41_Cluster.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Estimacion de las frecuencias de corte de servicio\n",
    "## Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "# Import the required packages\n",
    "import os\n",
    "import sys\n",
    "in_colab = 'google.colab' in sys.modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "if in_colab:\n",
    "    from google.colab import drive\n",
    "    drive.mount('/content/drive')\n",
    "    BASE_DIR = \"/content/drive/My Drive/Colab Notebooks/Data/\"\n",
    "else:\n",
    "    BASE_DIR = \"./Data/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import geopandas as gpd\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans, MiniBatchKMeans\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import accuracy_score\n",
    "from scipy.stats import stats\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from IPython.display import display\n",
    "from time import time\n",
    "pd.options.display.float_format = '{:.2f}'.format\n",
    "%matplotlib inline \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "### Extraer informacion de la base de datos y condensar en un solo dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "def prepare_data(Matriz):\n",
    "    \n",
    "    FIC = Matriz.FIC\n",
    "    bin = np.array(FIC.values)\n",
    "    FIC_bin = pd.cut(bin, bins=4, labels=False)\n",
    "    \n",
    "    X = Matriz.drop(['FIC'], axis=1)\n",
    "\n",
    "    return X, FIC, FIC_bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "def purity_score(y_true, y_pred):\n",
    "\n",
    "#    Args:\n",
    "#        y_true(np.ndarray): n*1 matrix Ground truth labels\n",
    "#        y_pred(np.ndarray): n*1 matrix Predicted clusters\n",
    "#    \n",
    "#    Returns:\n",
    "#        float: Purity score\n",
    "#    \n",
    "#    References:\n",
    "#        [1] https://nlp.stanford.edu/IR-book/html/htmledition/evaluation-of-clustering-1.html\n",
    "\n",
    "    # matriz que mantiene la mayoria de labels votados\n",
    "    \n",
    "    y_voted_labels = np.zeros(y_true.shape)\n",
    "    \n",
    "    labels = np.unique(y_true)\n",
    "    ordered_labels = np.arange(labels.shape[0])\n",
    "    for k in range(labels.shape[0]):\n",
    "        y_true[y_true==labels[k]] = ordered_labels[k]\n",
    "        \n",
    "    # Actualiza labels unicos\n",
    "    \n",
    "    labels = np.unique(y_true)\n",
    "    \n",
    "    bins = np.concatenate((labels, [np.max(labels)+1]), axis=0)\n",
    "\n",
    "    for cluster in np.unique(y_pred):\n",
    "        hist, _ = np.histogram(y_true[y_pred==cluster], bins=bins)\n",
    "        # Find the most present label in the cluster\n",
    "        winner = np.argmax(hist)\n",
    "        y_voted_labels[y_pred==cluster] = winner\n",
    "    \n",
    "    return accuracy_score(y_true, y_voted_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "# Adecuamos K-Means al dataset utilizando MiniBatchKmeans\n",
    "def Calculo_MiniBatchKmeans(true_k, data):\n",
    "    km = MiniBatchKMeans(n_clusters=true_k, init='k-means++', n_init=1,\n",
    "                         init_size=1000, batch_size=1000, random_state=0)\n",
    "    y_kmeans = km.fit_predict(data)\n",
    "    return km, y_kmeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Carga del dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "dataset = BASE_DIR+'UNTRD_ENF.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "Data = pd.read_csv(dataset)\n",
    "Data.set_index('COD_ID', drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3305, 21)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "          DISTANCE_SUB  CTMT_PNTMT_STD  CLO_CEN  TIP_CC_RES-Tipo3  POT_NOM  \\\n",
       "COD_ID                                                                       \n",
       "26879192       5104.49         2088.58     0.02              0.00    45.00   \n",
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       "26880293       3774.91         8194.44     0.03              0.00   112.50   \n",
       "26880295       3897.62         8194.44     0.01              0.00    75.00   \n",
       "26880295       3897.62         8194.44     0.03              0.00    75.00   \n",
       "...                ...             ...      ...               ...      ...   \n",
       "26878633      15873.00         4838.27     0.00              0.00    30.00   \n",
       "26878634      15732.31         4838.27     0.00              0.00    15.00   \n",
       "26878635      16620.79         4838.27     0.00              0.00    10.00   \n",
       "26878636      16872.15         4838.27     0.00              0.00    45.00   \n",
       "26878637      17049.51         4838.27     0.00              0.00    15.00   \n",
       "\n",
       "          PERD_A4_B  I_NOM_MED_R  CTMT_ENE_STD  CTMT_PNTMT_MED  UCBT_ENE_MED  \\\n",
       "COD_ID                                                                         \n",
       "26879192  389840.98       365.00      43960.32        18885.60       2922.50   \n",
       "26880293  408741.02       438.00      67863.54        22266.87      22227.00   \n",
       "26880293  408741.02       438.00      67863.54        22266.87      22227.00   \n",
       "26880295  408741.02       438.00      67863.54        22266.87       8893.00   \n",
       "26880295  408741.02       438.00      67863.54        22266.87       8893.00   \n",
       "...             ...          ...           ...             ...           ...   \n",
       "26878633  379666.04       168.00      93329.28        13712.42        353.00   \n",
       "26878634  379666.04       168.00      93329.28        13712.42        190.00   \n",
       "26878635  379666.04       168.00      93329.28        13712.42        246.00   \n",
       "26878636  379666.04       168.00      93329.28        13712.42        271.00   \n",
       "26878637  379666.04       168.00      93329.28        13712.42         77.50   \n",
       "\n",
       "          ...  CTMT_PNTBT_MAX  PAGE_RANK  TIP_TRAFO_M  FAS_CON_S_ABCN  \\\n",
       "COD_ID    ...                                                           \n",
       "26879192  ...       143337.13       0.00         0.00            0.00   \n",
       "26880293  ...       119688.30       0.00         0.00            0.00   \n",
       "26880293  ...       119688.30       0.00         0.00            0.00   \n",
       "26880295  ...       119688.30       0.00         0.00            0.00   \n",
       "26880295  ...       119688.30       0.00         0.00            0.00   \n",
       "...       ...             ...        ...          ...             ...   \n",
       "26878633  ...       320696.78       0.00         0.00            1.00   \n",
       "26878634  ...       320696.78       0.00         0.00            0.00   \n",
       "26878635  ...       320696.78       0.00         1.00            0.00   \n",
       "26878636  ...       320696.78       0.00         0.00            1.00   \n",
       "26878637  ...       320696.78       0.00         0.00            1.00   \n",
       "\n",
       "          CTMT_ENE_MED  GRU_TAR_B3  TIP_CC_RES-Tipo10   PERD_A4  \\\n",
       "COD_ID                                                            \n",
       "26879192    1421387.50        0.00               0.00 255476.21   \n",
       "26880293    1648865.40        0.00               0.00 350609.30   \n",
       "26880293    1648865.40        0.00               0.00 350609.30   \n",
       "26880295    1648865.40        0.00               0.00 350609.30   \n",
       "26880295    1648865.40        0.00               0.00 350609.30   \n",
       "...                ...         ...                ...       ...   \n",
       "26878633    1013653.45        0.00               0.00 198609.64   \n",
       "26878634    1013653.45        0.00               0.00 198609.64   \n",
       "26878635    1013653.45        0.00               0.00 198609.64   \n",
       "26878636    1013653.45        0.00               0.00 198609.64   \n",
       "26878637    1013653.45        0.00               0.00 198609.64   \n",
       "\n",
       "          TIP_CC_RES-Tipo5   FIC  \n",
       "COD_ID                            \n",
       "26879192              0.00  1.00  \n",
       "26880293              0.00  0.00  \n",
       "26880293              0.00  0.00  \n",
       "26880295              0.00  0.00  \n",
       "26880295              0.00  0.00  \n",
       "...                    ...   ...  \n",
       "26878633              0.00  9.00  \n",
       "26878634              0.00 16.00  \n",
       "26878635              0.00  8.00  \n",
       "26878636              0.00  8.00  \n",
       "26878637              0.00 13.00  \n",
       "\n",
       "[3305 rows x 21 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Split Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "Data_X, Data_FIC, FIC_bin = prepare_data(Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DISTANCE_SUB</th>\n",
       "      <th>CTMT_PNTMT_STD</th>\n",
       "      <th>CLO_CEN</th>\n",
       "      <th>TIP_CC_RES-Tipo3</th>\n",
       "      <th>POT_NOM</th>\n",
       "      <th>PERD_A4_B</th>\n",
       "      <th>I_NOM_MED_R</th>\n",
       "      <th>CTMT_ENE_STD</th>\n",
       "      <th>CTMT_PNTMT_MED</th>\n",
       "      <th>UCBT_ENE_MED</th>\n",
       "      <th>...</th>\n",
       "      <th>CTMT_PNTBT_MAX</th>\n",
       "      <th>PAGE_RANK</th>\n",
       "      <th>TIP_TRAFO_M</th>\n",
       "      <th>FAS_CON_S_ABCN</th>\n",
       "      <th>CTMT_ENE_MED</th>\n",
       "      <th>GRU_TAR_B3</th>\n",
       "      <th>TIP_CC_RES-Tipo10</th>\n",
       "      <th>PERD_A4</th>\n",
       "      <th>TIP_CC_RES-Tipo5</th>\n",
       "      <th>FIC_bin</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COD_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>2922.50</td>\n",
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       "      <td>22266.87</td>\n",
       "      <td>22227.00</td>\n",
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       "      <td>67863.54</td>\n",
       "      <td>22266.87</td>\n",
       "      <td>1080.50</td>\n",
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       "      <td>119688.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1648865.40</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>350609.30</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>67863.54</td>\n",
       "      <td>22266.87</td>\n",
       "      <td>17356.50</td>\n",
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       "      <td>0.00</td>\n",
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       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>26880299</th>\n",
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       "      <td>112.50</td>\n",
       "      <td>408741.02</td>\n",
       "      <td>438.00</td>\n",
       "      <td>67863.54</td>\n",
       "      <td>22266.87</td>\n",
       "      <td>23069.50</td>\n",
       "      <td>...</td>\n",
       "      <td>119688.30</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>1648865.40</td>\n",
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       "      <td>350609.30</td>\n",
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       "    <tr>\n",
       "      <th>26880300</th>\n",
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       "      <td>75.00</td>\n",
       "      <td>408741.02</td>\n",
       "      <td>438.00</td>\n",
       "      <td>67863.54</td>\n",
       "      <td>22266.87</td>\n",
       "      <td>13187.00</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>1648865.40</td>\n",
       "      <td>1.00</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          DISTANCE_SUB  CTMT_PNTMT_STD  CLO_CEN  TIP_CC_RES-Tipo3  POT_NOM  \\\n",
       "COD_ID                                                                       \n",
       "26879192       5104.49         2088.58     0.02              0.00    45.00   \n",
       "26880293       3774.91         8194.44     0.01              0.00   112.50   \n",
       "26880293       3774.91         8194.44     0.03              0.00   112.50   \n",
       "26880295       3897.62         8194.44     0.01              0.00    75.00   \n",
       "26880295       3897.62         8194.44     0.03              0.00    75.00   \n",
       "26880297       3835.13         8194.44     0.03              0.00   112.50   \n",
       "26880298       4275.24         8194.44     0.01              0.00    75.00   \n",
       "26880298       4275.24         8194.44     0.02              0.00    75.00   \n",
       "26880299       4238.75         8194.44     0.03              0.00   112.50   \n",
       "26880300       4017.00         8194.44     0.03              0.00    75.00   \n",
       "\n",
       "          PERD_A4_B  I_NOM_MED_R  CTMT_ENE_STD  CTMT_PNTMT_MED  UCBT_ENE_MED  \\\n",
       "COD_ID                                                                         \n",
       "26879192  389840.98       365.00      43960.32        18885.60       2922.50   \n",
       "26880293  408741.02       438.00      67863.54        22266.87      22227.00   \n",
       "26880293  408741.02       438.00      67863.54        22266.87      22227.00   \n",
       "26880295  408741.02       438.00      67863.54        22266.87       8893.00   \n",
       "26880295  408741.02       438.00      67863.54        22266.87       8893.00   \n",
       "26880297  408741.02       438.00      67863.54        22266.87       1080.50   \n",
       "26880298  408741.02       438.00      67863.54        22266.87      17356.50   \n",
       "26880298  408741.02       438.00      67863.54        22266.87      17356.50   \n",
       "26880299  408741.02       438.00      67863.54        22266.87      23069.50   \n",
       "26880300  408741.02       438.00      67863.54        22266.87      13187.00   \n",
       "\n",
       "          ...  CTMT_PNTBT_MAX  PAGE_RANK  TIP_TRAFO_M  FAS_CON_S_ABCN  \\\n",
       "COD_ID    ...                                                           \n",
       "26879192  ...       143337.13       0.00         0.00            0.00   \n",
       "26880293  ...       119688.30       0.00         0.00            0.00   \n",
       "26880293  ...       119688.30       0.00         0.00            0.00   \n",
       "26880295  ...       119688.30       0.00         0.00            0.00   \n",
       "26880295  ...       119688.30       0.00         0.00            0.00   \n",
       "26880297  ...       119688.30       0.00         0.00            1.00   \n",
       "26880298  ...       119688.30       0.00         0.00            0.00   \n",
       "26880298  ...       119688.30       0.00         0.00            0.00   \n",
       "26880299  ...       119688.30       0.00         0.00            0.00   \n",
       "26880300  ...       119688.30       0.00         0.00            0.00   \n",
       "\n",
       "          CTMT_ENE_MED  GRU_TAR_B3  TIP_CC_RES-Tipo10   PERD_A4  \\\n",
       "COD_ID                                                            \n",
       "26879192    1421387.50        0.00               0.00 255476.21   \n",
       "26880293    1648865.40        0.00               0.00 350609.30   \n",
       "26880293    1648865.40        0.00               0.00 350609.30   \n",
       "26880295    1648865.40        0.00               0.00 350609.30   \n",
       "26880295    1648865.40        0.00               0.00 350609.30   \n",
       "26880297    1648865.40        1.00               0.00 350609.30   \n",
       "26880298    1648865.40        0.00               0.00 350609.30   \n",
       "26880298    1648865.40        0.00               0.00 350609.30   \n",
       "26880299    1648865.40        0.00               0.00 350609.30   \n",
       "26880300    1648865.40        1.00               0.00 350609.30   \n",
       "\n",
       "          TIP_CC_RES-Tipo5  FIC_bin  \n",
       "COD_ID                               \n",
       "26879192              0.00        0  \n",
       "26880293              0.00        0  \n",
       "26880293              0.00        0  \n",
       "26880295              0.00        0  \n",
       "26880295              0.00        0  \n",
       "26880297              0.00        0  \n",
       "26880298              0.00        0  \n",
       "26880298              0.00        0  \n",
       "26880299              0.00        0  \n",
       "26880300              0.00        0  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Se genera un dataset con los valores discretizados de FIC \n",
    "df_FIC_bin = pd.DataFrame({'FIC_bin': FIC_bin})\n",
    "\n",
    "Data_X.reset_index(inplace=True)\n",
    "Data_bin = pd.concat((Data_X, df_FIC_bin), axis=1)\n",
    "Data_X.set_index('COD_ID', drop=True, inplace=True)\n",
    "Data_bin.set_index('COD_ID', drop=True, inplace=True)\n",
    "Data_bin.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "### Kmeans Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "Collapsed": "false"
   },
   "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": [
    "# Utilizamos el elbow method para encontrar el número óptimo de clusters con el dataset original con k-means\n",
    "\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    km = KMeans(n_clusters = i, init = 'k-means++', max_iter=100, random_state = 0) # n_jobs=-1\n",
    "    km.fit(Data)\n",
    "    wcss.append(km.inertia_)\n",
    "    \n",
    "plt.plot(range(1, 11), wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Número de clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "El resultado nos muestra que un numero posible de elección es 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "Collapsed": "false"
   },
   "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": [
    "# Utilizamos el elbow method para encontrar el número óptimo de clusters con el dataset original con Mini Batch k-means\n",
    "\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    km = MiniBatchKMeans(n_clusters=i, init='k-means++', n_init=1,\n",
    "         init_size=1000, batch_size=1000, random_state=0)\n",
    "    km.fit(Data)\n",
    "    wcss.append(km.inertia_)\n",
    "    \n",
