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  0.357865 -0.347737 -0.214767\n",
       "1  0.182294 -0.495110 -0.219267\n",
       "2 -0.074974 -0.522058  0.192601\n",
       "3  0.045108  0.050224  0.723261\n",
       "4 -0.008642 -0.186997  0.551114\n",
       "5 -0.164236 -0.543472  0.076999\n",
       "6  0.470298 -0.031651 -0.046571\n",
       "7  0.405968 -0.019052  0.092405\n",
       "8  0.473936  0.138197  0.136421\n",
       "9  0.439762  0.089916  0.081377"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Comp1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "Comp1['variables']=df.columns[1:]\n",
    "Comp1s=Comp1.set_index('variables')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                <tr>\n",
       "                        <th id=\"T_cf89635d_924a_11eb_98e0_eef48295f015level0_row0\" class=\"row_heading level0 row0\" >Waste2018</th>\n",
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       "                        <th id=\"T_cf89635d_924a_11eb_98e0_eef48295f015level0_row2\" class=\"row_heading level0 row2\" >WasteSel2018</th>\n",
       "                        <td id=\"T_cf89635d_924a_11eb_98e0_eef48295f015row2_col0\" class=\"data row2 col0\" >0.439762</td>\n",
       "                        <td id=\"T_cf89635d_924a_11eb_98e0_eef48295f015row2_col1\" class=\"data row2 col1\" >0.089916</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_cf89635d_924a_11eb_98e0_eef48295f015level0_row3\" class=\"row_heading level0 row3\" >IMI2018percapita</th>\n",
       "                        <td id=\"T_cf89635d_924a_11eb_98e0_eef48295f015row3_col0\" class=\"data row3 col0\" >0.405968</td>\n",
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       "                        <td id=\"T_cf89635d_924a_11eb_98e0_eef48295f015row3_col2\" class=\"data row3 col2\" >0.092405</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_cf89635d_924a_11eb_98e0_eef48295f015level0_row4\" class=\"row_heading level0 row4\" >price2018</th>\n",
       "                        <td id=\"T_cf89635d_924a_11eb_98e0_eef48295f015row4_col0\" class=\"data row4 col0\" >0.357865</td>\n",
       "                        <td id=\"T_cf89635d_924a_11eb_98e0_eef48295f015row4_col1\" class=\"data row4 col1\" >-0.347737</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_cf89635d_924a_11eb_98e0_eef48295f015level0_row5\" class=\"row_heading level0 row5\" >price2000</th>\n",
