4-1 Clustering.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Final_Exam_Clustering.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mwNEDkhFtxwC"
      },
      "source": [
        "# <font style=\"font-size:40px\"><center><b>Machine Learning</b></center></font><font                        style=\"font-size:30px\"><center><b>Master in Computational Finance (MCF) </b></center></font> \n",
        "\n",
        "<center> \n",
        "\n",
        "# ![Screen Shot 2021-07-11 at 13.37.02 PM.png](data:image/png;base64,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)\n",
        "\n",
        "<center/>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aihhzeTmt6TX"
      },
      "source": [
        "# <font color='tomato' style=\"font-size:40px\"><center><b>Final Exam</b></center></font></b></center></font> \n",
        "\n",
        "<center>\n",
        "<b>profesor: </b> Nikola Vasiljevic\n",
        "\n",
        "<b>students: </b> \n",
        "Nikola Krivacevic, Aleksandar Milinkovic\n",
        "</center>\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preliminaries"
      ],
      "metadata": {
        "id": "Jm-FTOBadFlQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import / install relevant Python packages\n",
        "import numpy as np\n",
        "from numpy import (where, unique)\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.metrics import silhouette_samples\n",
        "from sklearn.cluster import (KMeans, MiniBatchKMeans, AgglomerativeClustering, DBSCAN)\n",
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
        "from sklearn.mixture import GaussianMixture\n",
        "from sklearn.neighbors import NearestNeighbors\n",
        "\n",
        "from collections import Counter\n",
        "from sklearn import metrics\n",
        "\n",
        "from scipy.cluster.hierarchy import (linkage, dendrogram, set_link_color_palette)\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.gridspec import GridSpec\n",
        "plt.style.use('ggplot')\n",
        "import seaborn as sns\n"
      ],
      "metadata": {
        "id": "98RNKuUmdMxL"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1 Clustering "
      ],
      "metadata": {
        "id": "gK1awqn_sa6p"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Consider the Federal Reserve Bank of St. Louis’ monthly database of macroeconomic\n",
        "factors created by Michael W. McCracken et al. The data and the associated research paper\n",
        "(McCracken and Ng, 2016) are available on the shared drive in the folders ‘Homework’ and\n",
        "‘Readings’, respectively. The list of all variables and the respective recommended data\n",
        "transformations are listed in the appendix of the paper (pp. 586–588).\n",
        "The goal of this exercise to to apply various clustering techniques on FRED–MD dataset,\n",
        "and to compare the results with the classification of macroeconomic variables proposed in\n",
        "McCracken and Ng (2016)."
      ],
      "metadata": {
        "id": "n5jDSjFuJYi4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## (a) Data preprocessing\n",
        "\n",
        "Starting from your earlier results for data preprocessing\n",
        "(see Machine Learning Homework 2), discuss and implement any additional data\n",
        "transformations—on top of those suggested in McCracken and Ng (2016)—that are\n",
        "required in order to apply clustering methods on the FRED–MD dataset. (3 pts)"
      ],
      "metadata": {
        "id": "fmQyhK2ccGJj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**NOTE:** Majority of the code for preprocessing is copied/inherited form Homework 2. However, there are some aditional steps in the preprocessing. For more detailed information and diagrams, please refer to Homework 2."
      ],
      "metadata": {
        "id": "0Y1Ct94KTnrx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First of all, we are going to define helper functions (same like Homework 2) for data transformation and outlier coping."
      ],
      "metadata": {
        "id": "mFs8e-VCUNds"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Helper functions\n",
        "def transform(dataframe, feature, tcode):\n",
        "  if tcode == 1:\n",
        "    return dataframe[feature]\n",
        "  if tcode == 2:\n",
        "    return dataframe[feature] - dataframe[feature].shift(1)\n",
        "  if tcode == 3:\n",
        "    return (dataframe[feature] - dataframe[feature].shift(1)) - (dataframe[feature].shift(1) - dataframe[feature].shift(2))\n",
        "  if tcode == 4:\n",
        "    return np.log(dataframe[feature])\n",
        "  if tcode == 5:\n",
        "    return np.log(dataframe[feature]) - np.log(dataframe[feature].shift(1))\n",
        "  if tcode == 6:\n",
        "    return (np.log(dataframe[feature]) - np.log(dataframe[feature].shift(1))) - (np.log(dataframe[feature]).shift(1) - np.log(dataframe[feature].shift(2)))\n",
        "  if tcode == 7:\n",
        "    return (dataframe[feature]/dataframe[feature].shift(1) - 1) - (dataframe[feature].shift(1)/dataframe[feature].shift(2) -1)\n",
        "\n",
        "def update_outliers(dataframe, feature):\n",
        "  feature_data = dataframe[feature]\n",
        "  Q1 = feature_data.quantile(0.25)\n",
        "  Q3 = feature_data.quantile(0.75)\n",
        "  IQR = Q3-Q1\n",
        "  maximum = Q3+1.5*IQR\n",
        "  minimum = Q1-1.5*IQR\n",
        "  dataframe.loc[dataframe[feature]>maximum, feature] = maximum\n",
        "  dataframe.loc[dataframe[feature]<minimum, feature] = minimum\n"
      ],
      "metadata": {
        "id": "PjFIW35XgCkI"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Loading the data that was given:"
      ],
      "metadata": {
        "id": "AE896wq3UhwD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"McCracken_Macro_Dataset_Monthly.csv\")\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "Brz-pvCLhqHn",
        "outputId": "32a5547e-c318-440d-f0e8-2267d06e8396"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>CUSR0000SAS</th>\n",
              "      <th>CPIULFSL</th>\n",
              "      <th>CUSR0000SA0L2</th>\n",
              "      <th>CUSR0000SA0L5</th>\n",
              "      <th>PCEPI</th>\n",
              "      <th>DDURRG3M086SBEA</th>\n",
              "      <th>DNDGRG3M086SBEA</th>\n",
              "      <th>DSERRG3M086SBEA</th>\n",
              "      <th>CES0600000008</th>\n",
              "      <th>CES2000000008</th>\n",
              "      <th>CES3000000008</th>\n",
              "      <th>UMCSENTx</th>\n",
              "      <th>DTCOLNVHFNM</th>\n",
              "      <th>DTCTHFNM</th>\n",
              "      <th>INVEST</th>\n",
              "      <th>VXOCLSx</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Transform:</td>\n",
              "      <td>5.000</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.000</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>5.00000</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>2.0000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.00</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.000</td>\n",
              "      <td>6.000</td>\n",
              "      <td>6.000</td>\n",
              "      <td>6.000</td>\n",
              "      <td>6.000</td>\n",
              "      <td>6.000</td>\n",
              "      <td>6.000</td>\n",
              "      <td>6.000</td>\n",
              "      <td>6.000</td>\n",
              "      <td>6.000</td>\n",
              "      <td>6.000</td>\n",
              "      <td>6.000</td>\n",
              "      <td>6.000</td>\n",
              "      <td>6.000</td>\n",
              "      <td>6.00</td>\n",
              "      <td>6.00</td>\n",
              "      <td>6.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.00</td>\n",
              "      <td>6.00</td>\n",
              "      <td>6.0000</td>\n",
              "      <td>1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>01/01/1959</td>\n",
              "      <td>2442.158</td>\n",
              "      <td>2293.2</td>\n",
              "      <td>17.272</td>\n",
              "      <td>2.922664e+05</td>\n",
              "      <td>18235.77392</td>\n",
              "      <td>22.0733</td>\n",
              "      <td>23.4027</td>\n",
              "      <td>22.2858</td>\n",
              "      <td>31.5688</td>\n",
              "      <td>18.6601</td>\n",
              "      <td>38.1454</td>\n",
              "      <td>8.1760</td>\n",
              "      <td>20.2407</td>\n",
              "      <td>12.2794</td>\n",
              "      <td>30.6469</td>\n",
              "      <td>20.9189</td>\n",
              "      <td>19.9060</td>\n",
              "      <td>34.7139</td>\n",
              "      <td>80.1973</td>\n",
              "      <td>1357.0</td>\n",
              "      <td>0.333579</td>\n",
              "      <td>67936</td>\n",
              "      <td>63868</td>\n",
              "      <td>6.0</td>\n",
              "      <td>16.3</td>\n",
              "      <td>1574</td>\n",
              "      <td>1169</td>\n",
              "      <td>1396</td>\n",
              "      <td>594</td>\n",
              "      <td>802</td>\n",
              "      <td>291078</td>\n",
              "      <td>52478</td>\n",
              "      <td>18796</td>\n",
              "      <td>713.0</td>\n",
              "      <td>2993</td>\n",
              "      <td>14998</td>\n",
              "      <td>8740</td>\n",
              "      <td>6258</td>\n",
              "      <td>33682</td>\n",
              "      <td>...</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.88</td>\n",
              "      <td>1.53</td>\n",
              "      <td>1.54</td>\n",
              "      <td>1.64</td>\n",
              "      <td>2.39</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.3122</td>\n",
              "      <td>359.8417</td>\n",
              "      <td>2.8065</td>\n",
              "      <td>0.9671</td>\n",
              "      <td>33.1</td>\n",
              "      <td>33.4</td>\n",
              "      <td>30.6</td>\n",
              "      <td>31.6</td>\n",
              "      <td>3.00</td>\n",
              "      <td>32.5</td>\n",
              "      <td>29.010</td>\n",
              "      <td>44.800</td>\n",
              "      <td>29.300</td>\n",
              "      <td>21.100</td>\n",
              "      <td>33.300</td>\n",
              "      <td>38.100</td>\n",
              "      <td>22.900</td>\n",
              "      <td>28.900</td>\n",
              "      <td>30.700</td>\n",
              "      <td>29.600</td>\n",
              "      <td>16.042</td>\n",
              "      <td>56.918</td>\n",
              "      <td>17.791</td>\n",
              "      <td>11.326</td>\n",
              "      <td>2.13</td>\n",
              "      <td>2.45</td>\n",
              "      <td>2.04</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6476.00</td>\n",
              "      <td>12298.00</td>\n",
              "      <td>84.2043</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>02/01/1959</td>\n",
              "      <td>2451.778</td>\n",
              "      <td>2301.5</td>\n",
              "      <td>17.452</td>\n",
              "      <td>2.944247e+05</td>\n",
              "      <td>18369.56308</td>\n",
              "      <td>22.5056</td>\n",
              "      <td>23.7185</td>\n",
              "      <td>22.4788</td>\n",
              "      <td>31.8004</td>\n",
              "      <td>18.7580</td>\n",
              "      <td>38.5100</td>\n",
              "      <td>8.2854</td>\n",
              "      <td>20.8817</td>\n",
              "      <td>12.8450</td>\n",
              "      <td>31.1834</td>\n",
              "      <td>21.3034</td>\n",
              "      <td>19.8518</td>\n",
              "      <td>34.2411</td>\n",
              "      <td>81.4428</td>\n",
              "      <td>1421.0</td>\n",
              "      <td>0.358386</td>\n",
              "      <td>67649</td>\n",
              "      <td>63684</td>\n",
              "      <td>5.9</td>\n",
              "      <td>15.5</td>\n",
              "      <td>1554</td>\n",
              "      <td>1164</td>\n",
              "      <td>1277</td>\n",
              "      <td>545</td>\n",
              "      <td>732</td>\n",
              "      <td>282958</td>\n",
              "      <td>52688</td>\n",
              "      <td>18890</td>\n",
              "      <td>704.2</td>\n",
              "      <td>2980</td>\n",
              "      <td>15115</td>\n",
              "      <td>8839</td>\n",
              "      <td>6276</td>\n",
              "      <td>33798</td>\n",
              "      <td>...</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1.11</td>\n",
              "      <td>1.53</td>\n",
              "      <td>1.53</td>\n",
              "      <td>1.71</td>\n",
              "      <td>2.46</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.3133</td>\n",
              "      <td>359.8417</td>\n",
              "      <td>2.8093</td>\n",
              "      <td>0.9748</td>\n",
              "      <td>33.2</td>\n",
              "      <td>33.4</td>\n",
              "      <td>30.7</td>\n",
              "      <td>31.4</td>\n",
              "      <td>3.00</td>\n",
              "      <td>32.5</td>\n",
              "      <td>29.000</td>\n",
              "      <td>44.700</td>\n",
              "      <td>29.400</td>\n",
              "      <td>21.200</td>\n",
              "      <td>33.300</td>\n",
              "      <td>38.100</td>\n",
              "      <td>23.000</td>\n",
              "      <td>28.900</td>\n",
              "      <td>30.700</td>\n",
              "      <td>29.600</td>\n",
              "      <td>16.057</td>\n",
              "      <td>56.951</td>\n",
              "      <td>17.798</td>\n",
              "      <td>11.343</td>\n",
              "      <td>2.14</td>\n",
              "      <td>2.46</td>\n",
              "      <td>2.05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6476.00</td>\n",
              "      <td>12298.00</td>\n",
              "      <td>83.5280</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>03/01/1959</td>\n",
              "      <td>2467.594</td>\n",
              "      <td>2318.5</td>\n",
              "      <td>17.617</td>\n",
              "      <td>2.934187e+05</td>\n",
              "      <td>18523.05762</td>\n",
              "      <td>22.8298</td>\n",
              "      <td>23.8621</td>\n",
              "      <td>22.5892</td>\n",
              "      <td>31.8004</td>\n",
              "      <td>19.1254</td>\n",
              "      <td>38.3277</td>\n",
              "      <td>8.3749</td>\n",
              "      <td>21.4240</td>\n",
              "      <td>13.3511</td>\n",
              "      <td>31.6519</td>\n",
              "      <td>21.6367</td>\n",
              "      <td>19.9873</td>\n",
              "      <td>35.1079</td>\n",
              "      <td>82.4769</td>\n",
              "      <td>1524.0</td>\n",
              "      <td>0.400947</td>\n",
              "      <td>68068</td>\n",
              "      <td>64267</td>\n",
              "      <td>5.6</td>\n",
              "      <td>15.3</td>\n",
              "      <td>1459</td>\n",
              "      <td>1093</td>\n",
              "      <td>1210</td>\n",
              "      <td>530</td>\n",
              "      <td>680</td>\n",
              "      <td>260346</td>\n",
              "      <td>53014</td>\n",
              "      <td>19069</td>\n",
              "      <td>704.1</td>\n",
              "      <td>3013</td>\n",
              "      <td>15259</td>\n",
              "      <td>8965</td>\n",
              "      <td>6294</td>\n",
              "      <td>33945</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.81</td>\n",
              "      <td>1.19</td>\n",
              "      <td>1.19</td>\n",
              "      <td>1.33</td>\n",
              "      <td>2.05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.3228</td>\n",
              "      <td>359.8417</td>\n",
              "      <td>2.8127</td>\n",
              "      <td>0.9698</td>\n",
              "      <td>33.2</td>\n",
              "      <td>33.3</td>\n",
              "      <td>30.7</td>\n",
              "      <td>31.5</td>\n",
              "      <td>2.97</td>\n",
              "      <td>32.9</td>\n",
              "      <td>28.970</td>\n",
              "      <td>44.700</td>\n",
              "      <td>29.600</td>\n",
              "      <td>21.300</td>\n",
              "      <td>33.200</td>\n",
              "      <td>38.300</td>\n",
              "      <td>23.000</td>\n",
              "      <td>28.900</td>\n",
              "      <td>30.700</td>\n",
              "      <td>29.600</td>\n",
              "      <td>16.068</td>\n",
              "      <td>57.022</td>\n",
              "      <td>17.785</td>\n",
              "      <td>11.363</td>\n",
              "      <td>2.15</td>\n",
              "      <td>2.45</td>\n",
              "      <td>2.07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6508.00</td>\n",
              "      <td>12349.00</td>\n",
              "      <td>81.6405</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>04/01/1959</td>\n",
              "      <td>2483.671</td>\n",
              "      <td>2334.9</td>\n",
              "      <td>17.553</td>\n",
              "      <td>2.993228e+05</td>\n",
              "      <td>18534.46600</td>\n",
              "      <td>23.3161</td>\n",
              "      <td>24.2067</td>\n",
              "      <td>22.9201</td>\n",
              "      <td>32.3021</td>\n",
              "      <td>19.2478</td>\n",
              "      <td>38.9658</td>\n",
              "      <td>8.5937</td>\n",
              "      <td>22.0157</td>\n",
              "      <td>13.7530</td>\n",
              "      <td>32.6322</td>\n",
              "      <td>22.0981</td>\n",
              "      <td>20.0685</td>\n",
              "      <td>34.9503</td>\n",
              "      <td>83.9922</td>\n",
              "      <td>1589.0</td>\n",
              "      <td>0.444973</td>\n",
              "      <td>68339</td>\n",
              "      <td>64768</td>\n",
              "      <td>5.2</td>\n",
              "      <td>14.9</td>\n",
              "      <td>1494</td>\n",
              "      <td>934</td>\n",
              "      <td>1039</td>\n",
              "      <td>408</td>\n",
              "      <td>631</td>\n",
              "      <td>246413</td>\n",
              "      <td>53321</td>\n",
              "      <td>19269</td>\n",
              "      <td>705.2</td>\n",
              "      <td>3085</td>\n",
              "      <td>15385</td>\n",
              "      <td>9077</td>\n",
              "      <td>6308</td>\n",
              "      <td>34052</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.76</td>\n",
              "      <td>1.16</td>\n",
              "      <td>1.16</td>\n",
              "      <td>1.27</td>\n",
              "      <td>1.90</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.3226</td>\n",
              "      <td>359.8417</td>\n",
              "      <td>2.8165</td>\n",
              "      <td>0.9636</td>\n",
              "      <td>33.2</td>\n",
              "      <td>33.4</td>\n",
              "      <td>30.7</td>\n",
              "      <td>31.7</td>\n",
              "      <td>2.97</td>\n",
              "      <td>32.7</td>\n",
              "      <td>28.980</td>\n",
              "      <td>44.800</td>\n",
              "      <td>29.700</td>\n",
              "      <td>21.300</td>\n",
              "      <td>33.200</td>\n",
              "      <td>38.300</td>\n",
              "      <td>23.100</td>\n",
              "      <td>29.000</td>\n",
              "      <td>30.700</td>\n",
              "      <td>29.600</td>\n",
              "      <td>16.100</td>\n",
              "      <td>57.080</td>\n",
              "      <td>17.796</td>\n",
              "      <td>11.403</td>\n",
              "      <td>2.16</td>\n",
              "      <td>2.47</td>\n",
              "      <td>2.08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6620.00</td>\n",
              "      <td>12484.00</td>\n",
              "      <td>81.8099</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>748</th>\n",
              "      <td>04/01/2021</td>\n",
              "      <td>18261.337</td>\n",
              "      <td>14130.6</td>\n",
              "      <td>123.827</td>\n",
              "      <td>1.585555e+06</td>\n",
              "      <td>628751.00000</td>\n",
              "      <td>99.2301</td>\n",
              "      <td>98.3032</td>\n",
              "      <td>98.9459</td>\n",
              "      <td>99.4073</td>\n",
              "      <td>99.2308</td>\n",
              "      <td>99.3852</td>\n",
              "      <td>92.3234</td>\n",
              "      <td>100.2550</td>\n",
              "      <td>95.9528</td>\n",
              "      <td>98.3618</td>\n",
              "      <td>97.4914</td>\n",
              "      <td>106.4134</td>\n",
              "      <td>92.4188</td>\n",
              "      <td>75.0321</td>\n",
              "      <td>9193.0</td>\n",
              "      <td>0.936914</td>\n",
              "      <td>160988</td>\n",
              "      <td>151176</td>\n",
              "      <td>6.1</td>\n",
              "      <td>28.8</td>\n",
              "      <td>2414</td>\n",
              "      <td>1939</td>\n",
              "      <td>5386</td>\n",
              "      <td>1203</td>\n",
              "      <td>4183</td>\n",
              "      <td>621000</td>\n",
              "      <td>144326</td>\n",
              "      <td>20319</td>\n",
              "      <td>571.4</td>\n",
              "      <td>7439</td>\n",
              "      <td>12264</td>\n",
              "      <td>7605</td>\n",
              "      <td>4659</td>\n",
              "      <td>124007</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.79</td>\n",
              "      <td>1.56</td>\n",
              "      <td>2.83</td>\n",
              "      <td>3.53</td>\n",
              "      <td>103.8470</td>\n",
              "      <td>0.9218</td>\n",
              "      <td>109.0445</td>\n",
              "      <td>1.3845</td>\n",
              "      <td>1.2494</td>\n",
              "      <td>215.3</td>\n",
              "      <td>228.9</td>\n",
              "      <td>220.4</td>\n",
              "      <td>219.5</td>\n",
              "      <td>61.72</td>\n",
              "      <td>235.4</td>\n",
              "      <td>266.832</td>\n",
              "      <td>119.335</td>\n",
              "      <td>221.826</td>\n",
              "      <td>524.002</td>\n",
              "      <td>193.772</td>\n",
              "      <td>111.940</td>\n",
              "      <td>338.805</td>\n",
              "      <td>265.904</td>\n",
              "      <td>244.660</td>\n",
              "      <td>254.498</td>\n",
              "      <td>114.166</td>\n",
              "      <td>88.879</td>\n",
              "      <td>101.517</td>\n",
              "      <td>123.125</td>\n",
              "      <td>26.19</td>\n",
              "      <td>30.20</td>\n",
              "      <td>23.44</td>\n",
              "      <td>88.3</td>\n",
              "      <td>362666.77</td>\n",
              "      <td>751213.18</td>\n",
              "      <td>5072.8265</td>\n",
              "      <td>15.9924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>749</th>\n",
              "      <td>05/01/2021</td>\n",
              "      <td>17778.066</td>\n",
              "      <td>14145.3</td>\n",
              "      <td>123.235</td>\n",
              "      <td>1.552988e+06</td>\n",
              "      <td>620119.00000</td>\n",
              "      <td>99.8504</td>\n",
              "      <td>98.8957</td>\n",
              "      <td>99.7572</td>\n",
              "      <td>100.0709</td>\n",
              "      <td>101.6037</td>\n",
              "      <td>99.5827</td>\n",
              "      <td>93.8185</td>\n",
              "      <td>100.9100</td>\n",
              "      <td>96.6621</td>\n",
              "      <td>99.4804</td>\n",
              "      <td>98.2943</td>\n",
              "      <td>106.1077</td>\n",
              "      <td>92.9248</td>\n",
              "      <td>75.6418</td>\n",
              "      <td>9483.0</td>\n",
              "      <td>1.017926</td>\n",
              "      <td>160935</td>\n",
              "      <td>151620</td>\n",
              "      <td>5.8</td>\n",
              "      <td>29.9</td>\n",
              "      <td>2023</td>\n",
              "      <td>2126</td>\n",
              "      <td>5013</td>\n",
              "      <td>1262</td>\n",
              "      <td>3752</td>\n",
              "      <td>444400</td>\n",
              "      <td>144940</td>\n",
              "      <td>20335</td>\n",
              "      <td>576.1</td>\n",
              "      <td>7415</td>\n",
              "      <td>12300</td>\n",
              "      <td>7632</td>\n",
              "      <td>4668</td>\n",
              "      <td>124605</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.76</td>\n",
              "      <td>1.56</td>\n",
              "      <td>2.90</td>\n",
              "      <td>3.56</td>\n",
              "      <td>102.0529</td>\n",
              "      <td>0.9027</td>\n",
              "      <td>109.1120</td>\n",
              "      <td>1.4084</td>\n",
              "      <td>1.2125</td>\n",
              "      <td>217.2</td>\n",
              "      <td>231.1</td>\n",
              "      <td>225.9</td>\n",
              "      <td>235.5</td>\n",
              "      <td>65.17</td>\n",
              "      <td>274.9</td>\n",
              "      <td>268.551</td>\n",
              "      <td>120.793</td>\n",
              "      <td>226.538</td>\n",
              "      <td>523.594</td>\n",
              "      <td>195.860</td>\n",
              "      <td>115.352</td>\n",
              "      <td>340.108</td>\n",
              "      <td>267.704</td>\n",
              "      <td>246.655</td>\n",
              "      <td>256.313</td>\n",
              "      <td>114.774</td>\n",
              "      <td>90.669</td>\n",
              "      <td>101.658</td>\n",
              "      <td>123.575</td>\n",
              "      <td>26.32</td>\n",
              "      <td>30.32</td>\n",
              "      <td>23.60</td>\n",
              "      <td>82.9</td>\n",
              "      <td>365154.60</td>\n",
              "      <td>754248.23</td>\n",
              "      <td>5169.0271</td>\n",
              "      <td>17.9421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>750</th>\n",
              "      <td>06/01/2021</td>\n",
              "      <td>17716.168</td>\n",
              "      <td>14168.2</td>\n",
              "      <td>123.891</td>\n",
              "      <td>1.559648e+06</td>\n",
              "      <td>625405.00000</td>\n",
              "      <td>100.3398</td>\n",
              "      <td>98.7304</td>\n",
              "      <td>99.6633</td>\n",
              "      <td>99.7401</td>\n",
              "      <td>100.1333</td>\n",
              "      <td>99.5610</td>\n",
              "      <td>93.4728</td>\n",
              "      <td>102.2355</td>\n",
              "      <td>96.5788</td>\n",
              "      <td>99.4550</td>\n",
              "      <td>97.9699</td>\n",
              "      <td>109.8122</td>\n",
              "      <td>93.3134</td>\n",
              "      <td>75.3792</td>\n",
              "      <td>10185.0</td>\n",
              "      <td>1.073914</td>\n",
              "      <td>161086</td>\n",
              "      <td>151602</td>\n",
              "      <td>5.9</td>\n",
              "      <td>31.6</td>\n",
              "      <td>1981</td>\n",
              "      <td>2166</td>\n",
              "      <td>5314</td>\n",
              "      <td>1329</td>\n",
              "      <td>3985</td>\n",
              "      <td>394000</td>\n",
              "      <td>145902</td>\n",
              "      <td>20377</td>\n",
              "      <td>586.7</td>\n",
              "      <td>7413</td>\n",
              "      <td>12332</td>\n",
              "      <td>7663</td>\n",
              "      <td>4669</td>\n",
              "      <td>125525</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.76</td>\n",
              "      <td>1.44</td>\n",
              "      <td>2.71</td>\n",
              "      <td>3.36</td>\n",
              "      <td>102.8535</td>\n",
              "      <td>0.9078</td>\n",
              "      <td>110.1073</td>\n",
              "      <td>1.4025</td>\n",