    "plt.plot(range(1, 11), wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Número de clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "Nuevamente nos sugiere 4 Clusters"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "Si bien nos da 4 en ambos casos los resultados de las evaluaciones con 4 son pobres por lo que se haran un análisis con distinto número de clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Calculo para  2  Clusters\n",
      "\n",
      "Clustering sparse data with MiniBatchKMeans(batch_size=1000, compute_labels=True, init='k-means++',\n",
      "                init_size=1000, max_iter=100, max_no_improvement=10,\n",
      "                n_clusters=2, n_init=1, random_state=0, reassignment_ratio=0.01,\n",
      "                tol=0.0, verbose=0)\n",
      "done in 0.025s\n",
      "\n",
      "Homogeneity: 0.013\n",
      "Completeness: 0.044\n",
      "V-measure: 0.020\n",
      "Adjusted Rand-Index: -0.012\n",
      "Silhouette Coefficient: 0.504\n",
      "\n",
      "Métrica de Pureza: 0.345\n",
      "\n",
      "\n",
      "Calculo para  3  Clusters\n",
      "\n",
      "Clustering sparse data with MiniBatchKMeans(batch_size=1000, compute_labels=True, init='k-means++',\n",
      "                init_size=1000, max_iter=100, max_no_improvement=10,\n",
      "                n_clusters=3, n_init=1, random_state=0, reassignment_ratio=0.01,\n",
      "                tol=0.0, verbose=0)\n",
      "done in 0.023s\n",
      "\n",
      "Homogeneity: 0.042\n",
      "Completeness: 0.097\n",
      "V-measure: 0.059\n",
      "Adjusted Rand-Index: 0.005\n",
      "Silhouette Coefficient: 0.491\n",
      "\n",
      "Métrica de Pureza: 0.345\n",
      "\n",
      "\n",
      "Calculo para  4  Clusters\n",
      "\n",
      "Clustering sparse data with MiniBatchKMeans(batch_size=1000, compute_labels=True, init='k-means++',\n",
      "                init_size=1000, max_iter=100, max_no_improvement=10,\n",
      "                n_clusters=4, n_init=1, random_state=0, reassignment_ratio=0.01,\n",
      "                tol=0.0, verbose=0)\n",
      "done in 0.027s\n",
      "\n",
      "Homogeneity: 0.034\n",
      "Completeness: 0.055\n",
      "V-measure: 0.042\n",
      "Adjusted Rand-Index: 0.015\n",
      "Silhouette Coefficient: 0.529\n",
      "\n",
      "Métrica de Pureza: 0.345\n",
      "\n",
      "\n",
      "Calculo para  5  Clusters\n",
      "\n",
      "Clustering sparse data with MiniBatchKMeans(batch_size=1000, compute_labels=True, init='k-means++',\n",
      "                init_size=1000, max_iter=100, max_no_improvement=10,\n",
      "                n_clusters=5, n_init=1, random_state=0, reassignment_ratio=0.01,\n",
      "                tol=0.0, verbose=0)\n",
      "done in 0.033s\n",
      "\n",
      "Homogeneity: 0.048\n",
      "Completeness: 0.068\n",
      "V-measure: 0.056\n",
      "Adjusted Rand-Index: 0.024\n",
      "Silhouette Coefficient: 0.537\n",
      "\n",
      "Métrica de Pureza: 0.345\n",
      "\n",
      "\n",
      "Calculo para  6  Clusters\n",
      "\n",
      "Clustering sparse data with MiniBatchKMeans(batch_size=1000, compute_labels=True, init='k-means++',\n",
      "                init_size=1000, max_iter=100, max_no_improvement=10,\n",
      "                n_clusters=6, n_init=1, random_state=0, reassignment_ratio=0.01,\n",
      "                tol=0.0, verbose=0)\n",
      "done in 0.032s\n",
      "\n",
      "Homogeneity: 0.050\n",
      "Completeness: 0.070\n",
      "V-measure: 0.058\n",
      "Adjusted Rand-Index: 0.024\n",
      "Silhouette Coefficient: 0.545\n",
      "\n",
      "Métrica de Pureza: 0.348\n",
      "\n",
      "\n",
      "Calculo para  7  Clusters\n",
      "\n",
      "Clustering sparse data with MiniBatchKMeans(batch_size=1000, compute_labels=True, init='k-means++',\n",
      "                init_size=1000, max_iter=100, max_no_improvement=10,\n",
      "                n_clusters=7, n_init=1, random_state=0, reassignment_ratio=0.01,\n",
      "                tol=0.0, verbose=0)\n",
      "done in 0.045s\n",
      "\n",
      "Homogeneity: 0.092\n",
      "Completeness: 0.109\n",
      "V-measure: 0.100\n",
      "Adjusted Rand-Index: 0.036\n",
      "Silhouette Coefficient: 0.594\n",
      "\n",
      "Métrica de Pureza: 0.348\n",
      "\n"
     ]
    }
   ],
   "source": [
    "labels = Data['FIC']\n",
    "for c in range(2, 8):\n",
    "    true_k = c\n",
    "    km, y_kmeans = Calculo_MiniBatchKmeans(true_k, Data)\n",
    "    print()\n",
    "    print ('Calculo para ', c, ' Clusters')\n",
    "    print()\n",
    "    print(\"Clustering sparse data with %s\" % km)\n",
    "    t0 = time()\n",
    "    km.fit(Data)\n",
    "    print(\"done in %0.3fs\" % (time() - t0))\n",
    "    print()\n",
    "    print(\"Homogeneity: %0.3f\" % metrics.homogeneity_score(labels, km.labels_))\n",
    "    print(\"Completeness: %0.3f\" % metrics.completeness_score(labels, km.labels_))\n",
    "    print(\"V-measure: %0.3f\" % metrics.v_measure_score(labels, km.labels_))\n",
    "    print(\"Adjusted Rand-Index: %.3f\"\n",
    "          % metrics.adjusted_rand_score(labels, km.labels_))\n",
    "    print(\"Silhouette Coefficient: %0.3f\"\n",
    "          % metrics.silhouette_score(Data, km.labels_, sample_size=1000))\n",
    "    print()\n",
    "    print(\"Métrica de Pureza: %.3f\" % purity_score(Data_FIC, y_kmeans))\n",
    "\n",
    "    print()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "Se opta por hacer el analisis con 4 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "true_k = 4\n",
    "km, y_kmeans = Calculo_MiniBatchKmeans(true_k, Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import pairwise_distances_argmin \n",
    "means_cluster_centers = np.sort(km.cluster_centers_, axis = 0) \n",
    "means_labels = pairwise_distances_argmin(Data, km.cluster_centers_) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "Data['Cluster']=means_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     887\n",
       "1     270\n",
       "2     865\n",
       "3    1283\n",
       "Name: Cluster, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data['Cluster'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3305, 22)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = 'data\\ENF_6612_2017-12-31_M10_20180820-1410.gdb'\n",
    "DATA_CLUS0= Data['Cluster']data_dfo.reset_index(inplace=True)\n",
    "UNTD_GEO = gpd.read_file(data_path, driver='FileGDB', layer= 'UNTRD')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_CLUS=Data['Cluster'].to_frame().reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "COD_ID     object\n",
       "Cluster     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATA_CLUS['COD_ID']=DATA_CLUS['COD_ID'].apply(str)\n",
    "DATA_CLUS.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "UNTD_GEO=UNTD_GEO.merge(DATA_CLUS, on='COD_ID', how='left')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "Data_path = BASE_DIR+'UNTRD_Cluster.csv'\n",
    "Data.to_csv(Data_path, index=False)\n",
    "\n",
    "UNTD_GEO.to_file(driver = 'ESRI Shapefile', filename= BASE_DIR+\"UNTD_CLUS.shp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "### Elementos mas cercanos al centroide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "from scipy.spatial import cKDTree\n",
    "kdtree = cKDTree(Data.iloc[:, 0:21]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "distances, indices = kdtree.query(km.cluster_centers_, 100)\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2824, 2579, 2825, 2826, 2820, 2581, 2583, 2822, 2578, 2828, 3161,\n",
       "       2823, 3170, 2848, 2580, 2582, 2577, 2827, 2846, 3169, 2819, 3157,\n",
       "       3159, 3171, 3160, 2586, 2817, 2984, 2833, 2844, 3145, 2584, 3162,\n",
       "       3158, 3148, 2585, 3164, 3069, 3168, 2821, 3150, 2832, 2576, 2587,\n",
       "       2590, 2845, 2830, 2840, 2843, 2588, 3142, 2842, 2816, 3147, 3167,\n",
       "       3155, 2829, 2818, 2589, 2834, 2594, 2836, 2835, 3156, 2476, 3119,\n",
       "       2839, 3154, 2592, 2603, 2841, 3117, 2831, 2591, 3153, 3141, 3140,\n",
       "       3151, 2596, 2595, 2838, 2634, 2593, 3152, 2837, 3166, 3116, 3165,\n",
       "       2350, 3163, 3065, 2609, 3149, 2349, 2635, 2636, 2597, 3113, 2640,\n",
       "       2352])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster 0:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DISTANCE_SUB</th>\n",
       "      <th>CTMT_PNTMT_STD</th>\n",
       "      <th>CLO_CEN</th>\n",
       "      <th>TIP_CC_RES-Tipo3</th>\n",
       "      <th>POT_NOM</th>\n",
       "      <th>PERD_A4_B</th>\n",
       "      <th>I_NOM_MED_R</th>\n",
       "      <th>CTMT_ENE_STD</th>\n",
       "      <th>CTMT_PNTMT_MED</th>\n",
       "      <th>UCBT_ENE_MED</th>\n",
       "      <th>...</th>\n",
       "      <th>PAGE_RANK</th>\n",
       "      <th>TIP_TRAFO_M</th>\n",
       "      <th>FAS_CON_S_ABCN</th>\n",
       "      <th>CTMT_ENE_MED</th>\n",
       "      <th>GRU_TAR_B3</th>\n",
       "      <th>TIP_CC_RES-Tipo10</th>\n",
       "      <th>PERD_A4</th>\n",
       "      <th>TIP_CC_RES-Tipo5</th>\n",
       "      <th>FIC</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26876780</th>\n",
       "      <td>16214.96</td>\n",
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       "      <td>30.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2401.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>459459.02</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>26876823</th>\n",
       "      <td>16450.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2514.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876782</th>\n",
       "      <td>16704.62</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3694.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876783</th>\n",
       "      <td>16979.62</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1220.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>1.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876773</th>\n",
       "      <td>13555.22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1871.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>26876825</th>\n",
       "      <td>16259.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>634.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876827</th>\n",
       "      <td>16758.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>857.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876776</th>\n",
       "      <td>15172.63</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>375.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876821</th>\n",
       "      <td>14406.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>559.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876786</th>\n",
       "      <td>16019.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>313.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876799</th>\n",
       "      <td>13538.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1060.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876777</th>\n",
       "      <td>15636.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>102.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876811</th>\n",
       "      <td>14415.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>280.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876778</th>\n",
       "      <td>14964.22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>95.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876824</th>\n",
       "      <td>15339.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876826</th>\n",
       "      <td>15624.62</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876820</th>\n",
       "      <td>13813.59</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>594.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876784</th>\n",
       "      <td>16888.29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>402.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876818</th>\n",
       "      <td>13686.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>629.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876810</th>\n",
       "      <td>13525.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>784.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876772</th>\n",
       "      <td>14775.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876763</th>\n",
       "      <td>13525.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>721.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876766</th>\n",
       "      <td>14366.74</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>118.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876814</th>\n",
       "      <td>14418.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>93.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876767</th>\n",
       "      <td>14255.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>109.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876832</th>\n",
       "      <td>17870.79</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1185.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876769</th>\n",
       "      <td>12781.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3222.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26880590</th>\n",
       "      <td>17579.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>669.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876792</th>\n",
       "      <td>18255.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1983.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876816</th>\n",
       "      <td>13267.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>712.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876751</th>\n",
       "      <td>12617.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2069.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876828</th>\n",
       "      <td>17138.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>176.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876800</th>\n",
       "      <td>13618.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>271.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876765</th>\n",
       "      <td>13950.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876754</th>\n",
       "      <td>12838.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1171.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876829</th>\n",
       "      <td>17927.82</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>858.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>1.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876802</th>\n",
       "      <td>13554.82</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>230.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80001522</th>\n",
       "      <td>13292.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>402.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876809</th>\n",
       "      <td>13195.63</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>458.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876774</th>\n",
       "      <td>13177.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>340.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876756</th>\n",
       "      <td>13305.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876791</th>\n",
       "      <td>18510.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1061.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876819</th>\n",
       "      <td>13160.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>81.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876833</th>\n",
       "      <td>17869.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>148.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876836</th>\n",
       "      <td>18790.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3367.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876817</th>\n",
       "      <td>13109.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>99.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876789</th>\n",
       "      <td>18302.68</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>623.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876806</th>\n",
       "      <td>12123.58</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3417.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876815</th>\n",
       "      <td>12413.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>742.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876834</th>\n",
       "      <td>18035.86</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>76.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          DISTANCE_SUB  CTMT_PNTMT_STD  CLO_CEN  TIP_CC_RES-Tipo3  POT_NOM  \\\n",
       "26876780      16214.96            0.00     0.01              0.00    30.00   \n",
       "26876823      16450.76            0.00     0.01              0.00    45.00   \n",
       "26876782      16704.62            0.00     0.01              0.00    30.00   \n",
       "26876783      16979.62            0.00     0.01              0.00    30.00   \n",
       "26876773      13555.22            0.00     0.00              0.00    30.00   \n",
       "26876825      16259.99            0.00     0.00              0.00    15.00   \n",
       "26876827      16758.48            0.00     0.01              0.00    25.00   \n",
       "26876776      15172.63            0.00     0.00              0.00    15.00   \n",
       "26876821      14406.70            0.00     0.00              0.00    15.00   \n",
       "26876786      16019.06            0.00     0.00              1.00    10.00   \n",
       "26876799      13538.33            0.00     0.00              0.00    30.00   \n",
       "26876777      15636.38            0.00     0.00              0.00    10.00   \n",
       "26876811      14415.57            0.00     0.00              0.00    15.00   \n",
       "26876778      14964.22            0.00     0.00              0.00    10.00   \n",
       "26876824      15339.51            0.00     0.00              0.00     5.00   \n",
       "26876826      15624.62            0.00     0.00              0.00     5.00   \n",
       "26876820      13813.59            0.00     0.00              0.00    30.00   \n",