       "                        <td id=\"T_cf89635d_924a_11eb_98e0_eef48295f015row5_col0\" class=\"data row5 col0\" >0.182294</td>\n",
       "                        <td id=\"T_cf89635d_924a_11eb_98e0_eef48295f015row5_col1\" class=\"data row5 col1\" >-0.495110</td>\n",
       "                        <td id=\"T_cf89635d_924a_11eb_98e0_eef48295f015row5_col2\" class=\"data row5 col2\" >-0.219267</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_cf89635d_924a_11eb_98e0_eef48295f015level0_row6\" class=\"row_heading level0 row6\" >crime2019</th>\n",
       "                        <td id=\"T_cf89635d_924a_11eb_98e0_eef48295f015row6_col0\" class=\"data row6 col0\" >0.045108</td>\n",
       "                        <td id=\"T_cf89635d_924a_11eb_98e0_eef48295f015row6_col1\" class=\"data row6 col1\" >0.050224</td>\n",
       "                        <td id=\"T_cf89635d_924a_11eb_98e0_eef48295f015row6_col2\" class=\"data row6 col2\" >0.723261</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_cf89635d_924a_11eb_98e0_eef48295f015level0_row7\" class=\"row_heading level0 row7\" >crime1993</th>\n",
       "                        <td id=\"T_cf89635d_924a_11eb_98e0_eef48295f015row7_col0\" class=\"data row7 col0\" >-0.008642</td>\n",
       "                        <td id=\"T_cf89635d_924a_11eb_98e0_eef48295f015row7_col1\" class=\"data row7 col1\" >-0.186997</td>\n",
       "                        <td id=\"T_cf89635d_924a_11eb_98e0_eef48295f015row7_col2\" class=\"data row7 col2\" >0.551114</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_cf89635d_924a_11eb_98e0_eef48295f015level0_row8\" class=\"row_heading level0 row8\" >purchacingPower2017</th>\n",
       "                        <td id=\"T_cf89635d_924a_11eb_98e0_eef48295f015row8_col0\" class=\"data row8 col0\" >-0.074974</td>\n",
       "                        <td id=\"T_cf89635d_924a_11eb_98e0_eef48295f015row8_col1\" class=\"data row8 col1\" >-0.522058</td>\n",
       "                        <td id=\"T_cf89635d_924a_11eb_98e0_eef48295f015row8_col2\" class=\"data row8 col2\" >0.192601</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cf89635d_924a_11eb_98e0_eef48295f015level0_row9\" class=\"row_heading level0 row9\" >wage2018</th>\n",