              "      <td>1.2220</td>\n",
              "      <td>219.7</td>\n",
              "      <td>234.1</td>\n",
              "      <td>230.1</td>\n",
              "      <td>241.6</td>\n",
              "      <td>71.38</td>\n",
              "      <td>257.1</td>\n",
              "      <td>270.981</td>\n",
              "      <td>121.636</td>\n",
              "      <td>234.727</td>\n",
              "      <td>523.058</td>\n",
              "      <td>199.179</td>\n",
              "      <td>119.422</td>\n",
              "      <td>341.567</td>\n",
              "      <td>270.182</td>\n",
              "      <td>249.386</td>\n",
              "      <td>258.878</td>\n",
              "      <td>115.385</td>\n",
              "      <td>91.620</td>\n",
              "      <td>102.225</td>\n",
              "      <td>124.096</td>\n",
              "      <td>26.48</td>\n",
              "      <td>30.48</td>\n",
              "      <td>23.76</td>\n",
              "      <td>85.5</td>\n",
              "      <td>366098.79</td>\n",
              "      <td>756290.99</td>\n",
              "      <td>5235.2502</td>\n",
              "      <td>14.8913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>751</th>\n",
              "      <td>07/01/2021</td>\n",
              "      <td>17838.510</td>\n",
              "      <td>14201.1</td>\n",
              "      <td>123.712</td>\n",
              "      <td>NaN</td>\n",
              "      <td>614313.00000</td>\n",
              "      <td>101.1874</td>\n",
              "      <td>100.1329</td>\n",
              "      <td>101.2501</td>\n",
              "      <td>100.4491</td>\n",
              "      <td>105.1449</td>\n",
              "      <td>99.1077</td>\n",
              "      <td>97.0062</td>\n",
              "      <td>102.3782</td>\n",
              "      <td>98.5360</td>\n",
              "      <td>99.5572</td>\n",
              "      <td>99.5387</td>\n",
              "      <td>106.5678</td>\n",
              "      <td>92.7238</td>\n",
              "      <td>76.5691</td>\n",
              "      <td>10934.0</td>\n",
              "      <td>1.256493</td>\n",
              "      <td>161347</td>\n",
              "      <td>152645</td>\n",
              "      <td>5.4</td>\n",
              "      <td>29.5</td>\n",
              "      <td>2257</td>\n",
              "      <td>1861</td>\n",
              "      <td>4599</td>\n",
              "      <td>1174</td>\n",
              "      <td>3425</td>\n",
              "      <td>392800</td>\n",
              "      <td>146955</td>\n",
              "      <td>20441</td>\n",
              "      <td>593.2</td>\n",
              "      <td>7419</td>\n",
              "      <td>12384</td>\n",
              "      <td>7704</td>\n",
              "      <td>4680</td>\n",
              "      <td>126514</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.66</td>\n",
              "      <td>1.22</td>\n",
              "      <td>2.47</td>\n",
              "      <td>3.14</td>\n",
              "      <td>104.7021</td>\n",
              "      <td>0.9174</td>\n",
              "      <td>110.2143</td>\n",
              "      <td>1.3808</td>\n",
              "      <td>1.2530</td>\n",
              "      <td>221.0</td>\n",
              "      <td>235.4</td>\n",
              "      <td>234.1</td>\n",
              "      <td>244.9</td>\n",
              "      <td>72.49</td>\n",
              "      <td>256.1</td>\n",
              "      <td>272.265</td>\n",
              "      <td>121.687</td>\n",
              "      <td>236.252</td>\n",
              "      <td>524.431</td>\n",
              "      <td>200.675</td>\n",
              "      <td>120.190</td>\n",
              "      <td>342.581</td>\n",
              "      <td>271.353</td>\n",
              "      <td>250.619</td>\n",
              "      <td>260.156</td>\n",
              "      <td>115.860</td>\n",
              "      <td>91.911</td>\n",
              "      <td>102.809</td>\n",
              "      <td>124.565</td>\n",
              "      <td>26.59</td>\n",
              "      <td>30.58</td>\n",
              "      <td>23.88</td>\n",
              "      <td>81.2</td>\n",
              "      <td>366251.23</td>\n",
              "      <td>757292.96</td>\n",
              "      <td>5281.4744</td>\n",
              "      <td>15.5122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>752</th>\n",
              "      <td>08/01/2021</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>618676.00000</td>\n",
              "      <td>101.5949</td>\n",
              "      <td>100.8053</td>\n",
              "      <td>101.9616</td>\n",
              "      <td>101.2702</td>\n",
              "      <td>105.3528</td>\n",
              "      <td>100.0933</td>\n",
              "      <td>97.4941</td>\n",
              "      <td>102.4499</td>\n",
              "      <td>98.4315</td>\n",
              "      <td>99.2038</td>\n",
              "      <td>99.7006</td>\n",
              "      <td>110.4348</td>\n",
              "      <td>93.6916</td>\n",
              "      <td>76.6736</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>161537</td>\n",
              "      <td>153154</td>\n",
              "      <td>5.2</td>\n",
              "      <td>29.6</td>\n",
              "      <td>2083</td>\n",
              "      <td>2066</td>\n",
              "      <td>4343</td>\n",
              "      <td>1164</td>\n",
              "      <td>3179</td>\n",
              "      <td>356250</td>\n",
              "      <td>147190</td>\n",
              "      <td>20481</td>\n",
              "      <td>599.2</td>\n",
              "      <td>7416</td>\n",
              "      <td>12421</td>\n",
              "      <td>7735</td>\n",
              "      <td>4686</td>\n",
              "      <td>126709</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.68</td>\n",
              "      <td>1.19</td>\n",
              "      <td>2.46</td>\n",
              "      <td>3.15</td>\n",
              "      <td>105.0489</td>\n",
              "      <td>0.9147</td>\n",
              "      <td>109.8527</td>\n",
              "      <td>1.3797</td>\n",
              "      <td>1.2599</td>\n",
              "      <td>223.2</td>\n",
              "      <td>238.1</td>\n",
              "      <td>236.5</td>\n",
              "      <td>247.4</td>\n",
              "      <td>67.73</td>\n",
              "      <td>260.9</td>\n",
              "      <td>273.012</td>\n",
              "      <td>122.142</td>\n",
              "      <td>235.997</td>\n",
              "      <td>525.724</td>\n",
              "      <td>201.848</td>\n",
              "      <td>119.936</td>\n",
              "      <td>342.755</td>\n",
              "      <td>272.045</td>\n",
              "      <td>251.445</td>\n",
              "      <td>260.877</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>26.71</td>\n",
              "      <td>30.70</td>\n",
              "      <td>24.01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5335.6648</td>\n",
              "      <td>14.6821</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>753 rows × 128 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d6ddec5b-cf89-4376-a384-36c0c6f54f97')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        sasdate        RPI  W875RX1  ...   DTCTHFNM     INVEST  VXOCLSx\n",
              "0    Transform:      5.000      5.0  ...       6.00     6.0000   1.0000\n",
              "1    01/01/1959   2442.158   2293.2  ...   12298.00    84.2043      NaN\n",
              "2    02/01/1959   2451.778   2301.5  ...   12298.00    83.5280      NaN\n",
              "3    03/01/1959   2467.594   2318.5  ...   12349.00    81.6405      NaN\n",
              "4    04/01/1959   2483.671   2334.9  ...   12484.00    81.8099      NaN\n",
              "..          ...        ...      ...  ...        ...        ...      ...\n",
              "748  04/01/2021  18261.337  14130.6  ...  751213.18  5072.8265  15.9924\n",
              "749  05/01/2021  17778.066  14145.3  ...  754248.23  5169.0271  17.9421\n",
              "750  06/01/2021  17716.168  14168.2  ...  756290.99  5235.2502  14.8913\n",
              "751  07/01/2021  17838.510  14201.1  ...  757292.96  5281.4744  15.5122\n",
              "752  08/01/2021        NaN      NaN  ...        NaN  5335.6648  14.6821\n",
              "\n",
              "[753 rows x 128 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Storing the transformation codes into separate array:"
      ],
      "metadata": {
        "id": "aFnw5lZ1UlaK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_transformations = df.iloc[0]\n",
        "df.drop(index=df.index[0], axis=0, inplace=True)\n",
        "\n",
        "df_transformations"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-FICOMqdtFCD",
        "outputId": "0eae2f97-51cb-440e-f0a2-dc3acdce9d6a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sasdate            Transform:\n",
              "RPI                         5\n",
              "W875RX1                     5\n",
              "DPCERA3M086SBEA             5\n",
              "CMRMTSPLx                   5\n",
              "                      ...    \n",
              "UMCSENTx                    2\n",
              "DTCOLNVHFNM                 6\n",
              "DTCTHFNM                    6\n",
              "INVEST                      6\n",
              "VXOCLSx                     1\n",
              "Name: 0, Length: 128, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, our dataframe looks like this:"
      ],
      "metadata": {
        "id": "5WgGOkpwU7mG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.set_index('sasdate')\n",
        "df.index.rename('', inplace=True)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "FqVlq423tF-i",
        "outputId": "3a44b2f4-3bad-4d13-f8ff-258f61ce0dc0"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-4b5698b4-3598-4f6f-bddd-3a26e83e802a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RPI</th>\n",
              "      <th>W875RX1</th>\n",
              "      <th>DPCERA3M086SBEA</th>\n",
              "      <th>CMRMTSPLx</th>\n",
              "      <th>RETAILx</th>\n",
              "      <th>INDPRO</th>\n",
              "      <th>IPFPNSS</th>\n",
              "      <th>IPFINAL</th>\n",
              "      <th>IPCONGD</th>\n",
              "      <th>IPDCONGD</th>\n",
              "      <th>IPNCONGD</th>\n",
              "      <th>IPBUSEQ</th>\n",
              "      <th>IPMAT</th>\n",
              "      <th>IPDMAT</th>\n",
              "      <th>IPNMAT</th>\n",
              "      <th>IPMANSICS</th>\n",
              "      <th>IPB51222S</th>\n",
              "      <th>IPFUELS</th>\n",
              "      <th>CUMFNS</th>\n",
              "      <th>HWI</th>\n",
              "      <th>HWIURATIO</th>\n",
              "      <th>CLF16OV</th>\n",
              "      <th>CE16OV</th>\n",
              "      <th>UNRATE</th>\n",
              "      <th>UEMPMEAN</th>\n",
              "      <th>UEMPLT5</th>\n",
              "      <th>UEMP5TO14</th>\n",
              "      <th>UEMP15OV</th>\n",
              "      <th>UEMP15T26</th>\n",
              "      <th>UEMP27OV</th>\n",
              "      <th>CLAIMSx</th>\n",
              "      <th>PAYEMS</th>\n",
              "      <th>USGOOD</th>\n",
              "      <th>CES1021000001</th>\n",
              "      <th>USCONS</th>\n",
              "      <th>MANEMP</th>\n",
              "      <th>DMANEMP</th>\n",
              "      <th>NDMANEMP</th>\n",
              "      <th>SRVPRD</th>\n",
              "      <th>USTPU</th>\n",
              "      <th>...</th>\n",
              "      <th>TB3SMFFM</th>\n",
              "      <th>TB6SMFFM</th>\n",
              "      <th>T1YFFM</th>\n",
              "      <th>T5YFFM</th>\n",
              "      <th>T10YFFM</th>\n",
              "      <th>AAAFFM</th>\n",
              "      <th>BAAFFM</th>\n",
              "      <th>TWEXAFEGSMTHx</th>\n",
              "      <th>EXSZUSx</th>\n",
              "      <th>EXJPUSx</th>\n",
              "      <th>EXUSUKx</th>\n",
              "      <th>EXCAUSx</th>\n",
              "      <th>WPSFD49207</th>\n",
              "      <th>WPSFD49502</th>\n",
              "      <th>WPSID61</th>\n",
              "      <th>WPSID62</th>\n",
              "      <th>OILPRICEx</th>\n",
              "      <th>PPICMM</th>\n",
              "      <th>CPIAUCSL</th>\n",
              "      <th>CPIAPPSL</th>\n",
              "      <th>CPITRNSL</th>\n",
              "      <th>CPIMEDSL</th>\n",
              "      <th>CUSR0000SAC</th>\n",
              "      <th>CUSR0000SAD</th>\n",
              "      <th>CUSR0000SAS</th>\n",
              "      <th>CPIULFSL</th>\n",
              "      <th>CUSR0000SA0L2</th>\n",
              "      <th>CUSR0000SA0L5</th>\n",
              "      <th>PCEPI</th>\n",
              "      <th>DDURRG3M086SBEA</th>\n",
              "      <th>DNDGRG3M086SBEA</th>\n",
              "      <th>DSERRG3M086SBEA</th>\n",
              "      <th>CES0600000008</th>\n",
              "      <th>CES2000000008</th>\n",
              "      <th>CES3000000008</th>\n",
              "      <th>UMCSENTx</th>\n",
              "      <th>DTCOLNVHFNM</th>\n",
              "      <th>DTCTHFNM</th>\n",
              "      <th>INVEST</th>\n",
              "      <th>VXOCLSx</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>01/01/1959</th>\n",
              "      <td>2442.158</td>\n",
              "      <td>2293.2</td>\n",
              "      <td>17.272</td>\n",
              "      <td>2.922664e+05</td>\n",
              "      <td>18235.77392</td>\n",
              "      <td>22.0733</td>\n",
              "      <td>23.4027</td>\n",
              "      <td>22.2858</td>\n",
              "      <td>31.5688</td>\n",
              "      <td>18.6601</td>\n",
              "      <td>38.1454</td>\n",
              "      <td>8.1760</td>\n",
              "      <td>20.2407</td>\n",
              "      <td>12.2794</td>\n",
              "      <td>30.6469</td>\n",
              "      <td>20.9189</td>\n",
              "      <td>19.9060</td>\n",
              "      <td>34.7139</td>\n",
              "      <td>80.1973</td>\n",
              "      <td>1357.0</td>\n",
              "      <td>0.333579</td>\n",
              "      <td>67936</td>\n",
              "      <td>63868</td>\n",
              "      <td>6.0</td>\n",
              "      <td>16.3</td>\n",
              "      <td>1574</td>\n",
              "      <td>1169</td>\n",
              "      <td>1396</td>\n",
              "      <td>594</td>\n",
              "      <td>802</td>\n",
              "      <td>291078</td>\n",
              "      <td>52478</td>\n",
              "      <td>18796</td>\n",
              "      <td>713.0</td>\n",
              "      <td>2993</td>\n",
              "      <td>14998</td>\n",
              "      <td>8740</td>\n",
              "      <td>6258</td>\n",
              "      <td>33682</td>\n",
              "      <td>10774</td>\n",
              "      <td>...</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.88</td>\n",
              "      <td>1.53</td>\n",
              "      <td>1.54</td>\n",
              "      <td>1.64</td>\n",
              "      <td>2.39</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.3122</td>\n",
              "      <td>359.8417</td>\n",
              "      <td>2.8065</td>\n",
              "      <td>0.9671</td>\n",
              "      <td>33.1</td>\n",
              "      <td>33.4</td>\n",
              "      <td>30.6</td>\n",
              "      <td>31.6</td>\n",
              "      <td>3.00</td>\n",
              "      <td>32.5</td>\n",
              "      <td>29.010</td>\n",
              "      <td>44.800</td>\n",
              "      <td>29.300</td>\n",
              "      <td>21.100</td>\n",
              "      <td>33.300</td>\n",
              "      <td>38.100</td>\n",
              "      <td>22.900</td>\n",
              "      <td>28.900</td>\n",
              "      <td>30.700</td>\n",
              "      <td>29.600</td>\n",
              "      <td>16.042</td>\n",
              "      <td>56.918</td>\n",
              "      <td>17.791</td>\n",
              "      <td>11.326</td>\n",
              "      <td>2.13</td>\n",
              "      <td>2.45</td>\n",
              "      <td>2.04</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6476.00</td>\n",
              "      <td>12298.00</td>\n",
              "      <td>84.2043</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>02/01/1959</th>\n",
              "      <td>2451.778</td>\n",
              "      <td>2301.5</td>\n",
              "      <td>17.452</td>\n",
              "      <td>2.944247e+05</td>\n",
              "      <td>18369.56308</td>\n",
              "      <td>22.5056</td>\n",
              "      <td>23.7185</td>\n",
              "      <td>22.4788</td>\n",
              "      <td>31.8004</td>\n",
              "      <td>18.7580</td>\n",
              "      <td>38.5100</td>\n",
              "      <td>8.2854</td>\n",
              "      <td>20.8817</td>\n",
              "      <td>12.8450</td>\n",
              "      <td>31.1834</td>\n",
              "      <td>21.3034</td>\n",
              "      <td>19.8518</td>\n",
              "      <td>34.2411</td>\n",
              "      <td>81.4428</td>\n",
              "      <td>1421.0</td>\n",
              "      <td>0.358386</td>\n",
              "      <td>67649</td>\n",
              "      <td>63684</td>\n",
              "      <td>5.9</td>\n",
              "      <td>15.5</td>\n",
              "      <td>1554</td>\n",
              "      <td>1164</td>\n",
              "      <td>1277</td>\n",
              "      <td>545</td>\n",
              "      <td>732</td>\n",
              "      <td>282958</td>\n",
              "      <td>52688</td>\n",
              "      <td>18890</td>\n",
              "      <td>704.2</td>\n",
              "      <td>2980</td>\n",
              "      <td>15115</td>\n",
              "      <td>8839</td>\n",
              "      <td>6276</td>\n",
              "      <td>33798</td>\n",
              "      <td>10816</td>\n",
              "      <td>...</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1.11</td>\n",
              "      <td>1.53</td>\n",
              "      <td>1.53</td>\n",
              "      <td>1.71</td>\n",
              "      <td>2.46</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.3133</td>\n",
              "      <td>359.8417</td>\n",
              "      <td>2.8093</td>\n",
              "      <td>0.9748</td>\n",
              "      <td>33.2</td>\n",
              "      <td>33.4</td>\n",
              "      <td>30.7</td>\n",
              "      <td>31.4</td>\n",
              "      <td>3.00</td>\n",
              "      <td>32.5</td>\n",
              "      <td>29.000</td>\n",
              "      <td>44.700</td>\n",
              "      <td>29.400</td>\n",
              "      <td>21.200</td>\n",
              "      <td>33.300</td>\n",
              "      <td>38.100</td>\n",
              "      <td>23.000</td>\n",
              "      <td>28.900</td>\n",
              "      <td>30.700</td>\n",
              "      <td>29.600</td>\n",
              "      <td>16.057</td>\n",
              "      <td>56.951</td>\n",
              "      <td>17.798</td>\n",
              "      <td>11.343</td>\n",
              "      <td>2.14</td>\n",
              "      <td>2.46</td>\n",
              "      <td>2.05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6476.00</td>\n",
              "      <td>12298.00</td>\n",
              "      <td>83.5280</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>03/01/1959</th>\n",
              "      <td>2467.594</td>\n",
              "      <td>2318.5</td>\n",
              "      <td>17.617</td>\n",
              "      <td>2.934187e+05</td>\n",
              "      <td>18523.05762</td>\n",
              "      <td>22.8298</td>\n",
              "      <td>23.8621</td>\n",
              "      <td>22.5892</td>\n",
              "      <td>31.8004</td>\n",
              "      <td>19.1254</td>\n",
              "      <td>38.3277</td>\n",
              "      <td>8.3749</td>\n",
              "      <td>21.4240</td>\n",
              "      <td>13.3511</td>\n",
              "      <td>31.6519</td>\n",
              "      <td>21.6367</td>\n",
              "      <td>19.9873</td>\n",
              "      <td>35.1079</td>\n",
              "      <td>82.4769</td>\n",
              "      <td>1524.0</td>\n",
              "      <td>0.400947</td>\n",
              "      <td>68068</td>\n",
              "      <td>64267</td>\n",
              "      <td>5.6</td>\n",
              "      <td>15.3</td>\n",
              "      <td>1459</td>\n",
              "      <td>1093</td>\n",
              "      <td>1210</td>\n",
              "      <td>530</td>\n",
              "      <td>680</td>\n",
              "      <td>260346</td>\n",
              "      <td>53014</td>\n",
              "      <td>19069</td>\n",
              "      <td>704.1</td>\n",
              "      <td>3013</td>\n",
              "      <td>15259</td>\n",
              "      <td>8965</td>\n",
              "      <td>6294</td>\n",
              "      <td>33945</td>\n",
              "      <td>10873</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.81</td>\n",
              "      <td>1.19</td>\n",
              "      <td>1.19</td>\n",
              "      <td>1.33</td>\n",
              "      <td>2.05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.3228</td>\n",
              "      <td>359.8417</td>\n",
              "      <td>2.8127</td>\n",
              "      <td>0.9698</td>\n",
              "      <td>33.2</td>\n",
              "      <td>33.3</td>\n",
              "      <td>30.7</td>\n",
              "      <td>31.5</td>\n",
              "      <td>2.97</td>\n",
              "      <td>32.9</td>\n",
              "      <td>28.970</td>\n",
              "      <td>44.700</td>\n",
              "      <td>29.600</td>\n",
              "      <td>21.300</td>\n",
              "      <td>33.200</td>\n",
              "      <td>38.300</td>\n",
              "      <td>23.000</td>\n",
              "      <td>28.900</td>\n",
              "      <td>30.700</td>\n",
              "      <td>29.600</td>\n",
              "      <td>16.068</td>\n",
              "      <td>57.022</td>\n",
              "      <td>17.785</td>\n",
              "      <td>11.363</td>\n",
              "      <td>2.15</td>\n",
              "      <td>2.45</td>\n",
              "      <td>2.07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6508.00</td>\n",
              "      <td>12349.00</td>\n",
              "      <td>81.6405</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>04/01/1959</th>\n",
              "      <td>2483.671</td>\n",
              "      <td>2334.9</td>\n",
              "      <td>17.553</td>\n",
              "      <td>2.993228e+05</td>\n",
              "      <td>18534.46600</td>\n",
              "      <td>23.3161</td>\n",
              "      <td>24.2067</td>\n",
              "      <td>22.9201</td>\n",
              "      <td>32.3021</td>\n",
              "      <td>19.2478</td>\n",
              "      <td>38.9658</td>\n",
              "      <td>8.5937</td>\n",
              "      <td>22.0157</td>\n",
              "      <td>13.7530</td>\n",
              "      <td>32.6322</td>\n",
              "      <td>22.0981</td>\n",
              "      <td>20.0685</td>\n",
              "      <td>34.9503</td>\n",
              "      <td>83.9922</td>\n",
              "      <td>1589.0</td>\n",
              "      <td>0.444973</td>\n",
              "      <td>68339</td>\n",
              "      <td>64768</td>\n",
              "      <td>5.2</td>\n",
              "      <td>14.9</td>\n",
              "      <td>1494</td>\n",
              "      <td>934</td>\n",
              "      <td>1039</td>\n",
              "      <td>408</td>\n",
              "      <td>631</td>\n",
              "      <td>246413</td>\n",
              "      <td>53321</td>\n",
              "      <td>19269</td>\n",
              "      <td>705.2</td>\n",
              "      <td>3085</td>\n",
              "      <td>15385</td>\n",
              "      <td>9077</td>\n",
              "      <td>6308</td>\n",
              "      <td>34052</td>\n",
              "      <td>10905</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.76</td>\n",
              "      <td>1.16</td>\n",
              "      <td>1.16</td>\n",
              "      <td>1.27</td>\n",
              "      <td>1.90</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.3226</td>\n",
              "      <td>359.8417</td>\n",
              "      <td>2.8165</td>\n",
              "      <td>0.9636</td>\n",
              "      <td>33.2</td>\n",
              "      <td>33.4</td>\n",
              "      <td>30.7</td>\n",
              "      <td>31.7</td>\n",
              "      <td>2.97</td>\n",
              "      <td>32.7</td>\n",
              "      <td>28.980</td>\n",
              "      <td>44.800</td>\n",
              "      <td>29.700</td>\n",
              "      <td>21.300</td>\n",
              "      <td>33.200</td>\n",
              "      <td>38.300</td>\n",
              "      <td>23.100</td>\n",
              "      <td>29.000</td>\n",
              "      <td>30.700</td>\n",
              "      <td>29.600</td>\n",
              "      <td>16.100</td>\n",
              "      <td>57.080</td>\n",
              "      <td>17.796</td>\n",
              "      <td>11.403</td>\n",
              "      <td>2.16</td>\n",
              "      <td>2.47</td>\n",
              "      <td>2.08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6620.00</td>\n",
              "      <td>12484.00</td>\n",
              "      <td>81.8099</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>05/01/1959</th>\n",
              "      <td>2498.026</td>\n",
              "      <td>2350.4</td>\n",
              "      <td>17.765</td>\n",
              "      <td>3.013643e+05</td>\n",
              "      <td>18679.66354</td>\n",
              "      <td>23.6673</td>\n",
              "      <td>24.4077</td>\n",
              "      <td>23.1408</td>\n",
              "      <td>32.4565</td>\n",
              "      <td>19.6396</td>\n",
              "      <td>38.9658</td>\n",
              "      <td>8.8722</td>\n",
              "      <td>22.5828</td>\n",
              "      <td>14.2293</td>\n",
              "      <td>32.9253</td>\n",
              "      <td>22.4058</td>\n",
              "      <td>20.3664</td>\n",
              "      <td>34.2805</td>\n",
              "      <td>84.9159</td>\n",
              "      <td>1655.0</td>\n",
              "      <td>0.475711</td>\n",
              "      <td>68178</td>\n",
              "      <td>64699</td>\n",
              "      <td>5.1</td>\n",
              "      <td>14.7</td>\n",
              "      <td>1479</td>\n",
              "      <td>1005</td>\n",
              "      <td>965</td>\n",
              "      <td>390</td>\n",
              "      <td>575</td>\n",
              "      <td>246952</td>\n",
              "      <td>53550</td>\n",
              "      <td>19378</td>\n",
              "      <td>710.0</td>\n",
              "      <td>3087</td>\n",
              "      <td>15487</td>\n",
              "      <td>9160</td>\n",
              "      <td>6327</td>\n",
              "      <td>34172</td>\n",
              "      <td>10949</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.06</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.06</td>\n",
              "      <td>1.45</td>\n",
              "      <td>1.41</td>\n",
              "      <td>1.47</td>\n",
              "      <td>2.06</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.3228</td>\n",
              "      <td>359.8417</td>\n",
              "      <td>2.8145</td>\n",
              "      <td>0.9630</td>\n",
              "      <td>33.3</td>\n",
              "      <td>33.3</td>\n",
              "      <td>30.9</td>\n",
              "      <td>31.5</td>\n",
              "      <td>2.97</td>\n",
              "      <td>32.9</td>\n",
              "      <td>29.040</td>\n",
              "      <td>44.900</td>\n",
              "      <td>29.700</td>\n",
              "      <td>21.400</td>\n",
              "      <td>33.300</td>\n",
              "      <td>38.400</td>\n",
              "      <td>23.200</td>\n",
              "      <td>29.100</td>\n",
              "      <td>30.700</td>\n",