       "26876784      16888.29            0.00     0.01              0.00    45.00   \n",
       "26876818      13686.51            0.00     0.00              0.00    30.00   \n",
       "26876810      13525.92            0.00     0.00              0.00    15.00   \n",
       "26876772      14775.45            0.00     0.00              0.00    10.00   \n",
       "26876763      13525.92            0.00     0.00              0.00    25.00   \n",
       "26876766      14366.74            0.00     0.00              0.00    10.00   \n",
       "26876814      14418.51            0.00     0.00              0.00    15.00   \n",
       "26876767      14255.65            0.00     0.00              0.00    15.00   \n",
       "26876832      17870.79            0.00     0.00              0.00    45.00   \n",
       "26876769      12781.64            0.00     0.00              0.00    30.00   \n",
       "26880590      17579.08            0.00     0.00              0.00    15.00   \n",
       "26876792      18255.48            0.00     0.00              0.00    30.00   \n",
       "26876816      13267.09            0.00     0.00              0.00    45.00   \n",
       "26876751      12617.46            0.00     0.00              0.00    30.00   \n",
       "26876828      17138.10            0.00     0.00              0.00    15.00   \n",
       "26876800      13618.25            0.00     0.00              0.00    25.00   \n",
       "26876765      13950.81            0.00     0.00              0.00    10.00   \n",
       "26876754      12838.02            0.00     0.00              0.00    30.00   \n",
       "26876829      17927.82            0.00     0.01              0.00    30.00   \n",
       "26876802      13554.82            0.00     0.00              0.00    45.00   \n",
       "80001522      13292.01            0.00     0.00              0.00    25.00   \n",
       "26876809      13195.63            0.00     0.00              0.00    10.00   \n",
       "26876774      13177.57            0.00     0.00              0.00    30.00   \n",
       "26876756      13305.12            0.00     0.00              0.00     5.00   \n",
       "26876791      18510.98            0.00     0.00              0.00    45.00   \n",
       "26876819      13160.01            0.00     0.00              0.00     5.00   \n",
       "26876833      17869.70            0.00     0.00              0.00    15.00   \n",
       "26876836      18790.96            0.00     0.00              0.00    30.00   \n",
       "26876817      13109.16            0.00     0.00              0.00    10.00   \n",
       "26876789      18302.68            0.00     0.00              0.00    45.00   \n",
       "26876806      12123.58            0.00     0.00              0.00    45.00   \n",
       "26876815      12413.17            0.00     0.00              0.00    30.00   \n",
       "26876834      18035.86            0.00     0.00              0.00    15.00   \n",
       "\n",
       "          PERD_A4_B  I_NOM_MED_R  CTMT_ENE_STD  CTMT_PNTMT_MED  UCBT_ENE_MED  \\\n",
       "26876780  475052.12       438.00      21457.60            0.00       2401.50   \n",
       "26876823  475052.12       438.00      21457.60            0.00       2514.50   \n",
       "26876782  475052.12       438.00      21457.60            0.00       3694.50   \n",
       "26876783  475052.12       438.00      21457.60            0.00       1220.00   \n",
       "26876773  475052.12       438.00      21457.60            0.00       1871.50   \n",
       "26876825  475052.12       438.00      21457.60            0.00        634.00   \n",
       "26876827  475052.12       438.00      21457.60            0.00        857.50   \n",
       "26876776  475052.12       438.00      21457.60            0.00        375.50   \n",
       "26876821  475052.12       438.00      21457.60            0.00        559.50   \n",
       "26876786  475052.12       438.00      21457.60            0.00        313.00   \n",
       "26876799  475052.12       438.00      21457.60            0.00       1060.00   \n",
       "26876777  475052.12       438.00      21457.60            0.00        102.50   \n",
       "26876811  475052.12       438.00      21457.60            0.00        280.50   \n",
       "26876778  475052.12       438.00      21457.60            0.00         95.00   \n",
       "26876824  475052.12       438.00      21457.60            0.00          0.00   \n",
       "26876826  475052.12       438.00      21457.60            0.00          0.00   \n",
       "26876820  475052.12       438.00      21457.60            0.00        594.00   \n",
       "26876784  475052.12       438.00      21457.60            0.00        402.50   \n",
       "26876818  475052.12       438.00      21457.60            0.00        629.50   \n",
       "26876810  475052.12       438.00      21457.60            0.00        784.50   \n",
       "26876772  475052.12       438.00      21457.60            0.00          0.00   \n",
       "26876763  475052.12       438.00      21457.60            0.00        721.00   \n",
       "26876766  475052.12       438.00      21457.60            0.00        118.00   \n",
       "26876814  475052.12       438.00      21457.60            0.00         93.00   \n",
       "26876767  475052.12       438.00      21457.60            0.00        109.50   \n",
       "26876832  475052.12       438.00      21457.60            0.00       1185.00   \n",
       "26876769  475052.12       438.00      21457.60            0.00       3222.50   \n",
       "26880590  475052.12       438.00      21457.60            0.00        669.50   \n",
       "26876792  475052.12       438.00      21457.60            0.00       1983.00   \n",
       "26876816  475052.12       438.00      21457.60            0.00        712.50   \n",
       "26876751  475052.12       438.00      21457.60            0.00       2069.50   \n",
       "26876828  475052.12       438.00      21457.60            0.00        176.50   \n",
       "26876800  475052.12       438.00      21457.60            0.00        271.50   \n",
       "26876765  475052.12       438.00      21457.60            0.00         21.50   \n",
       "26876754  475052.12       438.00      21457.60            0.00       1171.00   \n",
       "26876829  475052.12       438.00      21457.60            0.00        858.00   \n",
       "26876802  475052.12       438.00      21457.60            0.00        230.00   \n",
       "80001522  475052.12       438.00      21457.60            0.00        402.50   \n",
       "26876809  475052.12       438.00      21457.60            0.00        458.00   \n",
       "26876774  475052.12       438.00      21457.60            0.00        340.50   \n",
       "26876756  475052.12       438.00      21457.60            0.00          0.00   \n",
       "26876791  475052.12       438.00      21457.60            0.00       1061.00   \n",
       "26876819  475052.12       438.00      21457.60            0.00         81.00   \n",
       "26876833  475052.12       438.00      21457.60            0.00        148.00   \n",
       "26876836  475052.12       438.00      21457.60            0.00       3367.00   \n",
       "26876817  475052.12       438.00      21457.60            0.00         99.50   \n",
       "26876789  475052.12       438.00      21457.60            0.00        623.00   \n",
       "26876806  475052.12       438.00      21457.60            0.00       3417.00   \n",
       "26876815  475052.12       438.00      21457.60            0.00        742.00   \n",
       "26876834  475052.12       438.00      21457.60            0.00         76.00   \n",
       "\n",
       "          ...  PAGE_RANK  TIP_TRAFO_M  FAS_CON_S_ABCN  CTMT_ENE_MED  \\\n",
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       "80001522  ...       0.00         1.00            0.00     459459.02   \n",
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       "26876834  ...       0.00         1.00            0.00     459459.02   \n",
       "\n",
       "          GRU_TAR_B3  TIP_CC_RES-Tipo10   PERD_A4  TIP_CC_RES-Tipo5   FIC  \\\n",
       "26876780        0.00               0.00 100388.53              0.00  6.00   \n",
       "26876823        0.00               0.00 100388.53              0.00  2.00   \n",
       "26876782        0.00               0.00 100388.53              0.00  6.00   \n",
       "26876783        0.00               0.00 100388.53              1.00 10.00   \n",
       "26876773        0.00               0.00 100388.53              0.00 14.00   \n",
       "26876825        0.00               0.00 100388.53              0.00 12.00   \n",
       "26876827        0.00               1.00 100388.53              0.00 12.00   \n",
       "26876776        0.00               0.00 100388.53              0.00  6.00   \n",
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       "26876778        0.00               0.00 100388.53              0.00 14.00   \n",
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       "26876826        0.00               0.00 100388.53              0.00 14.00   \n",
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       "26876784        0.00               0.00 100388.53              0.00 14.00   \n",
       "26876818        0.00               0.00 100388.53              0.00  8.00   \n",
       "26876810        0.00               0.00 100388.53              0.00 10.00   \n",
       "26876772        0.00               0.00 100388.53              0.00  4.00   \n",
       "26876763        0.00               0.00 100388.53              0.00 10.00   \n",
       "26876766        0.00               0.00 100388.53              0.00 10.00   \n",
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       "26876832        0.00               0.00 100388.53              0.00 16.00   \n",
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       "26880590        0.00               0.00 100388.53              0.00  2.00   \n",
       "26876792        0.00               0.00 100388.53              0.00 16.00   \n",
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       "26876834        0.00               0.00 100388.53              0.00  6.00   \n",
       "\n",
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       "\n",
       "[50 rows x 22 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster 1:\n"
     ]
    },
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DISTANCE_SUB</th>\n",
       "      <th>CTMT_PNTMT_STD</th>\n",
       "      <th>CLO_CEN</th>\n",
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       "      <td>605749.12</td>\n",
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       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
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       "    <tr>\n",
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       "      <td>605749.12</td>\n",
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       "      <td>141887.74</td>\n",
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       "      <td>605749.12</td>\n",
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       "    <tr>\n",
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       "      <td>605749.12</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>141887.74</td>\n",
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       "      <td>605749.12</td>\n",
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       "      <td>141887.74</td>\n",
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       "      <td>605749.12</td>\n",
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       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
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       "    <tr>\n",
       "      <th>26877274</th>\n",
       "      <td>4470.01</td>\n",
       "      <td>15779.90</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>4546.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>585114.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877304</th>\n",
       "      <td>3426.96</td>\n",
       "      <td>15779.90</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>230.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>5069.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>585114.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64822638</th>\n",
       "      <td>2603.61</td>\n",
       "      <td>15779.90</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>8277.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>585114.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877289</th>\n",
       "      <td>2777.82</td>\n",
       "      <td>15779.90</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>8738.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>585114.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877321</th>\n",
       "      <td>5450.05</td>\n",
       "      <td>15779.90</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>4256.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>585114.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877252</th>\n",
       "      <td>2751.96</td>\n",
       "      <td>15779.90</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>230.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>5454.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>585114.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877373</th>\n",
       "      <td>5800.07</td>\n",
       "      <td>15779.90</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>230.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>10199.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>585114.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877239</th>\n",
       "      <td>2163.71</td>\n",
       "      <td>15779.90</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>230.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>6752.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>585114.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877270</th>\n",
       "      <td>4378.79</td>\n",
       "      <td>15779.90</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>4182.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>585114.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877284</th>\n",
       "      <td>2147.22</td>\n",
       "      <td>15779.90</td>\n",
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       "      <td>1.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>230.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>6440.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>585114.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877256</th>\n",
       "      <td>2907.59</td>\n",
       "      <td>15779.90</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>5027.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>585114.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877303</th>\n",
       "      <td>4364.66</td>\n",
       "      <td>15779.90</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>112.50</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>3982.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>585114.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877343</th>\n",
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       "      <td>45.00</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>230.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>10329.50</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>585114.24</td>\n",
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       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877242</th>\n",
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       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>230.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>4871.50</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
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       "      <td>585114.24</td>\n",
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       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877266</th>\n",
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       "      <td>75.00</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>10837.00</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
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       "      <td>585114.24</td>\n",
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       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877236</th>\n",
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       "      <td>75.00</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>230.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>9088.50</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
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       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877320</th>\n",
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       "      <td>45.00</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>3477.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>585114.24</td>\n",
       "      <td>1.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877230</th>\n",
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       "      <td>1.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>8175.50</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
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       "      <td>585114.24</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877306</th>\n",
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       "      <td>75.00</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>10841.00</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
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       "      <td>585114.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67694955</th>\n",
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       "      <td>0.00</td>\n",
       "      <td>112.50</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>10244.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
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       "      <td>585114.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877262</th>\n",