       "                        <td id=\"T_cf89635d_924a_11eb_98e0_eef48295f015row9_col0\" class=\"data row9 col0\" >-0.164236</td>\n",
       "                        <td id=\"T_cf89635d_924a_11eb_98e0_eef48295f015row9_col1\" class=\"data row9 col1\" >-0.543472</td>\n",
       "                        <td id=\"T_cf89635d_924a_11eb_98e0_eef48295f015row9_col2\" class=\"data row9 col2\" >0.076999</td>\n",
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       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x244fbf09820>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "def moreThan05(val):\n",
    "    color = 'red' if val > 0.35 or val<-0.35 else 'black'\n",
    "    return 'color: %s' % color\n",
    "\n",
    "Comp1s=Comp1s.sort_values(by=[0, 1, 2], ascending=False)\n",
    "\n",
    "s = Comp1s.style.applymap(moreThan05)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Comp1=pd.DataFrame(principalComponents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.972826</td>\n",
       "      <td>-1.198204</td>\n",
       "      <td>0.136065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.005852</td>\n",
       "      <td>-1.252115</td>\n",
       "      <td>0.087374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.582760</td>\n",
       "      <td>-0.974156</td>\n",
       "      <td>-0.041264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.432546</td>\n",
       "      <td>-0.046321</td>\n",
       "      <td>0.237438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.497081</td>\n",
       "      <td>0.214608</td>\n",
       "      <td>0.228310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>-1.267692</td>\n",
       "      <td>0.385313</td>\n",
       "      <td>0.391458</td>\n",
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       "      <th>337</th>\n",
       "      <td>0.053560</td>\n",
       "      <td>-1.485031</td>\n",
       "      <td>-1.389320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>-1.840092</td>\n",
       "      <td>0.506480</td>\n",
       "      <td>0.216991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>-1.072990</td>\n",
       "      <td>0.251504</td>\n",
       "      <td>0.456633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>5.802427</td>\n",
       "      <td>3.826544</td>\n",