              "      <td>29.600</td>\n",
              "      <td>16.109</td>\n",
              "      <td>57.175</td>\n",
              "      <td>17.777</td>\n",
              "      <td>11.421</td>\n",
              "      <td>2.17</td>\n",
              "      <td>2.48</td>\n",
              "      <td>2.08</td>\n",
              "      <td>95.3</td>\n",
              "      <td>6753.00</td>\n",
              "      <td>12646.00</td>\n",
              "      <td>80.7315</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>04/01/2021</th>\n",
              "      <td>18261.337</td>\n",
              "      <td>14130.6</td>\n",
              "      <td>123.827</td>\n",
              "      <td>1.585555e+06</td>\n",
              "      <td>628751.00000</td>\n",
              "      <td>99.2301</td>\n",
              "      <td>98.3032</td>\n",
              "      <td>98.9459</td>\n",
              "      <td>99.4073</td>\n",
              "      <td>99.2308</td>\n",
              "      <td>99.3852</td>\n",
              "      <td>92.3234</td>\n",
              "      <td>100.2550</td>\n",
              "      <td>95.9528</td>\n",
              "      <td>98.3618</td>\n",
              "      <td>97.4914</td>\n",
              "      <td>106.4134</td>\n",
              "      <td>92.4188</td>\n",
              "      <td>75.0321</td>\n",
              "      <td>9193.0</td>\n",
              "      <td>0.936914</td>\n",
              "      <td>160988</td>\n",
              "      <td>151176</td>\n",
              "      <td>6.1</td>\n",
              "      <td>28.8</td>\n",
              "      <td>2414</td>\n",
              "      <td>1939</td>\n",
              "      <td>5386</td>\n",
              "      <td>1203</td>\n",
              "      <td>4183</td>\n",
              "      <td>621000</td>\n",
              "      <td>144326</td>\n",
              "      <td>20319</td>\n",
              "      <td>571.4</td>\n",
              "      <td>7439</td>\n",
              "      <td>12264</td>\n",
              "      <td>7605</td>\n",
              "      <td>4659</td>\n",
              "      <td>124007</td>\n",
              "      <td>27116</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.79</td>\n",
              "      <td>1.56</td>\n",
              "      <td>2.83</td>\n",
              "      <td>3.53</td>\n",
              "      <td>103.8470</td>\n",
              "      <td>0.9218</td>\n",
              "      <td>109.0445</td>\n",
              "      <td>1.3845</td>\n",
              "      <td>1.2494</td>\n",
              "      <td>215.3</td>\n",
              "      <td>228.9</td>\n",
              "      <td>220.4</td>\n",
              "      <td>219.5</td>\n",
              "      <td>61.72</td>\n",
              "      <td>235.4</td>\n",
              "      <td>266.832</td>\n",
              "      <td>119.335</td>\n",
              "      <td>221.826</td>\n",
              "      <td>524.002</td>\n",
              "      <td>193.772</td>\n",
              "      <td>111.940</td>\n",
              "      <td>338.805</td>\n",
              "      <td>265.904</td>\n",
              "      <td>244.660</td>\n",
              "      <td>254.498</td>\n",
              "      <td>114.166</td>\n",
              "      <td>88.879</td>\n",
              "      <td>101.517</td>\n",
              "      <td>123.125</td>\n",
              "      <td>26.19</td>\n",
              "      <td>30.20</td>\n",
              "      <td>23.44</td>\n",
              "      <td>88.3</td>\n",
              "      <td>362666.77</td>\n",
              "      <td>751213.18</td>\n",
              "      <td>5072.8265</td>\n",
              "      <td>15.9924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>05/01/2021</th>\n",
              "      <td>17778.066</td>\n",
              "      <td>14145.3</td>\n",
              "      <td>123.235</td>\n",
              "      <td>1.552988e+06</td>\n",
              "      <td>620119.00000</td>\n",
              "      <td>99.8504</td>\n",
              "      <td>98.8957</td>\n",
              "      <td>99.7572</td>\n",
              "      <td>100.0709</td>\n",
              "      <td>101.6037</td>\n",
              "      <td>99.5827</td>\n",
              "      <td>93.8185</td>\n",
              "      <td>100.9100</td>\n",
              "      <td>96.6621</td>\n",
              "      <td>99.4804</td>\n",
              "      <td>98.2943</td>\n",
              "      <td>106.1077</td>\n",
              "      <td>92.9248</td>\n",
              "      <td>75.6418</td>\n",
              "      <td>9483.0</td>\n",
              "      <td>1.017926</td>\n",
              "      <td>160935</td>\n",
              "      <td>151620</td>\n",
              "      <td>5.8</td>\n",
              "      <td>29.9</td>\n",
              "      <td>2023</td>\n",
              "      <td>2126</td>\n",
              "      <td>5013</td>\n",
              "      <td>1262</td>\n",
              "      <td>3752</td>\n",
              "      <td>444400</td>\n",
              "      <td>144940</td>\n",
              "      <td>20335</td>\n",
              "      <td>576.1</td>\n",
              "      <td>7415</td>\n",
              "      <td>12300</td>\n",
              "      <td>7632</td>\n",
              "      <td>4668</td>\n",
              "      <td>124605</td>\n",
              "      <td>27205</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.76</td>\n",
              "      <td>1.56</td>\n",
              "      <td>2.90</td>\n",
              "      <td>3.56</td>\n",
              "      <td>102.0529</td>\n",
              "      <td>0.9027</td>\n",
              "      <td>109.1120</td>\n",
              "      <td>1.4084</td>\n",
              "      <td>1.2125</td>\n",
              "      <td>217.2</td>\n",
              "      <td>231.1</td>\n",
              "      <td>225.9</td>\n",
              "      <td>235.5</td>\n",
              "      <td>65.17</td>\n",
              "      <td>274.9</td>\n",
              "      <td>268.551</td>\n",
              "      <td>120.793</td>\n",
              "      <td>226.538</td>\n",
              "      <td>523.594</td>\n",
              "      <td>195.860</td>\n",
              "      <td>115.352</td>\n",
              "      <td>340.108</td>\n",
              "      <td>267.704</td>\n",
              "      <td>246.655</td>\n",
              "      <td>256.313</td>\n",
              "      <td>114.774</td>\n",
              "      <td>90.669</td>\n",
              "      <td>101.658</td>\n",
              "      <td>123.575</td>\n",
              "      <td>26.32</td>\n",
              "      <td>30.32</td>\n",
              "      <td>23.60</td>\n",
              "      <td>82.9</td>\n",
              "      <td>365154.60</td>\n",
              "      <td>754248.23</td>\n",
              "      <td>5169.0271</td>\n",
              "      <td>17.9421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>06/01/2021</th>\n",
              "      <td>17716.168</td>\n",
              "      <td>14168.2</td>\n",
              "      <td>123.891</td>\n",
              "      <td>1.559648e+06</td>\n",
              "      <td>625405.00000</td>\n",
              "      <td>100.3398</td>\n",
              "      <td>98.7304</td>\n",
              "      <td>99.6633</td>\n",
              "      <td>99.7401</td>\n",
              "      <td>100.1333</td>\n",
              "      <td>99.5610</td>\n",
              "      <td>93.4728</td>\n",
              "      <td>102.2355</td>\n",
              "      <td>96.5788</td>\n",
              "      <td>99.4550</td>\n",
              "      <td>97.9699</td>\n",
              "      <td>109.8122</td>\n",
              "      <td>93.3134</td>\n",
              "      <td>75.3792</td>\n",
              "      <td>10185.0</td>\n",
              "      <td>1.073914</td>\n",
              "      <td>161086</td>\n",
              "      <td>151602</td>\n",
              "      <td>5.9</td>\n",
              "      <td>31.6</td>\n",
              "      <td>1981</td>\n",
              "      <td>2166</td>\n",
              "      <td>5314</td>\n",
              "      <td>1329</td>\n",
              "      <td>3985</td>\n",
              "      <td>394000</td>\n",
              "      <td>145902</td>\n",
              "      <td>20377</td>\n",
              "      <td>586.7</td>\n",
              "      <td>7413</td>\n",
              "      <td>12332</td>\n",
              "      <td>7663</td>\n",
              "      <td>4669</td>\n",
              "      <td>125525</td>\n",
              "      <td>27344</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.76</td>\n",
              "      <td>1.44</td>\n",
              "      <td>2.71</td>\n",
              "      <td>3.36</td>\n",
              "      <td>102.8535</td>\n",
              "      <td>0.9078</td>\n",
              "      <td>110.1073</td>\n",
              "      <td>1.4025</td>\n",
              "      <td>1.2220</td>\n",
              "      <td>219.7</td>\n",
              "      <td>234.1</td>\n",
              "      <td>230.1</td>\n",
              "      <td>241.6</td>\n",
              "      <td>71.38</td>\n",
              "      <td>257.1</td>\n",
              "      <td>270.981</td>\n",
              "      <td>121.636</td>\n",
              "      <td>234.727</td>\n",
              "      <td>523.058</td>\n",
              "      <td>199.179</td>\n",
              "      <td>119.422</td>\n",
              "      <td>341.567</td>\n",
              "      <td>270.182</td>\n",
              "      <td>249.386</td>\n",
              "      <td>258.878</td>\n",
              "      <td>115.385</td>\n",
              "      <td>91.620</td>\n",
              "      <td>102.225</td>\n",
              "      <td>124.096</td>\n",
              "      <td>26.48</td>\n",
              "      <td>30.48</td>\n",
              "      <td>23.76</td>\n",
              "      <td>85.5</td>\n",
              "      <td>366098.79</td>\n",
              "      <td>756290.99</td>\n",
              "      <td>5235.2502</td>\n",
              "      <td>14.8913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>07/01/2021</th>\n",
              "      <td>17838.510</td>\n",
              "      <td>14201.1</td>\n",
              "      <td>123.712</td>\n",
              "      <td>NaN</td>\n",
              "      <td>614313.00000</td>\n",
              "      <td>101.1874</td>\n",
              "      <td>100.1329</td>\n",
              "      <td>101.2501</td>\n",
              "      <td>100.4491</td>\n",
              "      <td>105.1449</td>\n",
              "      <td>99.1077</td>\n",
              "      <td>97.0062</td>\n",
              "      <td>102.3782</td>\n",
              "      <td>98.5360</td>\n",
              "      <td>99.5572</td>\n",
              "      <td>99.5387</td>\n",
              "      <td>106.5678</td>\n",
              "      <td>92.7238</td>\n",
              "      <td>76.5691</td>\n",
              "      <td>10934.0</td>\n",
              "      <td>1.256493</td>\n",
              "      <td>161347</td>\n",
              "      <td>152645</td>\n",
              "      <td>5.4</td>\n",
              "      <td>29.5</td>\n",
              "      <td>2257</td>\n",
              "      <td>1861</td>\n",
              "      <td>4599</td>\n",
              "      <td>1174</td>\n",
              "      <td>3425</td>\n",
              "      <td>392800</td>\n",
              "      <td>146955</td>\n",
              "      <td>20441</td>\n",
              "      <td>593.2</td>\n",
              "      <td>7419</td>\n",
              "      <td>12384</td>\n",
              "      <td>7704</td>\n",
              "      <td>4680</td>\n",
              "      <td>126514</td>\n",
              "      <td>27405</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.66</td>\n",
              "      <td>1.22</td>\n",
              "      <td>2.47</td>\n",
              "      <td>3.14</td>\n",
              "      <td>104.7021</td>\n",
              "      <td>0.9174</td>\n",
              "      <td>110.2143</td>\n",
              "      <td>1.3808</td>\n",
              "      <td>1.2530</td>\n",
              "      <td>221.0</td>\n",
              "      <td>235.4</td>\n",
              "      <td>234.1</td>\n",
              "      <td>244.9</td>\n",
              "      <td>72.49</td>\n",
              "      <td>256.1</td>\n",
              "      <td>272.265</td>\n",
              "      <td>121.687</td>\n",
              "      <td>236.252</td>\n",
              "      <td>524.431</td>\n",
              "      <td>200.675</td>\n",
              "      <td>120.190</td>\n",
              "      <td>342.581</td>\n",
              "      <td>271.353</td>\n",
              "      <td>250.619</td>\n",
              "      <td>260.156</td>\n",
              "      <td>115.860</td>\n",
              "      <td>91.911</td>\n",
              "      <td>102.809</td>\n",
              "      <td>124.565</td>\n",
              "      <td>26.59</td>\n",
              "      <td>30.58</td>\n",
              "      <td>23.88</td>\n",
              "      <td>81.2</td>\n",
              "      <td>366251.23</td>\n",
              "      <td>757292.96</td>\n",
              "      <td>5281.4744</td>\n",
              "      <td>15.5122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>08/01/2021</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>618676.00000</td>\n",
              "      <td>101.5949</td>\n",
              "      <td>100.8053</td>\n",
              "      <td>101.9616</td>\n",
              "      <td>101.2702</td>\n",
              "      <td>105.3528</td>\n",
              "      <td>100.0933</td>\n",
              "      <td>97.4941</td>\n",
              "      <td>102.4499</td>\n",
              "      <td>98.4315</td>\n",
              "      <td>99.2038</td>\n",
              "      <td>99.7006</td>\n",
              "      <td>110.4348</td>\n",
              "      <td>93.6916</td>\n",
              "      <td>76.6736</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>161537</td>\n",
              "      <td>153154</td>\n",
              "      <td>5.2</td>\n",
              "      <td>29.6</td>\n",
              "      <td>2083</td>\n",
              "      <td>2066</td>\n",
              "      <td>4343</td>\n",
              "      <td>1164</td>\n",
              "      <td>3179</td>\n",
              "      <td>356250</td>\n",
              "      <td>147190</td>\n",
              "      <td>20481</td>\n",
              "      <td>599.2</td>\n",
              "      <td>7416</td>\n",
              "      <td>12421</td>\n",
              "      <td>7735</td>\n",
              "      <td>4686</td>\n",
              "      <td>126709</td>\n",
              "      <td>27429</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.68</td>\n",
              "      <td>1.19</td>\n",
              "      <td>2.46</td>\n",
              "      <td>3.15</td>\n",
              "      <td>105.0489</td>\n",
              "      <td>0.9147</td>\n",
              "      <td>109.8527</td>\n",
              "      <td>1.3797</td>\n",
              "      <td>1.2599</td>\n",
              "      <td>223.2</td>\n",
              "      <td>238.1</td>\n",
              "      <td>236.5</td>\n",
              "      <td>247.4</td>\n",
              "      <td>67.73</td>\n",
              "      <td>260.9</td>\n",
              "      <td>273.012</td>\n",
              "      <td>122.142</td>\n",
              "      <td>235.997</td>\n",
              "      <td>525.724</td>\n",
              "      <td>201.848</td>\n",
              "      <td>119.936</td>\n",
              "      <td>342.755</td>\n",
              "      <td>272.045</td>\n",
              "      <td>251.445</td>\n",
              "      <td>260.877</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>26.71</td>\n",
              "      <td>30.70</td>\n",
              "      <td>24.01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5335.6648</td>\n",
              "      <td>14.6821</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>752 rows × 127 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4b5698b4-3598-4f6f-bddd-3a26e83e802a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                  RPI  W875RX1  DPCERA3M086SBEA  ...   DTCTHFNM     INVEST  VXOCLSx\n",
              "                                                 ...                               \n",
              "01/01/1959   2442.158   2293.2           17.272  ...   12298.00    84.2043      NaN\n",
              "02/01/1959   2451.778   2301.5           17.452  ...   12298.00    83.5280      NaN\n",
              "03/01/1959   2467.594   2318.5           17.617  ...   12349.00    81.6405      NaN\n",
              "04/01/1959   2483.671   2334.9           17.553  ...   12484.00    81.8099      NaN\n",
              "05/01/1959   2498.026   2350.4           17.765  ...   12646.00    80.7315      NaN\n",
              "...               ...      ...              ...  ...        ...        ...      ...\n",
              "04/01/2021  18261.337  14130.6          123.827  ...  751213.18  5072.8265  15.9924\n",
              "05/01/2021  17778.066  14145.3          123.235  ...  754248.23  5169.0271  17.9421\n",
              "06/01/2021  17716.168  14168.2          123.891  ...  756290.99  5235.2502  14.8913\n",
              "07/01/2021  17838.510  14201.1          123.712  ...  757292.96  5281.4744  15.5122\n",
              "08/01/2021        NaN      NaN              NaN  ...        NaN  5335.6648  14.6821\n",
              "\n",
              "[752 rows x 127 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Taking a quick look at statistical information:"
      ],
      "metadata": {
        "id": "-dIeHdJoVBnY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "6iAH9j7dvE5P",
        "outputId": "91219468-b2db-43a0-8914-65d5bc56e9cf"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RPI</th>\n",
              "      <th>W875RX1</th>\n",
              "      <th>DPCERA3M086SBEA</th>\n",
              "      <th>CMRMTSPLx</th>\n",
              "      <th>RETAILx</th>\n",
              "      <th>INDPRO</th>\n",
              "      <th>IPFPNSS</th>\n",
              "      <th>IPFINAL</th>\n",
              "      <th>IPCONGD</th>\n",
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              "      <th>IPNCONGD</th>\n",
              "      <th>IPBUSEQ</th>\n",
              "      <th>IPMAT</th>\n",
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              "      <th>IPB51222S</th>\n",
              "      <th>IPFUELS</th>\n",
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              "      <th>HWI</th>\n",
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              "      <th>CES1021000001</th>\n",
              "      <th>USCONS</th>\n",
              "      <th>MANEMP</th>\n",
              "      <th>DMANEMP</th>\n",
              "      <th>NDMANEMP</th>\n",
              "      <th>SRVPRD</th>\n",
              "      <th>USTPU</th>\n",
              "      <th>...</th>\n",
              "      <th>TB3SMFFM</th>\n",
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              "      <th>T1YFFM</th>\n",
              "      <th>T5YFFM</th>\n",
              "      <th>T10YFFM</th>\n",
              "      <th>AAAFFM</th>\n",
              "      <th>BAAFFM</th>\n",
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              "      <th>EXJPUSx</th>\n",
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              "      <th>WPSFD49207</th>\n",
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              "      <th>WPSID62</th>\n",
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              "      <th>CPIULFSL</th>\n",
              "      <th>CUSR0000SA0L2</th>\n",
              "      <th>CUSR0000SA0L5</th>\n",
              "      <th>PCEPI</th>\n",
              "      <th>DDURRG3M086SBEA</th>\n",
              "      <th>DNDGRG3M086SBEA</th>\n",
              "      <th>DSERRG3M086SBEA</th>\n",
              "      <th>CES0600000008</th>\n",
              "      <th>CES2000000008</th>\n",
              "      <th>CES3000000008</th>\n",
              "      <th>UMCSENTx</th>\n",
              "      <th>DTCOLNVHFNM</th>\n",
              "      <th>DTCTHFNM</th>\n",
              "      <th>INVEST</th>\n",
              "      <th>VXOCLSx</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>751.000000</td>\n",
              "      <td>751.000000</td>\n",
              "      <td>751.000000</td>\n",
              "      <td>7.500000e+02</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>751.000000</td>\n",
              "      <td>751.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>7.520000e+02</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>584.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.00000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>751.000000</td>\n",
              "      <td>751.000000</td>\n",
              "      <td>751.000000</td>\n",
              "      <td>751.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>597.000000</td>\n",
              "      <td>751.000000</td>\n",
              "      <td>751.000000</td>\n",
              "      <td>752.000000</td>\n",
              "      <td>710.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8561.801019</td>\n",
              "      <td>7332.305859</td>\n",
              "      <td>60.612336</td>\n",
              "      <td>8.327589e+05</td>\n",
              "      <td>198994.791505</td>\n",
              "      <td>66.026775</td>\n",
              "      <td>70.392132</td>\n",
              "      <td>69.132665</td>\n",
              "      <td>77.724911</td>\n",
              "      <td>63.819049</td>\n",
              "      <td>83.518358</td>\n",
              "      <td>51.687234</td>\n",
              "      <td>61.278919</td>\n",
              "      <td>50.615666</td>\n",
              "      <td>86.135095</td>\n",
              "      <td>66.076402</td>\n",
              "      <td>72.465026</td>\n",
              "      <td>67.292965</td>\n",
              "      <td>79.307126</td>\n",
              "      <td>3897.187750</td>\n",
              "      <td>0.605315</td>\n",
              "      <td>120380.183511</td>\n",
              "      <td>113116.750000</td>\n",
              "      <td>5.990559</td>\n",
              "      <td>17.218750</td>\n",
              "      <td>2628.158245</td>\n",
              "      <td>2126.655585</td>\n",
              "      <td>2509.522606</td>\n",
              "      <td>1043.759309</td>\n",
              "      <td>1465.752660</td>\n",
              "      <td>3.605422e+05</td>\n",
              "      <td>104723.582447</td>\n",
              "      <td>21848.339096</td>\n",
              "      <td>677.602527</td>\n",
              "      <td>5124.706117</td>\n",
              "      <td>15972.598404</td>\n",
              "      <td>9787.349734</td>\n",
              "      <td>6185.248670</td>\n",
              "      <td>82875.243351</td>\n",
              "      <td>20639.635638</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.438590</td>\n",
              "      <td>-0.301130</td>\n",
              "      <td>0.059654</td>\n",
              "      <td>0.724495</td>\n",
              "      <td>1.049189</td>\n",
              "      <td>2.048324</td>\n",
              "      <td>3.051742</td>\n",
              "      <td>111.451477</td>\n",
              "      <td>2.142961</td>\n",
              "      <td>196.626652</td>\n",
              "      <td>1.899167</td>\n",
              "      <td>1.191139</td>\n",
              "      <td>112.802660</td>\n",
              "      <td>115.243218</td>\n",
              "      <td>110.074601</td>\n",
              "      <td>112.35625</td>\n",
              "      <td>30.521878</td>\n",
              "      <td>120.346144</td>\n",
              "      <td>130.270927</td>\n",
              "      <td>99.996464</td>\n",
              "      <td>116.961633</td>\n",
              "      <td>198.862649</td>\n",
              "      <td>112.692286</td>\n",
              "      <td>90.668266</td>\n",
              "      <td>147.370741</td>\n",
              "      <td>130.141044</td>\n",
              "      <td>125.908856</td>\n",
              "      <td>126.831045</td>\n",
              "      <td>60.080546</td>\n",
              "      <td>98.604064</td>\n",
              "      <td>58.644186</td>\n",
              "      <td>56.034792</td>\n",
              "      <td>11.864761</td>\n",
              "      <td>13.784628</td>\n",
              "      <td>10.984801</td>\n",
              "      <td>86.430486</td>\n",
              "      <td>120078.048415</td>\n",
              "      <td>323953.161798</td>\n",
              "      <td>1121.704579</td>\n",
              "      <td>19.202106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4397.444144</td>\n",
              "      <td>3504.633702</td>\n",
              "      <td>31.638404</td>\n",
              "      <td>3.829910e+05</td>\n",
              "      <td>161257.705727</td>\n",
              "      <td>26.281483</td>\n",
              "      <td>27.012231</td>\n",
              "      <td>27.249858</td>\n",
              "      <td>23.327085</td>\n",
              "      <td>27.661764</td>\n",
              "      <td>21.191380</td>\n",
              "      <td>33.338389</td>\n",
              "      <td>26.234739</td>\n",
              "      <td>32.075397</td>\n",
              "      <td>23.251976</td>\n",
              "      <td>27.796731</td>\n",
              "      <td>26.517485</td>\n",
              "      <td>17.981125</td>\n",
              "      <td>5.154523</td>\n",
              "      <td>1344.396946</td>\n",
              "      <td>0.270700</td>\n",
              "      <td>30728.080848</td>\n",
              "      <td>28896.879589</td>\n",
              "      <td>1.659305</td>\n",
              "      <td>7.368212</td>\n",
              "      <td>737.215122</td>\n",
              "      <td>914.133637</td>\n",
              "      <td>1852.805398</td>\n",
              "      <td>603.904869</td>\n",
              "      <td>1358.754242</td>\n",
              "      <td>2.177330e+05</td>\n",
              "      <td>30004.431994</td>\n",
              "      <td>1924.002543</td>\n",
              "      <td>135.451481</td>\n",
              "      <td>1439.500592</td>\n",
              "      <td>2316.187083</td>\n",
              "      <td>1391.799870</td>\n",
              "      <td>962.630646</td>\n",
              "      <td>30140.122516</td>\n",
              "      <td>5478.385759</td>\n",
              "      <td>...</td>\n",
              "      <td>0.699994</td>\n",
              "      <td>0.754522</td>\n",
              "      <td>0.758511</td>\n",
              "      <td>1.326247</td>\n",
              "      <td>1.595884</td>\n",
              "      <td>1.905299</td>\n",
              "      <td>2.006718</td>\n",
              "      <td>16.041805</td>\n",
              "      <td>1.246873</td>\n",
              "      <td>102.946980</td>\n",
              "      <td>0.490007</td>\n",
              "      <td>0.162781</td>\n",
              "      <td>57.845038</td>\n",
              "      <td>61.675582</td>\n",
              "      <td>58.296723</td>\n",
              "      <td>67.00046</td>\n",
              "      <td>28.048131</td>\n",
              "      <td>66.117927</td>\n",
              "      <td>76.752931</td>\n",
              "      <td>31.202814</td>\n",
              "      <td>64.470583</td>\n",
              "      <td>159.416503</td>\n",
              "      <td>54.908614</td>\n",
              "      <td>32.525583</td>\n",
              "      <td>99.634852</td>\n",
              "      <td>76.743421</td>\n",
              "      <td>70.556101</td>\n",
              "      <td>72.769818</td>\n",
              "      <td>31.751902</td>\n",
              "      <td>27.224610</td>\n",
              "      <td>28.444154</td>\n",
              "      <td>34.933456</td>\n",
              "      <td>7.185550</td>\n",
              "      <td>8.010987</td>\n",
              "      <td>6.514713</td>\n",
              "      <td>12.341583</td>\n",
              "      <td>117724.117608</td>\n",
              "      <td>320249.261430</td>\n",
              "      <td>1182.896977</td>\n",
              "      <td>7.376781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2442.158000</td>\n",
              "      <td>2293.200000</td>\n",
              "      <td>17.272000</td>\n",
              "      <td>2.809324e+05</td>\n",
              "      <td>18235.773920</td>\n",
              "      <td>22.073300</td>\n",
