CH6.3 - 클러스터링.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pandas_datareader as dr\n",
    "from sklearn.preprocessing import Normalizer\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "sp500_url = 'https://en.wikipedia.org/wiki/List_of_S&P_500_companies'\n",
    "data_table = pd.read_html(sp500_url)\n",
    "tickers = data_table[0]['Symbol'].tolist()\n",
    "\n",
    "tickers = tickers[0:60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp500_url = 'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies'\n",
    "data_table_security = pd.read_html(sp500_url)\n",
    "security = data_table[0]['Security'].tolist()\n",
    "security = security[0:60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp500_url = 'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies'\n",
    "data_table_security = pd.read_html(sp500_url)\n",
    "sector = data_table[0]['GICS Sector'].tolist()\n",
    "sector = security[0:60]\n",
    "\n",
    "prices_list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60\n",
      "60\n",
      "60\n"
     ]
    }
   ],
   "source": [
    "print(len(tickers))\n",
    "print(len(security))\n",
    "print(len(sector))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>MMM</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ABBV</th>\n",
       "      <th>ABMD</th>\n",
       "      <th>ACN</th>\n",
       "      <th>ATVI</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>AMD</th>\n",
       "      <th>AAP</th>\n",
       "      <th>AES</th>\n",
       "      <th>...</th>\n",
       "      <th>AJG</th>\n",
       "      <th>AIZ</th>\n",
       "      <th>T</th>\n",
       "      <th>ATO</th>\n",
       "      <th>ADSK</th>\n",
       "      <th>ADP</th>\n",
       "      <th>AZO</th>\n",
       "      <th>AVB</th>\n",
       "      <th>AVY</th>\n",
       "      <th>BKR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>155.622498</td>\n",
       "      <td>36.016212</td>\n",
       "      <td>50.757782</td>\n",
       "      <td>112.360001</td>\n",
       "      <td>108.282211</td>\n",
       "      <td>35.534649</td>\n",
       "      <td>103.480003</td>\n",
       "      <td>11.43</td>\n",
       "      <td>167.308853</td>\n",
       "      <td>9.918479</td>\n",
       "      <td>...</td>\n",
       "      <td>47.586002</td>\n",
       "      <td>85.384369</td>\n",
       "      <td>32.314777</td>\n",
       "      <td>67.412842</td>\n",
       "      <td>76.180000</td>\n",
       "      <td>93.929581</td>\n",
       "      <td>791.460022</td>\n",
       "      <td>151.620651</td>\n",
       "      <td>64.486946</td>\n",
       "      <td>39.831497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>155.858475</td>\n",
       "      <td>36.302132</td>\n",
       "      <td>51.473480</td>\n",
       "      <td>115.739998</td>\n",
       "      <td>108.542549</td>\n",
       "      <td>36.232922</td>\n",
       "      <td>104.139999</td>\n",
       "      <td>11.43</td>\n",
       "      <td>168.681808</td>\n",
       "      <td>9.833416</td>\n",
       "      <td>...</td>\n",
       "      <td>48.187191</td>\n",
       "      <td>86.264328</td>\n",
       "      <td>32.126976</td>\n",
       "      <td>67.792641</td>\n",
       "      <td>77.519997</td>\n",
       "      <td>94.074783</td>\n",
       "      <td>793.039978</td>\n",
       "      <td>152.247375</td>\n",
       "      <td>66.325760</td>\n",
       "      <td>39.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>155.325317</td>\n",
       "      <td>36.615711</td>\n",
       "      <td>51.863861</td>\n",
       "      <td>114.809998</td>\n",
       "      <td>106.915443</td>\n",
       "      <td>36.795425</td>\n",
       "      <td>105.910004</td>\n",
       "      <td>11.24</td>\n",
       "      <td>168.564178</td>\n",
       "      <td>9.705822</td>\n",
       "      <td>...</td>\n",
       "      <td>48.296513</td>\n",
       "      <td>86.300613</td>\n",
       "      <td>32.036842</td>\n",
       "      <td>67.458046</td>\n",
       "      <td>76.930000</td>\n",
       "      <td>93.512093</td>\n",
       "      <td>792.000000</td>\n",
       "      <td>153.174500</td>\n",
       "      <td>65.672974</td>\n",
       "      <td>39.414993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>155.779831</td>\n",
       "      <td>37.611813</td>\n",
       "      <td>51.880135</td>\n",
       "      <td>115.419998</td>\n",
       "      <td>108.133453</td>\n",
       "      <td>36.766335</td>\n",
       "      <td>108.300003</td>\n",
       "      <td>11.32</td>\n",
       "      <td>166.357513</td>\n",
       "      <td>10.054584</td>\n",
       "      <td>...</td>\n",
       "      <td>48.405811</td>\n",
       "      <td>87.071747</td>\n",
       "      <td>31.398540</td>\n",
       "      <td>67.069221</td>\n",
       "      <td>79.300003</td>\n",
       "      <td>93.575630</td>\n",
       "      <td>792.909973</td>\n",
       "      <td>154.522278</td>\n",
       "      <td>66.564796</td>\n",
       "      <td>39.176113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>154.940765</td>\n",
       "      <td>37.574924</td>\n",
       "      <td>52.221714</td>\n",
       "      <td>117.110001</td>\n",
       "      <td>106.924728</td>\n",
       "      <td>36.562668</td>\n",
       "      <td>108.570000</td>\n",
       "      <td>11.49</td>\n",
       "      <td>166.259460</td>\n",
       "      <td>9.807898</td>\n",
       "      <td>...</td>\n",
       "      <td>47.731743</td>\n",
       "      <td>85.375290</td>\n",
       "      <td>31.003397</td>\n",
       "      <td>66.201118</td>\n",
       "      <td>79.589996</td>\n",
       "      <td>92.994804</td>\n",
       "      <td>790.340027</td>\n",
       "      <td>152.135773</td>\n",
       "      <td>66.711884</td>\n",
       "      <td>38.735107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   MMM        ABT       ABBV        ABMD         ACN  \\\n",
       "Date                                                                   \n",
       "2017-01-03  155.622498  36.016212  50.757782  112.360001  108.282211   \n",
       "2017-01-04  155.858475  36.302132  51.473480  115.739998  108.542549   \n",