       "      <td>-1.314745</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>341 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2\n",
       "0    1.972826 -1.198204  0.136065\n",
       "1    2.005852 -1.252115  0.087374\n",
       "2    0.582760 -0.974156 -0.041264\n",
       "3   -0.432546 -0.046321  0.237438\n",
       "4   -1.497081  0.214608  0.228310\n",
       "..        ...       ...       ...\n",
       "336 -1.267692  0.385313  0.391458\n",
       "337  0.053560 -1.485031 -1.389320\n",
       "338 -1.840092  0.506480  0.216991\n",
       "339 -1.072990  0.251504  0.456633\n",
       "340  5.802427  3.826544 -1.314745\n",
       "\n",
       "[341 rows x 3 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Comp1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\profc\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\cluster\\_kmeans.py:881: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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QtOUiksBihUW6ux/8k/eDwG3u/md3/xowMb6lpYa87AzOmVzKghXb6dJQlIgkqJhhYWYZwfN3AI9GLcvoYX05BnOnl1O3r5WXt+wJuxQRkR7FCot7gMVmdh9wAHgCwMwmEhmKkn5w/pQyMtNNF+iJSMLqNSzc/TvADcCvgbPc/eA4SRrw2fiWljqKhmRy5sQS5i+v5fBXLCKSOGKdDZULLHH3v7p7k5mdaGafB6rc/cWBKTE1zJlWzuZdB1ixbV/YpYiIvEGsYagFwFg4NPT0DDAeuMbM/ju+paWWC6eWkWZoKEpEElKssBjq7quD51cC97j7Z4G5wLy4VpZihuVnc/q4YSzQ1dwikoBihUX0APr5wCMA7t4GaPa7fjZ3ejlr6vezpr4x7FJERI4QKyyWmtkPguMUE4GHAczshHgXlopmT4tcLD9/mXoXIpJYYoXFp4AdRI5bvNPdm4P2qcAP4lhXSiorzOGU0ScwX8ctRCTBxAqLfODv7n6du78S1b6PyMFv6WdzqypYWbuPTTubY68sIjJAYoXFT4GSHtorgR/3fzkypyoyFLVgheaKEpHEESssprv74u6N7v4QMCM+JaW2UcW5VFUWaihKRBJKrLDIPMZlmNkvzazezJZHtX3DzLaa2cvB46KoZV8xszVm9pqZzY5qn2Vmy4JlPzEzi/WhBru5VRW8tGkPtXsPhF2KiAgQOyxWR/9CP8jM5gLrYmz7a2BOD+3/4+4zg8eDwf6mApcB04JtbjGz9GD9W4GrgEnBo6d9JpWDZ0Xd8/zmkCsREYmINXPs9cA/zOwDwJKgrRp4KzEuynP3x81sbB/ruAT4g7u3AuvNbA1wmpltAArd/RkAM7sbuBSY38f9DkoTh+czb0YFP310NSePLOIdJ5WFXZKIpLhYPYuLgU8ATwFjgsdiYIa7v36M73mtmS0NhqmGBm2VQPSf0VuCtsrgeff2HpnZVWZWY2Y1DQ0Nx1heYvj++05m2ohCPnfPS7y2XRfpiUi4YoXFSOC7wPeI9CjagDog9xjf71ZgAjATqAV+GLT3dBzCe2nvkbvf5u7V7l5dWlp6jCUmhiFZ6dx+RTW52Rl88u4X2NXUFnZJIpLCYk1R/gV3fxtQBtwI7AI+Diw3s5VH+2buXufune7eBdwOnBYs2gKMilp1JLAtaB/ZQ3tKqCgawm2Xz6JuXyv/9tsltHVohhURCUesnsVBQ4BCoCh4bAOeO9o3M7OKqJfvBg6eKXU/cJmZZZvZOCIHsp9391qg0czOCM6CugK472jfdzB7y+ihfP99M3h+/S7+877lut+FiISi1wPcZnYbkTOUGomEw9PAze6+O9aOzewe4FygxMy2AF8HzjWzmUSGkjYAnwZw9xVmdi+wEugArnH3zmBXVxM5s2oIkQPbSX1wuyeXzKzk9bpGfrZoLZPLCvj4WePCLklEUkyss6FGA9nAamArkWGhPX3Zsbt/qIfmO3tZ/zvAd3porwGq+vKeyeyGC09kdd1+vv2PlUwYns85kwf3MRkRGVxiHbOYA5zK4UkDbwBeMLOHzeyb8S5ODktLM/7ngzM5sbyQa3//Imvq94ddkoikkJjHLDxiOfAgkSGgp4ic0XRdnGuTbvKyM7j9illkZ6TxybteYE+zzpASkYER6x7cnzOzP5jZZuBxIhfivQa8BygegPqkm5FDc/nF5bPYtqeFz/zuRdo7dYaUiMRfrJ7FWOBPwGnuPt7dL3f3W9z9leD0VwnBrDHF/Nd7pvP02p186+9HfQaziMhR6/UAt7v/v4EqRI7O+2aNZHVdI794fB2Ty/K5/K1jwy5JRJJYX6+zkAT0pTlTeMeU4Xzj7yt5as2OsMsRkSSmsBjE0tOMH102kwmleXzmdy+yfkdT2CWJSJJSWAxyBTmZ3HnlqaQZfOKuF9h7oD3skkQkCSksksCo4lx+/tFZbNrZzLW/f5EOnSElIv1MYZEkTh8/jG9fWsUTq3fwnQdXhV2OiCSZWNN9yCBy2Wmjeb1uP798aj2Tywr40Gmjwy5JRJKEehZJ5saLpnD25FK+9rflPLtuZ9jliEiSUFgkmYz0NP73w29hzLBcrv7tEjbtbA67JBFJAgqLJFSYk8kdV55Kl0fOkGps0RlSInJ8FBZJalxJHrd+5BTW7Wjiuj+8TGeXbpokIsdOYZHE3jaxhG/8yzQefbWe7y54NexyRGQQ09lQSe7yM8awuq6R2x5fx6Th+by/elTsjUREulHPIgV8bd5Uzpw4jK/+dTk1G3aFXY6IDEIKixSQmZ7GLR+eReXQIXz6N0vYsltnSInI0VFYpIii3Exuv6Kats4uPnlXDU2tHWGXJCKDiMIihUwcns/PPnwKr9c1cv0fX6ZLZ0iJSB8pLFLM2ZNL+dq8qTyyso4fPPxa2OWIyCChs6FS0MfeNpbX6xq55bG1TC4r4NK3VIZdkogkOPUsUpCZ8c1/qeL0ccV86c9LeWnT7rBLEpEEp7BIUVkZadz60VmUF+Zw1W+WsG3PgbBLEpEEprBIYcV5WdxxZTUH2jr51N01NLfpDCkR6ZnCIsVNLivgJx+aycrafXzh/17RGVIi0iOFhXD+lDJunHsSDy7bzo8Xrg67HBFJQDobSgD45NvH8XpdIz9euJpJZfnMmzEi7JJEJIGoZyFA5Aypb7+7iuoxQ7nh3ldYumVP2CWJSAJRWMgh2Rnp/PzyWZTkZ/Opu2uo29cSdkkikiAUFnKEkvxs7riymsaWDq66u4aW9s6wSxKRBKCwkDc4qaKQH31wJku37uWLf1qKu86QEkl1cQsLM/ulmdWb2fKotmIze8TMVgc/h0Yt+4qZrTGz18xsdlT7LDNbFiz7iZlZvGqWw945rZwvzj6Rv7+yjXk/fZKFq+oUGiIpLJ49i18Dc7q1fRlY6O6TgIXBa8xsKnAZMC3Y5hYzSw+2uRW4CpgUPLrvU+Lk6nMmcPMHTqaxpYNP3FXDpT97isdeq1doiKSguIWFuz8OdL8t2yXAXcHzu4BLo9r/4O6t7r4eWAOcZmYVQKG7P+OR31B3R20jcWZmvOeUkSy84Ry+994Z7Gxq42O/eoH33vo0T67eodAQSSEDfcyizN1rAYKfw4P2SmBz1HpbgrbK4Hn39h6Z2VVmVmNmNQ0NDf1aeCrLTE/jA6eO4tEbzuW/3j2d7Xtb+Oidz/HB257l2XU7wy5PRAZAohzg7uk4hPfS3iN3v83dq929urS0tN+Kk4isjDQ+fPpoFn3xXL51yTQ27mzistue5cO3P6t7e4skuYEOi7pgaIngZ33QvgUYFbXeSGBb0D6yh3YJUXZGOle8dSyLv3ge/zlvKq/X7ed9P3+Gy+98jhc13blIUhrosLgfuDJ4fiVwX1T7ZWaWbWbjiBzIfj4Yqmo0szOCs6CuiNpGQpaTmc7HzxrHE186j69edBIrtu3jPbc8zb/+6nldAS6SZCxeBynN7B7gXKAEqAO+DvwNuBcYDWwC3u/uu4L1vwp8HOgArnf3+UF7NZEzq4YA84HPeh+Krq6u9pqamn79TNK7ptYO7npmA7c9vo49ze1cOLWM6y+YxLQRRWGXJiJ9ZGZL3L36De3JekaLwiI8jS3t/PqpDdz+xDr2tXQwt6qc6y+YzInlBWGXJiIxKCxkwO090M4vn1zPL59cz/62Di6eXsH