              "      <td>23.402700</td>\n",
              "      <td>22.285800</td>\n",
              "      <td>31.568800</td>\n",
              "      <td>17.068300</td>\n",
              "      <td>38.145400</td>\n",
              "      <td>8.176000</td>\n",
              "      <td>19.279200</td>\n",
              "      <td>10.954700</td>\n",
              "      <td>30.646900</td>\n",
              "      <td>20.842000</td>\n",
              "      <td>19.851800</td>\n",
              "      <td>33.492400</td>\n",
              "      <td>60.814200</td>\n",
              "      <td>1357.000000</td>\n",
              "      <td>0.152866</td>\n",
              "      <td>67649.000000</td>\n",
              "      <td>63684.000000</td>\n",
              "      <td>3.400000</td>\n",
              "      <td>6.100000</td>\n",
              "      <td>1431.000000</td>\n",
              "      <td>653.000000</td>\n",
              "      <td>339.000000</td>\n",
              "      <td>191.000000</td>\n",
              "      <td>115.000000</td>\n",
              "      <td>1.762630e+05</td>\n",
              "      <td>52478.000000</td>\n",
              "      <td>17627.000000</td>\n",
              "      <td>498.000000</td>\n",
              "      <td>2862.000000</td>\n",
              "      <td>11414.000000</td>\n",
              "      <td>6985.000000</td>\n",
              "      <td>4352.000000</td>\n",
              "      <td>33682.000000</td>\n",
              "      <td>10774.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>-5.370000</td>\n",
              "      <td>-5.010000</td>\n",
              "      <td>-5.000000</td>\n",
              "      <td>-6.310000</td>\n",
              "      <td>-6.510000</td>\n",
              "      <td>-6.270000</td>\n",
              "      <td>-4.050000</td>\n",
              "      <td>82.678300</td>\n",
              "      <td>0.780000</td>\n",
              "      <td>76.643000</td>\n",
              "      <td>1.093100</td>\n",
              "      <td>0.947800</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>33.100000</td>\n",
              "      <td>30.400000</td>\n",
              "      <td>29.00000</td>\n",
              "      <td>2.920000</td>\n",
              "      <td>32.200000</td>\n",
              "      <td>28.970000</td>\n",
              "      <td>44.700000</td>\n",
              "      <td>29.300000</td>\n",
              "      <td>21.100000</td>\n",
              "      <td>33.200000</td>\n",
              "      <td>37.800000</td>\n",
              "      <td>22.900000</td>\n",
              "      <td>28.900000</td>\n",
              "      <td>30.700000</td>\n",
              "      <td>29.600000</td>\n",
              "      <td>16.042000</td>\n",
              "      <td>56.570000</td>\n",
              "      <td>17.777000</td>\n",
              "      <td>11.326000</td>\n",
              "      <td>2.130000</td>\n",
              "      <td>2.450000</td>\n",
              "      <td>2.040000</td>\n",
              "      <td>51.700000</td>\n",
              "      <td>6476.000000</td>\n",
              "      <td>12298.000000</td>\n",
              "      <td>71.186400</td>\n",
              "      <td>8.016400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4788.852000</td>\n",
              "      <td>4277.200000</td>\n",
              "      <td>32.352000</td>\n",
              "      <td>5.090859e+05</td>\n",
              "      <td>47407.256143</td>\n",
              "      <td>44.171100</td>\n",
              "      <td>46.406625</td>\n",
              "      <td>44.575750</td>\n",
              "      <td>60.488550</td>\n",
              "      <td>40.638725</td>\n",
              "      <td>67.638925</td>\n",
              "      <td>20.040100</td>\n",
              "      <td>41.048375</td>\n",
              "      <td>23.346050</td>\n",
              "      <td>74.258400</td>\n",
              "      <td>42.229025</td>\n",
              "      <td>53.510175</td>\n",
              "      <td>57.664825</td>\n",
              "      <td>75.711000</td>\n",
              "      <td>3018.500000</td>\n",
              "      <td>0.407805</td>\n",
              "      <td>92419.000000</td>\n",
              "      <td>86309.750000</td>\n",
              "      <td>4.900000</td>\n",
              "      <td>12.500000</td>\n",
              "      <td>2229.000000</td>\n",
              "      <td>1649.750000</td>\n",
              "      <td>1275.750000</td>\n",
              "      <td>676.750000</td>\n",
              "      <td>603.500000</td>\n",
              "      <td>2.875750e+05</td>\n",
              "      <td>77640.750000</td>\n",
              "      <td>20213.750000</td>\n",
              "      <td>595.825000</td>\n",
              "      <td>3918.750000</td>\n",
              "      <td>14207.500000</td>\n",
              "      <td>8852.750000</td>\n",
              "      <td>5228.000000</td>\n",
              "      <td>55295.000000</td>\n",
              "      <td>15583.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.622500</td>\n",
              "      <td>-0.520000</td>\n",
              "      <td>-0.110000</td>\n",
              "      <td>0.130000</td>\n",
              "      <td>0.227500</td>\n",
              "      <td>0.995000</td>\n",
              "      <td>1.827500</td>\n",
              "      <td>101.687714</td>\n",
              "      <td>1.202850</td>\n",
              "      <td>109.792300</td>\n",
              "      <td>1.541325</td>\n",
              "      <td>1.058125</td>\n",
              "      <td>54.150000</td>\n",
              "      <td>54.475000</td>\n",
              "      <td>55.875000</td>\n",
              "      <td>61.12500</td>\n",
              "      <td>10.110000</td>\n",
              "      <td>70.225000</td>\n",
              "      <td>50.425000</td>\n",
              "      <td>70.950000</td>\n",
              "      <td>46.900000</td>\n",
              "      <td>43.575000</td>\n",
              "      <td>54.625000</td>\n",
              "      <td>52.825000</td>\n",
              "      <td>44.875000</td>\n",
              "      <td>49.175000</td>\n",
              "      <td>52.625000</td>\n",
              "      <td>50.850000</td>\n",
              "      <td>26.725000</td>\n",
              "      <td>72.793500</td>\n",
              "      <td>30.439500</td>\n",
              "      <td>20.145000</td>\n",
              "      <td>4.797500</td>\n",
              "      <td>6.440000</td>\n",
              "      <td>4.410000</td>\n",
              "      <td>77.200000</td>\n",
              "      <td>10651.500000</td>\n",
              "      <td>33128.000000</td>\n",
              "      <td>168.985300</td>\n",
              "      <td>14.163925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7717.737000</td>\n",
              "      <td>6761.400000</td>\n",
              "      <td>54.252000</td>\n",
              "      <td>7.490701e+05</td>\n",
              "      <td>158569.715050</td>\n",
              "      <td>62.010300</td>\n",
              "      <td>70.448250</td>\n",
              "      <td>68.688850</td>\n",
              "      <td>79.561550</td>\n",
              "      <td>58.131700</td>\n",
              "      <td>88.937300</td>\n",
              "      <td>41.943650</td>\n",
              "      <td>53.364450</td>\n",
              "      <td>34.070700</td>\n",
              "      <td>96.710700</td>\n",
              "      <td>61.882700</td>\n",
              "      <td>73.900700</td>\n",
              "      <td>65.409150</td>\n",
              "      <td>79.024500</td>\n",
              "      <td>3835.000000</td>\n",
              "      <td>0.544032</td>\n",
              "      <td>125766.500000</td>\n",
              "      <td>117704.000000</td>\n",
              "      <td>5.700000</td>\n",
              "      <td>15.300000</td>\n",
              "      <td>2641.500000</td>\n",
              "      <td>2167.000000</td>\n",
              "      <td>2194.000000</td>\n",
              "      <td>992.000000</td>\n",
              "      <td>1150.000000</td>\n",
              "      <td>3.362250e+05</td>\n",
              "      <td>108346.500000</td>\n",
              "      <td>22163.500000</td>\n",
              "      <td>640.150000</td>\n",
              "      <td>5022.500000</td>\n",
              "      <td>16973.000000</td>\n",
              "      <td>10194.500000</td>\n",
              "      <td>6682.500000</td>\n",
              "      <td>85654.000000</td>\n",
              "      <td>22188.500000</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.210000</td>\n",
              "      <td>-0.070000</td>\n",
              "      <td>0.140000</td>\n",
              "      <td>0.855000</td>\n",
              "      <td>1.195000</td>\n",
              "      <td>2.170000</td>\n",
              "      <td>3.095000</td>\n",
              "      <td>109.952200</td>\n",
              "      <td>1.571850</td>\n",
              "      <td>134.308550</td>\n",
              "      <td>1.712200</td>\n",
              "      <td>1.173050</td>\n",
              "      <td>117.600000</td>\n",
              "      <td>116.450000</td>\n",
              "      <td>113.000000</td>\n",
              "      <td>101.20000</td>\n",
              "      <td>20.826500</td>\n",
              "      <td>109.400000</td>\n",
              "      <td>129.000000</td>\n",
              "      <td>118.159500</td>\n",
              "      <td>117.600000</td>\n",
              "      <td>160.200000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>106.776000</td>\n",
              "      <td>137.650000</td>\n",
              "      <td>128.600000</td>\n",
              "      <td>126.500000</td>\n",
              "      <td>127.200000</td>\n",
              "      <td>62.577000</td>\n",
              "      <td>101.344000</td>\n",
              "      <td>61.998000</td>\n",
              "      <td>54.177000</td>\n",
              "      <td>11.410000</td>\n",
              "      <td>13.380000</td>\n",
              "      <td>10.720000</td>\n",
              "      <td>89.800000</td>\n",
              "      <td>71616.000000</td>\n",
              "      <td>177736.850000</td>\n",
              "      <td>597.720100</td>\n",
              "      <td>17.564050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>12389.173500</td>\n",
              "      <td>10480.350000</td>\n",
              "      <td>92.401000</td>\n",
              "      <td>1.189313e+06</td>\n",
              "      <td>338717.750000</td>\n",
              "      <td>93.025700</td>\n",
              "      <td>98.599925</td>\n",
              "      <td>97.462825</td>\n",
              "      <td>99.517350</td>\n",
              "      <td>92.618100</td>\n",
              "      <td>101.511125</td>\n",
              "      <td>85.497975</td>\n",
              "      <td>86.407550</td>\n",
              "      <td>84.190750</td>\n",
              "      <td>102.238550</td>\n",
              "      <td>95.715300</td>\n",
              "      <td>97.489825</td>\n",
              "      <td>81.603925</td>\n",
              "      <td>82.850850</td>\n",
              "      <td>4653.000000</td>\n",
              "      <td>0.755664</td>\n",
              "      <td>150102.250000</td>\n",
              "      <td>139386.500000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>3070.250000</td>\n",
              "      <td>2538.500000</td>\n",
              "      <td>2976.250000</td>\n",
              "      <td>1274.250000</td>\n",
              "      <td>1704.500000</td>\n",
              "      <td>3.936375e+05</td>\n",
              "      <td>132096.000000</td>\n",
              "      <td>23396.750000</td>\n",
              "      <td>721.400000</td>\n",
              "      <td>6510.000000</td>\n",
              "      <td>17763.500000</td>\n",
              "      <td>10895.500000</td>\n",
              "      <td>6920.500000</td>\n",
              "      <td>112144.500000</td>\n",
              "      <td>25546.500000</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.040000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>0.430000</td>\n",
              "      <td>1.580000</td>\n",
              "      <td>2.192500</td>\n",
              "      <td>3.392500</td>\n",
              "      <td>4.542500</td>\n",
              "      <td>119.466861</td>\n",
              "      <td>2.901775</td>\n",
              "      <td>295.467500</td>\n",
              "      <td>2.370475</td>\n",
              "      <td>1.321050</td>\n",
              "      <td>158.925000</td>\n",
              "      <td>164.500000</td>\n",
              "      <td>161.525000</td>\n",
              "      <td>165.52500</td>\n",
              "      <td>42.365000</td>\n",
              "      <td>166.400000</td>\n",
              "      <td>199.150000</td>\n",
              "      <td>125.834000</td>\n",
              "      <td>172.376250</td>\n",
              "      <td>329.300000</td>\n",
              "      <td>162.500000</td>\n",
              "      <td>114.500000</td>\n",
              "      <td>234.675000</td>\n",
              "      <td>200.300000</td>\n",
              "      <td>190.425000</td>\n",
              "      <td>192.525000</td>\n",
              "      <td>88.051000</td>\n",
              "      <td>121.771500</td>\n",
              "      <td>83.473000</td>\n",
              "      <td>86.131000</td>\n",
              "      <td>17.760000</td>\n",
              "      <td>19.612500</td>\n",
              "      <td>16.692500</td>\n",
              "      <td>95.100000</td>\n",
              "      <td>254038.260000</td>\n",
              "      <td>719133.445000</td>\n",
              "      <td>1870.125200</td>\n",
              "      <td>22.472800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>21267.506000</td>\n",
              "      <td>14225.600000</td>\n",
              "      <td>123.891000</td>\n",
              "      <td>1.591781e+06</td>\n",
              "      <td>628751.000000</td>\n",
              "      <td>104.165900</td>\n",
              "      <td>110.068300</td>\n",
              "      <td>108.580800</td>\n",
              "      <td>111.295700</td>\n",
              "      <td>107.784700</td>\n",
              "      <td>113.729100</td>\n",
              "      <td>105.203700</td>\n",
              "      <td>106.137600</td>\n",
              "      <td>105.024600</td>\n",
              "      <td>115.989300</td>\n",
              "      <td>108.501800</td>\n",
              "      <td>117.724400</td>\n",
              "      <td>101.779200</td>\n",
              "      <td>91.637900</td>\n",
              "      <td>10934.000000</td>\n",
              "      <td>1.434206</td>\n",
              "      <td>164579.000000</td>\n",
              "      <td>158735.000000</td>\n",
              "      <td>14.800000</td>\n",
              "      <td>40.700000</td>\n",
              "      <td>14282.000000</td>\n",
              "      <td>14804.000000</td>\n",
              "      <td>9130.000000</td>\n",
              "      <td>6536.000000</td>\n",
              "      <td>6800.000000</td>\n",
              "      <td>4.667750e+06</td>\n",
              "      <td>152523.000000</td>\n",
              "      <td>25163.000000</td>\n",
              "      <td>1177.400000</td>\n",
              "      <td>7726.000000</td>\n",
              "      <td>19553.000000</td>\n",
              "      <td>12320.000000</td>\n",
              "      <td>7233.000000</td>\n",
              "      <td>131386.000000</td>\n",
              "      <td>27876.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.070000</td>\n",
              "      <td>1.280000</td>\n",
              "      <td>1.750000</td>\n",
              "      <td>3.160000</td>\n",
              "      <td>3.850000</td>\n",
              "      <td>5.730000</td>\n",
              "      <td>8.820000</td>\n",
              "      <td>170.302034</td>\n",
              "      <td>4.349000</td>\n",
              "      <td>362.976400</td>\n",
              "      <td>2.816500</td>\n",
              "      <td>1.599700</td>\n",
              "      <td>223.200000</td>\n",
              "      <td>238.100000</td>\n",
              "      <td>236.500000</td>\n",
              "      <td>303.40000</td>\n",
              "      <td>133.930000</td>\n",
              "      <td>322.100000</td>\n",
              "      <td>273.012000</td>\n",
              "      <td>135.100000</td>\n",
              "      <td>236.252000</td>\n",
              "      <td>525.724000</td>\n",
              "      <td>201.848000</td>\n",
              "      <td>129.700000</td>\n",
              "      <td>342.755000</td>\n",
              "      <td>272.045000</td>\n",
              "      <td>251.445000</td>\n",
              "      <td>260.877000</td>\n",
              "      <td>115.860000</td>\n",
              "      <td>140.457000</td>\n",
              "      <td>102.809000</td>\n",
              "      <td>124.565000</td>\n",
              "      <td>26.710000</td>\n",
              "      <td>30.700000</td>\n",
              "      <td>24.010000</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>366251.230000</td>\n",
              "      <td>896985.060000</td>\n",
              "      <td>5335.664800</td>\n",
              "      <td>67.151000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 127 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-82bfff43-f38a-4465-ad3c-835c728ee342')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-82bfff43-f38a-4465-ad3c-835c728ee342 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-82bfff43-f38a-4465-ad3c-835c728ee342');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                RPI       W875RX1  ...       INVEST     VXOCLSx\n",
              "count    751.000000    751.000000  ...   752.000000  710.000000\n",
              "mean    8561.801019   7332.305859  ...  1121.704579   19.202106\n",
              "std     4397.444144   3504.633702  ...  1182.896977    7.376781\n",
              "min     2442.158000   2293.200000  ...    71.186400    8.016400\n",
              "25%     4788.852000   4277.200000  ...   168.985300   14.163925\n",
              "50%     7717.737000   6761.400000  ...   597.720100   17.564050\n",
              "75%    12389.173500  10480.350000  ...  1870.125200   22.472800\n",
              "max    21267.506000  14225.600000  ...  5335.664800   67.151000\n",
              "\n",
              "[8 rows x 127 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since there are outliers in the original dataset, we need to cope them. In this case we will use the IQR boundires."
      ],
      "metadata": {
        "id": "yzs0eR91VZ4V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for feature in df:\n",
        " update_outliers(df, feature)\n"
      ],
      "metadata": {
        "id": "cHHeM3eMhRlI"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since there are some missing values, we are going to fill them in with the **median** values for each feature (we used mean value in the previous homework)."
      ],
      "metadata": {
        "id": "nuXuKJsgfIng"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_full = df.fillna(df.median())\n",
        "df_full"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "7-9g0UREfCyE",
        "outputId": "4b8f94e1-023c-4f03-d1cc-d2d3dafb75c2"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-2a79ccf3-8376-4337-a641-adca0d62a4c0\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RPI</th>\n",
              "      <th>W875RX1</th>\n",
              "      <th>DPCERA3M086SBEA</th>\n",
              "      <th>CMRMTSPLx</th>\n",
              "      <th>RETAILx</th>\n",
              "      <th>INDPRO</th>\n",
              "      <th>IPFPNSS</th>\n",
              "      <th>IPFINAL</th>\n",
              "      <th>IPCONGD</th>\n",
              "      <th>IPDCONGD</th>\n",
              "      <th>IPNCONGD</th>\n",
              "      <th>IPBUSEQ</th>\n",
              "      <th>IPMAT</th>\n",
              "      <th>IPDMAT</th>\n",
              "      <th>IPNMAT</th>\n",
              "      <th>IPMANSICS</th>\n",
              "      <th>IPB51222S</th>\n",
              "      <th>IPFUELS</th>\n",
              "      <th>CUMFNS</th>\n",
              "      <th>HWI</th>\n",
              "      <th>HWIURATIO</th>\n",
              "      <th>CLF16OV</th>\n",
              "      <th>CE16OV</th>\n",
              "      <th>UNRATE</th>\n",
              "      <th>UEMPMEAN</th>\n",
              "      <th>UEMPLT5</th>\n",
              "      <th>UEMP5TO14</th>\n",
              "      <th>UEMP15OV</th>\n",
              "      <th>UEMP15T26</th>\n",
              "      <th>UEMP27OV</th>\n",
              "      <th>CLAIMSx</th>\n",
              "      <th>PAYEMS</th>\n",
              "      <th>USGOOD</th>\n",
              "      <th>CES1021000001</th>\n",
              "      <th>USCONS</th>\n",
              "      <th>MANEMP</th>\n",
              "      <th>DMANEMP</th>\n",
              "      <th>NDMANEMP</th>\n",
              "      <th>SRVPRD</th>\n",
              "      <th>USTPU</th>\n",
              "      <th>...</th>\n",
              "      <th>TB3SMFFM</th>\n",
              "      <th>TB6SMFFM</th>\n",
              "      <th>T1YFFM</th>\n",
              "      <th>T5YFFM</th>\n",
              "      <th>T10YFFM</th>\n",
              "      <th>AAAFFM</th>\n",
              "      <th>BAAFFM</th>\n",
              "      <th>TWEXAFEGSMTHx</th>\n",
              "      <th>EXSZUSx</th>\n",
              "      <th>EXJPUSx</th>\n",
              "      <th>EXUSUKx</th>\n",
              "      <th>EXCAUSx</th>\n",
              "      <th>WPSFD49207</th>\n",
              "      <th>WPSFD49502</th>\n",
              "      <th>WPSID61</th>\n",
              "      <th>WPSID62</th>\n",
              "      <th>OILPRICEx</th>\n",
              "      <th>PPICMM</th>\n",
              "      <th>CPIAUCSL</th>\n",
              "      <th>CPIAPPSL</th>\n",
              "      <th>CPITRNSL</th>\n",
              "      <th>CPIMEDSL</th>\n",
              "      <th>CUSR0000SAC</th>\n",
              "      <th>CUSR0000SAD</th>\n",
              "      <th>CUSR0000SAS</th>\n",
              "      <th>CPIULFSL</th>\n",
              "      <th>CUSR0000SA0L2</th>\n",
              "      <th>CUSR0000SA0L5</th>\n",
              "      <th>PCEPI</th>\n",
              "      <th>DDURRG3M086SBEA</th>\n",
              "      <th>DNDGRG3M086SBEA</th>\n",
              "      <th>DSERRG3M086SBEA</th>\n",
              "      <th>CES0600000008</th>\n",
              "      <th>CES2000000008</th>\n",
              "      <th>CES3000000008</th>\n",
              "      <th>UMCSENTx</th>\n",
              "      <th>DTCOLNVHFNM</th>\n",
              "      <th>DTCTHFNM</th>\n",
              "      <th>INVEST</th>\n",
              "      <th>VXOCLSx</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>01/01/1959</th>\n",
              "      <td>2442.158</td>\n",
              "      <td>2293.2</td>\n",
              "      <td>17.272</td>\n",
              "      <td>2.922664e+05</td>\n",
              "      <td>18235.77392</td>\n",
              "      <td>22.0733</td>\n",
              "      <td>23.4027</td>\n",
              "      <td>22.2858</td>\n",
              "      <td>31.5688</td>\n",
              "      <td>18.6601</td>\n",
              "      <td>38.1454</td>\n",
              "      <td>8.1760</td>\n",
              "      <td>20.2407</td>\n",
              "      <td>12.2794</td>\n",
              "      <td>32.288175</td>\n",
              "      <td>20.9189</td>\n",
              "      <td>19.9060</td>\n",
              "      <td>34.7139</td>\n",
              "      <td>80.1973</td>\n",
              "      <td>1357.00</td>\n",
              "      <td>0.333579</td>\n",
              "      <td>67936.0</td>\n",
              "      <td>63868.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>16.30</td>\n",
              "      <td>1574.0</td>\n",
              "      <td>1169.0</td>\n",
              "      <td>1396.0</td>\n",
              "      <td>594.0</td>\n",
              "      <td>802.0</td>\n",
              "      <td>291078.00</td>\n",
              "      <td>52478.0</td>\n",
              "      <td>18796.0</td>\n",
              "      <td>713.0</td>\n",
              "      <td>2993.0</td>\n",
              "      <td>14998.0</td>\n",
              "      <td>8740.0</td>\n",
              "      <td>6258.0</td>\n",
              "      <td>33682.0</td>\n",
              "      <td>10774.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.88</td>\n",
              "      <td>1.53</td>\n",
              "      <td>1.54</td>\n",
              "      <td>1.64</td>\n",
              "      <td>2.39</td>\n",
              "      <td>109.9522</td>\n",
              "      <td>4.3122</td>\n",
              "      <td>359.8417</td>\n",
              "      <td>2.8065</td>\n",
              "      <td>0.9671</td>\n",
              "      <td>33.1</td>\n",
              "      <td>33.4</td>\n",
              "      <td>30.6</td>\n",
              "      <td>31.6</td>\n",
              "      <td>3.00</td>\n",
              "      <td>32.5</td>\n",
              "      <td>29.010</td>\n",
              "      <td>44.800</td>\n",
              "      <td>29.300</td>\n",
              "      <td>21.100</td>\n",
              "      <td>33.300</td>\n",
              "      <td>38.100</td>\n",
              "      <td>22.900</td>\n",
              "      <td>28.900</td>\n",
              "      <td>30.700</td>\n",
              "      <td>29.600</td>\n",
              "      <td>16.042</td>\n",
              "      <td>56.918</td>\n",
              "      <td>17.791</td>\n",
              "      <td>11.326</td>\n",
              "      <td>2.13</td>\n",
              "      <td>2.45</td>\n",
              "      <td>2.04</td>\n",
              "      <td>89.8</td>\n",
              "      <td>6476.00</td>\n",
              "      <td>12298.00</td>\n",
              "      <td>84.20430</td>\n",
              "      <td>17.56405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>02/01/1959</th>\n",
              "      <td>2451.778</td>\n",
              "      <td>2301.5</td>\n",
              "      <td>17.452</td>\n",
              "      <td>2.944247e+05</td>\n",
              "      <td>18369.56308</td>\n",
              "      <td>22.5056</td>\n",
              "      <td>23.7185</td>\n",
              "      <td>22.4788</td>\n",
              "      <td>31.8004</td>\n",
              "      <td>18.7580</td>\n",
              "      <td>38.5100</td>\n",
              "      <td>8.2854</td>\n",
              "      <td>20.8817</td>\n",
              "      <td>12.8450</td>\n",
              "      <td>32.288175</td>\n",
              "      <td>21.3034</td>\n",
              "      <td>19.8518</td>\n",
              "      <td>34.2411</td>\n",
              "      <td>81.4428</td>\n",
              "      <td>1421.00</td>\n",
              "      <td>0.358386</td>\n",
              "      <td>67649.0</td>\n",
              "      <td>63684.0</td>\n",
              "      <td>5.9</td>\n",
              "      <td>15.50</td>\n",
              "      <td>1554.0</td>\n",
              "      <td>1164.0</td>\n",
              "      <td>1277.0</td>\n",
              "      <td>545.0</td>\n",
              "      <td>732.0</td>\n",
              "      <td>282958.00</td>\n",
              "      <td>52688.0</td>\n",
              "      <td>18890.0</td>\n",
              "      <td>704.2</td>\n",
              "      <td>2980.0</td>\n",
              "      <td>15115.0</td>\n",
              "      <td>8839.0</td>\n",
              "      <td>6276.0</td>\n",
              "      <td>33798.0</td>\n",
              "      <td>10816.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1.11</td>\n",
              "      <td>1.53</td>\n",
              "      <td>1.53</td>\n",
              "      <td>1.71</td>\n",
              "      <td>2.46</td>\n",
              "      <td>109.9522</td>\n",
              "      <td>4.3133</td>\n",
              "      <td>359.8417</td>\n",
              "      <td>2.8093</td>\n",
              "      <td>0.9748</td>\n",
              "      <td>33.2</td>\n",
              "      <td>33.4</td>\n",
              "      <td>30.7</td>\n",
              "      <td>31.4</td>\n",
              "      <td>3.00</td>\n",
              "      <td>32.5</td>\n",
              "      <td>29.000</td>\n",
              "      <td>44.700</td>\n",
              "      <td>29.400</td>\n",
              "      <td>21.200</td>\n",
              "      <td>33.300</td>\n",