       "2017-01-05  155.325317  36.615711  51.863861  114.809998  106.915443   \n",
       "2017-01-06  155.779831  37.611813  51.880135  115.419998  108.133453   \n",
       "2017-01-09  154.940765  37.574924  52.221714  117.110001  106.924728   \n",
       "\n",
       "                 ATVI        ADBE    AMD         AAP        AES  ...  \\\n",
       "Date                                                             ...   \n",
       "2017-01-03  35.534649  103.480003  11.43  167.308853   9.918479  ...   \n",
       "2017-01-04  36.232922  104.139999  11.43  168.681808   9.833416  ...   \n",
       "2017-01-05  36.795425  105.910004  11.24  168.564178   9.705822  ...   \n",
       "2017-01-06  36.766335  108.300003  11.32  166.357513  10.054584  ...   \n",
       "2017-01-09  36.562668  108.570000  11.49  166.259460   9.807898  ...   \n",
       "\n",
       "                  AJG        AIZ          T        ATO       ADSK        ADP  \\\n",
       "Date                                                                           \n",
       "2017-01-03  47.586002  85.384369  32.314777  67.412842  76.180000  93.929581   \n",
       "2017-01-04  48.187191  86.264328  32.126976  67.792641  77.519997  94.074783   \n",
       "2017-01-05  48.296513  86.300613  32.036842  67.458046  76.930000  93.512093   \n",
       "2017-01-06  48.405811  87.071747  31.398540  67.069221  79.300003  93.575630   \n",
       "2017-01-09  47.731743  85.375290  31.003397  66.201118  79.589996  92.994804   \n",
       "\n",
       "                   AZO         AVB        AVY        BKR  \n",
       "Date                                                      \n",
       "2017-01-03  791.460022  151.620651  64.486946  39.831497  \n",
       "2017-01-04  793.039978  152.247375  66.325760  39.660000  \n",
       "2017-01-05  792.000000  153.174500  65.672974  39.414993  \n",
       "2017-01-06  792.909973  154.522278  66.564796  39.176113  \n",
       "2017-01-09  790.340027  152.135773  66.711884  38.735107  \n",
       "\n",
       "[5 rows x 60 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for ticker in tickers:\n",
    "    try:\n",
    "        prices = dr.DataReader(ticker, 'yahoo', '01/01/2017')['Adj Close']\n",
    "        prices = pd.DataFrame(prices)\n",
    "        prices.columns = [ticker]\n",
    "        prices_list.append(prices)\n",
    "    except:\n",
    "        pass\n",
    "    prices_df = pd.concat(prices_list, axis = 1)\n",
    "\n",
    "prices_df.sort_index(inplace = True)\n",
    "prices_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Date</th>\n",
       "      <th>2017-01-04</th>\n",
       "      <th>2017-01-05</th>\n",
       "      <th>2017-01-06</th>\n",
       "      <th>2017-01-09</th>\n",
       "      <th>2017-01-10</th>\n",
       "      <th>2017-01-11</th>\n",
       "      <th>2017-01-12</th>\n",
       "      <th>2017-01-13</th>\n",
       "      <th>2017-01-17</th>\n",
       "      <th>2017-01-18</th>\n",
       "      <th>...</th>\n",
       "      <th>2021-06-29</th>\n",
       "      <th>2021-06-30</th>\n",
       "      <th>2021-07-01</th>\n",
       "      <th>2021-07-02</th>\n",
       "      <th>2021-07-06</th>\n",
       "      <th>2021-07-07</th>\n",
       "      <th>2021-07-08</th>\n",
       "      <th>2021-07-09</th>\n",
       "      <th>2021-07-12</th>\n",
       "      <th>2021-07-13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MMM</th>\n",
       "      <td>0.001516</td>\n",
       "      <td>-0.003421</td>\n",
       "      <td>0.002926</td>\n",
       "      <td>-0.005386</td>\n",
       "      <td>-0.003892</td>\n",
       "      <td>0.007419</td>\n",
       "      <td>-0.002530</td>\n",
       "      <td>-0.000282</td>\n",
       "      <td>-0.000733</td>\n",
       "      <td>0.006939</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002392</td>\n",
       "      <td>0.013263</td>\n",
       "      <td>0.002316</td>\n",
       "      <td>0.004018</td>\n",
       "      <td>-0.015008</td>\n",
       "      <td>0.015085</td>\n",
       "      <td>-0.007956</td>\n",
       "      <td>0.013769</td>\n",
       "      <td>-0.005075</td>\n",
       "      <td>-0.001900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABT</th>\n",
       "      <td>0.007939</td>\n",
       "      <td>0.008638</td>\n",
       "      <td>0.027204</td>\n",
       "      <td>-0.000981</td>\n",
       "      <td>0.013500</td>\n",
       "      <td>0.001341</td>\n",
       "      <td>-0.008764</td>\n",
       "      <td>0.005157</td>\n",
       "      <td>-0.000733</td>\n",
       "      <td>-0.003423</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013051</td>\n",
       "      <td>-0.010921</td>\n",
       "      <td>0.010610</td>\n",
       "      <td>0.007511</td>\n",
       "      <td>0.006608</td>\n",
       "      <td>0.008837</td>\n",
       "      <td>-0.005089</td>\n",
       "      <td>0.004025</td>\n",
       "      <td>-0.007767</td>\n",
       "      <td>-0.001347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABBV</th>\n",
       "      <td>0.014100</td>\n",
       "      <td>0.007584</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0.006584</td>\n",
       "      <td>-0.002180</td>\n",
       "      <td>-0.036103</td>\n",
       "      <td>0.002290</td>\n",
       "      <td>0.011586</td>\n",
       "      <td>-0.002097</td>\n",
       "      <td>-0.003233</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006195</td>\n",
       "      <td>0.003028</td>\n",
       "      <td>0.014382</td>\n",
       "      <td>0.007964</td>\n",
       "      <td>0.004862</td>\n",
       "      <td>0.008814</td>\n",
       "      <td>-0.003426</td>\n",
       "      <td>0.001977</td>\n",
       "      <td>0.009007</td>\n",
       "      <td>0.002380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABMD</th>\n",
       "      <td>0.030082</td>\n",
       "      <td>-0.008035</td>\n",
       "      <td>0.005313</td>\n",
       "      <td>0.014642</td>\n",
       "      <td>-0.041585</td>\n",
       "      <td>-0.008642</td>\n",
       "      <td>0.009886</td>\n",
       "      <td>0.029367</td>\n",