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       "      <td>15779.90</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>3399.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>585114.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877261</th>\n",
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       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>3442.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>585114.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877179</th>\n",
       "      <td>1156.38</td>\n",
       "      <td>15779.90</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>6493.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>585114.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877280</th>\n",
       "      <td>1845.38</td>\n",
       "      <td>15779.90</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.00</td>\n",
       "      <td>112.50</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>230.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>9742.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>585114.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877335</th>\n",
       "      <td>6012.47</td>\n",
       "      <td>15779.90</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>199.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>11360.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>585114.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877178</th>\n",
       "      <td>1032.34</td>\n",
       "      <td>15779.90</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>605749.12</td>\n",
       "      <td>438.00</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>62636.97</td>\n",
       "      <td>6548.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2276963.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>585114.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          DISTANCE_SUB  CTMT_PNTMT_STD  CLO_CEN  TIP_CC_RES-Tipo3  POT_NOM  \\\n",
       "79761050       4808.40        15779.90     0.01              0.00    75.00   \n",
       "26877318       4973.35        15779.90     0.01              0.00    45.00   \n",
       "26877308       4339.07        15779.90     0.01              0.00    45.00   \n",
       "26877322       5275.55        15779.90     0.01              0.00    45.00   \n",
       "26877388       6360.07        15779.90     0.01              0.00    45.00   \n",
       "26877267       4608.06        15779.90     0.01              0.00    75.00   \n",
       "26877307       3973.30        15779.90     0.01              0.00    75.00   \n",
       "26877389       6492.29        15779.90     0.01              0.00    45.00   \n",
       "26877333       6061.37        15779.90     0.01              0.00    75.00   \n",
       "26877263       3945.52        15779.90     0.01              0.00    45.00   \n",
       "26877344       6621.74        15779.90     0.01              0.00    75.00   \n",
       "62048302       6674.20        15779.90     0.01              0.00    45.00   \n",
       "26877340       6226.74        15779.90     0.01              0.00    75.00   \n",
       "26877392       7032.39        15779.90     0.01              0.00    45.00   \n",
       "26877319       4975.15        15779.90     0.01              0.00    45.00   \n",
       "26877317       4645.98        15779.90     0.01              0.00    45.00   \n",
       "26877328       5567.39        15779.90     0.01              0.00    45.00   \n",
       "26877260       3180.18        15779.90     0.01              0.00    30.00   \n",
       "26877386       6534.37        15779.90     0.01              0.00    45.00   \n",
       "26877259       3765.96        15779.90     0.01              0.00    30.00   \n",
       "67694966       2719.36        15779.90     0.01              0.00   112.50   \n",
       "26877246       2695.20        15779.90     0.01              0.00    75.00   \n",
       "26877298       3043.94        15779.90     0.01              0.00    45.00   \n",
       "26877268       4876.31        15779.90     0.01              0.00   112.50   \n",
       "26877274       4470.01        15779.90     0.01              0.00    45.00   \n",
       "26877304       3426.96        15779.90     0.01              0.00    45.00   \n",
       "64822638       2603.61        15779.90     0.01              0.00    75.00   \n",
       "26877289       2777.82        15779.90     0.01              0.00    75.00   \n",
       "26877321       5450.05        15779.90     0.01              0.00    30.00   \n",
       "26877252       2751.96        15779.90     0.01              0.00    75.00   \n",
       "26877373       5800.07        15779.90     0.01              0.00    75.00   \n",
       "26877239       2163.71        15779.90     0.01              0.00    45.00   \n",
       "26877270       4378.79        15779.90     0.01              0.00    30.00   \n",
       "26877284       2147.22        15779.90     0.01              1.00    75.00   \n",
       "26877256       2907.59        15779.90     0.01              0.00    75.00   \n",
       "26877303       4364.66        15779.90     0.01              0.00   112.50   \n",
       "26877343       6483.94        15779.90     0.01              0.00    45.00   \n",
       "26877242       2476.48        15779.90     0.01              0.00    30.00   \n",
       "26877266       4575.48        15779.90     0.01              0.00    75.00   \n",
       "26877236       1999.83        15779.90     0.01              1.00    75.00   \n",
       "26877320       5156.55        15779.90     0.01              0.00    45.00   \n",
       "26877230       1466.60        15779.90     0.01              1.00    75.00   \n",
       "26877306       3842.21        15779.90     0.01              0.00    75.00   \n",
       "67694955       2693.73        15779.90     0.01              0.00   112.50   \n",
       "26877262       4052.61        15779.90     0.01              0.00    45.00   \n",
       "26877261       3887.82        15779.90     0.01              0.00    45.00   \n",
       "26877179       1156.38        15779.90     0.01              1.00    45.00   \n",
       "26877280       1845.38        15779.90     0.01              1.00   112.50   \n",
       "26877335       6012.47        15779.90     0.01              0.00    75.00   \n",
       "26877178       1032.34        15779.90     0.01              1.00   300.00   \n",
       "\n",
       "          PERD_A4_B  I_NOM_MED_R  CTMT_ENE_STD  CTMT_PNTMT_MED  UCBT_ENE_MED  \\\n",
       "79761050  605749.12       438.00     141887.74        62636.97       6778.50   \n",
       "26877318  605749.12       438.00     141887.74        62636.97       6194.00   \n",
       "26877308  605749.12       438.00     141887.74        62636.97       7837.00   \n",
       "26877322  605749.12       438.00     141887.74        62636.97       8301.00   \n",
       "26877388  605749.12       230.00     141887.74        62636.97       7480.50   \n",
       "26877267  605749.12       438.00     141887.74        62636.97       8245.00   \n",
       "26877307  605749.12       438.00     141887.74        62636.97       7369.50   \n",
       "26877389  605749.12       230.00     141887.74        62636.97       6861.50   \n",
       "26877333  605749.12       168.00     141887.74        62636.97       8277.50   \n",
       "26877263  605749.12       438.00     141887.74        62636.97       7776.50   \n",
       "26877344  605749.12       230.00     141887.74        62636.97       7673.00   \n",
       "62048302  605749.12       230.00     141887.74        62636.97       7717.50   \n",
       "26877340  605749.12       168.00     141887.74        62636.97       6035.00   \n",
       "26877392  605749.12       230.00     141887.74        62636.97       7533.00   \n",
       "26877319  605749.12       438.00     141887.74        62636.97       5346.50   \n",
       "26877317  605749.12       438.00     141887.74        62636.97       9098.00   \n",
       "26877328  605749.12       230.00     141887.74        62636.97       5030.50   \n",
       "26877260  605749.12       438.00     141887.74        62636.97       6333.00   \n",
       "26877386  605749.12       149.00     141887.74        62636.97       5215.00   \n",
       "26877259  605749.12       438.00     141887.74        62636.97       5252.50   \n",
       "67694966  605749.12       438.00     141887.74        62636.97       6795.00   \n",
       "26877246  605749.12       438.00     141887.74        62636.97       6703.50   \n",
       "26877298  605749.12       438.00     141887.74        62636.97       5803.50   \n",
       "26877268  605749.12       438.00     141887.74        62636.97       9786.50   \n",
       "26877274  605749.12       438.00     141887.74        62636.97       4546.00   \n",
       "26877304  605749.12       230.00     141887.74        62636.97       5069.50   \n",
       "64822638  605749.12       438.00     141887.74        62636.97       8277.00   \n",
       "26877289  605749.12       438.00     141887.74        62636.97       8738.00   \n",
       "26877321  605749.12       438.00     141887.74        62636.97       4256.50   \n",
       "26877252  605749.12       230.00     141887.74        62636.97       5454.50   \n",
       "26877373  605749.12       230.00     141887.74        62636.97      10199.00   \n",
       "26877239  605749.12       230.00     141887.74        62636.97       6752.00   \n",
       "26877270  605749.12       438.00     141887.74        62636.97       4182.50   \n",
       "26877284  605749.12       230.00     141887.74        62636.97       6440.00   \n",
       "26877256  605749.12       438.00     141887.74        62636.97       5027.00   \n",
       "26877303  605749.12       438.00     141887.74        62636.97       3982.50   \n",
       "26877343  605749.12       230.00     141887.74        62636.97      10329.50   \n",
       "26877242  605749.12       230.00     141887.74        62636.97       4871.50   \n",
       "26877266  605749.12       438.00     141887.74        62636.97      10837.00   \n",
       "26877236  605749.12       230.00     141887.74        62636.97       9088.50   \n",
       "26877320  605749.12       438.00     141887.74        62636.97       3477.00   \n",
       "26877230  605749.12       438.00     141887.74        62636.97       8175.50   \n",
       "26877306  605749.12       438.00     141887.74        62636.97      10841.00   \n",
       "67694955  605749.12       438.00     141887.74        62636.97      10244.00   \n",
       "26877262  605749.12       438.00     141887.74        62636.97       3399.50   \n",
       "26877261  605749.12       438.00     141887.74        62636.97       3442.00   \n",
       "26877179  605749.12       438.00     141887.74        62636.97       6493.00   \n",
       "26877280  605749.12       230.00     141887.74        62636.97       9742.50   \n",
       "26877335  605749.12       199.00     141887.74        62636.97      11360.00   \n",
       "26877178  605749.12       438.00     141887.74        62636.97       6548.50   \n",
       "\n",
       "          ...  PAGE_RANK  TIP_TRAFO_M  FAS_CON_S_ABCN  CTMT_ENE_MED  \\\n",
       "79761050  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877318  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877308  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877322  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877388  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877267  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877307  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877389  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877333  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877263  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877344  ...       0.00         0.00            0.00    2276963.08   \n",
       "62048302  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877340  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877392  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877319  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877317  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877328  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877260  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877386  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877259  ...       0.00         0.00            0.00    2276963.08   \n",
       "67694966  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877246  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877298  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877268  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877274  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877304  ...       0.00         0.00            0.00    2276963.08   \n",
       "64822638  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877289  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877321  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877252  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877373  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877239  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877270  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877284  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877256  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877303  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877343  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877242  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877266  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877236  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877320  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877230  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877306  ...       0.00         0.00            0.00    2276963.08   \n",
       "67694955  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877262  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877261  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877179  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877280  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877335  ...       0.00         0.00            0.00    2276963.08   \n",
       "26877178  ...       0.00         0.00            0.00    2276963.08   \n",
       "\n",
       "          GRU_TAR_B3  TIP_CC_RES-Tipo10   PERD_A4  TIP_CC_RES-Tipo5   FIC  \\\n",
       "79761050        0.00               0.00 585114.24              0.00  2.00   \n",
       "26877318        0.00               0.00 585114.24              0.00  6.00   \n",
       "26877308        0.00               0.00 585114.24              0.00  2.00   \n",
       "26877322        0.00               0.00 585114.24              0.00  6.00   \n",
       "26877388        0.00               0.00 585114.24              0.00  6.00   \n",
       "26877267        0.00               0.00 585114.24              0.00  2.00   \n",
       "26877307        0.00               0.00 585114.24              0.00  2.00   \n",
       "26877389        0.00               0.00 585114.24              0.00  6.00   \n",
       "26877333        0.00               0.00 585114.24              0.00  6.00   \n",
       "26877263        0.00               0.00 585114.24              0.00  2.00   \n",
       "26877344        0.00               0.00 585114.24              1.00  6.00   \n",
       "62048302        0.00               0.00 585114.24              0.00  6.00   \n",
       "26877340        0.00               0.00 585114.24              0.00  6.00   \n",
       "26877392        0.00               0.00 585114.24              0.00  6.00   \n",
       "26877319        0.00               0.00 585114.24              0.00 14.00   \n",
       "26877317        0.00               0.00 585114.24              0.00 18.00   \n",
       "26877328        0.00               0.00 585114.24              0.00  6.00   \n",
       "26877260        0.00               0.00 585114.24              0.00 14.00   \n",
       "26877386        0.00               0.00 585114.24              0.00  2.00   \n",
       "26877259        0.00               0.00 585114.24              0.00 14.00   \n",
       "67694966        0.00               1.00 585114.24              0.00  6.00   \n",
       "26877246        0.00               0.00 585114.24              0.00 14.00   \n",
       "26877298        0.00               0.00 585114.24              0.00 14.00   \n",
       "26877268        0.00               0.00 585114.24              0.00 10.00   \n",
       "26877274        0.00               0.00 585114.24              0.00 14.00   \n",
       "26877304        0.00               0.00 585114.24              0.00  2.00   \n",
       "64822638        0.00               1.00 585114.24              0.00  6.00   \n",
       "26877289        0.00               0.00 585114.24              0.00 14.00   \n",
       "26877321        0.00               1.00 585114.24              0.00 18.00   \n",
       "26877252        0.00               1.00 585114.24              0.00 10.00   \n",
       "26877373        0.00               1.00 585114.24              0.00  6.00   \n",
       "26877239        0.00               0.00 585114.24              0.00  6.00   \n",
       "26877270        0.00               0.00 585114.24              0.00  6.00   \n",
       "26877284        0.00               0.00 585114.24              0.00  6.00   \n",
       "26877256        0.00               0.00 585114.24              0.00 14.00   \n",
       "26877303        0.00               0.00 585114.24              0.00  4.00   \n",
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       "[50 rows x 22 columns]"
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     "metadata": {},
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      "Cluster 2:\n"
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       "      <td>0.00</td>\n",
       "      <td>112.50</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>13070.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879207</th>\n",
       "      <td>3246.87</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>8213.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879249</th>\n",