              "      <td>38.100</td>\n",
              "      <td>23.000</td>\n",
              "      <td>28.900</td>\n",
              "      <td>30.700</td>\n",
              "      <td>29.600</td>\n",
              "      <td>16.057</td>\n",
              "      <td>56.951</td>\n",
              "      <td>17.798</td>\n",
              "      <td>11.343</td>\n",
              "      <td>2.14</td>\n",
              "      <td>2.46</td>\n",
              "      <td>2.05</td>\n",
              "      <td>89.8</td>\n",
              "      <td>6476.00</td>\n",
              "      <td>12298.00</td>\n",
              "      <td>83.52800</td>\n",
              "      <td>17.56405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>03/01/1959</th>\n",
              "      <td>2467.594</td>\n",
              "      <td>2318.5</td>\n",
              "      <td>17.617</td>\n",
              "      <td>2.934187e+05</td>\n",
              "      <td>18523.05762</td>\n",
              "      <td>22.8298</td>\n",
              "      <td>23.8621</td>\n",
              "      <td>22.5892</td>\n",
              "      <td>31.8004</td>\n",
              "      <td>19.1254</td>\n",
              "      <td>38.3277</td>\n",
              "      <td>8.3749</td>\n",
              "      <td>21.4240</td>\n",
              "      <td>13.3511</td>\n",
              "      <td>32.288175</td>\n",
              "      <td>21.6367</td>\n",
              "      <td>19.9873</td>\n",
              "      <td>35.1079</td>\n",
              "      <td>82.4769</td>\n",
              "      <td>1524.00</td>\n",
              "      <td>0.400947</td>\n",
              "      <td>68068.0</td>\n",
              "      <td>64267.0</td>\n",
              "      <td>5.6</td>\n",
              "      <td>15.30</td>\n",
              "      <td>1459.0</td>\n",
              "      <td>1093.0</td>\n",
              "      <td>1210.0</td>\n",
              "      <td>530.0</td>\n",
              "      <td>680.0</td>\n",
              "      <td>260346.00</td>\n",
              "      <td>53014.0</td>\n",
              "      <td>19069.0</td>\n",
              "      <td>704.1</td>\n",
              "      <td>3013.0</td>\n",
              "      <td>15259.0</td>\n",
              "      <td>8965.0</td>\n",
              "      <td>6294.0</td>\n",
              "      <td>33945.0</td>\n",
              "      <td>10873.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.81</td>\n",
              "      <td>1.19</td>\n",
              "      <td>1.19</td>\n",
              "      <td>1.33</td>\n",
              "      <td>2.05</td>\n",
              "      <td>109.9522</td>\n",
              "      <td>4.3228</td>\n",
              "      <td>359.8417</td>\n",
              "      <td>2.8127</td>\n",
              "      <td>0.9698</td>\n",
              "      <td>33.2</td>\n",
              "      <td>33.3</td>\n",
              "      <td>30.7</td>\n",
              "      <td>31.5</td>\n",
              "      <td>2.97</td>\n",
              "      <td>32.9</td>\n",
              "      <td>28.970</td>\n",
              "      <td>44.700</td>\n",
              "      <td>29.600</td>\n",
              "      <td>21.300</td>\n",
              "      <td>33.200</td>\n",
              "      <td>38.300</td>\n",
              "      <td>23.000</td>\n",
              "      <td>28.900</td>\n",
              "      <td>30.700</td>\n",
              "      <td>29.600</td>\n",
              "      <td>16.068</td>\n",
              "      <td>57.022</td>\n",
              "      <td>17.785</td>\n",
              "      <td>11.363</td>\n",
              "      <td>2.15</td>\n",
              "      <td>2.45</td>\n",
              "      <td>2.07</td>\n",
              "      <td>89.8</td>\n",
              "      <td>6508.00</td>\n",
              "      <td>12349.00</td>\n",
              "      <td>81.64050</td>\n",
              "      <td>17.56405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>04/01/1959</th>\n",
              "      <td>2483.671</td>\n",
              "      <td>2334.9</td>\n",
              "      <td>17.553</td>\n",
              "      <td>2.993228e+05</td>\n",
              "      <td>18534.46600</td>\n",
              "      <td>23.3161</td>\n",
              "      <td>24.2067</td>\n",
              "      <td>22.9201</td>\n",
              "      <td>32.3021</td>\n",
              "      <td>19.2478</td>\n",
              "      <td>38.9658</td>\n",
              "      <td>8.5937</td>\n",
              "      <td>22.0157</td>\n",
              "      <td>13.7530</td>\n",
              "      <td>32.632200</td>\n",
              "      <td>22.0981</td>\n",
              "      <td>20.0685</td>\n",
              "      <td>34.9503</td>\n",
              "      <td>83.9922</td>\n",
              "      <td>1589.00</td>\n",
              "      <td>0.444973</td>\n",
              "      <td>68339.0</td>\n",
              "      <td>64768.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>14.90</td>\n",
              "      <td>1494.0</td>\n",
              "      <td>934.0</td>\n",
              "      <td>1039.0</td>\n",
              "      <td>408.0</td>\n",
              "      <td>631.0</td>\n",
              "      <td>246413.00</td>\n",
              "      <td>53321.0</td>\n",
              "      <td>19269.0</td>\n",
              "      <td>705.2</td>\n",
              "      <td>3085.0</td>\n",
              "      <td>15385.0</td>\n",
              "      <td>9077.0</td>\n",
              "      <td>6308.0</td>\n",
              "      <td>34052.0</td>\n",
              "      <td>10905.0</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.76</td>\n",
              "      <td>1.16</td>\n",
              "      <td>1.16</td>\n",
              "      <td>1.27</td>\n",
              "      <td>1.90</td>\n",
              "      <td>109.9522</td>\n",
              "      <td>4.3226</td>\n",
              "      <td>359.8417</td>\n",
              "      <td>2.8165</td>\n",
              "      <td>0.9636</td>\n",
              "      <td>33.2</td>\n",
              "      <td>33.4</td>\n",
              "      <td>30.7</td>\n",
              "      <td>31.7</td>\n",
              "      <td>2.97</td>\n",
              "      <td>32.7</td>\n",
              "      <td>28.980</td>\n",
              "      <td>44.800</td>\n",
              "      <td>29.700</td>\n",
              "      <td>21.300</td>\n",
              "      <td>33.200</td>\n",
              "      <td>38.300</td>\n",
              "      <td>23.100</td>\n",
              "      <td>29.000</td>\n",
              "      <td>30.700</td>\n",
              "      <td>29.600</td>\n",
              "      <td>16.100</td>\n",
              "      <td>57.080</td>\n",
              "      <td>17.796</td>\n",
              "      <td>11.403</td>\n",
              "      <td>2.16</td>\n",
              "      <td>2.47</td>\n",
              "      <td>2.08</td>\n",
              "      <td>89.8</td>\n",
              "      <td>6620.00</td>\n",
              "      <td>12484.00</td>\n",
              "      <td>81.80990</td>\n",
              "      <td>17.56405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>05/01/1959</th>\n",
              "      <td>2498.026</td>\n",
              "      <td>2350.4</td>\n",
              "      <td>17.765</td>\n",
              "      <td>3.013643e+05</td>\n",
              "      <td>18679.66354</td>\n",
              "      <td>23.6673</td>\n",
              "      <td>24.4077</td>\n",
              "      <td>23.1408</td>\n",
              "      <td>32.4565</td>\n",
              "      <td>19.6396</td>\n",
              "      <td>38.9658</td>\n",
              "      <td>8.8722</td>\n",
              "      <td>22.5828</td>\n",
              "      <td>14.2293</td>\n",
              "      <td>32.925300</td>\n",
              "      <td>22.4058</td>\n",
              "      <td>20.3664</td>\n",
              "      <td>34.2805</td>\n",
              "      <td>84.9159</td>\n",
              "      <td>1655.00</td>\n",
              "      <td>0.475711</td>\n",
              "      <td>68178.0</td>\n",
              "      <td>64699.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>14.70</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>1005.0</td>\n",
              "      <td>965.0</td>\n",
              "      <td>390.0</td>\n",
              "      <td>575.0</td>\n",
              "      <td>246952.00</td>\n",
              "      <td>53550.0</td>\n",
              "      <td>19378.0</td>\n",
              "      <td>710.0</td>\n",
              "      <td>3087.0</td>\n",
              "      <td>15487.0</td>\n",
              "      <td>9160.0</td>\n",
              "      <td>6327.0</td>\n",
              "      <td>34172.0</td>\n",
              "      <td>10949.0</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.06</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.06</td>\n",
              "      <td>1.45</td>\n",
              "      <td>1.41</td>\n",
              "      <td>1.47</td>\n",
              "      <td>2.06</td>\n",
              "      <td>109.9522</td>\n",
              "      <td>4.3228</td>\n",
              "      <td>359.8417</td>\n",
              "      <td>2.8145</td>\n",
              "      <td>0.9630</td>\n",
              "      <td>33.3</td>\n",
              "      <td>33.3</td>\n",
              "      <td>30.9</td>\n",
              "      <td>31.5</td>\n",
              "      <td>2.97</td>\n",
              "      <td>32.9</td>\n",
              "      <td>29.040</td>\n",
              "      <td>44.900</td>\n",
              "      <td>29.700</td>\n",
              "      <td>21.400</td>\n",
              "      <td>33.300</td>\n",
              "      <td>38.400</td>\n",
              "      <td>23.200</td>\n",
              "      <td>29.100</td>\n",
              "      <td>30.700</td>\n",
              "      <td>29.600</td>\n",
              "      <td>16.109</td>\n",
              "      <td>57.175</td>\n",
              "      <td>17.777</td>\n",
              "      <td>11.421</td>\n",
              "      <td>2.17</td>\n",
              "      <td>2.48</td>\n",
              "      <td>2.08</td>\n",
              "      <td>95.3</td>\n",
              "      <td>6753.00</td>\n",
              "      <td>12646.00</td>\n",
              "      <td>80.73150</td>\n",
              "      <td>17.56405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>04/01/2021</th>\n",
              "      <td>18261.337</td>\n",
              "      <td>14130.6</td>\n",
              "      <td>123.827</td>\n",
              "      <td>1.585555e+06</td>\n",
              "      <td>628751.00000</td>\n",
              "      <td>99.2301</td>\n",
              "      <td>98.3032</td>\n",
              "      <td>98.9459</td>\n",
              "      <td>99.4073</td>\n",
              "      <td>99.2308</td>\n",
              "      <td>99.3852</td>\n",
              "      <td>92.3234</td>\n",
              "      <td>100.2550</td>\n",
              "      <td>95.9528</td>\n",
              "      <td>98.361800</td>\n",
              "      <td>97.4914</td>\n",
              "      <td>106.4134</td>\n",
              "      <td>92.4188</td>\n",
              "      <td>75.0321</td>\n",
              "      <td>7104.75</td>\n",
              "      <td>0.936914</td>\n",
              "      <td>160988.0</td>\n",
              "      <td>151176.0</td>\n",
              "      <td>6.1</td>\n",
              "      <td>28.75</td>\n",
              "      <td>2414.0</td>\n",
              "      <td>1939.0</td>\n",
              "      <td>5386.0</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>3356.0</td>\n",
              "      <td>552731.25</td>\n",
              "      <td>144326.0</td>\n",
              "      <td>20319.0</td>\n",
              "      <td>571.4</td>\n",
              "      <td>7439.0</td>\n",
              "      <td>12264.0</td>\n",
              "      <td>7605.0</td>\n",
              "      <td>4659.0</td>\n",
              "      <td>124007.0</td>\n",
              "      <td>27116.0</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.79</td>\n",
              "      <td>1.56</td>\n",
              "      <td>2.83</td>\n",
              "      <td>3.53</td>\n",
              "      <td>103.8470</td>\n",
              "      <td>0.9218</td>\n",
              "      <td>109.0445</td>\n",
              "      <td>1.3845</td>\n",
              "      <td>1.2494</td>\n",
              "      <td>215.3</td>\n",
              "      <td>228.9</td>\n",
              "      <td>220.4</td>\n",
              "      <td>219.5</td>\n",
              "      <td>61.72</td>\n",
              "      <td>235.4</td>\n",
              "      <td>266.832</td>\n",
              "      <td>119.335</td>\n",
              "      <td>221.826</td>\n",
              "      <td>524.002</td>\n",
              "      <td>193.772</td>\n",
              "      <td>111.940</td>\n",
              "      <td>338.805</td>\n",
              "      <td>265.904</td>\n",
              "      <td>244.660</td>\n",
              "      <td>254.498</td>\n",
              "      <td>114.166</td>\n",
              "      <td>88.879</td>\n",
              "      <td>101.517</td>\n",
              "      <td>123.125</td>\n",
              "      <td>26.19</td>\n",
              "      <td>30.20</td>\n",
              "      <td>23.44</td>\n",
              "      <td>88.3</td>\n",
              "      <td>362666.77</td>\n",
              "      <td>751213.18</td>\n",
              "      <td>4421.83505</td>\n",
              "      <td>15.99240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>05/01/2021</th>\n",
              "      <td>17778.066</td>\n",
              "      <td>14145.3</td>\n",
              "      <td>123.235</td>\n",
              "      <td>1.552988e+06</td>\n",
              "      <td>620119.00000</td>\n",
              "      <td>99.8504</td>\n",
              "      <td>98.8957</td>\n",
              "      <td>99.7572</td>\n",
              "      <td>100.0709</td>\n",
              "      <td>101.6037</td>\n",
              "      <td>99.5827</td>\n",
              "      <td>93.8185</td>\n",
              "      <td>100.9100</td>\n",
              "      <td>96.6621</td>\n",
              "      <td>99.480400</td>\n",
              "      <td>98.2943</td>\n",
              "      <td>106.1077</td>\n",
              "      <td>92.9248</td>\n",
              "      <td>75.6418</td>\n",
              "      <td>7104.75</td>\n",
              "      <td>1.017926</td>\n",
              "      <td>160935.0</td>\n",
              "      <td>151620.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>28.75</td>\n",
              "      <td>2023.0</td>\n",
              "      <td>2126.0</td>\n",
              "      <td>5013.0</td>\n",
              "      <td>1262.0</td>\n",
              "      <td>3356.0</td>\n",
              "      <td>444400.00</td>\n",
              "      <td>144940.0</td>\n",
              "      <td>20335.0</td>\n",
              "      <td>576.1</td>\n",
              "      <td>7415.0</td>\n",
              "      <td>12300.0</td>\n",
              "      <td>7632.0</td>\n",
              "      <td>4668.0</td>\n",
              "      <td>124605.0</td>\n",
              "      <td>27205.0</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.76</td>\n",
              "      <td>1.56</td>\n",
              "      <td>2.90</td>\n",
              "      <td>3.56</td>\n",
              "      <td>102.0529</td>\n",
              "      <td>0.9027</td>\n",
              "      <td>109.1120</td>\n",
              "      <td>1.4084</td>\n",
              "      <td>1.2125</td>\n",
              "      <td>217.2</td>\n",
              "      <td>231.1</td>\n",
              "      <td>225.9</td>\n",
              "      <td>235.5</td>\n",
              "      <td>65.17</td>\n",
              "      <td>274.9</td>\n",
              "      <td>268.551</td>\n",
              "      <td>120.793</td>\n",
              "      <td>226.538</td>\n",
              "      <td>523.594</td>\n",
              "      <td>195.860</td>\n",
              "      <td>115.352</td>\n",
              "      <td>340.108</td>\n",
              "      <td>267.704</td>\n",
              "      <td>246.655</td>\n",
              "      <td>256.313</td>\n",
              "      <td>114.774</td>\n",
              "      <td>90.669</td>\n",
              "      <td>101.658</td>\n",
              "      <td>123.575</td>\n",
              "      <td>26.32</td>\n",
              "      <td>30.32</td>\n",
              "      <td>23.60</td>\n",
              "      <td>82.9</td>\n",
              "      <td>365154.60</td>\n",
              "      <td>754248.23</td>\n",
              "      <td>4421.83505</td>\n",
              "      <td>17.94210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>06/01/2021</th>\n",
              "      <td>17716.168</td>\n",
              "      <td>14168.2</td>\n",
              "      <td>123.891</td>\n",
              "      <td>1.559648e+06</td>\n",
              "      <td>625405.00000</td>\n",
              "      <td>100.3398</td>\n",
              "      <td>98.7304</td>\n",
              "      <td>99.6633</td>\n",
              "      <td>99.7401</td>\n",
              "      <td>100.1333</td>\n",
              "      <td>99.5610</td>\n",
              "      <td>93.4728</td>\n",
              "      <td>102.2355</td>\n",
              "      <td>96.5788</td>\n",
              "      <td>99.455000</td>\n",
              "      <td>97.9699</td>\n",
              "      <td>109.8122</td>\n",
              "      <td>93.3134</td>\n",
              "      <td>75.3792</td>\n",
              "      <td>7104.75</td>\n",
              "      <td>1.073914</td>\n",
              "      <td>161086.0</td>\n",
              "      <td>151602.0</td>\n",
              "      <td>5.9</td>\n",
              "      <td>28.75</td>\n",
              "      <td>1981.0</td>\n",
              "      <td>2166.0</td>\n",
              "      <td>5314.0</td>\n",
              "      <td>1329.0</td>\n",
              "      <td>3356.0</td>\n",
              "      <td>394000.00</td>\n",
              "      <td>145902.0</td>\n",
              "      <td>20377.0</td>\n",
              "      <td>586.7</td>\n",
              "      <td>7413.0</td>\n",
              "      <td>12332.0</td>\n",
              "      <td>7663.0</td>\n",
              "      <td>4669.0</td>\n",
              "      <td>125525.0</td>\n",
              "      <td>27344.0</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.76</td>\n",
              "      <td>1.44</td>\n",
              "      <td>2.71</td>\n",
              "      <td>3.36</td>\n",
              "      <td>102.8535</td>\n",
              "      <td>0.9078</td>\n",
              "      <td>110.1073</td>\n",
              "      <td>1.4025</td>\n",
              "      <td>1.2220</td>\n",
              "      <td>219.7</td>\n",
              "      <td>234.1</td>\n",
              "      <td>230.1</td>\n",
              "      <td>241.6</td>\n",
              "      <td>71.38</td>\n",
              "      <td>257.1</td>\n",
              "      <td>270.981</td>\n",
              "      <td>121.636</td>\n",
              "      <td>234.727</td>\n",
              "      <td>523.058</td>\n",
              "      <td>199.179</td>\n",
              "      <td>119.422</td>\n",
              "      <td>341.567</td>\n",
              "      <td>270.182</td>\n",
              "      <td>249.386</td>\n",
              "      <td>258.878</td>\n",
              "      <td>115.385</td>\n",
              "      <td>91.620</td>\n",
              "      <td>102.225</td>\n",
              "      <td>124.096</td>\n",
              "      <td>26.48</td>\n",
              "      <td>30.48</td>\n",
              "      <td>23.76</td>\n",
              "      <td>85.5</td>\n",
              "      <td>366098.79</td>\n",
              "      <td>756290.99</td>\n",
              "      <td>4421.83505</td>\n",
              "      <td>14.89130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>07/01/2021</th>\n",
              "      <td>17838.510</td>\n",
              "      <td>14201.1</td>\n",
              "      <td>123.712</td>\n",
              "      <td>7.490701e+05</td>\n",
              "      <td>614313.00000</td>\n",
              "      <td>101.1874</td>\n",
              "      <td>100.1329</td>\n",
              "      <td>101.2501</td>\n",
              "      <td>100.4491</td>\n",
              "      <td>105.1449</td>\n",
              "      <td>99.1077</td>\n",
              "      <td>97.0062</td>\n",
              "      <td>102.3782</td>\n",
              "      <td>98.5360</td>\n",
              "      <td>99.557200</td>\n",
              "      <td>99.5387</td>\n",
              "      <td>106.5678</td>\n",
              "      <td>92.7238</td>\n",
              "      <td>76.5691</td>\n",
              "      <td>7104.75</td>\n",
              "      <td>1.256493</td>\n",
              "      <td>161347.0</td>\n",
              "      <td>152645.0</td>\n",
              "      <td>5.4</td>\n",
              "      <td>28.75</td>\n",
              "      <td>2257.0</td>\n",
              "      <td>1861.0</td>\n",
              "      <td>4599.0</td>\n",
              "      <td>1174.0</td>\n",
              "      <td>3356.0</td>\n",
              "      <td>392800.00</td>\n",
              "      <td>146955.0</td>\n",
              "      <td>20441.0</td>\n",
              "      <td>593.2</td>\n",
              "      <td>7419.0</td>\n",
              "      <td>12384.0</td>\n",
              "      <td>7704.0</td>\n",
              "      <td>4680.0</td>\n",
              "      <td>126514.0</td>\n",
              "      <td>27405.0</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.66</td>\n",
              "      <td>1.22</td>\n",
              "      <td>2.47</td>\n",
              "      <td>3.14</td>\n",
              "      <td>104.7021</td>\n",
              "      <td>0.9174</td>\n",
              "      <td>110.2143</td>\n",
              "      <td>1.3808</td>\n",
              "      <td>1.2530</td>\n",
              "      <td>221.0</td>\n",
              "      <td>235.4</td>\n",
              "      <td>234.1</td>\n",
              "      <td>244.9</td>\n",
              "      <td>72.49</td>\n",
              "      <td>256.1</td>\n",
              "      <td>272.265</td>\n",
              "      <td>121.687</td>\n",
              "      <td>236.252</td>\n",
              "      <td>524.431</td>\n",
              "      <td>200.675</td>\n",
              "      <td>120.190</td>\n",
              "      <td>342.581</td>\n",
              "      <td>271.353</td>\n",
              "      <td>250.619</td>\n",
              "      <td>260.156</td>\n",
              "      <td>115.860</td>\n",
              "      <td>91.911</td>\n",
              "      <td>102.809</td>\n",
              "      <td>124.565</td>\n",
              "      <td>26.59</td>\n",
              "      <td>30.58</td>\n",
              "      <td>23.88</td>\n",
              "      <td>81.2</td>\n",
              "      <td>366251.23</td>\n",
              "      <td>757292.96</td>\n",
              "      <td>4421.83505</td>\n",
              "      <td>15.51220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>08/01/2021</th>\n",
              "      <td>7717.737</td>\n",
              "      <td>6761.4</td>\n",
              "      <td>54.252</td>\n",
              "      <td>7.490701e+05</td>\n",
              "      <td>618676.00000</td>\n",
              "      <td>101.5949</td>\n",
              "      <td>100.8053</td>\n",
              "      <td>101.9616</td>\n",
              "      <td>101.2702</td>\n",
              "      <td>105.3528</td>\n",
              "      <td>100.0933</td>\n",
              "      <td>97.4941</td>\n",
              "      <td>102.4499</td>\n",
              "      <td>98.4315</td>\n",
              "      <td>99.203800</td>\n",
              "      <td>99.7006</td>\n",
              "      <td>110.4348</td>\n",
              "      <td>93.6916</td>\n",
              "      <td>76.6736</td>\n",
              "      <td>3835.00</td>\n",
              "      <td>0.544032</td>\n",
              "      <td>161537.0</td>\n",
              "      <td>153154.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>28.75</td>\n",
              "      <td>2083.0</td>\n",
              "      <td>2066.0</td>\n",
              "      <td>4343.0</td>\n",
              "      <td>1164.0</td>\n",
              "      <td>3179.0</td>\n",
              "      <td>356250.00</td>\n",
              "      <td>147190.0</td>\n",
              "      <td>20481.0</td>\n",
              "      <td>599.2</td>\n",
              "      <td>7416.0</td>\n",
              "      <td>12421.0</td>\n",
              "      <td>7735.0</td>\n",
              "      <td>4686.0</td>\n",
              "      <td>126709.0</td>\n",
              "      <td>27429.0</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.68</td>\n",
              "      <td>1.19</td>\n",
              "      <td>2.46</td>\n",
              "      <td>3.15</td>\n",
              "      <td>105.0489</td>\n",
              "      <td>0.9147</td>\n",
              "      <td>109.8527</td>\n",
              "      <td>1.3797</td>\n",
              "      <td>1.2599</td>\n",
              "      <td>223.2</td>\n",
              "      <td>238.1</td>\n",
              "      <td>236.5</td>\n",
              "      <td>247.4</td>\n",
              "      <td>67.73</td>\n",
              "      <td>260.9</td>\n",
              "      <td>273.012</td>\n",
              "      <td>122.142</td>\n",
              "      <td>235.997</td>\n",
              "      <td>525.724</td>\n",
              "      <td>201.848</td>\n",
              "      <td>119.936</td>\n",
              "      <td>342.755</td>\n",
              "      <td>272.045</td>\n",
              "      <td>251.445</td>\n",
              "      <td>260.877</td>\n",
              "      <td>62.577</td>\n",
              "      <td>101.344</td>\n",
              "      <td>61.998</td>\n",
              "      <td>54.177</td>\n",
              "      <td>26.71</td>\n",
              "      <td>30.70</td>\n",
              "      <td>24.01</td>\n",
              "      <td>89.8</td>\n",
              "      <td>71616.00</td>\n",
              "      <td>177736.85</td>\n",
              "      <td>4421.83505</td>\n",
              "      <td>14.68210</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>752 rows × 127 columns</p>\n",
              "</div>\n",
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              "          const element = document.querySelector('#df-2a79ccf3-8376-4337-a641-adca0d62a4c0');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                  RPI  W875RX1  ...      INVEST   VXOCLSx\n",
              "                                ...                      \n",
              "01/01/1959   2442.158   2293.2  ...    84.20430  17.56405\n",
              "02/01/1959   2451.778   2301.5  ...    83.52800  17.56405\n",
              "03/01/1959   2467.594   2318.5  ...    81.64050  17.56405\n",
              "04/01/1959   2483.671   2334.9  ...    81.80990  17.56405\n",
              "05/01/1959   2498.026   2350.4  ...    80.73150  17.56405\n",
              "...               ...      ...  ...         ...       ...\n",
              "04/01/2021  18261.337  14130.6  ...  4421.83505  15.99240\n",
              "05/01/2021  17778.066  14145.3  ...  4421.83505  17.94210\n",
              "06/01/2021  17716.168  14168.2  ...  4421.83505  14.89130\n",
              "07/01/2021  17838.510  14201.1  ...  4421.83505  15.51220\n",
              "08/01/2021   7717.737   6761.4  ...  4421.83505  14.68210\n",
              "\n",
              "[752 rows x 127 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are seven different transformations that are recommended in the paperwork that descbires FRED-MD dataset. We are going to apply each one of them based on the transformation code that is given for every time serie."
      ],
      "metadata": {
        "id": "WrVm48tlXtMm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_transformed = pd.DataFrame(columns=df.columns)\n",
        "for feature in df:\n",
        "  tscode = int(df_transformations[feature])\n",
        "  transformed_data = transform(df_full, feature, tscode)\n",
        "  df_transformed[feature] = transformed_data"
      ],
      "metadata": {
        "id": "41rJbXHFtimm"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will see that there are few NaN rows as some transformations require the data in the past."