       "      <td>-0.014265</td>\n",
       "      <td>-0.006578</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002656</td>\n",
       "      <td>-0.003703</td>\n",
       "      <td>0.002563</td>\n",
       "      <td>0.013135</td>\n",
       "      <td>0.030566</td>\n",
       "      <td>0.023936</td>\n",
       "      <td>-0.028368</td>\n",
       "      <td>-0.000831</td>\n",
       "      <td>-0.008098</td>\n",
       "      <td>-0.009344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACN</th>\n",
       "      <td>0.002404</td>\n",
       "      <td>-0.014990</td>\n",
       "      <td>0.011392</td>\n",
       "      <td>-0.011178</td>\n",
       "      <td>0.000522</td>\n",
       "      <td>0.009213</td>\n",
       "      <td>-0.002928</td>\n",
       "      <td>0.010105</td>\n",
       "      <td>-0.007866</td>\n",
       "      <td>-0.000690</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008608</td>\n",
       "      <td>-0.005532</td>\n",
       "      <td>0.017674</td>\n",
       "      <td>0.016133</td>\n",
       "      <td>0.001837</td>\n",
       "      <td>0.013785</td>\n",
       "      <td>-0.001486</td>\n",
       "      <td>0.011224</td>\n",
       "      <td>-0.003359</td>\n",
       "      <td>0.003402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1138 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Date  2017-01-04  2017-01-05  2017-01-06  2017-01-09  2017-01-10  2017-01-11  \\\n",
       "MMM     0.001516   -0.003421    0.002926   -0.005386   -0.003892    0.007419   \n",
       "ABT     0.007939    0.008638    0.027204   -0.000981    0.013500    0.001341   \n",
       "ABBV    0.014100    0.007584    0.000314    0.006584   -0.002180   -0.036103   \n",
       "ABMD    0.030082   -0.008035    0.005313    0.014642   -0.041585   -0.008642   \n",
       "ACN     0.002404   -0.014990    0.011392   -0.011178    0.000522    0.009213   \n",
       "\n",
       "Date  2017-01-12  2017-01-13  2017-01-17  2017-01-18  ...  2021-06-29  \\\n",
       "MMM    -0.002530   -0.000282   -0.000733    0.006939  ...   -0.002392   \n",
       "ABT    -0.008764    0.005157   -0.000733   -0.003423  ...    0.013051   \n",
       "ABBV    0.002290    0.011586   -0.002097   -0.003233  ...   -0.006195   \n",
       "ABMD    0.009886    0.029367   -0.014265   -0.006578  ...    0.002656   \n",
       "ACN    -0.002928    0.010105   -0.007866   -0.000690  ...    0.008608   \n",
       "\n",
       "Date  2021-06-30  2021-07-01  2021-07-02  2021-07-06  2021-07-07  2021-07-08  \\\n",
       "MMM     0.013263    0.002316    0.004018   -0.015008    0.015085   -0.007956   \n",
       "ABT    -0.010921    0.010610    0.007511    0.006608    0.008837   -0.005089   \n",
       "ABBV    0.003028    0.014382    0.007964    0.004862    0.008814   -0.003426   \n",
       "ABMD   -0.003703    0.002563    0.013135    0.030566    0.023936   -0.028368   \n",
       "ACN    -0.005532    0.017674    0.016133    0.001837    0.013785   -0.001486   \n",
       "\n",
       "Date  2021-07-09  2021-07-12  2021-07-13  \n",
       "MMM     0.013769   -0.005075   -0.001900  \n",
       "ABT     0.004025   -0.007767   -0.001347  \n",
       "ABBV    0.001977    0.009007    0.002380  \n",
       "ABMD   -0.000831   -0.008098   -0.009344  \n",
       "ACN     0.011224   -0.003359    0.003402  \n",
       "\n",
       "[5 rows x 1138 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = prices_df.pct_change().iloc[1:].T\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00151634, -0.00342078,  0.0029262 , ...,  0.01376908,\n",
       "        -0.00507465, -0.00190014],\n",
       "       [ 0.00793862,  0.00863805,  0.02720421, ...,  0.00402478,\n",
       "        -0.00776683, -0.00134665],\n",
       "       [ 0.01410027,  0.00758412,  0.00031377, ...,  0.00197682,\n",
       "         0.00900665,  0.0023804 ],\n",
       "       ...,\n",
       "       [ 0.0041335 ,  0.00608959,  0.00879897, ...,  0.01638513,\n",
       "         0.01421908, -0.01227843],\n",
       "       [ 0.02851451, -0.00984212,  0.01357975, ...,  0.01383271,\n",
       "         0.00038025, -0.01050226],\n",
       "       [-0.00430557, -0.00617767, -0.00606064, ...,  0.03459264,\n",
       "         0.01275851, -0.0282363 ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "companies = list(df.index)\n",
    "companies\n",
    "movements = df.values\n",
    "movements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Date</th>\n",
       "      <th>2017-01-04</th>\n",
       "      <th>2017-01-05</th>\n",
       "      <th>2017-01-06</th>\n",
       "      <th>2017-01-09</th>\n",
       "      <th>2017-01-10</th>\n",
       "      <th>2017-01-11</th>\n",
       "      <th>2017-01-12</th>\n",
       "      <th>2017-01-13</th>\n",
       "      <th>2017-01-17</th>\n",
       "      <th>2017-01-18</th>\n",
       "      <th>...</th>\n",
       "      <th>2021-06-29</th>\n",
       "      <th>2021-06-30</th>\n",
       "      <th>2021-07-01</th>\n",
       "      <th>2021-07-02</th>\n",
       "      <th>2021-07-06</th>\n",
       "      <th>2021-07-07</th>\n",
       "      <th>2021-07-08</th>\n",
       "      <th>2021-07-09</th>\n",
       "      <th>2021-07-12</th>\n",
       "      <th>2021-07-13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MMM</th>\n",
       "      <td>0.002787</td>\n",
       "      <td>-0.006288</td>\n",
       "      <td>0.005379</td>\n",
       "      <td>-0.009901</td>\n",
       "      <td>-0.007155</td>\n",
       "      <td>0.013638</td>\n",
       "      <td>-0.004650</td>\n",
       "      <td>-0.000518</td>\n",
       "      <td>-0.001347</td>\n",
       "      <td>0.012756</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004397</td>\n",
       "      <td>0.024382</td>\n",
       "      <td>0.004257</td>\n",
       "      <td>0.007387</td>\n",
       "      <td>-0.027589</td>\n",
       "      <td>0.027729</td>\n",
       "      <td>-0.014624</td>\n",
       "      <td>0.025311</td>\n",
       "      <td>-0.009329</td>\n",
       "      <td>-0.003493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABT</th>\n",