       "      <td>6064.59</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>13072.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879228</th>\n",
       "      <td>5952.86</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>112.50</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>13051.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879213</th>\n",
       "      <td>4961.00</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>4534.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879209</th>\n",
       "      <td>3926.36</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>5336.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879189</th>\n",
       "      <td>5693.45</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>13356.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879188</th>\n",
       "      <td>4650.36</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>12743.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879165</th>\n",
       "      <td>2657.15</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>112.50</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>8880.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879221</th>\n",
       "      <td>4954.38</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>3991.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879225</th>\n",
       "      <td>5784.89</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>13567.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879196</th>\n",
       "      <td>5775.51</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>112.50</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>13566.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879224</th>\n",
       "      <td>5671.68</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>3461.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879198</th>\n",
       "      <td>2986.86</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>10944.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879148</th>\n",
       "      <td>2336.37</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>112.50</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>7630.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879166</th>\n",
       "      <td>2745.48</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>112.50</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>10874.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879145</th>\n",
       "      <td>2258.55</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>112.50</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>8459.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879200</th>\n",
       "      <td>3153.27</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>5378.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879200</th>\n",
       "      <td>3153.27</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>5378.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879174</th>\n",
       "      <td>3925.60</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>112.50</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>12882.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879143</th>\n",
       "      <td>2124.45</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>10060.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879192</th>\n",
       "      <td>5104.49</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>2922.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879206</th>\n",
       "      <td>3594.66</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>13633.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879173</th>\n",
       "      <td>3762.42</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>14008.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879208</th>\n",
       "      <td>3815.43</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>14080.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879157</th>\n",
       "      <td>1644.17</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>11410.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879178</th>\n",
       "      <td>3572.30</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>2877.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879186</th>\n",
       "      <td>5566.19</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>112.50</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>15520.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879220</th>\n",
       "      <td>4712.07</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>1896.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879164</th>\n",
       "      <td>2382.51</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>13376.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879162</th>\n",
       "      <td>2484.71</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>13647.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879245</th>\n",
       "      <td>6189.14</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>112.50</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>15977.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879205</th>\n",
       "      <td>3865.59</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>14941.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26879146</th>\n",
       "      <td>2172.02</td>\n",
       "      <td>2088.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>389840.98</td>\n",
       "      <td>365.00</td>\n",
       "      <td>43960.32</td>\n",
       "      <td>18885.60</td>\n",
       "      <td>13468.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1421387.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255476.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          DISTANCE_SUB  CTMT_PNTMT_STD  CLO_CEN  TIP_CC_RES-Tipo3  POT_NOM  \\\n",
       "26879246       6586.65         2088.58     0.01              0.00   112.50   \n",
       "26879229       5968.43         2088.58     0.02              0.00    45.00   \n",
       "26879191       5813.85         2088.58     0.02              0.00   112.50   \n",
       "26879247       6832.07         2088.58     0.01              0.00    75.00   \n",
       "26879216       5359.71         2088.58     0.02              0.00    75.00   \n",
       "26879223       5692.10         2088.58     0.02              0.00    75.00   \n",
       "26879181       4277.49         2088.58     0.02              0.00    75.00   \n",
       "26879193       5866.00         2088.58     0.02              0.00    75.00   \n",
       "26879248       6273.53         2088.58     0.02              0.00    75.00   \n",
       "26879214       4164.33         2088.58     0.02              0.00    75.00   \n",
       "26879217       5494.79         2088.58     0.02              0.00    75.00   \n",
       "26879211       4109.64         2088.58     0.02              0.00    75.00   \n",
       "26879171       3748.00         2088.58     0.03              0.00    45.00   \n",
       "26879179       5481.97         2088.58     0.02              0.00    75.00   \n",
       "26879202       3458.65         2088.58     0.00              0.00   112.50   \n",
       "26879202       3458.65         2088.58     0.03              0.00   112.50   \n",
       "26879232       6357.48         2088.58     0.02              0.00   112.50   \n",
       "26879207       3246.87         2088.58     0.02              0.00    75.00   \n",
       "26879249       6064.59         2088.58     0.02              0.00    75.00   \n",
       "26879228       5952.86         2088.58     0.02              0.00   112.50   \n",
       "26879213       4961.00         2088.58     0.02              0.00    45.00   \n",
       "26879209       3926.36         2088.58     0.02              0.00    45.00   \n",
       "26879189       5693.45         2088.58     0.02              0.00    75.00   \n",
       "26879188       4650.36         2088.58     0.02              0.00    75.00   \n",
       "26879165       2657.15         2088.58     0.02              0.00   112.50   \n",
       "26879221       4954.38         2088.58     0.02              0.00    45.00   \n",
       "26879225       5784.89         2088.58     0.02              0.00    75.00   \n",
       "26879196       5775.51         2088.58     0.02              0.00   112.50   \n",
       "26879224       5671.68         2088.58     0.02              0.00    75.00   \n",
       "26879198       2986.86         2088.58     0.02              0.00    45.00   \n",
       "26879148       2336.37         2088.58     0.02              0.00   112.50   \n",
       "26879166       2745.48         2088.58     0.02              0.00   112.50   \n",
       "26879145       2258.55         2088.58     0.02              0.00   112.50   \n",
       "26879200       3153.27         2088.58     0.00              0.00    45.00   \n",
       "26879200       3153.27         2088.58     0.03              0.00    45.00   \n",
       "26879174       3925.60         2088.58     0.03              0.00   112.50   \n",
       "26879143       2124.45         2088.58     0.02              0.00    75.00   \n",
       "26879192       5104.49         2088.58     0.02              0.00    45.00   \n",
       "26879206       3594.66         2088.58     0.02              0.00    75.00   \n",
       "26879173       3762.42         2088.58     0.02              0.00    75.00   \n",
       "26879208       3815.43         2088.58     0.02              0.00    75.00   \n",
       "26879157       1644.17         2088.58     0.02              0.00    75.00   \n",
       "26879178       3572.30         2088.58     0.02              0.00    45.00   \n",
       "26879186       5566.19         2088.58     0.02              0.00   112.50   \n",
       "26879220       4712.07         2088.58     0.02              0.00    30.00   \n",
       "26879164       2382.51         2088.58     0.01              0.00    75.00   \n",
       "26879162       2484.71         2088.58     0.02              0.00    75.00   \n",
       "26879245       6189.14         2088.58     0.02              0.00   112.50   \n",
       "26879205       3865.59         2088.58     0.03              0.00    75.00   \n",
       "26879146       2172.02         2088.58     0.02              0.00    75.00   \n",
       "\n",
       "          PERD_A4_B  I_NOM_MED_R  CTMT_ENE_STD  CTMT_PNTMT_MED  UCBT_ENE_MED  \\\n",
       "26879246  389840.98       365.00      43960.32        18885.60       7237.50   \n",
       "26879229  389840.98       365.00      43960.32        18885.60       7795.50   \n",
       "26879191  389840.98       365.00      43960.32        18885.60       9377.50   \n",
       "26879247  389840.98       365.00      43960.32        18885.60      10846.00   \n",
       "26879216  389840.98       365.00      43960.32        18885.60       9323.00   \n",
       "26879223  389840.98       365.00      43960.32        18885.60      11306.50   \n",
       "26879181  389840.98       365.00      43960.32        18885.60       8050.50   \n",
       "26879193  389840.98       365.00      43960.32        18885.60       5144.50   \n",
       "26879248  389840.98       365.00      43960.32        18885.60       4895.50   \n",
       "26879214  389840.98       365.00      43960.32        18885.60       6924.50   \n",
       "26879217  389840.98       365.00      43960.32        18885.60      11966.00   \n",
       "26879211  389840.98       365.00      43960.32        18885.60      10441.50   \n",
       "26879171  389840.98       365.00      43960.32        18885.60       9306.50   \n",
       "26879179  389840.98       365.00      43960.32        18885.60       4621.50   \n",
       "26879202  389840.98       365.00      43960.32        18885.60       9553.50   \n",
       "26879202  389840.98       365.00      43960.32        18885.60       9553.50   \n",
       "26879232  389840.98       365.00      43960.32        18885.60      13070.50   \n",
       "26879207  389840.98       365.00      43960.32        18885.60       8213.00   \n",
       "26879249  389840.98       365.00      43960.32        18885.60      13072.50   \n",
       "26879228  389840.98       365.00      43960.32        18885.60      13051.00   \n",
       "26879213  389840.98       365.00      43960.32        18885.60       4534.50   \n",
       "26879209  389840.98       365.00      43960.32        18885.60       5336.00   \n",
       "26879189  389840.98       365.00      43960.32        18885.60      13356.00   \n",
       "26879188  389840.98       365.00      43960.32        18885.60      12743.00   \n",
       "26879165  389840.98       365.00      43960.32        18885.60       8880.50   \n",
       "26879221  389840.98       365.00      43960.32        18885.60       3991.00   \n",
       "26879225  389840.98       365.00      43960.32        18885.60      13567.00   \n",
       "26879196  389840.98       365.00      43960.32        18885.60      13566.50   \n",
       "26879224  389840.98       365.00      43960.32        18885.60       3461.50   \n",
       "26879198  389840.98       365.00      43960.32        18885.60      10944.00   \n",
       "26879148  389840.98       365.00      43960.32        18885.60       7630.50   \n",
       "26879166  389840.98       365.00      43960.32        18885.60      10874.00   \n",
       "26879145  389840.98       365.00      43960.32        18885.60       8459.50   \n",
       "26879200  389840.98       365.00      43960.32        18885.60       5378.00   \n",
       "26879200  389840.98       365.00      43960.32        18885.60       5378.00   \n",
       "26879174  389840.98       365.00      43960.32        18885.60      12882.50   \n",
       "26879143  389840.98       365.00      43960.32        18885.60      10060.50   \n",
       "26879192  389840.98       365.00      43960.32        18885.60       2922.50   \n",
       "26879206  389840.98       365.00      43960.32        18885.60      13633.50   \n",
       "26879173  389840.98       365.00      43960.32        18885.60      14008.50   \n",
       "26879208  389840.98       365.00      43960.32        18885.60      14080.50   \n",
       "26879157  389840.98       365.00      43960.32        18885.60      11410.50   \n",
       "26879178  389840.98       365.00      43960.32        18885.60       2877.50   \n",
       "26879186  389840.98       365.00      43960.32        18885.60      15520.00   \n",
       "26879220  389840.98       365.00      43960.32        18885.60       1896.00   \n",
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       "26879162  389840.98       365.00      43960.32        18885.60      13647.50   \n",
       "26879245  389840.98       365.00      43960.32        18885.60      15977.50   \n",
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       "26879146  389840.98       365.00      43960.32        18885.60      13468.50   \n",
       "\n",
       "          ...  PAGE_RANK  TIP_TRAFO_M  FAS_CON_S_ABCN  CTMT_ENE_MED  \\\n",
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       "\n",
       "          GRU_TAR_B3  TIP_CC_RES-Tipo10   PERD_A4  TIP_CC_RES-Tipo5   FIC  \\\n",
       "26879246        0.00               0.00 255476.21              0.00  0.00   \n",
       "26879229        0.00               0.00 255476.21              0.00 14.00   \n",
       "26879191        0.00               0.00 255476.21              0.00 14.00   \n",
       "26879247        0.00               0.00 255476.21              0.00  0.00   \n",
       "26879216        0.00               0.00 255476.21              0.00 14.00   \n",
       "26879223        0.00               0.00 255476.21              0.00  0.00   \n",
       "26879181        0.00               0.00 255476.21              1.00 14.00   \n",
       "26879193        0.00               0.00 255476.21              0.00 14.00   \n",
       "26879248        0.00               0.00 255476.21              0.00 14.00   \n",
       "26879214        0.00               0.00 255476.21              1.00  6.00   \n",
       "26879217        0.00               0.00 255476.21              0.00 14.00   \n",
       "26879211        0.00               0.00 255476.21              0.00 14.00   \n",
       "26879171        0.00               0.00 255476.21              1.00 14.00   \n",
       "26879179        1.00               0.00 255476.21              0.00 14.00   \n",
       "26879202        0.00               0.00 255476.21              1.00 14.00   \n",
       "26879202        0.00               0.00 255476.21              1.00 14.00   \n",
       "26879232        0.00               0.00 255476.21              0.00 14.00   \n",
       "26879207        0.00               0.00 255476.21              1.00 14.00   \n",
       "26879249        0.00               0.00 255476.21              0.00  0.00   \n",
       "26879228        0.00               0.00 255476.21              0.00  0.00   \n",
       "26879213        1.00               0.00 255476.21              0.00 14.00   \n",
       "26879209        0.00               0.00 255476.21              1.00  6.00   \n",
       "26879189        0.00               0.00 255476.21              0.00 14.00   \n",
       "26879188        0.00               0.00 255476.21              1.00 14.00   \n",
       "26879165        0.00               0.00 255476.21              1.00 14.00   \n",
       "26879221        0.00               0.00 255476.21              0.00 14.00   \n",
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       "26879196        0.00               0.00 255476.21              0.00  0.00   \n",
       "26879224        1.00               0.00 255476.21              0.00  0.00   \n",
       "26879198        0.00               0.00 255476.21              0.00 14.00   \n",
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       "26879200        1.00               0.00 255476.21              0.00 14.00   \n",
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       "26879146        0.00               0.00 255476.21              0.00  6.00   \n",
       "\n",
       "          Cluster  \n",
       "26879246     2.00  \n",
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       "26879216     2.00  \n",
       "26879223     2.00  \n",
       "26879181     2.00  \n",
       "26879193     2.00  \n",