      ],
      "metadata": {
        "id": "KTuXP18MX9za"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_transformed = df_transformed.iloc[2:]\n",
        "df_transformed.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "id": "YWxP7SI90-iz",
        "outputId": "ba29de2b-58fa-4f8a-abf7-66ae605a357e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ca2de78f-d0f1-450b-8079-e0bf732415ec\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RPI</th>\n",
              "      <th>W875RX1</th>\n",
              "      <th>DPCERA3M086SBEA</th>\n",
              "      <th>CMRMTSPLx</th>\n",
              "      <th>RETAILx</th>\n",
              "      <th>INDPRO</th>\n",
              "      <th>IPFPNSS</th>\n",
              "      <th>IPFINAL</th>\n",
              "      <th>IPCONGD</th>\n",
              "      <th>IPDCONGD</th>\n",
              "      <th>IPNCONGD</th>\n",
              "      <th>IPBUSEQ</th>\n",
              "      <th>IPMAT</th>\n",
              "      <th>IPDMAT</th>\n",
              "      <th>IPNMAT</th>\n",
              "      <th>IPMANSICS</th>\n",
              "      <th>IPB51222S</th>\n",
              "      <th>IPFUELS</th>\n",
              "      <th>CUMFNS</th>\n",
              "      <th>HWI</th>\n",
              "      <th>HWIURATIO</th>\n",
              "      <th>CLF16OV</th>\n",
              "      <th>CE16OV</th>\n",
              "      <th>UNRATE</th>\n",
              "      <th>UEMPMEAN</th>\n",
              "      <th>UEMPLT5</th>\n",
              "      <th>UEMP5TO14</th>\n",
              "      <th>UEMP15OV</th>\n",
              "      <th>UEMP15T26</th>\n",
              "      <th>UEMP27OV</th>\n",
              "      <th>CLAIMSx</th>\n",
              "      <th>PAYEMS</th>\n",
              "      <th>USGOOD</th>\n",
              "      <th>CES1021000001</th>\n",
              "      <th>USCONS</th>\n",
              "      <th>MANEMP</th>\n",
              "      <th>DMANEMP</th>\n",
              "      <th>NDMANEMP</th>\n",
              "      <th>SRVPRD</th>\n",
              "      <th>USTPU</th>\n",
              "      <th>...</th>\n",
              "      <th>TB3SMFFM</th>\n",
              "      <th>TB6SMFFM</th>\n",
              "      <th>T1YFFM</th>\n",
              "      <th>T5YFFM</th>\n",
              "      <th>T10YFFM</th>\n",
              "      <th>AAAFFM</th>\n",
              "      <th>BAAFFM</th>\n",
              "      <th>TWEXAFEGSMTHx</th>\n",
              "      <th>EXSZUSx</th>\n",
              "      <th>EXJPUSx</th>\n",
              "      <th>EXUSUKx</th>\n",
              "      <th>EXCAUSx</th>\n",
              "      <th>WPSFD49207</th>\n",
              "      <th>WPSFD49502</th>\n",
              "      <th>WPSID61</th>\n",
              "      <th>WPSID62</th>\n",
              "      <th>OILPRICEx</th>\n",
              "      <th>PPICMM</th>\n",
              "      <th>CPIAUCSL</th>\n",
              "      <th>CPIAPPSL</th>\n",
              "      <th>CPITRNSL</th>\n",
              "      <th>CPIMEDSL</th>\n",
              "      <th>CUSR0000SAC</th>\n",
              "      <th>CUSR0000SAD</th>\n",
              "      <th>CUSR0000SAS</th>\n",
              "      <th>CPIULFSL</th>\n",
              "      <th>CUSR0000SA0L2</th>\n",
              "      <th>CUSR0000SA0L5</th>\n",
              "      <th>PCEPI</th>\n",
              "      <th>DDURRG3M086SBEA</th>\n",
              "      <th>DNDGRG3M086SBEA</th>\n",
              "      <th>DSERRG3M086SBEA</th>\n",
              "      <th>CES0600000008</th>\n",
              "      <th>CES2000000008</th>\n",
              "      <th>CES3000000008</th>\n",
              "      <th>UMCSENTx</th>\n",
              "      <th>DTCOLNVHFNM</th>\n",
              "      <th>DTCTHFNM</th>\n",
              "      <th>INVEST</th>\n",
              "      <th>VXOCLSx</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>7.500000e+02</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>7.500000e+02</td>\n",
              "      <td>7.500000e+02</td>\n",
              "      <td>7.500000e+02</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.001529</td>\n",
              "      <td>0.001437</td>\n",
              "      <td>0.001512</td>\n",
              "      <td>0.001245</td>\n",
              "      <td>0.004689</td>\n",
              "      <td>0.002010</td>\n",
              "      <td>0.001929</td>\n",
              "      <td>0.002016</td>\n",
              "      <td>0.001544</td>\n",
              "      <td>0.002301</td>\n",
              "      <td>0.001274</td>\n",
              "      <td>0.003287</td>\n",
              "      <td>0.002121</td>\n",
              "      <td>0.002715</td>\n",
              "      <td>0.001497</td>\n",
              "      <td>0.002058</td>\n",
              "      <td>0.002288</td>\n",
              "      <td>0.001342</td>\n",
              "      <td>-0.006359</td>\n",
              "      <td>3.218667</td>\n",
              "      <td>0.000248</td>\n",
              "      <td>0.001161</td>\n",
              "      <td>0.001170</td>\n",
              "      <td>-0.000933</td>\n",
              "      <td>0.017667</td>\n",
              "      <td>0.000391</td>\n",
              "      <td>0.000765</td>\n",
              "      <td>0.001632</td>\n",
              "      <td>0.001012</td>\n",
              "      <td>0.001958</td>\n",
              "      <td>0.000307</td>\n",
              "      <td>0.001370</td>\n",
              "      <td>0.000108</td>\n",
              "      <td>-0.000215</td>\n",
              "      <td>0.001216</td>\n",
              "      <td>-0.000262</td>\n",
              "      <td>-0.000178</td>\n",
              "      <td>-0.000390</td>\n",
              "      <td>0.001762</td>\n",
              "      <td>0.001241</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.377123</td>\n",
              "      <td>-0.235440</td>\n",
              "      <td>0.126413</td>\n",
              "      <td>0.778240</td>\n",
              "      <td>1.090453</td>\n",
              "      <td>2.081715</td>\n",
              "      <td>3.064800</td>\n",
              "      <td>-0.000061</td>\n",
              "      <td>-0.002068</td>\n",
              "      <td>-0.001582</td>\n",
              "      <td>-0.000948</td>\n",
              "      <td>0.000342</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>9.249584e-06</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>-0.000091</td>\n",
              "      <td>0.000025</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>0.000008</td>\n",
              "      <td>-0.000006</td>\n",
              "      <td>-0.000003</td>\n",
              "      <td>0.000008</td>\n",
              "      <td>-0.000003</td>\n",
              "      <td>-0.000005</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>-0.000823</td>\n",
              "      <td>0.000129</td>\n",
              "      <td>-0.000675</td>\n",
              "      <td>-0.001112</td>\n",
              "      <td>-2.413699e-07</td>\n",
              "      <td>-2.091617e-07</td>\n",
              "      <td>7.188486e-07</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.002176</td>\n",
              "      <td>-0.001933</td>\n",
              "      <td>0.000011</td>\n",
              "      <td>18.804237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.033058</td>\n",
              "      <td>0.027873</td>\n",
              "      <td>0.031381</td>\n",
              "      <td>0.029712</td>\n",
              "      <td>0.015842</td>\n",
              "      <td>0.010160</td>\n",
              "      <td>0.009966</td>\n",
              "      <td>0.010880</td>\n",
              "      <td>0.010639</td>\n",
              "      <td>0.032528</td>\n",
              "      <td>0.007749</td>\n",
              "      <td>0.017487</td>\n",
              "      <td>0.012441</td>\n",
              "      <td>0.018618</td>\n",
              "      <td>0.011762</td>\n",
              "      <td>0.011576</td>\n",
              "      <td>0.035283</td>\n",
              "      <td>0.021113</td>\n",
              "      <td>0.765190</td>\n",
              "      <td>210.034248</td>\n",
              "      <td>0.050321</td>\n",
              "      <td>0.003111</td>\n",
              "      <td>0.006723</td>\n",
              "      <td>0.286825</td>\n",
              "      <td>0.756881</td>\n",
              "      <td>0.063720</td>\n",
              "      <td>0.062957</td>\n",
              "      <td>0.057311</td>\n",
              "      <td>0.080064</td>\n",
              "      <td>0.071315</td>\n",
              "      <td>0.059825</td>\n",
              "      <td>0.006083</td>\n",
              "      <td>0.006597</td>\n",
              "      <td>0.016342</td>\n",
              "      <td>0.010746</td>\n",
              "      <td>0.006175</td>\n",
              "      <td>0.007793</td>\n",
              "      <td>0.004547</td>\n",
              "      <td>0.006106</td>\n",
              "      <td>0.005388</td>\n",
              "      <td>...</td>\n",
              "      <td>0.490874</td>\n",
              "      <td>0.523681</td>\n",
              "      <td>0.518807</td>\n",
              "      <td>1.150787</td>\n",
              "      <td>1.461372</td>\n",
              "      <td>1.806702</td>\n",
              "      <td>1.973771</td>\n",
              "      <td>0.015228</td>\n",
              "      <td>0.024627</td>\n",
              "      <td>0.023241</td>\n",
              "      <td>0.021363</td>\n",
              "      <td>0.013380</td>\n",
              "      <td>0.006903</td>\n",
              "      <td>0.008908</td>\n",
              "      <td>7.480820e-03</td>\n",
              "      <td>0.041327</td>\n",
              "      <td>0.101052</td>\n",
              "      <td>0.032929</td>\n",
              "      <td>0.002690</td>\n",
              "      <td>0.005823</td>\n",
              "      <td>0.011334</td>\n",
              "      <td>0.002751</td>\n",
              "      <td>0.005318</td>\n",
              "      <td>0.003222</td>\n",
              "      <td>0.002540</td>\n",
              "      <td>0.002951</td>\n",
              "      <td>0.003603</td>\n",
              "      <td>0.002949</td>\n",
              "      <td>0.022722</td>\n",
              "      <td>0.004738</td>\n",
              "      <td>0.019619</td>\n",
              "      <td>0.030581</td>\n",
              "      <td>3.990238e-03</td>\n",
              "      <td>9.087767e-03</td>\n",
              "      <td>4.757243e-03</td>\n",
              "      <td>5.868708</td>\n",
              "      <td>0.064236</td>\n",
              "      <td>0.056591</td>\n",
              "      <td>0.010823</td>\n",
              "      <td>6.016176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-0.837838</td>\n",
              "      <td>-0.742089</td>\n",
              "      <td>-0.824316</td>\n",
              "      <td>-0.733383</td>\n",
              "      <td>-0.158918</td>\n",
              "      <td>-0.146100</td>\n",
              "      <td>-0.153453</td>\n",
              "      <td>-0.163314</td>\n",
              "      <td>-0.122299</td>\n",
              "      <td>-0.472578</td>\n",
              "      <td>-0.048760</td>\n",
              "      <td>-0.289115</td>\n",
              "      <td>-0.136392</td>\n",
              "      <td>-0.199027</td>\n",
              "      <td>-0.090059</td>\n",
              "      <td>-0.172309</td>\n",
              "      <td>-0.124546</td>\n",
              "      <td>-0.170917</td>\n",
              "      <td>-7.201475</td>\n",
              "      <td>-3269.750000</td>\n",
              "      <td>-0.712460</td>\n",
              "      <td>-0.039122</td>\n",
              "      <td>-0.153750</td>\n",
              "      <td>-1.750000</td>\n",
              "      <td>-10.600000</td>\n",
              "      <td>-0.321281</td>\n",
              "      <td>-0.220383</td>\n",
              "      <td>-0.182773</td>\n",
              "      <td>-0.363052</td>\n",
              "      <td>-0.214410</td>\n",
              "      <td>-0.255151</td>\n",
              "      <td>-0.147448</td>\n",
              "      <td>-0.120490</td>\n",
              "      <td>-0.190394</td>\n",
              "      <td>-0.145302</td>\n",
              "      <td>-0.108178</td>\n",
              "      <td>-0.119826</td>\n",
              "      <td>-0.088982</td>\n",
              "      <td>-0.151864</td>\n",
              "      <td>-0.123275</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.496250</td>\n",
              "      <td>-1.405000</td>\n",
              "      <td>-0.920000</td>\n",
              "      <td>-2.045000</td>\n",
              "      <td>-2.720000</td>\n",
              "      <td>-2.601250</td>\n",
              "      <td>-2.245000</td>\n",
              "      <td>-0.048328</td>\n",
              "      <td>-0.087458</td>\n",
              "      <td>-0.105227</td>\n",
              "      <td>-0.110761</td>\n",
              "      <td>-0.060094</td>\n",
              "      <td>-0.036765</td>\n",
              "      <td>-0.047035</td>\n",
              "      <td>-4.432701e-02</td>\n",
              "      <td>-0.315969</td>\n",
              "      <td>-0.852587</td>\n",
              "      <td>-0.222063</td>\n",
              "      <td>-0.013503</td>\n",
              "      <td>-0.029322</td>\n",
              "      <td>-0.074197</td>\n",
              "      <td>-0.015446</td>\n",
              "      <td>-0.034738</td>\n",
              "      <td>-0.028265</td>\n",
              "      <td>-0.024230</td>\n",
              "      <td>-0.013615</td>\n",
              "      <td>-0.021154</td>\n",
              "      <td>-0.013383</td>\n",
              "      <td>-0.620093</td>\n",
              "      <td>-0.014374</td>\n",
              "      <td>-0.511467</td>\n",
              "      <td>-0.836343</td>\n",
              "      <td>-2.950920e-02</td>\n",
              "      <td>-6.807429e-02</td>\n",
              "      <td>-2.686752e-02</td>\n",
              "      <td>-32.200000</td>\n",
              "      <td>-1.632417</td>\n",
              "      <td>-1.450770</td>\n",
              "      <td>-0.080819</td>\n",
              "      <td>8.016400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000634</td>\n",
              "      <td>0.000236</td>\n",
              "      <td>-0.000069</td>\n",
              "      <td>-0.003907</td>\n",
              "      <td>-0.001104</td>\n",
              "      <td>-0.001746</td>\n",
              "      <td>-0.002272</td>\n",
              "      <td>-0.002438</td>\n",
              "      <td>-0.003311</td>\n",
              "      <td>-0.007921</td>\n",
              "      <td>-0.003509</td>\n",
              "      <td>-0.003220</td>\n",
              "      <td>-0.002542</td>\n",
              "      <td>-0.003656</td>\n",
              "      <td>-0.003471</td>\n",
              "      <td>-0.002333</td>\n",
              "      <td>-0.014118</td>\n",
              "      <td>-0.009649</td>\n",
              "      <td>-0.338725</td>\n",
              "      <td>-70.750000</td>\n",
              "      <td>-0.016617</td>\n",
              "      <td>-0.000258</td>\n",
              "      <td>-0.000398</td>\n",
              "      <td>-0.100000</td>\n",
              "      <td>-0.300000</td>\n",
              "      <td>-0.034433</td>\n",
              "      <td>-0.035056</td>\n",
              "      <td>-0.031213</td>\n",
              "      <td>-0.046862</td>\n",
              "      <td>-0.036135</td>\n",
              "      <td>-0.027703</td>\n",
              "      <td>0.000458</td>\n",
              "      <td>-0.001770</td>\n",
              "      <td>-0.003612</td>\n",
              "      <td>-0.002648</td>\n",
              "      <td>-0.001757</td>\n",
              "      <td>-0.002015</td>\n",
              "      <td>-0.001793</td>\n",
              "      <td>0.000877</td>\n",
              "      <td>0.000129</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.627500</td>\n",
              "      <td>-0.520000</td>\n",
              "      <td>-0.110000</td>\n",
              "      <td>0.130000</td>\n",
              "      <td>0.222500</td>\n",
              "      <td>0.985000</td>\n",
              "      <td>1.822500</td>\n",
              "      <td>-0.006828</td>\n",
              "      <td>-0.013722</td>\n",
              "      <td>-0.011365</td>\n",
              "      <td>-0.010708</td>\n",
              "      <td>-0.005962</td>\n",
              "      <td>-0.003089</td>\n",
              "      <td>-0.004145</td>\n",
              "      <td>-3.054893e-03</td>\n",
              "      <td>-0.014601</td>\n",
              "      <td>-0.030304</td>\n",
              "      <td>-0.015130</td>\n",
              "      <td>-0.001413</td>\n",
              "      <td>-0.003017</td>\n",
              "      <td>-0.004512</td>\n",
              "      <td>-0.001273</td>\n",
              "      <td>-0.002550</td>\n",
              "      <td>-0.001639</td>\n",
              "      <td>-0.001056</td>\n",
              "      <td>-0.001664</td>\n",
              "      <td>-0.001819</td>\n",
              "      <td>-0.001553</td>\n",
              "      <td>-0.001133</td>\n",
              "      <td>-0.001777</td>\n",
              "      <td>-0.002619</td>\n",
              "      <td>-0.000838</td>\n",
              "      <td>-2.178819e-03</td>\n",
              "      <td>-3.456027e-03</td>\n",
              "      <td>-2.375804e-03</td>\n",
              "      <td>-2.400000</td>\n",
              "      <td>-0.006990</td>\n",
              "      <td>-0.004204</td>\n",
              "      <td>-0.005742</td>\n",
              "      <td>14.340950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.002799</td>\n",
              "      <td>0.002868</td>\n",
              "      <td>0.002571</td>\n",
              "      <td>0.002129</td>\n",
              "      <td>0.004800</td>\n",
              "      <td>0.002519</td>\n",
              "      <td>0.002415</td>\n",
              "      <td>0.002273</td>\n",
              "      <td>0.001819</td>\n",
              "      <td>0.002798</td>\n",
              "      <td>0.001609</td>\n",
              "      <td>0.004043</td>\n",
              "      <td>0.002779</td>\n",
              "      <td>0.003972</td>\n",
              "      <td>0.001677</td>\n",
              "      <td>0.002282</td>\n",
              "      <td>0.003295</td>\n",
              "      <td>0.001963</td>\n",
              "      <td>0.030950</td>\n",
              "      <td>4.375000</td>\n",
              "      <td>0.002389</td>\n",
              "      <td>0.001212</td>\n",
              "      <td>0.001357</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.001904</td>\n",
              "      <td>-0.000808</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.000273</td>\n",
              "      <td>0.001611</td>\n",
              "      <td>0.000817</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.002188</td>\n",
              "      <td>0.000288</td>\n",
              "      <td>0.000554</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.001856</td>\n",
              "      <td>0.001441</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.210000</td>\n",
              "      <td>-0.070000</td>\n",
              "      <td>0.135000</td>\n",
              "      <td>0.850000</td>\n",
              "      <td>1.190000</td>\n",
              "      <td>2.170000</td>\n",
              "      <td>3.105000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.000278</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000018</td>\n",
              "      <td>-0.000046</td>\n",
              "      <td>-0.000005</td>\n",
              "      <td>-0.000008</td>\n",
              "      <td>-2.963138e-07</td>\n",
              "      <td>-0.000070</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.000009</td>\n",
              "      <td>-0.000009</td>\n",
              "      <td>-0.000006</td>\n",
              "      <td>-0.000009</td>\n",
              "      <td>-0.000023</td>\n",
              "      <td>-0.000006</td>\n",
              "      <td>-0.000004</td>\n",
              "      <td>-0.000011</td>\n",
              "      <td>-0.000008</td>\n",
              "      <td>-0.000008</td>\n",
              "      <td>-0.000009</td>\n",
              "      <td>-0.000012</td>\n",
              "      <td>-0.000008</td>\n",
              "      <td>0.000042</td>\n",
              "      <td>-0.000077</td>\n",
              "      <td>-1.288158e-05</td>\n",
              "      <td>-2.377488e-05</td>\n",
              "      <td>-1.028432e-05</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000388</td>\n",
              "      <td>0.000576</td>\n",
              "      <td>0.000421</td>\n",
              "      <td>17.564050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.004840</td>\n",
              "      <td>0.004961</td>\n",
              "      <td>0.005727</td>\n",
              "      <td>0.008586</td>\n",
              "      <td>0.010864</td>\n",
              "      <td>0.006510</td>\n",
              "      <td>0.006520</td>\n",
              "      <td>0.007016</td>\n",
              "      <td>0.006676</td>\n",
              "      <td>0.013312</td>\n",
              "      <td>0.006241</td>\n",
              "      <td>0.010831</td>\n",
              "      <td>0.007597</td>\n",
              "      <td>0.010798</td>\n",
              "      <td>0.006900</td>\n",
              "      <td>0.007205</td>\n",
              "      <td>0.019357</td>\n",
              "      <td>0.011718</td>\n",
              "      <td>0.353875</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>0.021361</td>\n",
              "      <td>0.002643</td>\n",
              "      <td>0.002869</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.036048</td>\n",
              "      <td>0.032955</td>\n",
              "      <td>0.029130</td>\n",
              "      <td>0.047746</td>\n",
              "      <td>0.037038</td>\n",
              "      <td>0.023308</td>\n",
              "      <td>0.002738</td>\n",
              "      <td>0.002744</td>\n",
              "      <td>0.004880</td>\n",
              "      <td>0.005383</td>\n",
              "      <td>0.002099</td>\n",
              "      <td>0.002929</td>\n",
              "      <td>0.001416</td>\n",
              "      <td>0.002968</td>\n",
              "      <td>0.002710</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.040000</td>\n",
              "      <td>0.067500</td>\n",
              "      <td>0.427500</td>\n",
              "      <td>1.580000</td>\n",
              "      <td>2.197500</td>\n",
              "      <td>3.397500</td>\n",
              "      <td>4.547500</td>\n",
              "      <td>0.006794</td>\n",
              "      <td>0.011124</td>\n",
              "      <td>0.009444</td>\n",
              "      <td>0.010507</td>\n",
              "      <td>0.007064</td>\n",
              "      <td>0.002999</td>\n",
              "      <td>0.004615</td>\n",
              "      <td>3.191703e-03</td>\n",
              "      <td>0.015669</td>\n",
              "      <td>0.017512</td>\n",
              "      <td>0.017355</td>\n",
              "      <td>0.001525</td>\n",
              "      <td>0.002518</td>\n",
              "      <td>0.004489</td>\n",
              "      <td>0.001320</td>\n",
              "      <td>0.002711</td>\n",
              "      <td>0.001661</td>\n",
              "      <td>0.001152</td>\n",
              "      <td>0.001817</td>\n",
              "      <td>0.002006</td>\n",
              "      <td>0.001681</td>\n",
              "      <td>0.001160</td>\n",
              "      <td>0.001804</td>\n",
              "      <td>0.002915</td>\n",
              "      <td>0.000791</td>\n",
              "      <td>1.988345e-03</td>\n",
              "      <td>3.602492e-03</td>\n",
              "      <td>2.130600e-03</td>\n",
              "      <td>2.575000</td>\n",
              "      <td>0.006842</td>\n",
              "      <td>0.004905</td>\n",
              "      <td>0.005908</td>\n",
              "      <td>22.212850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.184835</td>\n",
              "      <td>0.040121</td>\n",
              "      <td>0.081153</td>\n",
              "      <td>0.076862</td>\n",
              "      <td>0.166979</td>\n",
              "      <td>0.060116</td>\n",
              "      <td>0.066007</td>\n",
              "      <td>0.078725</td>\n",
              "      <td>0.076816</td>\n",
              "      <td>0.316900</td>\n",
              "      <td>0.026339</td>\n",
              "      <td>0.117652</td>\n",
              "      <td>0.101665</td>\n",
              "      <td>0.168423</td>\n",
              "      <td>0.063119</td>\n",
              "      <td>0.070018</td>\n",
              "      <td>0.154974</td>\n",
              "      <td>0.148639</td>\n",
              "      <td>5.063100</td>\n",
              "      <td>817.000000</td>\n",
              "      <td>0.182579</td>\n",
              "      <td>0.017105</td>\n",
              "      <td>0.034916</td>\n",
              "      <td>5.750000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>0.497480</td>\n",
              "      <td>0.774256</td>\n",
              "      <td>0.526385</td>\n",
              "      <td>0.648536</td>\n",
              "      <td>0.411931</td>\n",
              "      <td>0.957343</td>\n",
              "      <td>0.035790</td>\n",
              "      <td>0.036533</td>\n",
              "      <td>0.203396</td>\n",
              "      <td>0.069309</td>\n",
              "      <td>0.028916</td>\n",
              "      <td>0.036943</td>\n",
              "      <td>0.023616</td>\n",
              "      <td>0.037458</td>\n",
              "      <td>0.039323</td>\n",
              "      <td>...</td>\n",
              "      <td>0.833750</td>\n",
              "      <td>0.955000</td>\n",
              "      <td>1.240000</td>\n",
              "      <td>3.160000</td>\n",
              "      <td>3.850000</td>\n",
              "      <td>5.730000</td>\n",
              "      <td>8.615000</td>\n",
              "      <td>0.152241</td>\n",
              "      <td>0.116871</td>\n",
              "      <td>0.080657</td>\n",
              "      <td>0.095205</td>\n",
              "      <td>0.112920</td>\n",
              "      <td>0.049266</td>\n",
              "      <td>0.069090</td>\n",
              "      <td>4.438688e-02</td>\n",
              "      <td>0.246151</td>\n",
              "      <td>1.113746</td>\n",
              "      <td>0.141751</td>\n",
              "      <td>0.017938</td>\n",
              "      <td>0.036532</td>\n",
              "      <td>0.061971</td>\n",
              "      <td>0.012680</td>\n",
              "      <td>0.025418</td>\n",
              "      <td>0.030068</td>\n",
              "      <td>0.014395</td>\n",
              "      <td>0.012510</td>\n",
              "      <td>0.019376</td>\n",
              "      <td>0.015534</td>\n",
              "      <td>0.007804</td>\n",
              "      <td>0.094529</td>\n",
              "      <td>0.027671</td>\n",
              "      <td>0.015975</td>\n",
              "      <td>1.930253e-02</td>\n",
              "      <td>5.305046e-02</td>\n",
              "      <td>2.482810e-02</td>\n",
              "      <td>32.200000</td>\n",
              "      <td>0.187308</td>\n",
              "      <td>0.258050</td>\n",
              "      <td>0.049909</td>\n",
              "      <td>34.936113</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 127 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ca2de78f-d0f1-450b-8079-e0bf732415ec')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ca2de78f-d0f1-450b-8079-e0bf732415ec');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              RPI     W875RX1  ...      INVEST     VXOCLSx\n",
              "count  750.000000  750.000000  ...  750.000000  750.000000\n",
              "mean     0.001529    0.001437  ...    0.000011   18.804237\n",
              "std      0.033058    0.027873  ...    0.010823    6.016176\n",
              "min     -0.837838   -0.742089  ...   -0.080819    8.016400\n",
              "25%      0.000634    0.000236  ...   -0.005742   14.340950\n",
              "50%      0.002799    0.002868  ...    0.000421   17.564050\n",
              "75%      0.004840    0.004961  ...    0.005908   22.212850\n",
              "max      0.184835    0.040121  ...    0.049909   34.936113\n",
              "\n",
              "[8 rows x 127 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we have a new dataframe with timeseries.\n",
        "Le't calculate mean, std, kurtosis and skewness for each one of them and store it in the different dataframe."
      ],
      "metadata": {
        "id": "76OiNqFnbEd8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_moments = pd.DataFrame(index=['mean', 'std', 'kurtosis', 'skew'], columns=df_transformed.columns)\n",
        "\n",
        "df_moments.at['mean'] = df_transformed.mean()\n",
        "df_moments.at['std'] = df_transformed.std()\n",
        "df_moments.at['kurtosis'] = df_transformed.kurtosis()\n",
        "df_moments.at['skew'] = df_transformed.skew()\n"
      ],
      "metadata": {
        "id": "KD2WCSF_DuFF"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We want our timeseries to be observations, and statistical moments to be features. We also need to remove/cope outliers as some of the algorithms are sensitive to outliers."
      ],
      "metadata": {
        "id": "uL69D-qIcbT2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_final = df_moments.T\n",
        "\n",
        "for feature in df_final:\n",
        "  update_outliers(df_final, feature)\n",
        "\n",
        "df_final"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "nzQWNam2ciSv",
        "outputId": "d19de980-ca3d-48ee-e09f-619723514925"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-4252b763-a95a-429c-9176-47abb5609a3a\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>kurtosis</th>\n",
              "      <th>skew</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>RPI</th>\n",
              "      <td>0.00152894</td>\n",
              "      <td>0.0330576</td>\n",
              "      <td>193.313</td>\n",
              "      <td>-8.61199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>W875RX1</th>\n",
              "      <td>0.00143689</td>\n",
              "      <td>0.0278733</td>\n",
              "      <td>193.313</td>\n",
              "      <td>-8.61199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DPCERA3M086SBEA</th>\n",
              "      <td>0.00151225</td>\n",
              "      <td>0.0313811</td>\n",
              "      <td>193.313</td>\n",
              "      <td>-8.61199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CMRMTSPLx</th>\n",
              "      <td>0.00124508</td>\n",
              "      <td>0.029712</td>\n",
              "      <td>193.313</td>\n",
              "      <td>-8.61199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RETAILx</th>\n",
              "      <td>0.00468918</td>\n",
              "      <td>0.0158418</td>\n",
              "      <td>34.8318</td>\n",
              "      <td>0.154204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>UMCSENTx</th>\n",
              "      <td>0</td>\n",
              "      <td>0.71361</td>\n",
              "      <td>6.4019</td>\n",
              "      <td>-0.0479609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DTCOLNVHFNM</th>\n",
              "      <td>-0.002176</td>\n",
              "      <td>0.0642365</td>\n",
              "      <td>193.313</td>\n",
              "      <td>-8.61199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DTCTHFNM</th>\n",
              "      <td>-0.00193259</td>\n",
              "      <td>0.0565907</td>\n",
              "      <td>193.313</td>\n",
              "      <td>-8.61199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>INVEST</th>\n",
              "      <td>1.07521e-05</td>\n",
              "      <td>0.0108226</td>\n",
              "      <td>5.42379</td>\n",
              "      <td>-0.401394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VXOCLSx</th>\n",
              "      <td>0.00553654</td>\n",
              "      <td>0.71361</td>\n",
              "      <td>0.448271</td>\n",
              "      <td>0.928025</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>127 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4252b763-a95a-429c-9176-47abb5609a3a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4252b763-a95a-429c-9176-47abb5609a3a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4252b763-a95a-429c-9176-47abb5609a3a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                        mean        std  kurtosis       skew\n",
              "RPI               0.00152894  0.0330576   193.313   -8.61199\n",
              "W875RX1           0.00143689  0.0278733   193.313   -8.61199\n",
              "DPCERA3M086SBEA   0.00151225  0.0313811   193.313   -8.61199\n",
              "CMRMTSPLx         0.00124508   0.029712   193.313   -8.61199\n",
              "RETAILx           0.00468918  0.0158418   34.8318   0.154204\n",
              "...                      ...        ...       ...        ...\n",
              "UMCSENTx                   0    0.71361    6.4019 -0.0479609\n",
              "DTCOLNVHFNM        -0.002176  0.0642365   193.313   -8.61199\n",
              "DTCTHFNM         -0.00193259  0.0565907   193.313   -8.61199\n",
              "INVEST           1.07521e-05  0.0108226   5.42379  -0.401394\n",
              "VXOCLSx           0.00553654    0.71361  0.448271   0.928025\n",
              "\n",
              "[127 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## (b) Clustering algorithms\n",
        "\n",
        "Run the algorithms discussed in the lecture: K-means,\n",
        "Gaussian mixture model, hierarchical agglomerative clustering, and DBSCAN. (12\n",
        "pts)"
      ],
      "metadata": {
        "id": "vO3vOCoQcNxk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- For each algorithm, try to replicate the analysis provided in the lecture notebook\n",
        "(up to adjustments which might be necessary given the specific characteristics of\n",
        "the FRED–MD dataset)."
      ],
      "metadata": {
        "id": "wHT11rRxcbaA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### K-means algorithm"
      ],
      "metadata": {
        "id": "Nd9p4wdgdZHM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This algorithm needs number of clusters in advance. We somehow need to determine what is the optimal number of clusters. First of all, we will use Elbow plot."