       "      <td>0.014625</td>\n",
       "      <td>0.015913</td>\n",
       "      <td>0.050116</td>\n",
       "      <td>-0.001807</td>\n",
       "      <td>0.024870</td>\n",
       "      <td>0.002470</td>\n",
       "      <td>-0.016144</td>\n",
       "      <td>0.009501</td>\n",
       "      <td>-0.001351</td>\n",
       "      <td>-0.006306</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024043</td>\n",
       "      <td>-0.020118</td>\n",
       "      <td>0.019546</td>\n",
       "      <td>0.013837</td>\n",
       "      <td>0.012173</td>\n",
       "      <td>0.016279</td>\n",
       "      <td>-0.009375</td>\n",
       "      <td>0.007414</td>\n",
       "      <td>-0.014308</td>\n",
       "      <td>-0.002481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABBV</th>\n",
       "      <td>0.023127</td>\n",
       "      <td>0.012439</td>\n",
       "      <td>0.000515</td>\n",
       "      <td>0.010799</td>\n",
       "      <td>-0.003576</td>\n",
       "      <td>-0.059214</td>\n",
       "      <td>0.003756</td>\n",
       "      <td>0.019003</td>\n",
       "      <td>-0.003440</td>\n",
       "      <td>-0.005303</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.010160</td>\n",
       "      <td>0.004966</td>\n",
       "      <td>0.023589</td>\n",
       "      <td>0.013063</td>\n",
       "      <td>0.007975</td>\n",
       "      <td>0.014456</td>\n",
       "      <td>-0.005619</td>\n",
       "      <td>0.003242</td>\n",
       "      <td>0.014772</td>\n",
       "      <td>0.003904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABMD</th>\n",
       "      <td>0.033440</td>\n",
       "      <td>-0.008932</td>\n",
       "      <td>0.005906</td>\n",
       "      <td>0.016277</td>\n",
       "      <td>-0.046228</td>\n",
       "      <td>-0.009607</td>\n",
       "      <td>0.010990</td>\n",
       "      <td>0.032646</td>\n",
       "      <td>-0.015857</td>\n",
       "      <td>-0.007312</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002953</td>\n",
       "      <td>-0.004116</td>\n",
       "      <td>0.002849</td>\n",
       "      <td>0.014601</td>\n",
       "      <td>0.033978</td>\n",
       "      <td>0.026608</td>\n",
       "      <td>-0.031535</td>\n",
       "      <td>-0.000923</td>\n",
       "      <td>-0.009002</td>\n",
       "      <td>-0.010387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACN</th>\n",
       "      <td>0.004444</td>\n",
       "      <td>-0.027707</td>\n",
       "      <td>0.021056</td>\n",
       "      <td>-0.020661</td>\n",
       "      <td>0.000964</td>\n",
       "      <td>0.017028</td>\n",
       "      <td>-0.005412</td>\n",
       "      <td>0.018678</td>\n",
       "      <td>-0.014540</td>\n",
       "      <td>-0.001275</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015911</td>\n",
       "      <td>-0.010226</td>\n",
       "      <td>0.032666</td>\n",
       "      <td>0.029819</td>\n",
       "      <td>0.003395</td>\n",
       "      <td>0.025479</td>\n",
       "      <td>-0.002746</td>\n",
       "      <td>0.020746</td>\n",
       "      <td>-0.006208</td>\n",
       "      <td>0.006288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATVI</th>\n",
       "      <td>0.026407</td>\n",
       "      <td>0.020863</td>\n",
       "      <td>-0.001062</td>\n",
       "      <td>-0.007444</td>\n",
       "      <td>0.024239</td>\n",
       "      <td>0.028712</td>\n",
       "      <td>-0.002400</td>\n",
       "      <td>-0.006182</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004485</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002958</td>\n",
       "      <td>-0.002389</td>\n",
       "      <td>-0.021684</td>\n",
       "      <td>0.005295</td>\n",
       "      <td>0.001853</td>\n",
       "      <td>-0.015517</td>\n",
       "      <td>-0.016274</td>\n",
       "      <td>0.002916</td>\n",
       "      <td>0.007710</td>\n",
       "      <td>0.004918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADBE</th>\n",
       "      <td>0.009033</td>\n",
       "      <td>0.024071</td>\n",
       "      <td>0.031959</td>\n",
       "      <td>0.003531</td>\n",
       "      <td>-0.004044</td>\n",
       "      <td>0.009550</td>\n",
       "      <td>-0.005198</td>\n",
       "      <td>-0.000782</td>\n",
       "      <td>-0.006916</td>\n",
       "      <td>0.010360</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004690</td>\n",
       "      <td>-0.012250</td>\n",
       "      <td>-0.002201</td>\n",
       "      <td>0.020200</td>\n",
       "      <td>0.009146</td>\n",
       "      <td>0.021046</td>\n",
       "      <td>0.000421</td>\n",
       "      <td>-0.003389</td>\n",
       "      <td>-0.010074</td>\n",
       "      <td>0.011350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMD</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.013661</td>\n",
       "      <td>0.005849</td>\n",
       "      <td>0.012341</td>\n",
       "      <td>-0.003576</td>\n",
       "      <td>-0.017240</td>\n",
       "      <td>-0.032285</td>\n",
       "      <td>-0.013747</td>\n",
       "      <td>-0.059032</td>\n",
       "      <td>0.005021</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023027</td>\n",
       "      <td>0.040484</td>\n",
       "      <td>-0.005424</td>\n",
       "      <td>0.012242</td>\n",
       "      <td>-0.001996</td>\n",
       "      <td>-0.034187</td>\n",
       "      <td>-0.007261</td>\n",
       "      <td>0.010623</td>\n",
       "      <td>-0.000814</td>\n",
       "      <td>-0.004977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAP</th>\n",
       "      <td>0.010725</td>\n",
       "      <td>-0.000911</td>\n",
       "      <td>-0.017110</td>\n",
       "      <td>-0.000770</td>\n",
       "      <td>0.003007</td>\n",
       "      <td>0.026998</td>\n",
       "      <td>0.007838</td>\n",
       "      <td>0.002397</td>\n",
       "      <td>-0.013385</td>\n",
       "      <td>0.007404</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005049</td>\n",
       "      <td>-0.001019</td>\n",
       "      <td>0.024211</td>\n",
       "      <td>0.013199</td>\n",