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       "26879211     2.00  \n",
       "26879171     2.00  \n",
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       "26879202     2.00  \n",
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       "26879249     2.00  \n",
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       "26879188     2.00  \n",
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       "26879206     2.00  \n",
       "26879173     2.00  \n",
       "26879208     2.00  \n",
       "26879157     2.00  \n",
       "26879178     2.00  \n",
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       "\n",
       "[50 rows x 22 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster 3:\n"
     ]
    },
    {
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       "      <td>909.23</td>\n",
       "      <td>4599.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>1.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876330</th>\n",
       "      <td>10043.28</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>2867.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876373</th>\n",
       "      <td>13778.56</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>5171.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876372</th>\n",
       "      <td>14154.35</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>5679.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>1.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876318</th>\n",
       "      <td>9130.79</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>9152.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>1.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876420</th>\n",
       "      <td>14317.01</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>5163.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>1.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876339</th>\n",
       "      <td>10618.84</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>112.50</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>9690.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876354</th>\n",
       "      <td>12138.89</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>2177.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876319</th>\n",
       "      <td>9301.19</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>9678.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876422</th>\n",
       "      <td>14576.97</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>4668.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876348</th>\n",
       "      <td>11549.67</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>1894.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876340</th>\n",
       "      <td>10751.24</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>1687.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876349</th>\n",
       "      <td>11560.71</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>1708.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876383</th>\n",
       "      <td>14890.28</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>6692.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876378</th>\n",
       "      <td>14514.73</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>3916.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876423</th>\n",
       "      <td>14676.36</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>3778.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63800272</th>\n",
       "      <td>11116.58</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>1346.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876388</th>\n",
       "      <td>15268.22</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>5930.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876293</th>\n",
       "      <td>8453.67</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>130.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>1860.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876352</th>\n",
       "      <td>11931.29</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>1367.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876434</th>\n",
       "      <td>15405.18</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>6178.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876324</th>\n",
       "      <td>9621.44</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>1256.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876278</th>\n",
       "      <td>6767.02</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>149.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>3077.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876416</th>\n",
       "      <td>14114.50</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>2387.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>1.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876297</th>\n",
       "      <td>9379.99</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>130.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>1172.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876321</th>\n",
       "      <td>9464.81</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>1084.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876273</th>\n",
       "      <td>6171.97</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>3783.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876271</th>\n",
       "      <td>5784.29</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>6869.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876357</th>\n",
       "      <td>11994.84</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>926.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876355</th>\n",
       "      <td>12187.34</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>843.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876350</th>\n",
       "      <td>11807.73</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>651.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876336</th>\n",
       "      <td>11486.73</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>543.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876425</th>\n",
       "      <td>14686.12</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>9557.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876326</th>\n",
       "      <td>9850.31</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>519.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876275</th>\n",
       "      <td>6156.10</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>2902.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876389</th>\n",
       "      <td>15415.45</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>8647.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876338</th>\n",
       "      <td>10662.61</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>11344.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>1.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876376</th>\n",
       "      <td>14062.52</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>1515.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154081659</th>\n",
       "      <td>13621.37</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>1157.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876374</th>\n",
       "      <td>14354.26</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>1657.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876397</th>\n",
       "      <td>16014.71</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>4154.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876334</th>\n",
       "      <td>11266.62</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>317.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>1.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           DISTANCE_SUB  CTMT_PNTMT_STD  CLO_CEN  TIP_CC_RES-Tipo3  POT_NOM  \\\n",
       "26876337       10536.27          236.63     0.01              0.00    45.00   \n",
       "26876345       11037.20          236.63     0.01              0.00    45.00   \n",
       "26876322        9583.59          236.63     0.01              0.00    30.00   \n",
       "26876325        9620.73          236.63     0.01              1.00    30.00   \n",
       "26876320        9422.00          236.63     0.01              0.00    45.00   \n",
       "26876342       10910.11          236.63     0.01              0.00    45.00   \n",
       "26876353       12138.64          236.63     0.01              0.00    45.00   \n",
       "26876347       11234.99          236.63     0.01              0.00    45.00   \n",
       "26876365       13064.00          236.63     0.01              0.00    45.00   \n",
       "26876330       10043.28          236.63     0.01              0.00    45.00   \n",
       "26876373       13778.56          236.63     0.01              0.00    45.00   \n",
       "26876372       14154.35          236.63     0.01              0.00    45.00   \n",
       "26876318        9130.79          236.63     0.01              0.00    75.00   \n",
       "26876420       14317.01          236.63     0.01              0.00    75.00   \n",
       "26876339       10618.84          236.63     0.01              0.00   112.50   \n",
       "26876354       12138.89          236.63     0.01              0.00    15.00   \n",
       "26876319        9301.19          236.63     0.01              0.00    75.00   \n",
       "26876422       14576.97          236.63     0.01              0.00    30.00   \n",
       "26876348       11549.67          236.63     0.01              0.00    45.00   \n",
       "26876340       10751.24          236.63     0.01              0.00    45.00   \n",
       "26876349       11560.71          236.63     0.01              0.00    10.00   \n",
       "26876383       14890.28          236.63     0.01              0.00    45.00   \n",
       "26876378       14514.73          236.63     0.01              0.00    30.00   \n",
       "26876423       14676.36          236.63     0.01              0.00    15.00   \n",
       "63800272       11116.58          236.63     0.01              0.00    45.00   \n",
       "26876388       15268.22          236.63     0.01              0.00    45.00   \n",
       "26876293        8453.67          236.63     0.00              0.00    25.00   \n",
       "26876352       11931.29          236.63     0.01              0.00    25.00   \n",
       "26876434       15405.18          236.63     0.01              0.00    45.00   \n",
       "26876324        9621.44          236.63     0.01              0.00    75.00   \n",
       "26876278        6767.02          236.63     0.00              0.00    45.00   \n",
       "26876416       14114.50          236.63     0.01              0.00    30.00   \n",
       "26876297        9379.99          236.63     0.00              0.00    45.00   \n",
       "26876321        9464.81          236.63     0.01              0.00    15.00   \n",
       "26876273        6171.97          236.63     0.00              0.00    45.00   \n",
       "26876271        5784.29          236.63     0.00              0.00    45.00   \n",
       "26876357       11994.84          236.63     0.01              0.00    15.00   \n",
       "26876355       12187.34          236.63     0.01              0.00    25.00   \n",
       "26876350       11807.73          236.63     0.01              0.00    10.00   \n",
       "26876336       11486.73          236.63     0.01              0.00    30.00   \n",
       "26876425       14686.12          236.63     0.01              0.00    75.00   \n",
       "26876326        9850.31          236.63     0.01              0.00    30.00   \n",
       "26876275        6156.10          236.63     0.00              0.00    45.00   \n",
       "26876389       15415.45          236.63     0.01              0.00    30.00   \n",
       "26876338       10662.61          236.63     0.01              0.00    75.00   \n",
       "26876376       14062.52          236.63     0.01              0.00    30.00   \n",
       "154081659      13621.37          236.63     0.01              0.00    30.00   \n",
       "26876374       14354.26          236.63     0.01              0.00    25.00   \n",
       "26876397       16014.71          236.63     0.01              0.00    30.00   \n",
       "26876334       11266.62          236.63     0.01              0.00     5.00   \n",
       "\n",
       "           PERD_A4_B  I_NOM_MED_R  CTMT_ENE_STD  CTMT_PNTMT_MED  UCBT_ENE_MED  \\\n",
       "26876337   506374.22       438.00      49923.49          909.23       5945.50   \n",
       "26876345   506374.22       438.00      49923.49          909.23       6208.50   \n",
       "26876322   506374.22       438.00      49923.49          909.23       5791.50   \n",
       "26876325   506374.22       438.00      49923.49          909.23       5498.50   \n",
       "26876320   506374.22       438.00      49923.49          909.23       6799.00   \n",
       "26876342   506374.22       438.00      49923.49          909.23       4111.50   \n",
       "26876353   506374.22       438.00      49923.49          909.23       7097.00   \n",
       "26876347   506374.22       438.00      49923.49          909.23       7941.00   \n",
       "26876365   506374.22       438.00      49923.49          909.23       4599.00   \n",
       "26876330   506374.22       438.00      49923.49          909.23       2867.50   \n",
       "26876373   506374.22       438.00      49923.49          909.23       5171.50   \n",
       "26876372   506374.22       438.00      49923.49          909.23       5679.00   \n",
       "26876318   506374.22       438.00      49923.49          909.23       9152.50   \n",
       "26876420   506374.22       438.00      49923.49          909.23       5163.50   \n",
       "26876339   506374.22       438.00      49923.49          909.23       9690.00   \n",
       "26876354   506374.22       438.00      49923.49          909.23       2177.00   \n",
       "26876319   506374.22       438.00      49923.49          909.23       9678.50   \n",
       "26876422   506374.22       438.00      49923.49          909.23       4668.50   \n",
       "26876348   506374.22       438.00      49923.49          909.23       1894.00   \n",
       "26876340   506374.22       438.00      49923.49          909.23       1687.50   \n",
       "26876349   506374.22       438.00      49923.49          909.23       1708.00   \n",
       "26876383   506374.22       438.00      49923.49          909.23       6692.50   \n",
       "26876378   506374.22       438.00      49923.49          909.23       3916.00   \n",
       "26876423   506374.22       438.00      49923.49          909.23       3778.00   \n",
       "63800272   506374.22       438.00      49923.49          909.23       1346.50   \n",
       "26876388   506374.22       438.00      49923.49          909.23       5930.00   \n",
       "26876293   506374.22       130.00      49923.49          909.23       1860.00   \n",
       "26876352   506374.22       438.00      49923.49          909.23       1367.50   \n",
       "26876434   506374.22       438.00      49923.49          909.23       6178.00   \n",
       "26876324   506374.22       438.00      49923.49          909.23       1256.50   \n",
       "26876278   506374.22       149.00      49923.49          909.23       3077.50   \n",
       "26876416   506374.22       438.00      49923.49          909.23       2387.50   \n",
       "26876297   506374.22       130.00      49923.49          909.23       1172.00   \n",
       "26876321   506374.22       438.00      49923.49          909.23       1084.00   \n",
       "26876273   506374.22       438.00      49923.49          909.23       3783.50   \n",
       "26876271   506374.22       438.00      49923.49          909.23       6869.50   \n",
       "26876357   506374.22       438.00      49923.49          909.23        926.50   \n",
       "26876355   506374.22       438.00      49923.49          909.23        843.00   \n",
       "26876350   506374.22       438.00      49923.49          909.23        651.50   \n",
       "26876336   506374.22       438.00      49923.49          909.23        543.50   \n",
       "26876425   506374.22       438.00      49923.49          909.23       9557.00   \n",
       "26876326   506374.22       438.00      49923.49          909.23        519.50   \n",
       "26876275   506374.22       438.00      49923.49          909.23       2902.50   \n",
       "26876389   506374.22       438.00      49923.49          909.23       8647.00   \n",
       "26876338   506374.22       438.00      49923.49          909.23      11344.00   \n",
       "26876376   506374.22       438.00      49923.49          909.23       1515.50   \n",
       "154081659  506374.22       438.00      49923.49          909.23       1157.50   \n",
       "26876374   506374.22       438.00      49923.49          909.23       1657.00   \n",
       "26876397   506374.22       438.00      49923.49          909.23       4154.00   \n",
       "26876334   506374.22       438.00      49923.49          909.23        317.50   \n",
       "\n",
       "           ...  PAGE_RANK  TIP_TRAFO_M  FAS_CON_S_ABCN  CTMT_ENE_MED  \\\n",
       "26876337   ...       0.00         0.00            0.00     844268.40   \n",
       "26876345   ...       0.00         0.00            0.00     844268.40   \n",
       "26876322   ...       0.00         0.00            0.00     844268.40   \n",
       "26876325   ...       0.00         0.00            0.00     844268.40   \n",
       "26876320   ...       0.00         0.00            0.00     844268.40   \n",
       "26876342   ...       0.00         0.00            0.00     844268.40   \n",
       "26876353   ...       0.00         0.00            0.00     844268.40   \n",
       "26876347   ...       0.00         0.00            0.00     844268.40   \n",
       "26876365   ...       0.00         0.00            0.00     844268.40   \n",
       "26876330   ...       0.00         0.00            0.00     844268.40   \n",
       "26876373   ...       0.00         0.00            0.00     844268.40   \n",
       "26876372   ...       0.00         0.00            0.00     844268.40   \n",