      ],
      "metadata": {
        "id": "foeQ6td6wRrS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define parametrs that will be used K-means evaluation\n",
        "n_clusters_lb = 1\n",
        "n_clusters_ub = 30\n",
        "max_iter = 1000\n",
        "n_init = 10\n",
        "init_type = 'k-means++'\n"
      ],
      "metadata": {
        "id": "Y4SMdxcSP0q8"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "4eb43dbb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "outputId": "0886439f-40ef-4ecc-ad40-bd4ab0b15732"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Initiate the cluster inertia (WCSS) vector \n",
        "kmeans_interia = []\n",
        "\n",
        "# Fit K-means model for different numbers of clusters\n",
        "for ix_clusters in range(n_clusters_lb, n_clusters_ub):\n",
        "    kmeans_model = KMeans(n_clusters=ix_clusters,\n",
        "                          init=init_type,\n",
        "                          n_init=n_init,\n",
        "                          max_iter=max_iter,\n",
        "                          random_state=1)\n",
        "    kmeans_model.fit(df_final)\n",
        "    kmeans_interia.append(kmeans_model.inertia_)\n",
        "\n",
        "# Create the elbow plot\n",
        "plt.figure(figsize=(16,8))    \n",
        "plt.plot(range(n_clusters_lb,n_clusters_ub), kmeans_interia, marker='s')\n",
        "plt.title('K-Means Clustering: Elbow Plot', size=16)\n",
        "plt.xlabel('Number of clusters', size=16)\n",
        "plt.ylabel('Cluster Inertia (WCSS)', size=16)\n",
        "plt.xticks(size=16) \n",
        "plt.yticks(size=16)  \n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Based on the elbow plot for the above, we can see that WCSS score is rapidly falling. However, the scale is to large on the y axis. It turns out that we might pick something between 5-10 clusters.\n",
        "\n",
        "The second approach is to calculate Average Silhouette Scores for model that has up to 30 clusters."
      ],
      "metadata": {
        "id": "bcBcKN6xRqCV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Initiate the average silhouette score vector \n",
        "kmeans_silhouette_avg = []\n",
        "\n",
        "# Fit K-means model for different numbers of clusters\n",
        "for ix_clusters in range(n_clusters_lb+1, n_clusters_ub):\n",
        "    kmeans_model = KMeans(n_clusters=ix_clusters,\n",
        "                          init=init_type,\n",
        "                          n_init=n_init,\n",
        "                          max_iter=max_iter,\n",
        "                          random_state=1)\n",
        "    y_pred = kmeans_model.fit_predict(df_final)\n",
        "    silhouette_vals = silhouette_samples(df_final, y_pred, metric='euclidean')\n",
        "    kmeans_silhouette_avg.append(np.mean(silhouette_vals))\n",
        "\n",
        "# Create the elbow plot\n",
        "plt.figure(figsize=(16,8))    \n",
        "plt.plot(range(n_clusters_lb+1,n_clusters_ub), kmeans_silhouette_avg, marker='s')\n",
        "plt.title('K-Means Clustering: Average Silhouette Score Plot', size=16)\n",
        "plt.xlabel('Number of clusters', size=16)\n",
        "plt.ylabel('Average silhouette score', size=16)\n",
        "plt.xticks(size=16) \n",
        "plt.yticks(size=16)  \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "02RanlLDxJ7T",
        "outputId": "0952f7c1-9185-4876-8b64-1496ef5d3586"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The diagram above shows us that we can get pretty high scores if we pick something between 4 and 7 cluster. After 7th cluster, the Average Silhouette Score is dropping, so we will pick something in between - 6 clusters."
      ],
      "metadata": {
        "id": "UN7nWDlfxPGZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's see the individual scores for each observation i.e. timeseries when we pick 6 clusters."
      ],
      "metadata": {
        "id": "qYiAY3ZMx6Pq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "7a4c6067",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 985
        },
        "outputId": "d00c31b7-5877-4197-efe5-6b0f9720516d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8aVs9PY4dHqta652llOU0vbLX6lgBAKtQ2QsAbFd7qfwxaRID35am8u/wFYsdmOaD/MrbOAnIdyW5K8lz0vSLLEneOejxugOD/2N+ur28fUe3zwyt+wdpEr2fStMa4OBa63611kPaiYOelF2g1vpXaSqZT0xzCf0NaSa/e2GS95dSzplgc59NMkjKfP9axrkWaq0vSXOJ/qvSJNa/nqbC9TeTfLKUsjOXoA+O+RvHPObnjdjG9qpF506t9Su11ncl+cE0lb5JU/U5DZ8R9t7Nrzd4zy8b89x6/y6MIUl+YJw4dvJ1Ntdtk6FtqLU+utb6rFrrmydI9A7b3ccKAFjFNPwjBwBMsVrrPyf58TT9Pr89TYXvtw79/oTtJCKOHmPby2mqeA9Mcxlxkpw/Zmg3tvcTVUK2E2cd2z58fq31PSMuQf7W7CLthFzn1lqPq7UenqaicjBZ2YtLKT8x5nbuSvLB9uGzd0Gog4T7aomcVScUq7VeXWt9Za31R9JUYD87yf9NUzH9pzvRI3anjvkUGLRDObCUslo15q7qA7uq2kxsOOgDe1C6rcD8Unu/2jF+2Ijlk/t3zk7DufXlbPsyYj2c4zs8VqWUvbOtSvlL21sOAFg7kr0AwA61k149K00l6aPTJHwXVl9rbIP+vA9Mck2bXB7Hh9v7H5/w9RbSTKCVbOvNu9IzJtzmTqu1XlNrPTHJP7VPPXWC1QeVwI8ppeyoZUaSZGgyvB0ZTJB3SJsgH+WoMbeVWuvWWuslaaq4kyax+Z1DiwxaK6wW3+CYH72DpOm0+d/t/WBytPsopXxbmkkRu3Lb0M9bO4si+Zf2/omr9A9+Wnt/W5oeswODc/Zh2b7tnbODc+vHdhjhfY1z7u5wufYLnMH4N+m41oXBsfrOEVd8DGzMttaB/7Lid4OJ+3a2QhkAGEGyFwAYS9tP9qfSzkaf5LJSysrJlXbGxUnekOT3sq26dxzntfc/WkpZNUGzIs5bsy3J8L0jlj0syUsniGMsqyRMBwa9XB+06lL39p5sSxKfU0r5gR3E8Igkm8fc9ifSHOuSEZXD7YR9P7ud11ntvQ73rB1+r19t71frwXxhmgTfQUnOWGW5lce8U20F+z+0D//HdhZ7+a547VLKQinlcTtYZo9s67v92VUm29od3pMmKfrQNO1O7qVNAA/21XtWTOL2f9v7w0spjx+x7lOSPHk7r/v2NOPC95RSXrJagCPOrcG5u2qle8Y7x89r708opazanmUKzvFL07ynB2bE+VtKeUCati1JM+HeF1YsMs7+AAAmJNkLAIyt1npZmgTf1jT9V99XSjngfm5za6311Frr/6i1XjzBeu9LkxgqSd5bSnl5KeWbl5+XUg4upfxUKeUvkvz+0Hq3ZluC9E8GibBSyh6llKcn+UB2TaXZfy2l/E0p5efahPIgzoeUUn4tzeR0SfI3426wnWisl2bCqocm+cdSymtKKd8ztP0HlFKeUEo5O8m1SZ4w5ra3JrmofXh2KeWH2320RynlmCSX5d6J22GXl1IWSykbhytwSymPzbZk1n9kW3Iu2dYz9jGllCduJ6YvJzm9fXhaKeXcUso3J9krpexTSnlKKeXNST40zvvcjV7V3v9YKeV/lVIOSZJSygGllN9K8svZNmnWfZRS3l9KqaWU90/4uocm+ddSymWllBPahP9gm3uXUo5Ok7QbVBwvTrj9NdVOADmoWH9tKeXEUsqDkqQ91n+Z5Ig0Vxm8esS6H20fnldK+d52vQeWUp6T5M/TTMI46nWvSXJ2+/CPSilnlVK+WSFcStm/lHJMKeWCNF86DPtkmt7jB5ZSRn4B0hqc4z9TStleYviP04xPe6fpkf7i4TG2lHJoKeX5pZQPpJlAszO11tuS/E778ORSyq+XUvZLkrbS951pJv27J80kgCsN9scL28QwALAGJHsBgInUWv8yyXPT9Md8QpK/KqU8uKNwXpgmgbN3ktcnubGUcnMp5atp+l++N6P72b4sTaLye9Mkwr6Wpifx5WmSpr+wC2ItaSa7e0eSG0opXyul3Jwm+fSa9vfntJO4ja3Wel2a4/D+JPsm+bUk15RStpZSvpwmMf+RJL+a5AFJLphg86en2Y8PT1OZemuaytq/SXPJ/JnbWe+ANNXRH0jytVLKTaWUO5JcleRH0iTqjh+eiK/W+skkV6S55PufSilfLqV8pr09aWi5N6WpFqxJfjHJx9t9eVOaY3hFkpMyZZNG1Vovz7b99QtJvtDGfFOaKuXfS/Jv7e+/voYvfXeaffWMJG9L8plSyp3ta9+R5O+TPL1d9n+mmbywa/89zZcJD0ry1iS3tn8rH0/zpcjXk/xcrfUTI9Y9Oc37OjLJ/yml3JrmvOgnuTLJH63yuqcmeXOaz0inJbmulHJLKeUraRLxf5Pk+Wn+jr6pTXq+s324VEr5ytC5u2lo0fPT/D3+cJLlUsr17TL/OLStu9L0FP9gmkkuz0lyc/v38LU0X5JckKY9Qk33fjdNVXRJk3z/SntuXZemZcs9SV5aa71ixLr/q73/1TTjxGfb/fG7uyFuAJhZkr0AwMRqre9N8oI0kwk9OcnFpYMeqrXW22qtP52mn/B7ktyQJuH5wCRb0iR4fj4r2jLUWj+S5D9nW6XfA5N8MU1i6XHZlnRbS3+W5MVp2ij8vzSVgPulSd78RZKfrLWuevn49tRa/6OdBO2ZaRIo16ZJqB6QJln7gTQJ0u+otY6qsNvedj+V5IlpEllfSpPk+nya5PSTs+0y7JV+Mckr0yQSP5dkcG5cm+QPkxxZa/3bEev9TJpk3KfT7JtHtLd7JW5rra9O8v1pEmGfTPM/7YPT7Mu/SZO0e8q473N3qbX+VppE3hVpkuZ7pklAvqDW+vJsawPwldFb2KnXvDbNPvzl3PvcOyBN8v7/ptmP/7nW+itttXinaq23p+lZ+4tpvmS4Pc3f9WfTnN/fW2u9aDvrfiRNMvXiNPtxzzQtSV6e5CeybRK3Uet+o9b6S+36F7Sv96A059/n0vyd/kqSTSNWPynJWWnO8Qdl27m739D2r03zN/q+NMnjQ9tl7tVjuNb6xTS9u5+f5K/S/O3t3/762jTJ1V6S127vvewu7T57UZp9cmmafT4Y196Z5Am11pEJ9lrr29KMiR9Nc1wenmZ/rFU/eACYS6XWafhCGAAA5ldbHf/lNInCR9VaP9NtRAAArEcqewEAoHsnp0n0flKiFwCAnbVn1wEAAMA8KKX8fpL/k+Sva603ts8dmuSX0vRaTprevQAAsFO0cQAAgN2gnYjrye3DO9vbQ4YWOT/Ji6p/0AEA2EmSvQAAsBuUUn48zcRaT0wzOdd+aSYI/Ockf1JrfXeH4QEAMAMkewEAAAAAZsA89eyV1QYAAAAAZkVZ+cQ8JXtzww03dB0CMCUWFhayvLzcdRjAFDEuAMOMCdDYtGlTkmRpaanjSLplTABW6npc2LBhw8jn99jNcQAAAAAAsAvMVWUvAAAAML7jjz++6xAAmIBkLwAAADDSscce23UIAExAGwcAAABgpOuvvz7XX39912EAMCaVvQAAAMBIp5xyShITtAGsFyp7AQAAAABmgGQvAAAAAMAMkOwFAAAAAJgBkr0AAAAAADPABG0AAADASCeeeGLXIQAwAcleAAAAYKRjjjmm6xAAmIA2DgAAAMBIW7ZsyZYtW7oOA4AxqewFAAAARjrttNOSJEtLSx1HAsA4VPYCAAAAAMwAyV4AAAAAgBkg2QsAAAAAMAMkewEAAAAAZoAJ2gAAAICRTj755K5DAGACkr0AAADASBs3buw6BAAmoI0DAAAAMNJVV12Vq666quswABiTyl4AAABgpDPPPDNJsrS01G0gAIxFZS8AAAAAwAyQ7AUAAAAAmAGSvQAAAAAAM0CyFwAAAABgBpigDQAAABjpFa94RdchADAByV4AAABgpKOOOqrrEACYgDYOAAAAwEhXXnllrrzyyq7DAGBMKnsBAACAkV73utclSZaWljqOBIBxqOwFAAAAAJgBkr0AAAAAADNAshcAAAAAYAZI9gIAAAAAzAATtAEAAAAjnXnmmV2HAMAEJHsBAACAkY488siuQwBgAto4AAAAACNdccUVueKKK7oOA4AxqewFAAAARlpcXEySbNy4seNIABiHyl4AAAAAgBkwV5W9m68+vusQAAAAmBHHPfb8rkMAgHtR2QsAAAAAMAMkewEAAAAAZsC6aOPQ6/UeneRXkhyT5GFJ7k5yQ5IPJ/nTfr//gQ7DAwAAgJn02te+tusQAJjA1Cd7e73eyUnekGSv9qmvtT9/d3u7J8lYyV79lICBhYWFLC8vdx0GMEWMC8AwYwI0jjjiiK5DAGACU93GodfrvSTJG9MkpV+X5BH9fn//fr+/T5LDkrwwyYc6DBEAAABm1qWXXppLL7206zAAGNPUVvb2er1HJvn99uFJ/X7/3OHf9/v9LySZqFR389XHr01wAADAN7mCDmbXOeeckyQ55phjOo4EgHFMc2XvKUn2TfKRlYleAAAAAADubZqTvT/X3r+z0ygAAAAAANaBqWzj0Ov1viPJIe3Df+31ek9K8utJfihNte9nk1yc5A39fv+L3UQJAAAAADA9pjLZm+Q7h34+OskZSR6Q5NYkNcmj29vze73eM/v9/tXjbFQvMWDADNvASsYFAABgvSu11q5juI9er/fcbGvfcE+Sjyf5+X6//5Fer7dHkh9Ncl6a6t+PJzmy3+/fPWI7JyY5MUn6/f7jt27duhuiB9aDPffcM3fffZ9hA5hjxgVgmDEBGtddd12S5OEPf3jHkXTLmACs1PW4sNdeeyVJWfn8tCZ7fy7JO9qH30jy2H6///EVy/xEkkvah8f1+/3+DjZbz77s6WsbKAAAO8UVV0w71f7AMGMCsFLX48KGDRuSEcneaZ2g7WtDP79vZaI3Sfr9/l8m+UT7UBYXAAAA1thFF12Uiy66qOswABjTtPbsvWHo5/skelf87ruSzPf1JAAAALALnH9+cyXGscce23EkAIxjWit7r0nTq3dc09eLAgAAAABgN5rKyt5+v397r9f7cJInJ3n0KosOfveZcbarNxww0HVvHWD6GBcAAID1blore5Pk7e39j/V6vfskfNsJ2r6rffhXuy0qAAAAAIApNJWVva0/SXJKksckeU+v1/v5fr//0V6vt0eSY5L8cbvcP2XMZO/mq4/fJYECAKwlVyMBAAA7Y2qTvf1+/+5er/fsJO9Pk/D9SK/XuzXJA5Ls2y52TZJN/X5fz14AAABYY+ecc07XIQAwgWlu45B+v/+pJN+b5DVpErt7ppmM7V+SnJ7kCf1+//ruIgQAAIDZdfDBB+fggw/uOgwAxjS1lb0D/X7/liS/0d4AAACA3WTz5s1JkuOOO67jSAAYx1RX9gIAAADdufDCC3PhhRd2HQYAY5r6yt61ZLITYGBhYSHLy8tdhwFMEeMCAACw3qnsBQAAAACYAXNV2bv56uO7DgEAmCOuKgIAAHYnlb0AAAAAADNgrip7AQAAgPGdf76rVADWE8leAAAAYKR99tmn6xAAmMBcJXv1zQMGFhYWsry83HUYwBQxLgDAfZ133nlJkhNOOKHTOAAYj569AAAAwEiXXHJJLrnkkq7DAGBMc1XZe8iW07sOAZgWW5JDuo4BmC7GBWDYTowJXzzirF0SCgDAuFT2AgAAAADMAMleAAAAAIAZMLVtHHq93glJ3raDxW7r9/v77YZwAAAAAACm2tQme4fcleSm7fzutkk2pIcWMLCwsJDl5eWuwwCmiHEBGGZMgMbS0lLXIQAwgfWQ7P1Qv98/uusgAAAAAACm2XpI9q6ZQ7ac3nUIwLTYiRm2gRlnXICJuXIOZt9b3vKWJMlJJ53UcSQAjMMEbQAAAMBIl19+eS6//PKuwwBgTJK9AAAAAAAzYD20cXhsr9e7Osm3J7k7yWeTXJZksd/vf7rTyAAAAAAApsR6SPYuJHlokpuTHJDkse3tJb1e7xf7/f6fjbshPcWAATNsAysZFwAAgPVumpO9NyR5ZZJ3J/lkv9/f2uv1HpTk6UnekOQxSf601+t9vt/vXzFqA71e78QkJyZJv9/PwsLC7okcmHp77jHaIA4AACAASURBVLmnMQG4F+MCMMyYAI39998/Seb+78GYAKw0reNCqbV2HcPEer3egUn+OckRST7c7/d/aIzV6t1XvGjXBgYA7LSur8BR2QsMMyYAw4wJwEpdjwsbNmxIkrLy+XU5QVu/378lye+0D5/U6/WmL40OAAAAALAbrctkb+sj7X1J8qguAwEAAIBZdPbZZ+fss8/uOgwAxrSek70AAADALvTBD34wH/zgB7sOA4Axredk7xOHfv5MV0EAAAAAAEyDPbsOYJRer1f6/f52Z47r9XoHJDmtffjRfr//pXG22/XEL8D06LqROgAAAMBam8pkb5JH9Hq9dyU5N8ll/X7/c0nS6/X2SvK0JG9I8l1J7kly+rgbPWTL2IsCs25LckjXMcA640tTAACA6Tatyd6kadPwxCTp9Xp3JrktyQFJHtj+/vYkJ/X7/b/rJjwAAACYbQcddFDXIQAwgWlN9t6Y5OQkP5zk+5N8S5ID0yR8P5nkb5O8ud/vf7azCAEAAGDGnXvuuV2HAMAEpjLZ2+/370jypvYGAAAAAMAO7NF1AAAAAMB0Ouuss3LWWfr2A6wXU1nZu6uYWAYYWFhYyPLyctdhAADAVPvYxz7WdQgATEBlLwAAAADADJiryt5DtpzedQiwLqmKBwAAAJh+KnsBAAAAAGbAXFX2AgAAAOM77LDDug4BgAlI9gIAAAAjvelNb+o6BAAmMFfJXn1HAQAAAIBZpWcvAAAAMNIZZ5yRM844o+swABjTXFX2Li4udh0CAAAArBuXXnppkmRhYWHsdU4++eRdFQ4AO6CyFwAAAABgBkj2AgAAAADMAMleAAAAAIAZMFc9e/UNAgYWFhayvLzcdRjAFDEuAMOMCdD4/Oc/n8TnaYD1Yq6SvQAAAMD4Xv/613cdAgATmKtk7+LiYtchAAAAwMxSAQzQLT17AQAAgJHe97735X3ve1/XYQAwprmq7AUAAADGd9NNN3UdAgATUNkLAAAAADADJHsBAAAAAGbAXLVx0CgeGFhYWMjy8nLXYQBTxLgADDMmQOOKK65I4vM0wHoxV8nexcXFrkMAAACAdWPr1q1J7v15WuIXYHrNVbIXAAAAGN8znvGMrkMAYAJ69gIAAAAAzADJXgAAAGCkiy++OBdffHHXYQAwJm0cAAAAgJFuvfXWrkMAYAJzlezVRB4YMMM2sJJxARhmTIDGFVdckcTnaYD1QhsHAAAAAIAZMFeVvYuLi12HAAAAALuUKlyA+TVXyV4AAABgfI9//OO7DgGACUj2AgAAACOdfvrpXYcAwAT07AUAAAAAmAFzVdmrbxEwYIZtYCXjAjDMmACNF7/4xUmSc889t+NIABjHXCV7AQAAgPHdfPPNXYcAwATmKtm7uLjYdQgAAACwS7mqFWB+6dkLAAAAADADJHsBAAAAAGbAXLVxAAAAAMb35Cc/uesQAJhAqbV2HcPuUm+44YauYwCmhBm2gZWMC8AwYwIwzJgArNT1uLBhw4YkKSuf18YBAAAAAGAGzFUbh8XFxa5DAAAAgPvl5JNP3m2v9YIXvCBJcsEFF+y21wRg581VshcAAAAY35133tl1CABMQBsHAAAAAIAZINkLAAAAADADJHsBAAAAAGbAXPXs3Z1N7IHptrCwkOXl5a7DAKaIcQEYZkyAxjOe8YyuQwBgAnOV7F1cXOw6BAAAABjLNBQsnXTSSV2HAMAEtHEAAAAAAJgBkr0AAADASJs2bcqmTZu6DgOAMUn2AgAAAADMAMleAAAAAIAZMFcTtE1Dc3tgOphhG1jJuAAMMyYAAOuRyl4AAAAAgBkwV5W9i4uLXYcAAADAlHI16H0961nP6joEACYwV8leAAAAYHwnnHBC1yEAMAFtHAAAAICR7rjjjtxxxx1dhwHAmNZNZW+v19svyf9L8rD2qZ/v9/vndRcRAAAAzLbjjz8+SbK0tNRxJACMY90ke5O8OtsSvTtF/yVgwAzbwErGBWCYMQEAWI/WRRuHXq/3n5L8SpKPdB0LAAAAAMA0mvrK3l6vt0eSt7YP/2uSf9nZbS0uLq5JTAAAAKxfrvoEYFath8relyb5wSRv7vf7/9p1MAAAAAAA02iqK3t7vd7hSX47yY1JfqPjcAAAAGCuPOc5z+k6BAAmMNXJ3iRvSrJ/kl/q9/u3dB0MAAAAzJPjjjuu6xAAmMDUJnt7vd6zk/x0kvf3+/0L1mKb+jIBA2bYBlYyLgDDjAnQuOmmm5IkBx98cMeRADCOqUz29nq9Byf5wyR3Jfnl+7GdE5OcmCT9fj8LCwtrEyCw7u25557GBOBejAvAMGMCNJ73vOclSS677LKOI+mWMQFYaVrHhalM9iZ5VZJvS/L6fr9/zc5upN/vn5PknPZhPeOMM9YiNgAAgLnjSsn5dNdddyXJ3Fe6q/YHVup6XNiwYcPI5/fYzXHsUK/Xe1ySU5JclybpCwAAAADADkxjZe8bkzwgya8nKb1eb7/tLPeg9nf39Pv923dbdAAAAAAAU2jqKnuTPKK9f3uSW0fcBt7SPt7pNg8AAAAAALNiGit7AQAAgClw/PHHdx0CABMotdauY5hIr9cbBPzz/X7/vAlWrTfccMMuiAhYj7pupA5MH+MCMMyYAAwzJgArdT0utBO0lZXPz1Vl7+LiYtchAAAAa+Tkk0/uOgSYeddff32S5PDDD+84EgDGMVfJXgAAAGB8p5xySpJkaWmp40gAGMc0TtAGAAAAAMCE1l1lb7/fv08vCgAAAACAeaeyFwAAAABgBqy7yt77wwQOwEDXs2YC08e4AAAArHdzlewFAAAAxnfiiSd2HQIAE5irZO/i4mLXIQAAADvBVXrQjWOOOabrEACYgJ69AAAAwEhbtmzJli1bug4DgDHNVWUvAAAAML7TTjstSbK0tNRxJACMQ2UvAAAAAMAMmKvKXn2+gIGFhYUsLy93HQYwRYwLAADAeqeyFwAAAABgBsxVZe/i4mLXIQAAwLrjCjkAgPVhrpK9AAAAwPh82QOwvkj2AgAAACNt3Lix6xAAmICevQAAAMBIV111Va666qquwwBgTHNV2evyE2BgYWEhy8vLXYcBTBHjAgDc15lnnpkkWVpa6jYQAMaishcAAAAAYAbMVWXv4uJi1yEAAMBUcNUbAMDsUdkLAAAAADADJHsBAAAAAGbAXLVxAAAAAMb3ile8ousQAJjAXCV79SUDBhYWFrK8vNx1GMAUMS4AwH0dddRRXYcAwAS0cQAAAABGuvLKK3PllVd2HQYAY5qryt7FxcWuQwAAYA24Ygtg93jd616XJFlaWuo4EgDGobIXAAAAAGAGSPYCAAAAAMwAyV4AAAAAgBkg2