1F0xi4nCFhkiiUlhIaPY0t3H7E+v41VMbONDeySUnj+C6CyYzriQv7NJEpBuFhYRuV1Mbv3h8LXc/vZG2zi7e/ZZKPnf+JEYPyw27NBEJKCwkYTQ0tvKLxWv5zbMb6exy3jdrJNeeP5GRQxUaImFTWEjCqd/Xwi2PreX3z23CcT5QPYprz59IRdGQsEsTSVkKC0lYtXsP8LNFa/jjC5sxjA+fPpqrz51AWWFO2KWJpByFhSS8Lbub+dmiNfxfzRbS04yPnjGGfztnAqUF2WGXJpIyFBYyaGza2cxPH13NX17aSma6ceVbx3LV2eMZlq/QEIk3hYUMOut3NPGThau57+WtZGWk8Y4pZVw8o4LzThzOkKz02DsQkaOmsJBBa039fu56egPzl9eyY38buVnpXHBSGfNmVHD25FJyMhUcIv1FYSGDXkdnF8+v38Xfl9ayYHktu5vbKcjO4MKpZcw7uYKzJpaSlZEos+6LDE4KC0kq7Z1dPL12J/9Yuo0Fy7ezr6WDwpwMZk8rZ97JI3jbhGFkpis4RI6WwkKSVltHF0+uaeCBpbU8sqKOxtYOhuZmMqeqnHkzRnD6uGIyFBwifaKwkJTQ0t7J469HguOfq+pobuukJD+LuVUVXDyjglPHFpOe1tMNGEUEFBaSglraO1n0aj0PLK1l4at1tLR3Mbwgm4umVzBvRgWnjB5KmoJD5AgKC0lpTa0dPPpqPQ8s3cai1xpo6+iioiiHi6dHehwzR51A5GaMIqlNYSESaGxp55+r6vjH0loWv95Ae6czcugQLp5RwbzpI6iqLFRwSMpSWIj0YO+Bdh5esZ1/LKvlydU76Ohyxg7LjQTHjBFMKS9QcEhKUViIxLC7qY2HVmzngaW1PL12B10OE0rzuHjGCN41o4JJZbrDnyQ/hYXIUdixv5UFy7fzwNJtPLd+F+5wYlkBF04tY+aoE5gxsojhmhVXkpDCQuQY1e9rYX4QHEs27qYr+CdTXpjD9JFFzKgsYvrIIqZXFmmyQxn0FBYi/aC5rYOV2/axdMtelm3dy9Ite1i3o4mD/4wqTxjCjJFFQYicwPTKIopyM8MtWuQovFlYZIRRjMhglZuVQfXYYqrHFh9qa2xpZ/nWfSzbuudQiMxfvv3Q8jHDcpleWRQJkcoTqKospCBHASKDi8JC5DgV5GTy1gnDeOuEYYfa9ja3R3oeW/ewbMteXtq0hweW1h5aPr40Lxi+ihz/mDaikNws/XOUxKX/O0XioCg3k7MmlXDWpJJDbTv3t7Js616WbdnL0q17eXbdLv728jYA0gwmDs9neuUJh4axplYUavp1SRg6ZiESovp9LcGxj8PHQHbsbwMgPc2YXFZw6AD6jJFFnFheQHaGAkTiRwe4RQYBd2f7vpZIeAQ9kGVb9rC7uR2AzHRjSnkhUysKGV+ax4TSfMaX5jG6OFcz60q/0AFukUHAzKgoGkJF0RBmTysHIgGyZfeBQz2QpVv2sPDVev5Y03pou8x0Y8ywPMaX5DFheP6hEJlQkq+zsaRfKCxEEpyZMao4l1HFuVw0veJQ+97mdtbu2M+6hibWNuxnbf1+1u1oYtFr9bR3Hh4xKMnPYnxpJEAmBL2RCaX5VA4dounapc8UFiKDVFFuJqeMHsopo4ce0d7R2cXm3QdYW7+ftQ2Hw+TgrWgPyspIY9ywvEPDWROG5zG+JNIj0am90p3CQiTJZKSnMa4kj3EleVxA2RHLdjW1sS4qQNY27Oe17Y08vLKOzq7DvZGywmzGlxwOkMjQVh4jioboHiApSmEhkkKK87IozjvyokKI3Jp2064m1gYhcjBM7n95G/taOg6tl5OZxrig9zGhNJ9xJbmMGZbH2GF5DM3N1Ay9SSyUsDCzDUAj0Al0uHu1mRUDfwTGAhuAD7j77mD9rwCfCNb/nLs/FELZIkkrKyONicMLmDj8yJl13Z2dTW3BkFYT64LeyPKte5m/rJaozggFORmMGXYwPHIZU5zHmGG5jC3JY3hBtoJkkAuzZ3Geu++Iev1lYKG732RmXw5e/7uZTQUuA6YBI4B/mtlkd+8c+JJFUouZUZKfTUl+NqePH3bEspb2TrbsbmbjzmY27Gxm484mNu5sZsXWvTy0fDsdUUmSk5l2KDwOB0rk9YgTdKB9MEikYahLgHOD53cBjwH/HrT/wd1bgfVmtgY4DXgmhBpFJJCTmd5jbwQiB9m37Wlhw86mQyGyYWcz63c0sfj1Blo7ug6tm5lujBqay+hhuYcC5GCgjBqaS1aGrh9JBGGFhQMPm5kDv3D324Ayd68FcPdaMxserFsJPBu17ZagTUQSVEZ6GqOHRQIASo9Y1tXl1DW2sGFHM5t2NR3RK6nZsJv9rYePkaQZjDhhyBHDW6OL8xhbksvo4lzNpzWAwvqmz3T3bUEgPGJmr/aybk/90x4vOzezq4CrAEaPHn38VYpIv0tLO3zhYfTki3D4GMnGIECig2T+siNP/QUYXpDNmGG5VJ4whPKiIVQU5QSPIZQX5TAsL0tnb/WTUMLC3bcFP+vN7K9EhpXqzKwi6FVUAPXB6luAUVGbjwS2vcl+bwNug8h0H/GqX0TiI/oYyawxQ9+wfO+BdjbtbD5ieGvjzmaWbNrN9r21R1yMCJCVnkZZUTYVhZHwqCjKCX4eDpaS/GwFSh8MeFiYWR6Q5u6NwfN3At8C7geuBG4Kft4XbHI/8Hszu5nIAe5JwPMDXbeIhK9oSGbkroQji96wrKsr0ivZvreF2r0HqN3bQu3eFrYHz1/ZsocFK1poizpeApCRZpQVRgdJDuVFQxgRFSylBdkpfxA+jJ5FGfDX4DS6DOD37r7AzF4A7jWzTwCbgPcDuPsKM7sXWAl0ANfoTCgR6S4tzSgtyKa0ILvHMIHIMNeuprYgRA6HyvYgWFZs28cjK+uOOAAPkRmAywqyD4VHebfhroqiHIYXZCf1ZI6adVZEJIq7s6e5PRIi+w6wbc/hMNm+L+ix7GnhQPuRf7OmGRTnZVOSn3UotEoLsinNP/x8eEE2pfk5FA7JSNjrTjTrrIhIH5gZQ/OyGJqXxdQRhT2u4+7sO9BBbVR4bN97gIb9bTQ0ttKwv5V1DU00NLbS1tn1hu2z0tMoLcimpFuYRIfL8ILIsZshWYlx/xKFhYjIUTIzinIzKcrNZEp5z4ECh0OlYX8L9Y2tkSAJwuTg8y27m3l58252NrXR00BPQXbG4WCJDpduITMsLyuuw2AKCxGROIkOlZ4uXozW0dnFrqa2SKhEhUl0uKzato/HG1tpjLoW5fB7QXFuZAjsz1e/jbzs/v31rrAQEUkAGelpDC/MYXhhTsx1D7R1smN/6+HeSlS47NzfSm4chq4UFiIig8yQrPRDN8QaKMl7npeIiPQbhYWIiMSksBARkZgUFiIiEpPCQkREYlJYiIhITAoLERGJSWEhIiIxJe2ss2bWAGwMu47jVALsCLuIBKHv4kj6Po6k7+Ow4/0uxrh7affGpA2LZGBmNT1NFZyK9F0cSd/HkfR9HBav70LDUCIiEpPCQkREYlJYJLbbwi4ggei7OJK+jyPp+zgsLt+FjlmIiEhM6lmIiEhMCgsREYlJYZFgzGyUmS0ys1VmtsLMrgu7pkRgZulm9pKZPRB2LWEzsxPM7E9m9mrw/8lbw64pLGb2+eDfyXIzu8fMYt9mLomY2S/NrN7Mlke1FZvZI2a2Ovg5tD/eS2GReDqAG9z9JOAM4BozmxpyTYngOmBV2EUkiB8DC9x9CnAyKfq9mFkl8Dmg2t2rgHTgsnCrGnC/BuZ0a/sysNDdJwELg9fHTWGRYNy91t1fDJ43EvlFUBluVeEys5HAxcAdYdcSNjMrBM4G7gRw9zZ33xNqUeHKAIaYWQaQC2wLuZ4B5e6PA7u6NV8C3BU8vwu4tD/eS2GRwMxsLPAW4LmQSwnbj4AvAV0h15EIxgMNwK+CYbk7zCwv7KLC4O5bgR8Am4BaYK+7PxxuVQmhzN1rIfLHJzC8P3aqsEhQZpYP/Bm43t33hV1PWMxsHlDv7kvCriVBZACnALe6+1uAJvppmGGwCcbiLwHGASOAPDP7aLhVJS+FRQIys0wiQfE7d/9L2PWE7EzgX8xsA/AH4Hwz+224JYVqC7DF3Q/2Nv9EJDxS0QXAendvcPd24C/A20KuKRHUmVkFQPCzvj92qrBIMGZmRMajV7n7zWHXEzZ3/4q7j3T3sUQOXj7q7in716O7bwc2m9mJQdM7gJUhlhSmTcAZZpYb/Lt5Byl6sL+b+4Erg+dXAvf1x04z+mMn0q/OBC4HlpnZy0Hbje7+YHglSYL5LPA7M8sC1gH/GnI9oXD358zsT8CLRM4ifIkUm/bDzO4BzgVKzGwL8HXgJuBeM/sEkUB9f7+8l6b7EBGRWDQMJSIiMSksREQkJoWFiIjEpLAQEZGYFBYiIhKTwkIGJTNzM/th1OsvmNk3+mnfvzaz9/XHvmK8z/uDWWMXxbMuMxtrZh8++gpFDlNYyGDVCrzHzErCLiSamaUfxeqfAD7j7ufFq57AWOCowuIoP4ekAIWFDFYdRC7A+nz3Bd3/Ajez/cHPc81ssZnda2avm9lNZvYRM3vezJaZ2YSo3VxgZk8E680Ltk83s++b2QtmttTMPh2130Vm9