       "26876318   ...       0.00         0.00            0.00     844268.40   \n",
       "26876420   ...       0.00         0.00            0.00     844268.40   \n",
       "26876339   ...       0.00         0.00            0.00     844268.40   \n",
       "26876354   ...       0.00         0.00            0.00     844268.40   \n",
       "26876319   ...       0.00         0.00            0.00     844268.40   \n",
       "26876422   ...       0.00         0.00            0.00     844268.40   \n",
       "26876348   ...       0.00         0.00            0.00     844268.40   \n",
       "26876340   ...       0.00         0.00            0.00     844268.40   \n",
       "26876349   ...       0.00         1.00            0.00     844268.40   \n",
       "26876383   ...       0.00         0.00            0.00     844268.40   \n",
       "26876378   ...       0.00         0.00            0.00     844268.40   \n",
       "26876423   ...       0.00         0.00            0.00     844268.40   \n",
       "63800272   ...       0.00         0.00            0.00     844268.40   \n",
       "26876388   ...       0.00         0.00            0.00     844268.40   \n",
       "26876293   ...       0.00         1.00            0.00     844268.40   \n",
       "26876352   ...       0.00         1.00            0.00     844268.40   \n",
       "26876434   ...       0.00         0.00            0.00     844268.40   \n",
       "26876324   ...       0.00         0.00            0.00     844268.40   \n",
       "26876278   ...       0.00         0.00            0.00     844268.40   \n",
       "26876416   ...       0.00         0.00            0.00     844268.40   \n",
       "26876297   ...       0.00         0.00            0.00     844268.40   \n",
       "26876321   ...       0.00         0.00            0.00     844268.40   \n",
       "26876273   ...       0.00         0.00            0.00     844268.40   \n",
       "26876271   ...       0.00         0.00            0.00     844268.40   \n",
       "26876357   ...       0.00         1.00            0.00     844268.40   \n",
       "26876355   ...       0.00         1.00            0.00     844268.40   \n",
       "26876350   ...       0.00         1.00            0.00     844268.40   \n",
       "26876336   ...       0.00         0.00            0.00     844268.40   \n",
       "26876425   ...       0.00         0.00            0.00     844268.40   \n",
       "26876326   ...       0.00         0.00            0.00     844268.40   \n",
       "26876275   ...       0.00         0.00            0.00     844268.40   \n",
       "26876389   ...       0.00         0.00            0.00     844268.40   \n",
       "26876338   ...       0.00         0.00            0.00     844268.40   \n",
       "26876376   ...       0.00         0.00            0.00     844268.40   \n",
       "154081659  ...       0.00         0.00            0.00     844268.40   \n",
       "26876374   ...       0.00         0.00            0.00     844268.40   \n",
       "26876397   ...       0.00         0.00            0.00     844268.40   \n",
       "26876334   ...       0.00         0.00            0.00     844268.40   \n",
       "\n",
       "           GRU_TAR_B3  TIP_CC_RES-Tipo10   PERD_A4  TIP_CC_RES-Tipo5   FIC  \\\n",
       "26876337         0.00               0.00 180278.34              1.00 12.00   \n",
       "26876345         0.00               0.00 180278.34              1.00 10.00   \n",
       "26876322         0.00               0.00 180278.34              0.00  6.00   \n",
       "26876325         0.00               0.00 180278.34              0.00 18.00   \n",
       "26876320         0.00               0.00 180278.34              1.00 14.00   \n",
       "26876342         0.00               0.00 180278.34              0.00  6.00   \n",
       "26876353         0.00               0.00 180278.34              1.00  4.00   \n",
       "26876347         0.00               0.00 180278.34              0.00 18.00   \n",
       "26876365         0.00               0.00 180278.34              1.00 18.00   \n",
       "26876330         0.00               0.00 180278.34              0.00  6.00   \n",
       "26876373         0.00               0.00 180278.34              1.00 14.00   \n",
       "26876372         0.00               0.00 180278.34              1.00 16.00   \n",
       "26876318         0.00               0.00 180278.34              1.00  8.00   \n",
       "26876420         0.00               0.00 180278.34              1.00 10.00   \n",
       "26876339         0.00               0.00 180278.34              1.00  4.00   \n",
       "26876354         0.00               0.00 180278.34              0.00  6.00   \n",
       "26876319         0.00               0.00 180278.34              1.00  4.00   \n",
       "26876422         0.00               0.00 180278.34              0.00 18.00   \n",
       "26876348         0.00               0.00 180278.34              1.00  4.00   \n",
       "26876340         0.00               0.00 180278.34              1.00  4.00   \n",
       "26876349         0.00               0.00 180278.34              0.00 10.00   \n",
       "26876383         0.00               0.00 180278.34              0.00 18.00   \n",
       "26876378         0.00               0.00 180278.34              0.00 10.00   \n",
       "26876423         0.00               0.00 180278.34              0.00  1.00   \n",
       "63800272         0.00               0.00 180278.34              0.00 16.00   \n",
       "26876388         0.00               1.00 180278.34              0.00 18.00   \n",
       "26876293         0.00               1.00 180278.34              0.00 18.00   \n",
       "26876352         0.00               0.00 180278.34              0.00 18.00   \n",
       "26876434         0.00               0.00 180278.34              0.00 18.00   \n",
       "26876324         0.00               0.00 180278.34              0.00  2.00   \n",
       "26876278         0.00               0.00 180278.34              1.00  6.00   \n",
       "26876416         0.00               0.00 180278.34              1.00 11.00   \n",
       "26876297         0.00               0.00 180278.34              0.00  8.00   \n",
       "26876321         0.00               0.00 180278.34              0.00  4.00   \n",
       "26876273         0.00               0.00 180278.34              0.00  6.00   \n",
       "26876271         0.00               0.00 180278.34              0.00 16.00   \n",
       "26876357         0.00               0.00 180278.34              0.00  8.00   \n",
       "26876355         0.00               0.00 180278.34              0.00  2.00   \n",
       "26876350         0.00               1.00 180278.34              0.00  8.00   \n",
       "26876336         0.00               0.00 180278.34              1.00  6.00   \n",
       "26876425         0.00               0.00 180278.34              0.00 18.00   \n",
       "26876326         0.00               0.00 180278.34              0.00  8.00   \n",
       "26876275         0.00               0.00 180278.34              0.00 14.00   \n",
       "26876389         0.00               0.00 180278.34              1.00  2.00   \n",
       "26876338         0.00               0.00 180278.34              1.00 12.00   \n",
       "26876376         0.00               0.00 180278.34              0.00 14.00   \n",
       "154081659        0.00               0.00 180278.34              0.00 14.00   \n",
       "26876374         0.00               0.00 180278.34              0.00 14.00   \n",
       "26876397         0.00               0.00 180278.34              0.00 14.00   \n",
       "26876334         0.00               0.00 180278.34              1.00 18.00   \n",
       "\n",
       "           Cluster  \n",
       "26876337      3.00  \n",
       "26876345      3.00  \n",
       "26876322      3.00  \n",
       "26876325      3.00  \n",
       "26876320      3.00  \n",
       "26876342      3.00  \n",
       "26876353      3.00  \n",
       "26876347      3.00  \n",
       "26876365      3.00  \n",
       "26876330      3.00  \n",
       "26876373      3.00  \n",
       "26876372      3.00  \n",
       "26876318      3.00  \n",
       "26876420      3.00  \n",
       "26876339      3.00  \n",
       "26876354      3.00  \n",
       "26876319      3.00  \n",
       "26876422      3.00  \n",
       "26876348      3.00  \n",
       "26876340      3.00  \n",
       "26876349      3.00  \n",
       "26876383      3.00  \n",
       "26876378      3.00  \n",
       "26876423      3.00  \n",
       "63800272      3.00  \n",
       "26876388      3.00  \n",
       "26876293      3.00  \n",
       "26876352      3.00  \n",
       "26876434      3.00  \n",
       "26876324      3.00  \n",
       "26876278      3.00  \n",
       "26876416      3.00  \n",
       "26876297      3.00  \n",
       "26876321      3.00  \n",
       "26876273      3.00  \n",
       "26876271      3.00  \n",
       "26876357      3.00  \n",
       "26876355      3.00  \n",
       "26876350      3.00  \n",
       "26876336      3.00  \n",
       "26876425      3.00  \n",
       "26876326      3.00  \n",
       "26876275      3.00  \n",
       "26876389      3.00  \n",
       "26876338      3.00  \n",
       "26876376      3.00  \n",
       "154081659     3.00  \n",
       "26876374      3.00  \n",
       "26876397      3.00  \n",
       "26876334      3.00  \n",
       "\n",
       "[50 rows x 22 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "elem_clus = pd.DataFrame([])\n",
    "for i in range(true_k):\n",
    "    print(\"Cluster %d:\" % i, end='')\n",
    "    print()\n",
    "    for ind in indices[i, :50]:\n",
    "        elem_clus = elem_clus.append((pd.DataFrame(Data.iloc[ind]).transpose()))\n",
    "    display (elem_clus)\n",
    "    elem_clus = pd.DataFrame([])    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "### Reduccion de Dimensionalidad con PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DISTANCE_SUB</th>\n",
       "      <th>CTMT_PNTMT_STD</th>\n",
       "      <th>CLO_CEN</th>\n",
       "      <th>TIP_CC_RES-Tipo3</th>\n",
       "      <th>POT_NOM</th>\n",
       "      <th>PERD_A4_B</th>\n",
       "      <th>I_NOM_MED_R</th>\n",
       "      <th>CTMT_ENE_STD</th>\n",
       "      <th>CTMT_PNTMT_MED</th>\n",
       "      <th>UCBT_ENE_MED</th>\n",
       "      <th>I_NOM_MIN_R</th>\n",
       "      <th>CTMT_PNTBT_MAX</th>\n",
       "      <th>PAGE_RANK</th>\n",
       "      <th>TIP_TRAFO_M</th>\n",
       "      <th>FAS_CON_S_ABCN</th>\n",
       "      <th>CTMT_ENE_MED</th>\n",
       "      <th>GRU_TAR_B3</th>\n",
       "      <th>TIP_CC_RES-Tipo10</th>\n",
       "      <th>PERD_A4</th>\n",
       "      <th>TIP_CC_RES-Tipo5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.63</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>0.40</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>0.79</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>0.26</td>\n",
       "      <td>-0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.78</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.18</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>1.17</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>1.23</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>0.86</td>\n",
       "      <td>-0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.78</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.68</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>1.17</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.64</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>1.23</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>0.86</td>\n",
       "      <td>-0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.77</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>0.44</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>1.23</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>0.86</td>\n",
       "      <td>-0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.77</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.68</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>0.44</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.66</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>1.23</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>0.86</td>\n",
       "      <td>-0.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DISTANCE_SUB  CTMT_PNTMT_STD  CLO_CEN  TIP_CC_RES-Tipo3  POT_NOM  \\\n",
       "0         -0.63           -0.54     0.45             -0.28    -0.14   \n",
       "1         -0.78            0.16     0.18             -0.28     1.17   \n",
       "2         -0.78            0.16     1.68             -0.28     1.17   \n",
       "3         -0.77            0.16     0.19             -0.28     0.44   \n",
       "4         -0.77            0.16     1.68             -0.28     0.44   \n",
       "\n",
       "   PERD_A4_B  I_NOM_MED_R  CTMT_ENE_STD  CTMT_PNTMT_MED  UCBT_ENE_MED  \\\n",
       "0      -0.16        -0.03         -0.46            0.40         -0.15   \n",
       "1      -0.02         0.76          0.27            0.48          0.79   \n",
       "2      -0.02         0.76          0.27            0.48          0.79   \n",
       "3      -0.02         0.76          0.27            0.48          0.14   \n",
       "4      -0.02         0.76          0.27            0.48          0.14   \n",
       "\n",
       "   I_NOM_MIN_R  CTMT_PNTBT_MAX  PAGE_RANK  TIP_TRAFO_M  FAS_CON_S_ABCN  \\\n",
       "0         0.78            0.45      -0.01        -0.53           -0.38   \n",
       "1         0.18            0.20       0.03        -0.53           -0.38   \n",
       "2         0.18            0.20       0.64        -0.53           -0.38   \n",
       "3         0.18            0.20       0.03        -0.53           -0.38   \n",
       "4         0.18            0.20       0.66        -0.53           -0.38   \n",
       "\n",
       "   CTMT_ENE_MED  GRU_TAR_B3  TIP_CC_RES-Tipo10  PERD_A4  TIP_CC_RES-Tipo5  \n",
       "0          0.79       -0.28              -0.23     0.26             -0.34  \n",
       "1          1.23       -0.28              -0.23     0.86             -0.34  \n",
       "2          1.23       -0.28              -0.23     0.86             -0.34  \n",
       "3          1.23       -0.28              -0.23     0.86             -0.34  \n",
       "4          1.23       -0.28              -0.23     0.86             -0.34  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature Scaling\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(Data_X)\n",
    "dfx = pd.DataFrame(data=X_train,columns=Data_X.columns[0:])\n",
    "dfx.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "# Aplicamos PCA\n",
    "pca = PCA(n_components = None , random_state=0)\n",
    "dfx_pca = pca.fit(dfx)\n",
    "explained_variance = dfx_pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graficamos los porcentajes de la varianza de las variables generadas\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(x=[i+1 for i in range(len(explained_variance))],\n",
    "            y=dfx_pca.explained_variance_ratio_,\n",
    "           s=200, alpha=0.75,c='orange',edgecolor='k')\n",
    "plt.grid(True)\n",
    "plt.title(\"Explained variance ratio del fit\\nde los vectores principales\\n\",fontsize=25)\n",
    "plt.xlabel(\"Componente Principal\",fontsize=15)\n",
    "plt.xticks([i+1 for i in range(len(dfx_pca.explained_variance_ratio_))],fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.ylabel(\"Explained variance ratio\",fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La suma de los cuatro primeros componentes nos da  0.5325892306655606\n",
      "lo que abarca mas de la mitad de la variaza por lo que tomaremos estos cuatro valores\n"
     ]
    }
   ],
   "source": [
    "print('La suma de los cuatro primeros componentes nos da ',sum(explained_variance[0:4]))\n",
    "print(\"lo que abarca mas de la mitad de la variaza por lo que tomaremos estos cuatro valores\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "dfx_trans = pca.transform(dfx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.64</td>\n",
       "      <td>0.46</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.26</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>0.67</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.89</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.10</td>\n",
       "      <td>1.63</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.03</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>0.38</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.28</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>0.28</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.99</td>\n",
       "      <td>1.47</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.85</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>0.23</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.77</td>\n",
       "      <td>1.53</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.52</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>0.33</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>0.40</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>0.24</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.66</td>\n",
       "      <td>1.37</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.33</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>0.76</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1     2    3    4     5     6     7     8     9    10    11   12  \\\n",
       "0 0.64 0.46 -0.64 0.11 0.26 -0.21  0.04 -0.26 -0.47  0.67 -0.02 -0.07 0.89   \n",
       "1 1.10 1.63 -0.18 0.13 0.72  1.03 -0.11  0.16 -0.16  0.38 -0.38  0.06 0.52   \n",
       "2 1.99 1.47 -0.15 0.37 0.79  0.85 -0.43 -0.26  0.23 -0.10 -0.74 -0.26 0.87   \n",
       "3 0.77 1.53 -0.15 0.09 0.60  0.52 -0.18 -0.26 -0.52  0.33 -0.33 -0.26 0.40   \n",
       "4 1.66 1.37 -0.13 0.33 0.67  0.33 -0.50 -0.68 -0.13 -0.15 -0.69 -0.58 0.76   \n",
       "\n",
       "     13    14    15    16    17    18    19  \n",
       "0 -0.55 -0.25 -0.06 -0.55 -0.23 -0.25 -0.34  \n",
       "1  0.28 -0.46  0.28 -0.15  0.09 -0.16 -0.31  \n",
       "2  0.19 -0.15 -0.01 -0.44 -0.34 -0.40  0.12  \n",