       "      <td>-0.014553</td>\n",
       "      <td>0.004634</td>\n",
       "      <td>-0.012044</td>\n",
       "      <td>0.037539</td>\n",
       "      <td>-0.009429</td>\n",
       "      <td>-0.018255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AES</th>\n",
       "      <td>-0.011544</td>\n",
       "      <td>-0.017466</td>\n",
       "      <td>0.048370</td>\n",
       "      <td>-0.033026</td>\n",
       "      <td>-0.023350</td>\n",
       "      <td>0.052276</td>\n",
       "      <td>-0.021730</td>\n",
       "      <td>0.016274</td>\n",
       "      <td>0.021822</td>\n",
       "      <td>0.002261</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008118</td>\n",
       "      <td>-0.015314</td>\n",
       "      <td>0.013425</td>\n",
       "      <td>-0.008691</td>\n",
       "      <td>-0.008233</td>\n",
       "      <td>-0.000518</td>\n",
       "      <td>-0.025897</td>\n",
       "      <td>0.012674</td>\n",
       "      <td>0.011510</td>\n",
       "      <td>-0.053429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 1138 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Date  2017-01-04  2017-01-05  2017-01-06  2017-01-09  2017-01-10  2017-01-11  \\\n",
       "MMM     0.002787   -0.006288    0.005379   -0.009901   -0.007155    0.013638   \n",
       "ABT     0.014625    0.015913    0.050116   -0.001807    0.024870    0.002470   \n",
       "ABBV    0.023127    0.012439    0.000515    0.010799   -0.003576   -0.059214   \n",
       "ABMD    0.033440   -0.008932    0.005906    0.016277   -0.046228   -0.009607   \n",
       "ACN     0.004444   -0.027707    0.021056   -0.020661    0.000964    0.017028   \n",
       "ATVI    0.026407    0.020863   -0.001062   -0.007444    0.024239    0.028712   \n",
       "ADBE    0.009033    0.024071    0.031959    0.003531   -0.004044    0.009550   \n",
       "AMD     0.000000   -0.013661    0.005849    0.012341   -0.003576   -0.017240   \n",
       "AAP     0.010725   -0.000911   -0.017110   -0.000770    0.003007    0.026998   \n",
       "AES    -0.011544   -0.017466    0.048370   -0.033026   -0.023350    0.052276   \n",
       "\n",
       "Date  2017-01-12  2017-01-13  2017-01-17  2017-01-18  ...  2021-06-29  \\\n",
       "MMM    -0.004650   -0.000518   -0.001347    0.012756  ...   -0.004397   \n",
       "ABT    -0.016144    0.009501   -0.001351   -0.006306  ...    0.024043   \n",
       "ABBV    0.003756    0.019003   -0.003440   -0.005303  ...   -0.010160   \n",
       "ABMD    0.010990    0.032646   -0.015857   -0.007312  ...    0.002953   \n",
       "ACN    -0.005412    0.018678   -0.014540   -0.001275  ...    0.015911   \n",
       "ATVI   -0.002400   -0.006182    0.000000    0.004485  ...    0.002958   \n",
       "ADBE   -0.005198   -0.000782   -0.006916    0.010360  ...    0.004690   \n",
       "AMD    -0.032285   -0.013747   -0.059032    0.005021  ...    0.023027   \n",
       "AAP     0.007838    0.002397   -0.013385    0.007404  ...    0.005049   \n",
       "AES    -0.021730    0.016274    0.021822    0.002261  ...   -0.008118   \n",
       "\n",
       "Date  2021-06-30  2021-07-01  2021-07-02  2021-07-06  2021-07-07  2021-07-08  \\\n",
       "MMM     0.024382    0.004257    0.007387   -0.027589    0.027729   -0.014624   \n",
       "ABT    -0.020118    0.019546    0.013837    0.012173    0.016279   -0.009375   \n",
       "ABBV    0.004966    0.023589    0.013063    0.007975    0.014456   -0.005619   \n",
       "ABMD   -0.004116    0.002849    0.014601    0.033978    0.026608   -0.031535   \n",
       "ACN    -0.010226    0.032666    0.029819    0.003395    0.025479   -0.002746   \n",
       "ATVI   -0.002389   -0.021684    0.005295    0.001853   -0.015517   -0.016274   \n",
       "ADBE   -0.012250   -0.002201    0.020200    0.009146    0.021046    0.000421   \n",
       "AMD     0.040484   -0.005424    0.012242   -0.001996   -0.034187   -0.007261   \n",
       "AAP    -0.001019    0.024211    0.013199   -0.014553    0.004634   -0.012044   \n",
       "AES    -0.015314    0.013425   -0.008691   -0.008233   -0.000518   -0.025897   \n",
       "\n",
       "Date  2021-07-09  2021-07-12  2021-07-13  \n",
       "MMM     0.025311   -0.009329   -0.003493  \n",
       "ABT     0.007414   -0.014308   -0.002481  \n",
       "ABBV    0.003242    0.014772    0.003904  \n",
       "ABMD   -0.000923   -0.009002   -0.010387  \n",
       "ACN     0.020746   -0.006208    0.006288  \n",
       "ATVI    0.002916    0.007710    0.004918  \n",
       "ADBE   -0.003389   -0.010074    0.011350  \n",
       "AMD     0.010623   -0.000814   -0.004977  \n",
       "AAP     0.037539   -0.009429   -0.018255  \n",
       "AES     0.012674    0.011510   -0.053429  \n",
       "\n",
       "[10 rows x 1138 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalize = Normalizer()\n",
    "array_norm = normalize.fit_transform(df)\n",
    "df_norm = pd.DataFrame(array_norm, columns = df.columns)\n",
    "final_df = df_norm.set_index(df.index)\n",
    "final_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_mask = df.isnull().any(axis = 0)\n",
    "row_mask = df.isnull().any(axis=1)\n",
    "df.loc[row_mask, col_mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Cluster_Numbers</th>\n",
       "      <th>Error_Term</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0.313787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0.286009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>0.275741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>0.262935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>0.258895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>0.246709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.238527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>0.228684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>0.225541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>0.217082</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cluster_Numbers  Error_Term\n",
       "0                2    0.313787\n",
       "1                3    0.286009\n",
       "2                4    0.275741\n",
       "3                5    0.262935\n",
       "4                6    0.258895\n",