ntgWQ/1fCjY/3Iz+27Q9p/AWcDPzez7PWzzpWCbV8zsph6WbzgYlGZWbWaPBc/PMbOXg8dLZlZA5CKttwdtn+/r5zCzPDP7R1DDcjP7YF/+w0hy0hXcMpj9DFhqZt87im1OBk4iMq3zOuAOdz/NIjeZ+ixwfbDeWOAcYAKwyMwmAlcQmdn0VDPLBp4ys4OznJ4GVLn7+ug3M7MRwHeBWcBu4GEzu9Tdv2Vm5wNfcPeabtvMJTKt9Onu3mxmxUfx+b4AXOPuTwWTUbYQmWjwC+5+MPSu6svnMLP3Atvc/eJgu6KjqEOSjHoWMmgFs/HeTeQGOH31QnDPkFZgLXDwl+QyIgFx0L3u3uXuq4mEyhTgncAVwTQszwHDgEnB+s93D4rAqcBjwWR3HcDviNyPojcXAL9y9+bgc3a/X0FvngJuNrPPAScE79ldXz/HMiI9rO+a2dvdfe9R1CFJRmEhg92PiIz9R9/ToYPg/+1ggrmsqGWtUc+7ol53cWRPu/s8OA4Y8Fl3nxk8xkXdP6HpTeqzPn6O7tvEmofn0GcEDt1K1N1vAj4JDAGeNbMpb7L/mJ/D3V8n0iNaBvx3MHQmKUphIYNa8Ff3vUQC46ANRH7JQeR+B5nHsOv3m1lacBxjPPAa8BBwdTCFPGY2uQ83HnoOOMfMSoKDxh8CFsfY5mHg42aWG7xPT8NQGzj8Gd97sNHMJrj7Mnf/LlBDpEfUCBREbdunzxEMoTW7+2+J3GQoVadCF3TMQpLDD4Fro17fDtxnZs8TuQfxm/3V35vXiPxSLwP+zd1bzOwOIkNVLwY9lgZi3LLS3WvN7CvAIiJ/0T/o7r1OGe3uC8xsJlBjZm3Ag8CN3Vb7JnCnmd3IkXdSvN7MzgM6iUxdPp9Ir6nDzF4hcs/mH/fxc0wHvm9mXUA7cHVvdUty06yzIiISk4ahREQkJoWFiIjEpLAQEZGYFBYiIhKTwkJERGJSWIiISEwKCxERien/A6ZC17QozTwJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from matplotlib import pyplot as plt\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    model =KMeans(n_clusters=i, random_state=1)\n",
    "    model.fit(Comp1)\n",
    "    wcss.append(model.inertia_)\n",
    "plt.plot(range(1, 11), wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4299584387809169"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "kmeans_model = KMeans(n_clusters=3, random_state=1).fit(Comp1)\n",
    "labels = kmeans_model.labels_\n",
    "metrics.silhouette_score(Comp1, labels, metric='euclidean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x244ff22c8e0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.scatter(Comp1[0], Comp1[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x24480473640>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.scatter(Comp1[1], Comp1[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x244804c8cd0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.scatter(Comp1[0], Comp1[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(projection='3d')\n",
    "ax.scatter(Comp1[0], Comp1[1], Comp1[2], c='r', marker='o')\n",
    "\n",
    "ax.set_xlabel('X Label')\n",
    "ax.set_ylabel('Y Label')\n",
    "ax.set_zlabel('Z Label')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}