       "3 -0.04 -0.41  0.24 -0.20  0.04 -0.17 -0.34  \n",
       "4 -0.14 -0.09 -0.04 -0.48 -0.38 -0.41  0.08  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfx_trans = pd.DataFrame(data=dfx_trans)\n",
    "dfx_trans.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(dfx_trans[0], dfx_trans[1], c=Data_FIC, edgecolors='k', alpha=0.75, s=150)\n",
    "plt.grid(True)\n",
    "plt.title(\"Separacion de clases usando los dos primeros componentes principales\\n utilizando la clase original\\n\",fontsize=20)\n",
    "plt.xlabel(\"Componente Principal 1\",fontsize=15)\n",
    "plt.ylabel(\"Componente Principal 2\",fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.19263033, 0.16716397, 0.1036861 , 0.06910769])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aplicamos PCA con los tres componentes principales seleccionados\n",
    "pca = PCA(n_components = 4 , random_state=0)\n",
    "dfx_pca = pca.fit(dfx)\n",
    "X_train = pca.fit_transform(dfx)\n",
    "explained_variance = dfx_pca.explained_variance_ratio_\n",
    "explained_variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+ANwJPAB0AvemtmqZrSEeY8+Bbl7bsivdVREROS4jBUy2uyc6Aj4B3OvuP3P3/wzMPd6DmtlnzWyNma02swfNrMDMYmb2jJmtDT8rkta/w8zWmdmbZnZ5UvlCM1sVln3bzCyU55vZw6F8uZnVHm9d06UxXgmgy2QiMmGNGDBmlhPeXww8m7QsZ5D1R2RmNcBtQL27LwCygUXAV4Al7j4PWBI+Y2anh+VnAFcA3zOz7LC7e4AbgHnhdUUovx5od/e5wN3A14+nruk0vayAOZVF6ugXkQlrpIB5EFhqZo8B+4B/AzCzuUSXyY5XDlAYwqsI2AJcBdwflt8PXB3eXwU85O4H3L0ZWAc0mNkMoNTdn3N3J7p0l7xNYl+LgYsTrZuJpKE2xooNbfSqH0ZEJqBhA8bdvwZ8HrgPuCD8Ik9sd+vxHNDdNwPfADYCW4FOd38amObuW8M6W4GpYZMaYFPSLlpCWU14P7C83zbu3k0UhpXHU990aqyrpKPrEGu370l3VUREjtlIo8iKgJXu/qi77zWz+Wb2WWCBu794PAcMfStXAXFgJlBsZn8+3CaDlPkw5cNtM7AuN5hZk5k1tba2Dl/xNGgM98OoH0ZEJqKRLpH9GqiFvstizwF1wM1m9j+P85iXAM3u3uruh4CfA+cB28JlL8LP7WH9FmB20vaziC6ptYT3A8v7bRMuw5UBR3RmuPu97l7v7vXV1dXHeTqpM6uikJllBSxXP4yITEAjBUyFu68N768DHnT3W4E/Aq48zmNuBM4xs6LQL3Ix8DrweDhG4liPhfePA4vCyLA4UWf+inAZbbeZnRP2c+2AbRL7ugZ4Nuny3oRhZjTEY6xobmMCVl9EMtxIAZP8W+3DwDMA7n4QOK6ZGN19OVHH+4vAqlCHe4nusbnUzNYCl4bPuPsa4BHgNaIW1c3unnga143A94k6/t8GngzlPwAqzWwd8DnCiLSJqCFeSevuAzTv2JvuqoiIHJORhhq/ambfADYT3ffyNICZlZ/IQd39q8BXBxQfIGrNDLb+14CvDVLeBCwYpHw/8LETqeN40Vh3eF6yuuqSNNdGROTojdSC+WtgB1E/zGXu3hXKTycaCSYpVldVTFVJvvphRGTCGakFUwL8IlymSraL6HKVpJiZ0Rj6YUREJpKRWjDfAaoGKa8BvjX61ZHBNMRjbO7YR0t718gri4iMEyMFzJnuvnRgobs/BZyVmirJQInnwyxfr1aMiEwcIwVM7nEuk1E0f9oUygpzdZlMRCaUkQJmrZl9ZGChmf0RsD41VZKBsrKM99fGdEe/iEwoI3Xyfwb4lZl9HFgZyuqBczn+Gy3lOJxTF+M3r29j2679TCstSHd1RERGNFIL5o+Jpr7/AzAnvJYCZ7n7WymumyRJ9MPoMpmITBQjBcwsomep/CNRy+UgsI1oin0ZQ6fPKKUkP0eXyURkwhj2Epm7fwHAzPKIAuY84FPAP5tZh7ufnvoqCkBOdhYL51SoBSMiE8ZILZiEQqCUaFbiMqJZi5enqlIyuIZ4jLe27aFt78F0V0VEZETDtmDM7F6iRxXvJgqUfwfucvf2MaibDHBO0rxkVyyYnubaiIgMb6QWzElAPvAu0YSXLUBHqislgzuzppz8nCxdJhORCWGkPpgrwrNWziDqf/k8sMDM2oDnwqzIMkbycrJ430kV6ugXkQlhxD4Yj6wGniB63sofgJOB21NcNxlEY12M17buYtf+Q+muiojIsIYNGDO7zcweMrNNwDKimyvfBP4UiI1B/WSAhngMd2jaoMtkIjK+jXQnfy3R0yc/Gx5RLGn2vpMqyM02lje38eFTp6W7OiIiQxqpD+ZzY1UROToFudmcPatcMyuLyLh3tPfByDjSWBdj9eZO9h7oTndVRESGpICZgBrilXT3Oi9t1IhxERm/FDAT0MI5FWRnmYYri8i4poCZgEryc1gws5TluuFSRMYxBcwE1RCP8fKmDvYf6kl3VUREBqWAmaAa45Uc7O7llU3qhxGR8UkBM0G9vzaGmR5AJiLjlwJmgiorymX+tCnqhxGRcUsBM4GdU1fJynfaOdTTm+6qiIgcIS0BY2blZrbYzN4ws9fN7Fwzi5nZM2a2NvysSFr/DjNbZ2ZvmtnlSeULzWxVWPbtMPMzZpZvZg+H8uVmVjv2Z5l6DfEY+w71sGpzZ7qrIiJyhHS1YL4F/NrdTwXOBl4HvgIscfd5wJLwGTM7HVhE9MiAK4DvmVl22M89wA3AvPC6IpRfD7S7+1zgbuDrY3FSY60hfvgBZCIi482YB4yZlQIfAH4A4O4H3b0DuAq4P6x2P3B1eH8V8JC7H3D3ZmAd0GBmM4BSd3/O3R14YMA2iX0tBi5OtG4mk6qSfE6uLlbAiMi4lI4WTB3QCvyLmb1kZt83s2JgWmLG5vBzali/BtiUtH1LKKsJ7weW99vG3buBTqByYEXM7AYzazKzptbW1tE6vzHVEK/kheY2eno93VUREeknHQGTA7wPuMfd3wvsJVwOG8JgLQ8fpny4bfoXuN/r7vXuXl9dXT18rcepc+pi7D7Qzetbd6W7KiIi/aQjYFqAFndfHj4vJgqcbeGyF+Hn9qT1ZydtPwvYEspnDVLebxszywHKgEl5HSnRD6PhyiIy3ox5wLj7u8AmM5sfii4GXgMeB64LZdcBj4X3jwOLwsiwOFFn/opwGW23mZ0T+leuHbBNYl/XAM+GfppJZ0ZZISfFilihiS9FZJwZ6YmWqXIr8BMzywPWA39JFHaPmNn1wEbgYwDuvsbMHiEKoW7gZndPTMB1I3AfUAg8GV4QDSD4kZmtI2q5LBqLk0qXhniMJa9vw92ZhGMZRGSCSkvAuPvLQP0giy4eYv2vAV8bpLwJWDBI+X5CQGWChniMxStbWLt9D6dMm5Lu6oiIALqTf1I4Jx4NkFM/jIiMJwqYSWB2rJDppQUsX69+GBEZPxQwk4CZ0VgXY0VzG5N0LIOITEAKmEmiIR5j++4DLHl9+8gri4iMAQXMJHHFGdOpqy7mrx5o4o6fr2L3/kPprpKIZDgFzCRRWZLPE7ddyN98oI6HX9jI5XcvY9lbE3P6GxGZHBQwk0hBbjZ3fOQ0fnbjeRTl53DtD1fw5cWvskutGRFJAwXMJPTekyr45a0XcONFJ/PTlZu47K5l/PYN9c2IyNhSwExSBbnZfPmKU3n0pvMpLczhL+97gS/89BU6u9SaEZGxoYCZ5M6eXc4vbr2AWz40l0df2sxl31zKkte3pbtaIpIBFDAZID8nmy9cPp//d9P5VBTlcf39TXzu4Zfp6DqY7qqJyCSmgMkgZ84q4/FbLuC2i+fx+CtbuPTuZTy95t10V0tEJikFTIbJy8nic5eewmO3nE9VST43/Ggltz/0Eu171ZoRkdGlgMlQZ8ws4/Fbzuezl5zCE6u2cundS/n16q3prpaITCIKmAyWm53F7ZfM4/FbLmB6WQGf/vGL3PKvL7Jzz4F0V01EJgEFjHDajFIevel8vnDZKTy15l0uu3sZT6xSa0ZETowCRoCoNXPLh+fxy1svZGZ5ITf95EVu+slKdqg1IyLHSQEj/cyfPoVHbzqPL10xn9+8tp1L71rKL17ZoscAiMgxU8DIEXKys7jporn86rYLOKmymFsffIlP/3gl23fvT3fVRGQCUcDIkOZNm8LPPn0ud/zRqfz2zVYuu3sZj728Wa0ZETkqChgZVk52Fn/zwZN54rYLiVcVc/tDL3PDj1ayfZdaMyIyPAWMHJW5U0tY/Onz+Ls/Po1lb7Vy6d3L+PmLLWrNiMiQFDBy1LKzjL+6sI4nb7+QeVNL+Nwjr/BX9zfxbqdaMyJyJAWMHLO66hIe/ptz+S9Xns4f3t7BpXcv5adNm9SaEZF+FDByXLKzjE9dEOfXt3+A06aX8sXFr/KX973A1s596a6aiIwTChg5IbVVxTx0wzn814+ewfL1bVx21zIeeG4D2zQIQCTjmS5rROrr672pqSnd1ZjQNu7s4ks/e4Xn17cBUFtZREM8RmO8koZ4jFkVhZhZmmspIqPJzFa6e/2gy9IVMGaWDTQBm939SjOLAQ8DtcAG4OPu3h7WvQO4HugBbnP3p0L5QuA+oBB4Arjd3d3M8oEHgIXATuAT7r5huPooYEaHu7NqcycrmttY3tzGiuY2OvdFj2meWVZAQzxGQ7ySxroYdVXFChyRCW68BszngHqgNATMPwJt7n6nmX0FqHD3L5vZ6cCDQAMwE/gNcIq795jZCuB24HmigPm2uz9pZjcBZ7n7p81sEfAn7v6J4eqjgEmN3l7nre27+wJn+fq2vvnNqkryosCpjULn1OlTyMpS4IhMJMMFTM5YVwbAzGYBfwx8DfhcKL4KuCi8vx/4HfDlUP6Qux8Ams1sHdBgZhuIwum5sM8HgKuBJ8M2fx/2tRj4rpmZ63rgmMvKMk6dXsqp00u59txa3J3mHXtZEVo3y5vbeGJV9FTN0oKc0MKJAmfBzFJystVNKDJRpSVggG8CXwKmJJVNc/etAO6+1cymhvIaohZKQksoOxTeDyxPbLMp7KvbzDqBSmBHciXM7AbgBoCTTjrpxM9KRmRm1FWXUFddwqKG6Dtvae/qC5wVzW385vXtABTlZbNwTgWNIXDOmlVGQW52OqsvIsdgzAPGzK4Etrv7SjO76Gg2GaTMhykfbpv+Be73AvdCdInsKOoiKTCroohZFUX86ftmAbB99/5+gfONp98Cosc9v3d2eV/gvG9OOUV56fobSURGko5/necDHzWzjwAFQKmZ/RjYZmYzQutlBrA9rN8CzE7afhawJZTPGqQ8eZsWM8sByoC2VJ2QjK6pUwq48qyZXHnWTAA6ug7ywoZ2lq/fyYoNbXz3t+vofXYdOVnGgpoyGutiNMZjLJwTo6wwN821F5GEtA5TDi2YL4RO/n8CdiZ18sfc/Utmdgbwrxzu5F8CzAud/C8AtwLLiTr5v+PuT5jZzcCZSZ38f+ruHx+uLurknzh27z/Eixs7osBpbuOVlg4O9ThmcNr0UhriMc6piwKnekp+uqsrMqmNu07+IdwJPGJm1wMbgY8BuPsaM3sEeA3oBm52956wzY0cHqb8ZHgB/AD4URgQ0AYsGquTkNSbUpDLB0+p5oOnVAOw/1APL23sCIMGdvLQCxu57983ADCjrIAFNWWcVVPGglllnFlTRlWJQkdkLOhGy0AtmMnjYHcvqzZ38NLGDlZt7mRVSyfrd+ztWz6zrIAzQ9icOaucM2vKiBXnpbHGIhPXRGnBiIyKvJwsFs6JLpEl7Np/iDWbd7F6c2cUOps7eWrNtr7lNeWFIXBC8NSUUaHQETkhChjJCKUFuZx7ciXnnlzZV7Zr/yFWb+5k9eZOXm2JQufXa97tWz6ropCzZpWFS2xRS6esSIMIRI6WAkYyVmlBLuedXMV5J1f1lXV2HWL1lsOtnFUtnX03ggKcFCvq19JZUFOmkWsiQ1DAiCQpK8rl/LlVnD/3cOh0dB1k9eZdIXQ6eHVzB79atbVv+ZzKor7LameGFk9pgUJHRAEjMoLyojwumFfFBfMOh0773oOs3hJdWlu9uZOXNnbwy1cPh068qrhv9Nr86VOIVxUzs7yQbM21JhlEASNyHCqK87hwXjUXzqvuK2vbe5BVfX06Hbz4Tju/eGVL3/K87CxOqiyitrKYuupiaiuLqa0qoq6qhGml+ZpZWiYdBYzIKIkV5/W7Pwdg554DrNu+h+Yde2neuZcNO/bSvGMvy9a2crC7t2+9wtxs5lQWJQVPMXVV0c/K4jyFj0xIChiRFKosyaeyJJ/Gusp+5b29ztZd+2lu7R88b2zdzdNrttHde/j+tCn5OcQHCZ54ZbFGtcm4poARSYOsLKOmvJCa8sJ+fTsA3T29tLTv6xc8zTv28tKmdn7x6haS742OFedRW1nUFziJIIpXFVOcr3/ekl76P1BknMnJzqI2tFKY33/Zge4eNrV10byji+Yde2je0cWGHXv593U7+fmLm/utO3VKfr/gOSlWxOyKIk6KFanlI2NCASMygeTnZDN36hTmTp0CTOu3rOtgN+/s7Opr8WzYsZcNO/ey5I1t7Gg62G/dKQU5zK4oYnasMAqdyqK+z1ZFISIAAAxySURBVLMqivTcHRkVChiRSaIoL4fTZpRy2ozSI5bt2n+ITW1dbGrbR0t7FxvbutjU1sXbrXv53ZutHEgacABQPSWf2RWFUasntHxmhTCaUVagJ43KUVHAiGSA0oJczphZxhkzy45Y5u607jnQF0Cb2rrY1B69f2FDO4+/soWkMQfkZBkzywv7Wj+z+0KokNmxIo16kz4KGJEMZ2ZMnVLA1CkFLJxz5PJDPb1s7dgfQicKn40hiH7z+jZ27Ol/+a0oL5tZFYOHz+xYESUafJAx9F9aRIaVG24QPamyaNDlXQe7aWnfx8adh1s+iTB6fv1O9h7s6bd+eVFu3wi6moqoz6emvJBZFVFZeVGuWkCThAJGRE5IUV4Op0ybwinTphyxzN1p7zqU1PLpYnP7PjZ37KN5x15+v24HXQMCKNECSgRQTXlR+BmFUHVJPlmacmdCUMCISMqYGbHiPGLFeZw9u/yI5e5OR9chNndEgw9aQvgkQuilTR10dB3qt01edhYzywv6QqemvCgKpPBZgxDGDwWMiKSNmVFRnEdFcR4Lao4cgACw50B3CJyo9dMSAqilfR+/fbOV1t0H+q2fZTC9tKDf5beafi2iQg3DHiMKGBEZ10ryc5g/fQrzpx95CQ5g/6Eetnbu7wuhlvZ9fUG0ormNd3ftp6e3/6Phq0ry+gdPeSE1SWGkZ/yMDgWMiExoBbnZxKui6XEG093Ty7bdB2hp6+p3+W1zxz7e2LqbJa9vP+I+oCn5OUe0epJ/Vpdo9uujoYARkUktJzurr5UyGHdnx56DSeFzeCBCS/s+VmxoY/f+7n7b5OUc3udgIaR+oIgCRkQymplRPSWf6in5vGeQgQgQzYSwub1/6ydxGW7JG9vZsWfofqChRsNlQj+QAkZEZASlBbmUzsgddBoeiPqBtnT0HwGXCKAXNrTzi1e3HtEPVFmc1xc4M8Orpryg7/1kmBFBASMicoIKcrOpqy6hrrpk0OWJfqDBLsGt3b6H373Zyr5D/e8Hyg+X4aLAKUgKoeg1vaxg3LeCFDAiIinWvx8odsRyd6dz3yFa2vexpSO8Ovf3tYR+92Yr2wcMxwaoKsnv1+qp6fezgFiaW0EKGBGRNDMzyovyKC8a+n6gA909bOs8wOYQQMk/39q2m9++uZ39h/qPhktuBSW3hhLvU90KUsCIiEwA+TnZw84Jl5iWJzl8otd+Wjr28eyb24+4KRWiVtC5J1fynf/43lGv85gHjJnNBh4ApgO9wL3u/i0ziwEPA7XABuDj7t4etrkDuB7oAW5z96dC+ULgPqAQeAK43d3dzPLDMRYCO4FPuPuGMTpFEZExlzwtz3CtoHfDpbctHdHNqVs69lFZkpeSOqWjBdMNfN7dXzSzKcBKM3sG+CSwxN3vNLOvAF8BvmxmpwOLgDOAmcBvzOwUd+8B7gFuAJ4nCpgrgCeJwqjd3eea2SLg68AnxvQsRUTGmfycbOZUFjOncvCbUkfbmN8J5O5b3f3F8H438DpQA1wF3B9Wux+4Ory/CnjI3Q+4ezOwDmgwsxlAqbs/5+5O1GJJ3iaxr8XAxTbRx/uJiEwwab3V1MxqgfcCy4Fp7r4VohACpobVaoBNSZu1hLKa8H5geb9t3L0b6AQqBzn+DWbWZGZNra2to3NSIiICpDFgzKwE+BnwGXffNdyqg5T5MOXDbdO/wP1ed6939/rq6uqRqiwiIscgLQFjZrlE4fITd/95KN4WLnsRfm4P5S3A7KTNZwFbQvmsQcr7bWNmOUAZ0Db6ZyIiIkMZ84AJfSE/AF5397uSFj0OXBfeXwc8llS+yMzyzSwOzANWhMtou83snLDPawdsk9jXNcCzoZ9GRETGSDpGkZ0P/AWwysxeDmX/CbgTeMTMrgc2Ah8DcPc1ZvYI8BrRCLSbwwgygBs5PEz5yfCCKMB+ZGbriFoui1J9UiIi0p/pD/tIfX29NzU1pbsaIiITipmtdPf6wZbpgQUiIpISasEEZtYKvJPuepygKmBHuisxjuj76E/fx2H6Lvo7ke9jjrsPOgxXATOJmFnTUE3VTKTvoz99H4fpu+gvVd+HLpGJiEhKKGBERCQlFDCTy73prsA4o++jP30fh+m76C8l34f6YEREJCXUghERkZRQwIiISEooYCYBM5ttZr81s9fNbI2Z3Z7uOqWbmWWb2Utm9st01yXdzKzczBab2Rvh/5Fz012ndDKzz4Z/J6vN7EEzK0h3ncaSmf3QzLab2eqkspiZPWNma8PPitE4lgJmckg8JfQ04Bzg5vAk0Ex2O9HD7AS+Bfza3U8FziaDvxczqwFuA+rdfQGQTebNVXgf0dN/k32F6InC84Al4fMJU8BMAsM8JTQjmdks4I+B76e7LulmZqXAB4gmgMXdD7p7R3prlXY5QGF4lEcRhx/zkRHcfRlHPr5kqCcKnxAFzCQz4CmhmeqbwJeA3nRXZByoA1qBfwmXDL9vZmPzQPZxyN03A98gmrF9K9Dp7k+nt1bjwlBPFD4hCphJ5BieEjppmdmVwHZ3X5nuuowTOcD7gHvc/b3AXkbp8sdEFPoWrgLiwEyg2Mz+PL21mrwUMJPEEE8JzUTnAx81sw3AQ8CHzezH6a1SWrUALe6eaNEuJgqcTHUJ0Ozure5+CPg5cF6a6zQeDPVE4ROigJkEhnlKaMZx9zvcfZa71xJ13j7r7hn7F6q7vwtsMrP5oehioof3ZaqNwDlmVhT+3VxMBg96SDLUE4VPSDqeaCmjb9CnhLr7E2msk4wftwI/MbM8YD3wl2muT9q4+3IzWwy8SDT68iUybNoYM3sQuAioMrMW4KsM8UThEz6WpooREZFU0CUyERFJCQWMiIikhAJGRERSQgEjIiIpoYARSWJmN4cbVkXkBClgJCOYmZvZ/0r6/AUz+/sB6/wFEHP3PWNdv6GY2X1mds0o7Oe4zsnMrtbEqXK8FDCSKQ4Af2pmVcOskw3891QcPEysOBFdDRxTwEzgc5VRpoCRTNFNdEPdZwcuSLQS3P0+d/fEX/tmdpGZLTWzR8zsLTO708z+zMxWmNkqMzs5rFdtZj8zsxfC6/xQ/vdmdq+ZPQ08YGYFZvYvYduXzOxDg9TFzOy7Zvaamf2KpEkHzWxhqM9KM3sqMbXHgO2nmdmjZvZKeJ03YPlFyc/ICcf6ZHh/Zzjuq2b2jbDtR4F/MrOXzezk8Pp1qMO/mdmpSd/hXWb2W+DrZvbBsM3L4VynHOt/MJn49JeGZJL/DbxqZv94DNucDZxGNL35euD77t5g0UPdbgU+Q/S8lbvd/fdmdhLwVNgGYCFwgbvvM7PPA7j7meEX89Nmdoq770863p8A84EzgWlE07r8MMw19x3gKndvNbNPAF8DPjWgvt8Glrr7n5hZNnBU/UlmFgvHPjWEbLm7d5jZ48Av3X1xWG8J8Gl3X2tmjcD3gA+H3ZwCXOLuPWb2C+Bmd/9D6NPaf+RRZbJTwEjGcPddZvYA0QOn9h3lZi8kpjE3s7eBxNTuq4BEC+QS4PRoaisASpP+Yn/c3RPHuoAoJHD3N8zsHaJfyq8mHe8DwIPu3gNsMbNnQ/l8YAHwTDhONtF08wN9GLg2HKMH6DzK89xFFALfDy2nI54EGoLiPOCnSeean7TKT8MxAf4A3GVmPwF+7u4tR1kPmUQUMJJpvkk0D9W/JJV1Ey4XhwkQ85KWHUh635v0uZfD/36ygHOTgoSwL4imx+8rOso6DjZ/kwFr3P1EH3fcd65BAYC7d5tZA9Hkj4uAWzjcMknIAjrc/T1D7LvvXN39zhBUHwGeN7NL3P2NE6y7TDDqg5GM4u5twCPA9UnFG4guZUH0rJDcY9zt00S/kAEws6F+AS8D/iyscwpwEvDmIOssMrPs0MeSaCW9CVSb2blh+1wzO2OQYywBbgzrZFv0RMtk7xC1tvLNrIwoUBKtk7IwQepngMQ57AamQNQCBJrN7GNhGzOzswc7UTM72d1XufvXgSbg1CG+E5nEFDCSif4XkDya7J+BD5rZCqCR/q2Oo3EbUB86x18DPj3Eet8Dss1sFfAw8El3PzBgnUeBtUSX4O4BlkL0qGPgGqIO9FeAlxn8OSa3Ax8Kx1gJ9Ashd99EFLCvAj8hmk0YohD5pZm9Go6ZGAzxEPDF0FF/MlFAXh/qsIYokAfzGTNbHdbbBzw5xHoyiWk2ZRERSQm1YEREJCUUMCIikhIKGBERSQkFjIiIpIQCRkREUkIBIyIiKaGAERGRlPj/PWtUNR1KRDoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Utilizamos el elbow method para encontrar el número óptimo de clusters con las variables reducidas por PCA\n",
    "Data_pca = np.insert(X_train, 2, Data_FIC, axis=1)\n",
    "\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 0)\n",
    "    kmeans.fit(Data_pca)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.plot(range(1, 11), wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Número de clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "Si bien nos dice que un número a seleccionar seria de 6 clusters se hace un análisis con distintos valores \n",
    "de clusters para evaluar resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Calculo para  2  Clusters\n",
      "\n",
      "Clustering sparse data with MiniBatchKMeans(batch_size=1000, compute_labels=True, init='k-means++',\n",
      "                init_size=1000, max_iter=100, max_no_improvement=10,\n",
      "                n_clusters=2, n_init=1, random_state=0, reassignment_ratio=0.01,\n",
      "                tol=0.0, verbose=0)\n",
      "done in 0.025s\n",
      "\n",
      "Homogeneity: 0.019\n",
      "Completeness: 0.093\n",
      "V-measure: 0.032\n",
      "Adjusted Rand-Index: -0.001\n",
      "Silhouette Coefficient: 0.505\n",
      "\n",
      "Métrica de Pureza: 0.000\n",
      "\n",
      "\n",
      "Calculo para  3  Clusters\n",
      "\n",
      "Clustering sparse data with MiniBatchKMeans(batch_size=1000, compute_labels=True, init='k-means++',\n",
      "                init_size=1000, max_iter=100, max_no_improvement=10,\n",
      "                n_clusters=3, n_init=1, random_state=0, reassignment_ratio=0.01,\n",
      "                tol=0.0, verbose=0)\n",
      "done in 0.037s\n",
      "\n",
      "Homogeneity: 0.042\n",
      "Completeness: 0.097\n",
      "V-measure: 0.059\n",
      "Adjusted Rand-Index: 0.005\n",
      "Silhouette Coefficient: 0.474\n",
      "\n",
      "Métrica de Pureza: 0.357\n",
      "\n",
      "\n",
      "Calculo para  4  Clusters\n",
      "\n",
      "Clustering sparse data with MiniBatchKMeans(batch_size=1000, compute_labels=True, init='k-means++',\n",
      "                init_size=1000, max_iter=100, max_no_improvement=10,\n",
      "                n_clusters=4, n_init=1, random_state=0, reassignment_ratio=0.01,\n",
      "                tol=0.0, verbose=0)\n",
      "done in 0.046s\n",
      "\n",
      "Homogeneity: 0.034\n",
      "Completeness: 0.055\n",
      "V-measure: 0.042\n",
      "Adjusted Rand-Index: 0.015\n",
      "Silhouette Coefficient: 0.527\n",
      "\n",
      "Métrica de Pureza: 0.359\n",
      "\n",
      "\n",
      "Calculo para  5  Clusters\n",
      "\n",
      "Clustering sparse data with MiniBatchKMeans(batch_size=1000, compute_labels=True, init='k-means++',\n",
      "                init_size=1000, max_iter=100, max_no_improvement=10,\n",
      "                n_clusters=5, n_init=1, random_state=0, reassignment_ratio=0.01,\n",
      "                tol=0.0, verbose=0)\n",
      "done in 0.040s\n",
      "\n",
      "Homogeneity: 0.048\n",
      "Completeness: 0.068\n",
      "V-measure: 0.056\n",
      "Adjusted Rand-Index: 0.024\n",
      "Silhouette Coefficient: 0.540\n",
      "\n",
      "Métrica de Pureza: 0.404\n",
      "\n",
      "\n",
      "Calculo para  6  Clusters\n",
      "\n",
      "Clustering sparse data with MiniBatchKMeans(batch_size=1000, compute_labels=True, init='k-means++',\n",
      "                init_size=1000, max_iter=100, max_no_improvement=10,\n",
      "                n_clusters=6, n_init=1, random_state=0, reassignment_ratio=0.01,\n",
      "                tol=0.0, verbose=0)\n",
      "done in 0.050s\n",
      "\n",
      "Homogeneity: 0.050\n",
      "Completeness: 0.070\n",
      "V-measure: 0.058\n",
      "Adjusted Rand-Index: 0.024\n",
      "Silhouette Coefficient: 0.539\n",
      "\n",
      "Métrica de Pureza: 0.399\n",
      "\n",
      "\n",
      "Calculo para  7  Clusters\n",
      "\n",
      "Clustering sparse data with MiniBatchKMeans(batch_size=1000, compute_labels=True, init='k-means++',\n",
      "                init_size=1000, max_iter=100, max_no_improvement=10,\n",
      "                n_clusters=7, n_init=1, random_state=0, reassignment_ratio=0.01,\n",
      "                tol=0.0, verbose=0)\n",
      "done in 0.057s\n",
      "\n",
      "Homogeneity: 0.092\n",
      "Completeness: 0.109\n",
      "V-measure: 0.100\n",
      "Adjusted Rand-Index: 0.036\n",
      "Silhouette Coefficient: 0.586\n",
      "\n",
      "Métrica de Pureza: 0.403\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Calculamos K-means con las variables reducidas por PCA\n",
    "labels = Data['FIC']\n",
    "for c in range(2, 8):\n",
    "    true_k = c\n",
    "    km, y_kmeans = Calculo_MiniBatchKmeans(true_k, Data_pca)\n",
    "    print()\n",
    "    print ('Calculo para ', c, ' Clusters')\n",
    "    print()\n",
    "    print(\"Clustering sparse data with %s\" % km)\n",
    "    t0 = time()\n",
    "    km.fit(Data)\n",
    "    print(\"done in %0.3fs\" % (time() - t0))\n",
    "    print()\n",
    "    print(\"Homogeneity: %0.3f\" % metrics.homogeneity_score(labels, km.labels_))\n",
    "    print(\"Completeness: %0.3f\" % metrics.completeness_score(labels, km.labels_))\n",
    "    print(\"V-measure: %0.3f\" % metrics.v_measure_score(labels, km.labels_))\n",
    "    print(\"Adjusted Rand-Index: %.3f\"\n",
    "          % metrics.adjusted_rand_score(labels, km.labels_))\n",
    "    print(\"Silhouette Coefficient: %0.3f\"\n",
    "          % metrics.silhouette_score(Data, km.labels_, sample_size=1000))\n",
    "    print()\n",
    "    print(\"Métrica de Pureza: %.3f\" % purity_score(Data_FIC, y_kmeans))\n",
    "\n",
    "    print()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "Se opta por hacer el analisis con 7 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "true_k = 7\n",
    "km, y_kmeans = Calculo_MiniBatchKmeans(true_k, Data_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x28dda0e0438>"
      ]
     },
     "execution_count": 25,
     "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": [
    "# Visualización  de clusters\n",
    "\n",
    "# Predicting the clusters\n",
    "labels = y_kmeans\n",
    "# Getting the cluster centers\n",
    "C = km.cluster_centers_\n",
    "colores=['red','green','blue','cyan','yellow', 'magenta', 'black']\n",
    "asignar=[]\n",
    "for row in labels:\n",
    "    asignar.append(colores[row])\n",
    " \n",
    "fig = plt.figure()\n",
    "ax = Axes3D(fig)\n",
    "ax.scatter(Data_pca[:, 0], Data_pca[:, 1], Data_pca[:, 2], c=asignar,s=60)\n",
    "ax.scatter(C[:, 0], C[:, 1], C[:, 2], marker='*', c=colores, s=1000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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