       "5                7    0.246709\n",
       "6                8    0.238527\n",
       "7                9    0.228684\n",
       "8               10    0.225541\n",
       "9               11    0.217082"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_of_clusters = range(2,12)\n",
    "error = []\n",
    "\n",
    "for num_clusters in num_of_clusters:\n",
    "    clusters = KMeans(num_clusters)\n",
    "    clusters.fit(final_df)\n",
    "    error.append(clusters.inertia_/100)\n",
    "    \n",
    "table = pd.DataFrame({\"Cluster_Numbers\":num_of_clusters, \"Error_Term\":error})\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(table.Cluster_Numbers, table.Error_Term, marker = \"D\", color = 'red')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('SSE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 1, 3, 1, 3, 3, 3, 4, 0, 0, 3, 2, 3, 0, 2, 5, 3, 3, 2, 6, 1,\n",
       "       3, 3, 5, 3, 2, 6, 0, 6, 0, 0, 5, 6, 1, 1, 2, 3, 2, 3, 3, 1, 1, 2,\n",
       "       0, 3, 3, 0, 2, 3, 1, 1, 1, 6, 3, 1, 4, 5, 2, 0], dtype=int32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters = KMeans(7)\n",
    "clusters.fit(final_df)\n",
    "clusters.labels_\n",
    "\n",
    "labels = clusters.predict(movements)\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>labels</th>\n",
       "      <th>tickers</th>\n",
       "      <th>full-name</th>\n",
       "      <th>sector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0</td>\n",
       "      <td>BKR</td>\n",
       "      <td>Baker Hughes</td>\n",
       "      <td>Baker Hughes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0</td>\n",
       "      <td>AXP</td>\n",
       "      <td>American Express</td>\n",
       "      <td>American Express</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>ALK</td>\n",
       "      <td>Alaska Air Group</td>\n",
       "      <td>Alaska Air Group</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>AAL</td>\n",
       "      <td>American Airlines Group</td>\n",
       "      <td>American Airlines Group</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0</td>\n",
       "      <td>APA</td>\n",
       "      <td>APA Corporation</td>\n",
       "      <td>APA Corporation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>AES</td>\n",
       "      <td>AES Corp</td>\n",
       "      <td>AES Corp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0</td>\n",
       "      <td>APTV</td>\n",
       "      <td>Aptiv</td>\n",
       "      <td>Aptiv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>AFL</td>\n",
       "      <td>Aflac</td>\n",
       "      <td>Aflac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0</td>\n",
       "      <td>AIG</td>\n",
       "      <td>American International Group</td>\n",
       "      <td>American International Group</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1</td>\n",
       "      <td>AJG</td>\n",
       "      <td>Arthur J. Gallagher &amp; Co.</td>\n",
       "      <td>Arthur J. Gallagher &amp; Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1</td>\n",
       "      <td>AIZ</td>\n",
       "      <td>Assurant</td>\n",
       "      <td>Assurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "      <td>AMP</td>\n",
       "      <td>Ameriprise Financial</td>\n",
       "      <td>Ameriprise Financial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>ACN</td>\n",
       "      <td>Accenture</td>\n",
       "      <td>Accenture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1</td>\n",
       "      <td>T</td>\n",
       "      <td>AT&amp;T</td>\n",
       "      <td>AT&amp;T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1</td>\n",
       "      <td>AON</td>\n",
       "      <td>Aon</td>\n",
       "      <td>Aon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>ABBV</td>\n",
       "      <td>AbbVie</td>\n",
       "      <td>AbbVie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1</td>\n",
       "      <td>ANTM</td>\n",
       "      <td>Anthem</td>\n",
       "      <td>Anthem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1</td>\n",
       "      <td>ADP</td>\n",
       "      <td>Automatic Data Processing</td>\n",
       "      <td>Automatic Data Processing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>ALL</td>\n",
       "      <td>Allstate Corp</td>\n",
       "      <td>Allstate Corp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1</td>\n",
       "      <td>ABC</td>\n",
       "      <td>AmerisourceBergen</td>\n",
       "      <td>AmerisourceBergen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2</td>\n",
       "      <td>AVY</td>\n",
       "      <td>Avery Dennison</td>\n",
       "      <td>Avery Dennison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2</td>\n",
       "      <td>AOS</td>\n",
       "      <td>A. O. Smith</td>\n",
       "      <td>A. O. Smith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2</td>\n",
       "      <td>APH</td>\n",
       "      <td>Amphenol Corp</td>\n",
       "      <td>Amphenol Corp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2</td>\n",
       "      <td>ADM</td>\n",
       "      <td>ADM</td>\n",
       "      <td>ADM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2</td>\n",
       "      <td>AME</td>\n",
       "      <td>Ametek</td>\n",
       "      <td>Ametek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2</td>\n",
       "      <td>AMCR</td>\n",
       "      <td>Amcor</td>\n",
       "      <td>Amcor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>MMM</td>\n",
       "      <td>3M</td>\n",
       "      <td>3M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>APD</td>\n",
       "      <td>Air Products &amp; Chemicals</td>\n",
       "      <td>Air Products &amp; Chemicals</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>ALLE</td>\n",
       "      <td>Allegion</td>\n",
       "      <td>Allegion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Albemarle Corporation</td>\n",
       "      <td>Albemarle Corporation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>ABMD</td>\n",
       "      <td>Abiomed</td>\n",
       "      <td>Abiomed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>ATVI</td>\n",
       "      <td>Activision Blizzard</td>\n",
       "      <td>Activision Blizzard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>ADBE</td>\n",
       "      <td>Adobe</td>\n",
       "      <td>Adobe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>3</td>\n",
       "      <td>ANET</td>\n",
       "      <td>Arista Networks</td>\n",
       "      <td>Arista Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Advanced Micro Devices</td>\n",
       "      <td>Advanced Micro Devices</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>3</td>\n",
       "      <td>AMAT</td>\n",
       "      <td>Applied Materials</td>\n",
       "      <td>Applied Materials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>3</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Apple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>Agilent Technologies</td>\n",
       "      <td>Agilent Technologies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>Amazon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>AKAM</td>\n",
       "      <td>Akamai Technologies</td>\n",
       "      <td>Akamai Technologies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>3</td>\n",
       "      <td>ANSS</td>\n",
       "      <td>Ansys</td>\n",
       "      <td>Ansys</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>3</td>\n",
       "      <td>ADI</td>\n",
       "      <td>Analog Devices</td>\n",
       "      <td>Analog Devices</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>3</td>\n",
       "      <td>AMGN</td>\n",
       "      <td>Amgen</td>\n",
       "      <td>Amgen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3</td>\n",
       "      <td>ALXN</td>\n",
       "      <td>Alexion Pharmaceuticals</td>\n",
       "      <td>Alexion Pharmaceuticals</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3</td>\n",
       "      <td>ALGN</td>\n",
       "      <td>Align Technology</td>\n",
       "      <td>Align Technology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>GOOGL</td>\n",
       "      <td>Alphabet (Class A)</td>\n",
       "      <td>Alphabet (Class A)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3</td>\n",
       "      <td>GOOG</td>\n",
       "      <td>Alphabet (Class C)</td>\n",
       "      <td>Alphabet (Class C)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>ABT</td>\n",
       "      <td>Abbott Laboratories</td>\n",
       "      <td>Abbott Laboratories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>3</td>\n",
       "      <td>ADSK</td>\n",
       "      <td>Autodesk</td>\n",
       "      <td>Autodesk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>4</td>\n",
       "      <td>AZO</td>\n",
       "      <td>AutoZone</td>\n",
       "      <td>AutoZone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>AAP</td>\n",
       "      <td>Advance Auto Parts</td>\n",
       "      <td>Advance Auto Parts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5</td>\n",
       "      <td>ARE</td>\n",
       "      <td>Alexandria Real Estate Equities</td>\n",
       "      <td>Alexandria Real Estate Equities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5</td>\n",
       "      <td>AMT</td>\n",
       "      <td>American Tower</td>\n",
       "      <td>American Tower</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>5</td>\n",
       "      <td>AVB</td>\n",
       "      <td>AvalonBay Communities</td>\n",
       "      <td>AvalonBay Communities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5</td>\n",
       "      <td>MO</td>\n",
       "      <td>Altria Group</td>\n",
       "      <td>Altria Group</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>6</td>\n",
       "      <td>AEE</td>\n",
       "      <td>Ameren Corp</td>\n",
       "      <td>Ameren Corp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>6</td>\n",
       "      <td>AWK</td>\n",
       "      <td>American Water Works</td>\n",
       "      <td>American Water Works</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6</td>\n",
       "      <td>LNT</td>\n",
       "      <td>Alliant Energy</td>\n",
       "      <td>Alliant Energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>6</td>\n",
       "      <td>ATO</td>\n",
       "      <td>Atmos Energy</td>\n",
       "      <td>Atmos Energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>6</td>\n",
       "      <td>AEP</td>\n",
       "      <td>American Electric Power</td>\n",
       "      <td>American Electric Power</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    labels tickers                        full-name  \\\n",
       "59       0     BKR                     Baker Hughes   \n",
       "30       0     AXP                 American Express   \n",
       "14       0     ALK                 Alaska Air Group   \n",
       "28       0     AAL          American Airlines Group   \n",
       "44       0     APA                  APA Corporation   \n",
       "9        0     AES                         AES Corp   \n",
       "47       0    APTV                            Aptiv   \n",
       "10       0     AFL                            Aflac   \n",
       "31       0     AIG     American International Group   \n",
       "50       1     AJG        Arthur J. Gallagher & Co.   \n",
       "51       1     AIZ                         Assurant   \n",
       "34       1     AMP             Ameriprise Financial   \n",
       "4        1     ACN                        Accenture   \n",
       "52       1       T                             AT&T   \n",
       "42       1     AON                              Aon   \n",
       "2        1    ABBV                           AbbVie   \n",
       "41       1    ANTM                           Anthem   \n",
       "55       1     ADP        Automatic Data Processing   \n",
       "21       1     ALL                    Allstate Corp   \n",
       "35       1     ABC                AmerisourceBergen   \n",
       "58       2     AVY                   Avery Dennison   \n",
       "43       2     AOS                      A. O. Smith   \n",
       "38       2     APH                    Amphenol Corp   \n",
       "48       2     ADM                              ADM   \n",
       "36       2     AME                           Ametek   \n",
       "26       2    AMCR                            Amcor   \n",
       "0        2     MMM                               3M   \n",
       "12       2     APD         Air Products & Chemicals   \n",
       "19       2    ALLE                         Allegion   \n",
       "15       2     ALB            Albemarle Corporation   \n",
       "3        3    ABMD                          Abiomed   \n",
       "5        3    ATVI              Activision Blizzard   \n",
       "6        3    ADBE                            Adobe   \n",
       "49       3    ANET                  Arista Networks   \n",
       "7        3     AMD           Advanced Micro Devices   \n",
       "46       3    AMAT                Applied Materials   \n",
       "45       3    AAPL                            Apple   \n",
       "11       3       A             Agilent Technologies   \n",
       "25       3    AMZN                           Amazon   \n",
       "13       3    AKAM              Akamai Technologies   \n",
       "40       3    ANSS                            Ansys   \n",
       "39       3     ADI                   Analog Devices   \n",
       "37       3    AMGN                            Amgen   \n",
       "17       3    ALXN          Alexion Pharmaceuticals   \n",
       "18       3    ALGN                 Align Technology   \n",
       "22       3   GOOGL               Alphabet (Class A)   \n",
       "23       3    GOOG               Alphabet (Class C)   \n",
       "1        3     ABT              Abbott Laboratories   \n",
       "54       3    ADSK                         Autodesk   \n",
       "56       4     AZO                         AutoZone   \n",
       "8        4     AAP               Advance Auto Parts   \n",
       "16       5     ARE  Alexandria Real Estate Equities   \n",
       "32       5     AMT                   American Tower   \n",
       "57       5     AVB            AvalonBay Communities   \n",
       "24       5      MO                     Altria Group   \n",
       "27       6     AEE                      Ameren Corp   \n",
       "33       6     AWK             American Water Works   \n",
       "20       6     LNT                   Alliant Energy   \n",
       "53       6     ATO                     Atmos Energy   \n",
       "29       6     AEP          American Electric Power   \n",
       "\n",
       "                             sector  \n",
       "59                     Baker Hughes  \n",
       "30                 American Express  \n",
       "14                 Alaska Air Group  \n",
       "28          American Airlines Group  \n",
       "44                  APA Corporation  \n",
       "9                          AES Corp  \n",
       "47                            Aptiv  \n",
       "10                            Aflac  \n",
       "31     American International Group  \n",
       "50        Arthur J. Gallagher & Co.  \n",
       "51                         Assurant  \n",
       "34             Ameriprise Financial  \n",
       "4                         Accenture  \n",
       "52                             AT&T  \n",
       "42                              Aon  \n",
       "2                            AbbVie  \n",
       "41                           Anthem  \n",
       "55        Automatic Data Processing  \n",
       "21                    Allstate Corp  \n",
       "35                AmerisourceBergen  \n",
       "58                   Avery Dennison  \n",
       "43                      A. O. Smith  \n",
       "38                    Amphenol Corp  \n",
       "48                              ADM  \n",
       "36                           Ametek  \n",
       "26                            Amcor  \n",
       "0                                3M  \n",
       "12         Air Products & Chemicals  \n",
       "19                         Allegion  \n",
       "15            Albemarle Corporation  \n",
       "3                           Abiomed  \n",
       "5               Activision Blizzard  \n",
       "6                             Adobe  \n",
       "49                  Arista Networks  \n",
       "7            Advanced Micro Devices  \n",
       "46                Applied Materials  \n",
       "45                            Apple  \n",
       "11             Agilent Technologies  \n",
       "25                           Amazon  \n",
       "13              Akamai Technologies  \n",
       "40                            Ansys  \n",
       "39                   Analog Devices  \n",
       "37                            Amgen  \n",
       "17          Alexion Pharmaceuticals  \n",
       "18                 Align Technology  \n",
       "22               Alphabet (Class A)  \n",
       "23               Alphabet (Class C)  \n",
       "1               Abbott Laboratories  \n",
       "54                         Autodesk  \n",
       "56                         AutoZone  \n",
       "8                Advance Auto Parts  \n",
       "16  Alexandria Real Estate Equities  \n",
       "32                   American Tower  \n",
       "57            AvalonBay Communities  \n",
       "24                     Altria Group  \n",
       "27                      Ameren Corp  \n",
       "33             American Water Works  \n",
       "20                   Alliant Energy  \n",
       "53                     Atmos Energy  \n",
       "29          American Electric Power  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustered_result = pd.DataFrame({'labels':labels, 'tickers':companies, 'full-name':security, 'sector':sector})\n",
    "clustered_result.sort_values('labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['Cluster'] = clusters.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.countplot(x = 'Cluster', data = final_df, palette = 'magma')\n",
    "plt.title('Cluster_count')\n",
    "plt.show()\n",
    "plt.savefig('cluster_count.png', dpi = 300)"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}