QsAAAAAMAPmaoI2E3kAAwsLC1leXu46DGCKGBcA4L7OPPPMrkMAYAJzlewFAAAAxnfkkUd2HQIAE5irZO/i4mLXIQAA0HLVFcD0u+KKK5IkGzdu7DgSAMYxV8leAAAAYHyDoinJXoD1wQRtAAAAAAAzQLIXAAAAAGAGzFUbB33hgIGFhYUsLy93HQYwRYwLAADAeqeyFwAAAABgBsxVZe+gsTwAMB9c1QMA989rX/varkMAYAJzlewFAAAAxnfEEUd0HQIAE9DGAQAAABjp0ksvzaWXXtp1GACMSWUvAAAAMNI555yTJDnmmGM6jgSAccxVslffPmBgYWEhy8vLXYcBAAAAsGa0cQAAAAAAmAFzVdm7uLjYdQgw9VTAAwAAAKxPKnsBAAAAAGbAXFX2AgAAAON74xvf2HUIAExAshcAAAAY6fDDD+86BAAmMFfJXr1IAQAAYHwXXXRRkuTYY4/tOBIAxjFXyV4AAABgfOeff34SyV6A9WKukr2Li4tdh8A6pzocAAAAgGm1R9cBAAAAAABw/0n2AgAAAADMAMleAAAAAIAZMFc9ewEAAIDxnXPOOV2HAMAE5irZa3ItAAAAGN/BBx/cdQgATEAbBwAAAGCkzZs3Z/PmzV2HAcCY5qqy9+LNX+k6BGBqGA+AlYwLwDBjArPn2cc9ZOJ1LrzwwiTJcccdt9bhALALqOwFAAAAAJgBkr0AAAAAADNAshcAAAAAYAbMVc/enelPBMymhYWFLC8vdx0GMEWMC8AwYwIAsB7NVbIXAAAAGN/555/fdQgATGCukr0XbzajLjBgPABWMi4Aw4wJrE9rfUXrPvvss6bbA2DX0rMXAAAAGOm8887Leeed13UYAIxJshcAAAAY6ZJLLskll1zSdRgAjEmyFwAAAABgBsxVz9617l0ErF9m2AZWMi4Aw4wJAMB6pLIXAAAAAGAGzFVl78WbzagLDBgPgJWMC8AwY8I8cjUoAOvdXCV7AQAAgPEtLS11HQIAE9DGAQAAAABgBkxtZW+v1/vBJMcmOSrJEUm+JcneSZaT/HOSt/X7/T/vLkIAAACYbW95y1uSJCeddFLHkQAwjqlN9ib5xSQvGXr8tST3JNmQ5CeT/GSv13t3kuf1+/27xtmg/kvAgBm2gZWMC8AwYwI0Lr/88iSSvQDrxTS3cfhwkpcleXyS/fv9/v79fn+fJN+W5A3tMj+b5LSO4gMAAAAAmBpTW9nb7/f/dDvPX5fk1F6vd1iSFyQ5Iclvj7PNizebURcYMB4AKxkXgGHGhHng6k8AZs00V/buyJXt/YZOowAAAAAAmAJTW9k7hh9q7z/daRQAAAAwo/bee++uQwBgAusq2dvr9fZL8u1pJm47rn36D7uLCAAAAGbXBRdc0HUIAExg6pO9vV7vYUmuG/GrO5O8pt/v/9FuDgkAAAAAYOpMfbI3yTeS3Nj+fFCSvZLcneSsJP9zkg1pvg8MLCwsZHl5ueswgCliXACGGROgcfbZZydJXvayl3UcCQDjKLXWrmMYW6/X2yPJEUlekeT/S/K5JP+l3+9fvZ3lT0xyYpL0+/3Hb926dXeFCky5PffcM3fffXfXYQBTxLgADDMmQOOZz3xmkuSyyy7rOJJuGROAlboeF/baa68kKSufX1fJ3mG9Xu/3kvy3JP+a5Af7/f49O1ilvvXsa3Z9YAAAAHPAlZPzYdOmTUmSpaWljiPplmp/YKWux4UNGzYkI5K9e+z+UNbMm9r7H2hvAAAAAABzaz0ne68f+vk7OosCAAAAAGAKrIcJ2rbnUUM/f62zKAAAAGBGHXTQQV2HAMAEpjLZ2+v1HpDknn6/v1pD4Ze393cn+fA429VTChjourcOMH2MC8AwYwI0zj333K5DAGACU5nsTfLwJO/u9Xr/M8ml/X7/80nS6/X2SPJ9aRK9P9cu+6Z+v39zN2ECAAAAAEyHaU32Jsl/SvLHSdLr9e5M06ph/yQPGlrmvCSnjrvBizd/ZQ3DA9Y34wGwknEBppmr9KAbZ511VpLk9NNP7zgSAMYxrcneG5Icl+TpSZ6Q5LAkD01yZ5J/T9O24W39fv+DnUUIAAAAM+5jH/tY1yEAMIGpTPb2+/2tSfrtDQAAAACAHdij6wAAAAAAALj/prKyd1fR5wsYMMM2sJJxAQAAWO/mKtkLAAAAjO+www7rOgQAJjBXyd6LN5tlGxgwHgArGRdgWrlCD7rzpje9qesQAJiAnr0AAAAAADNAshcAAAAY6YwzzsgZZ5zRdRgAjGmu2jgAAAAA47vmmmu6DgGACcxVslevL2BgYWEhy8vLXYcBTBHjAgAAsN5p4wAAAAAAMAPmqrL34s1m2QYGjAfASsYF2BFXygEATLe5SvYCAAAA4/v2b//2rkMAYAKSvQAAAMBIr3/967sOAYAJ6NkLAAAAADADJHsBAACAkU499dSceuqpXYcBwJjmqo2DCSWAgYWFhSwvL3cdBjBFjAsAcF+f+tSnug4BgAnMVbL34s1m2QYGjAesT764BAAAYHu0cQAAAAAAmAGSvQAAAAAAM2Cu2jgAAAAA43vMYx7TdQgATECyFwAAABjpVa96VdchADCBuUr2mtQGGFhYWMjy8nLXYQAAAACsGT17AQAAgJFe+tKX5qUvfWnXYQAwprmq7D32Hdd2HQIAAACsG9f+739P4vM0989Fz//urkOAuaGyFwAAAABgBkj2AgAAAADMAMleAAAAAIAZMFc9e/WIAQYWFhayvLzcdRjAFDEuAMOMCdA463NPSZKcPuefp40JwHoxV8leAAAAYHynn3561yEAMIG5SvaaPRQAAABg93GVNexeevYCAAAAI215+5nZ8vYzuw4DgDHNVWUvAAAAML67b/9q1yEAMAGVvQAAAAAAM2CuKnv1iQEGzKYLrGRcAIYZE6Cx6b37JkmW5vzztDEBWC9U9gIAAAAAzIC5quw99h3Xdh0CAAAArBs37P/oJD5Ps3NcYQ2731wlewEAAIDxbXjG8V2HAMAEtHEAAAAAAJgBkr0AAADASJ/449PyiT8+reswABiTNg4AAADASPfctbXrEACYwFwlezUGBwYWFhayvLzcdRjAFDEuAMOMCdDY9N59kyRLc/552pgArBdzlew1eygAAACM79obb0/i8zQ7R9Ed7H569gIAAAAAzIC5quwFAAAAxveQ73lS1yEAMAHJXgAAAGCkQ5/a6zoEACagjQMAAAAAwAyYq8pejcGBAbPpAisZF4BhxgRobNq0KUmytLTUcSTdMiYA64XKXgAAAACAGTBXlb3HvuParkMAAACAdePaG29P4vM0k3N1NXRDZS8AAAAAwAyQ7AUAAAAAmAFz1cYBAAAAGN/B3/fUrkMAYAKl1tp1DLtLveGGG7qOAZgSZtMFVjIuAMOMCcAwYwKwUtfjwoYNG5KkrHxeGwcAAABgpDvuuCN33HFH12EAMKa5auNg9lAAAAAY37Vv+W9Jku8+6fc7joT15KLnf3fXIcDcUtkLAAAAADADJHsBAAAAAGaAZC8AAAAAwAyYq569esYAA13PmglMH+MCMMyYAI1N7903SbI055+njQnAejFXyV4AAABgfM95znO6DgGACZRaa9cx7C71qDf8XdcxAAAAAMwsV1UzL7qu+N+wYUOSlJXP69kLAAAAjHTXbbfkrttu6ToMAMYk2QsAAACM9O/n/1b+/fzf6joMAMYk2QsAAAAAMAMkewEAAAAAZsCeXQewO2kSDgx03UgdmD7GBWCYMQEam967b5Jkac4/TxsTgPVirpK9x77j2q5DAAAAgHXj2htvT+LzNONRZAfdm6tkLwAAADC+Q5707K5DAGACkr0AAADASAc/7ke6DgGACZigDQAAABhp61e+mK1f+WLXYQAwJsleAAAAYKRPveu1+dS7Xtt1GACMaa7aOGgUDgyYTRdYybgADDMmQGPTe/dNkizN+edpYwKwXqjsBQAAAACYAXNV2XvsO67tOgQAAABYN6698fYkPk+zY66mhumgshcAAAAAYAbMVWUvAAAAML5DN27qOgQAJiDZCwAAAIz0kMf8UNchADCBuUr26h8DDJhNF1jJuAAMMyZAY8uWLUmSI444ouNIumVMANaLuUr2AgAAAOM77bTTkiRLS0sdRwLAOOYq2Wv2UAAAABjftTfensTnaXbM1dQwHfboOgAAAAAAAO4/yV4AAAAAgBkg2QsAAAAAMAPmqmev/jHAgNl0gZWMC8AwYwI0rnh4M0Hbxo3z/XnamACsF3OV7AUAAADGt3Hjxq5DAGACc5XsNXsoAAAAjO/2G7YkSfbd8P+zd/fRltXlneC/l0KCJSLRo63XN+xgKAkxpqV6XMmExIntMp3IbdPXbfcgaWYSCDNZ6NKZaTEzY4g9k5ZxRhu7kyaiaVQqWW7PpBqko8P40pImYQadqK14K6AhvFwtvJaIWhCkOPPHubc5Hg/UPqdu1e+cvT+ftVi37rnnnP3961lrPzz7+Z1WOAnzztPUMB/s7AUAAAAmuuPa38sd1/5e6RgANKTZCwAAAADQApq9AAAAAAAt0KmdvfbHAFucpguMUxeAUWoCDK3u3Zkk6Xf8flpNABaFyV4AAAAAgBbo1GTvyp610hEAAABgYXznJ/9xEvfTPDZPUsP86FSzFwAAAGjupFN/rHQEAKZgjQMAAAAw0Xdu/2K+c/sXS8cAoCHNXgAAAGCiuz763tz10feWjgFAQ5q9AAAAAAAt0KmdvRaGA1t6vV42NjZKxwDmiLoAjFITYGh1784kSb/j99NqArAoTPYCAAAAALRApyZ7V/aslY4AAAAAC2Nt/8Ek7qd5dJ6ihvnSqWYvAAAA0NxzzvlvS0cAYAqavQAAAMBEO5dPKx0BgCnY2QsAAABMdN+tn8l9t36mdAwAGloaDAalMxwrg/X19dIZgDnhNF1gnLoAjFITYGh1dTVJ0u/3CycpS00AxpWuC8vLy0myNP66yV4AAAAAgBbo1M5ep4cCAABAc2v7DyZxP82ju+bcXaUjACNM9gIAAAAAtIBmLwAAAABAC3RqjQMAAADQ3Km//IbSEQCYQqeavfbIAFtKn5oJzB91ARilJsAW99GJmgAsDmscAAAAgImuv/76XH/99aVjANBQpyZ7nR4KAAAAza1d8S+SJLu+/pzCSZgXnpqG+WayFwAAAACgBTR7AQAAAABaQLMXAAAAAKAFOrWz114ZYIvTdIFx6gIwSk2AodW9O5Mk/Y7fT6sJwKLoVLMXAAAAaO7yyy8vHQGAKXSq2buyZ610BAAAAFhA7qe7ztPSsBjs7AUAAAAmOvDZT+bAZz9ZOgYADXVqshcAAABo7p6bPpwkefKLXlo4CQBNmOwFAAAAAGgBzV4AAAAAgBbo1BoHy8SBLb1eLxsbG6VjAHNEXQBGqQkwtLp3Z5Kk3/H7aTUBWBQmewEAAAAAWmBpMBiUznCsDHa//ROlMwAAAMDC+N53v5UkedwTnlQ4CaV5Whq+X+mJ/+Xl5SRZGn+9U2scAAAAgOY0eQEWizUOAAAAwEQbn/5oNj790dIxAGhIsxcAAACYaOPT12fj09eXjgFAQ51a42C/DLCl9G4dYP6oC8AoNQGGVvfuTJL0O34/rSYAi8JkLwAAAABAC3Rqsndlz1rpCAAAALAw1vYfTOJ+uus8KQ2Lw2QvAAAAAEALdGqyFwAAAGju+f/175SOAMAUNHsBAACAiXaccGLpCABMoVPNXjtmgC1O0wXGqQvAKDUBhq666qokyfnnn180R2lqArAo7OwFAAAAJrruuuty3XXXlY4BQEOdmux1eigAAAA0t7b/YBL3013mKWlYLCZ7AQAAAABaQLMXAAAAAKAFNHsBAAAAAFpgaTAYlM5wrAzW19dLZwDmhNN0gXHqAjBKTQBGqQnAuNJ1YXl5OUmWxl832QsAAAAA0ALHz/KhqqpO3vznd+u6PrSNeY4qp4cCAABAc1/7VJ0kefrPVoWTUMI15+4qHQGY0qyTvfcmOZBkeRuzAAAAAHPk3i/dlHu/dFPpGAA0NNNkb5LvJHmorus7tzMMAAAAAACzmXWy96+S7KyqatZmMQAAAAAA22jWZm+d5HFJ/sE2ZgEAAAAAYEazTua+Pck5SX6/qqpv1nX98W3MdNRYLA5s6fV62djYKB0DmCPqAjBKTYCh137kyUmSqzt+P60mAIti1mbvJUk+keQFSa6vqurzSf48ydeTHHq0D9V1/dYZrwcAAAAcY1dffXXpCABMYdZm76VJBkmWNn//iSQvfIz3L22+v2izd2XPWsnLAwAAACwMT0jD4pm12fv+DJu3AAAAQEutf+wDSZLll51XOAkATczU7K3r+vxtzgEAAADMmftu+4skmr0Ai+K40gEAAAAAADhys65xWEh2zQBbnKYLjFMXgFFqAgyt7t2ZJOl3/H5aTQAWxRE1e6uqel6SNyT5e0meneTEuq6PH/n7KUlel+F+37fVdf29I7keAAAAAACTzdzsrarqVRke1LYzydLmy993aFtd1/dWVfVfJPmZJLck+T9nvd52WNmzVvLyAAAAsFBu+/awbeB+uns8HQ2LaaZmb1VVu5LsSXJikt/f/PcfJ3nKhLdfmeTsJL+Uws1eAAAAoLnTfuXS0hEAmMKsk73/Q4aN3nfWdf3fJUlVVYce5b0f2/z5d2e8FgAAAAAAh3HcjJ/7+QxXNvxvh3tjXdf7k3w3w52+AAAAwIK46yPvyV0feU/pGAA0NOtk79OTfHuzkdvE3yQ5acZrbRv7ZoAtTtMFxqkLwCg1AYZW996eJOl3/H5aTQAWxayTvd9N8oSqqnYc7o1VVT0xySlJDsx4LQAAAAAADmPWyd4vJvnpJC9O8v8e5r2vybCp/JkZr7VtnB4KAAAAza3tP5jE/XRXeCIaFt+sk711kqUk/6yqqkf9jqqqfjzJ2zLc77tnxmsBAAAAAHAYs072/n6SX0vysiQfr6rqXVvftdngfW6SX0hyfpLHJ/kPST54pGEBAACAY+eEJ/VKRwBgCkuDwWCmD1ZV9fQk1yY5K8PJ3Ynfn+SmJOfUdV16k/lg99s/UTgCAAAAwHyyxgGaK31w4/LycjLsvX6fmZu9SVJV1fEZTu/+kyS7k5yw+adDST6d5Kok763r+qGZL7J9Buvr66UzAHOidFEG5o+6AIxSE4BRagIwrnRdeLRm76xrHJIkm03c9yR5T1VVO5I8OcM9wN+YkwYvAAAAMKO3vOUtSZK3vvWthZMA0MQRNXtH1XV9KMnXt+v7jganhwIAAEBza5/8dJLkc+6nO8EaB1h8x83yoaqqvlJV1U1TvP9Pq6r68izXAgAAAADg8Gad7D01yYlTvP9ZSZ4z47UAAAAAADiMmSZ7Z/C4JA8fo2sBAAAAAHTOtu3sfTRVVZ2c5GlJvnm0rwUAAABsnxN7zyodAYApNGr2VlX1wiQvGnv58VVV/cpjfGwpySlJfjnJjiQ3z5RwG1k0Dmzp9XrZ2NgoHQOYI+oCMEpNgE3nvrt0grmgJgCLoulk76uSvGXstZOT/JsGn11K8mCSfz5FrqNixemhAAAAAD/AgBy0Q9Nm7+1Jbhj5/WeTfC/Jnz/G215VAQAAIABJREFUZx5Ocl+SLyb5QF3X+2YJCAAAAJRxe/8dSZJTV99YOAkATTRq9tZ1/b4k79v6vaqqh5McqOv6pUcrGAAAAFDWAxt3lY4AwBRmPaDtv0py/3YGAQAAAABgdjM1ezcnfQEAAAAAmBMzNXurqjoxyXOSPFDX9R1jf9uZ5Lcy3Ov7Q0k+muR/rev6O0eY9YhZNg5scZouME5dAEapCTC0undnkqTf8ftpNQFYFMfN+LlfT/KlJL85+mJVVTsyPMjtv0/yd5P8RJJ/muRjVVXNujICAAAAKOCMM87IGWecUToGAA3N2oB9xebPD4y9/o+S/J0M9/levvnzjUl2J7kgyb+e8XrbYmXPWsnLAwAAwGI5/b9M4n66zTwFDe0ya7P3Rzd/fm7s9X+cZJDkf67r+h1JUlXVLUk+lGEjuGizFwAAAACgrWZd4/DUJPeN7uGtquq4JGdv/nr1yHuvSfJwkh+b8VoAAABAAV/5o9/JV/7od0rHAKChWZu9J2R4+NqoH09yUpIv1XV9z9aLdV0/lOTeJE+c8VoAAABAAQ9+ayMPfsvBZACLYtY1Dl9N8pyqqn6krusvb772S5s//3TC+5+Q5BszXmvb2EMDbHGaLjBOXQBGqQkwtLp3Z5Kk3/H7aTUBWBSzNnv/NMm5Sf6Pqqp+Lclykosz3Nf7kdE3VlX1/AyngNePICcAAAAAAI9h1mbv/57kNUlemWT/5mtLSW5Jct3Ye//+5s//Z8ZrbRunhwIAAEBza/sPJnE/3WaegoZ2mWlnb13Xn0/yqiR/nWGTd5Dkk0leWdf1w2Nvv2Dz58dmDQkAAAAceyc994yc9NwzSscAoKFZJ3tT1/WfJPnbVVU9Ncl36rq+f/w9VVUdn+G6hyT50qzXAgAAAI69Z/3Cr5WOAMAUZm72bqnr+uuP8beHknzuSK8BAAAAAMBjO+Jm7yKxhwbY4jRdYJy6AIxSE2DogguGmxmvvPLKwknKUhOARTFTs7eqql+Z5XN1Xb9/ls8BAAAAx943v/nN0hEAmMKsk71XZXgo27SKNnudHgoAAADNre0/mMT9dFt5AhraZ9Zm7w157Gbvk5K8IMkPJbk39vYCAAAAABxVMzV767r+ucO9p6qqnUnemOS3kny8ruv/ZZZrAQAAAABweMcdrS+u6/rgZoP30iS/XVXVK4/WtQAAAIDtd/JpP5mTT/vJ0jEAaOioNXtH/MsMVz688RhcCwAAANgmyy87L8svO690DAAamnVnb2N1Xd9XVdV9SV50tK91OBaPA1t6vV42NjZKxwDmiLoAjFITgFFqArAojnqzt6qqpyY5Jcl3j/a1DsfpoQAAANDcX773kiTJj/7q2wonYTsZhoP2OqrN3qqqTkjyrzZ//fzRvBYAAACwvR7+3oOlIwAwhZmavVVVveUwbzkxybOSvDzJUzPc2fvOWa4FAAAAAMDhzTrZe2mGDdzHsrT58/4kl9R13Z/xWgAAAAAAHMaszd7357GbvQ8luTfJf0zy4bquvznjdQAAAAAAaGBpMDjcgG5rDNbX10tnAOaE03SBceoCMEpNgKErrrgiSXLRRRcVTlKWmgCMK10XlpeXk0c2K/wnR/WANgAAAGBxdb3JC7BoOtXsXdmzVjoCAAAAQDHXnLurdATgKDpss7eqquds18Xqur5ju74LAAAAOLrWrnhjkmTXRe8onASAJppM9v7VNl1r0PB6AAAAAABMqUnz9QcW/c5ou74HAAAAAIAxS4PBoHSGY2Wwvr5eOgMwJ0qfmgnMH3UBGKUmwNDq6mqSpN/vF05SlpoAjCtdF5aXl5MJw7XHHfsoAAAAAABst8Y7dKuq+rkkZyf5dl3X72z4mTcmOSnJJ+q6/g8zJdxGK3vWSkcAAACAhXHP39qdxP10m1xz7q7SEYCjqNFkb1VVJya5OslvJblriu+/M8mlSd5XVdXjpk4HAAAAFPO0n1rJ035qpXQMABpqusbhHyZZTvLv67r+UNMv33zvp5KcmuRVU6cDAAAAijn04AM59OADpWMA0FDTZu8/SDJI8q9muMa/zHBZ8D+c4bMAAABAIbf+wW/m1j/4zdIxAGio6c7eF2/+/NgM19j6zFkzfHZb2UsDbCl9aiYwf9QFYJSaAEOre3cmSfodv59WE4BF0XSy92kZHsz27WkvUNf1fUm+neRvTftZAAAAAACaaTrZu5TmjeFH+/zSEXx+Wzg9FAAAAJpb238wifvptvDEM7Rf0wbuRpInVFX1w9NeYPMzJ21+BwAAAAAAR0HTyd7PJXlWklck+aMpr/H3N39+fsrPAQAAAAX1znp56QgATKFps/cjSX4pyf9UVdUf13X9N00+VFXVDyX5H5MMkvy7aYJVVfWcJL+c5OeT/ESGO38fTPKVzTyX13X91Wm+EwAAAGiud9YrSkcAYApN1zhcleRrSXYl+VBVVU883AeqqjopyYc2P3NPkvc1DVVV1bOT3J7knRk2mZ+d5IEkj0/ywiRvSvLFqqpe2vQ7AQAAgOl877vfyve++63SMQBoaGkwGDR6Y1VVr0yyN8OD1u5OcnmSD9d1/Zdj7/vRJOckuTjD1Q+DJL9c1/W1TUNVVXVqhhO8f5Jho/njdV1/s6qqEzKc9P3dJM9Lcl+S0+u6/lqDrx2sr683jQC0XK/Xy8aGVeLAI9QFYJSaAEOrq6tJkn6/XzhJWWoCMK50XVheXk6Gfdrv07jZmyRVVZ2f5F8n+aEMm7hJ8jdJvrn57x/e/NvWxf4myW/Udf0H04StqupJSU6t6/pzj/L3XUn+IsmJSS6t6/q3G3ztYPfbPzFNDAAAAOi0tSvemCTZddE7CidhO1xz7q7SEaA15rXZ23SNQ5KkruurkvxneWT/7lKGDddnbP534shF/l2Sl0zb6N28zrcerdG7+fe1JDdt/vriab8fAAAAAKBtmh7Q9p/Udf35JK+sqmo5yc8leUGSp2z++RtJvpTkU3Vd371dIR/FNzZ/7jjK1wEAAAAAmHtTN3u31HW9nuQPtzFLY1VVHZ/kpzd//UKJDAAAAAAA82TmZm9hv5Hk6UkeTvK+wlkAAACglZ72kleWjgDAFKY6oG0eVFX1wgz39T4+ybvqun59w48O1tfXj14wYKGUXqQOzB91ARilJgCj1ARgXOm68GgHtC3UZG9VVc9I8m8zbPR+JsmbDvP+C5NcmCR1XafX6x31jMBiOP7449UE4PuoC8AoNQGG7rzzziTJs5/97MJJylITgHHzWhcWZrK3qqonJ/lUkjOT3JrkZ+q63j/FVwx2v/0TRyUbAAAAtNHaFW9Mkuy66B2Fk7Adrjl3V+kI0BrzOtl73LGPMr2qqp6U5P/KsNF7R5KXTdnoBQAAAABotblv9lZV9YQkf5LkrCRfy7DRe0fZVAAAAAAA82Wum71VVT0+yYeT/FSSb2TY6L21bCoAAAAAgPkztwe0VVV1QpI/TvLSJPcmeXld1188ku+0mwbYUnq3DjB/1AVglJoAQ6t7dyZJ+h2/n1YTgEUxl83eqqp2JPnDJK9I8u0kv1DX9f9XNhUAAAB0y4UXXlg6AgBTWBoMBqUz/ICqqs5O8qnNXx9I8q3HePuddV3vbvC1g91v/8QRZwMAAABYNJ52hu1VeuJ/eXk5SZbGX5/Lyd58/y7hEzf/ezQPHOUsAAAA0EkP3HNnkuTEpz27cBIAmpjLZm9d1/8+EzrTAAAAwLFz+x+/M0my66J3FE4CQBPHHf4tAAAAAADMu7mc7D1a7KcBtpTerQPMH3UBGKUmwNDq3p1Jkn7H76fVBGBRmOwFAAAAAGiBTk32ruxZKx0BAAAAFsba/oNJ3E8vOk86Q3d0qtkLAAAANLf88+eWjgDAFDR7AQAAgIlOfv6LS0cAYAp29gIAAAATHVy/LQfXbysdA4CGNHsBAACAie649vdyx7W/VzoGAA11ao2DheTAll6vl42NjdIxgDmiLgCj1AQYWt27M0nS7/j9tJoALIpONXudHgoAAADNre0/mMT99KIy9AbdY40DAAAAAEALaPYCAAAAALRAp9Y4AAAAAM096xW/WjoCAFPQ7AUAAAAmOunUHysdAYApdKrZazE5sMVpusA4dQEYpSbA0M0335wk2b17d+EkZakJwKLoVLMXAAAAaO6yyy5LkvT7/cJJAGiiU83elT1rpSMAAADAwljbfzCJ++lF5Olm6KbjSgcAAAAAAODIafYCAAAAALSAZi8AAAAAQAssDQaD0hmOlcH6+nrpDMCccJouME5dAEapCTD0hS98IUly5plnFk5SlpoAjCtdF5aXl5Nkafz1Th3QBgAAADTX9SYvwKLpVLPX6aEAAADQ3H23fiZJcvLzX1w4CdO65txdpSMABXSq2QsAAAA0t/7xPUk0ewEWhQPaAAAAAABaQLMXAAAAAKAFOrXGwb4aYEvpUzOB+aMuAKPUBBha3bszSdLv+P20mgAsCpO9AAAAAAAtsDQYDEpnOFYGu9/+idIZAAAAYGE8cM+dSZITn/bswkmYlqeb4egqPfG/vLycJEvjr3dqjQMAAADQnCYvwGKxxgEAAACY6N5b/iz33vJnpWMA0JDJXgAAAGCir93QT5KccsZPFU4CQBOdavbaVwNsKb1bB5g/6gIwSk2AodW9O5Mk/Y7fT6sJwKKwxgEAAAAAoAU6Ndm7smetdAQAAABYGGv7DyZxP71oPNkM3WWyFwAAAACgBTo12QsAAAA097f/0SWlIwAwBc1eAAAAYKITTnla6QgATMEaBwAAAGCiA5/9ZA589pOlYwDQ0NJgMCid4VgZrK+vl84AzIler5eNjY3SMYA5oi4Ao9QEGFpdXU2S9Pv9wknKUhOAcaXrwvLycpIsjb9ushcAAAAAoAU6tbN3Zc9a6QgAAACwMNb2H0zifnqRXHPurtIRgIJM9gIAAAAAtIBmLwAAAABAC3RqjQMAAADQ3I+c91ulIwAwhU41e+2tAbaUPjUTmD/qAjBKTQBGqQnAorDGAQAAAJjogx/8YD74wQ+WjgFAQ52a7HV6KAAAADS3dsX7kiR/+NBPFE5CU55qhm4z2QsAAAAA0AKavQAAAAAALaDZCwAAAADQAp3a2WtvDbDFabrAOHUBGKUmwNDq3p1Jkn7H76fVBGBRdKrZCwAAADT3gQ98oHQEAKbQqWbvyp610hEAAAAAtpUnmYEtdvYCAAAAE93zZ9fknj+7pnQMABrS7AUAAAAmOvD5T+XA5z9VOgYADWn2AgAAAAC0QKd29tphA2xxmi4wTl0ARqkJMLS6d2eSpN/x+2k1AVgUJnsBAAAAAFqgU5O9K3vWSkcAAACAhbG2/2AS99PzzpPMwJZONXsBAACA5nZd9I7SEQCYgjUOAAAAAAAtoNkLAAAATPS1T9X52qfq0jEAaEizFwAAAJjo3i/dlHu/dFPpGAA01KmdvRaWA1t6vV42NjZKxwDmiLoAjFITYGh1784kSb/j99NqArAoTPYCAAAAALRApyZ7V/aslY4AAAAAC2Nt/8Ek7qfnkaeXgUk61ewFAAAAmjvucSeUjgDAFDR7AQAAgIl+9FffVjoCAFOwsxcAAAAAoAU6Ndlrnw2wxWm6wDh1ARilJsDQO9/5ziTJG97whsJJylITgEVhshcAAACY6MYbb8yNN95YOgYADXVqstfpoQAAANDc2v6DSdxPzxtPLgOPxmQvAAAAAEALaPYCAAAAALRAp9Y4AAAAAM0dv/Pk0hEAmMLSYDAoneFYGayvr5fOAMwJp+kC49QFYJSaAIxSE4BxpevC8vJykiyNv26NAwAAAABAC3RqjYPTQwEAAKC5uz7yniTJs37h1wonIUmuOXdX6QjAnOtUsxcAAABo7jt/fUvpCABMwRoHAAAAAIAW0OwFAAAAAGiBTq1xsNsG2FL61Exg/qgLwCg1AYZW9+5MkvQ7fj+tJgCLolPNXgAAAKC5ZzzjGaUjADCFpcFgUDrDsTLY/fZPlM4AAAAA0JinlGE+lZ74X15eTpKl8dft7AUAAAAAaAHNXgAAAGCiO6793dxx7e+WjgFAQ3b2AgAAABMdXP9y6QgATMFkLwAAAABAC3RqstdSc2BL6UXqwPxRF4BRagIMre7dmSTpd/x+Wk0AFoXJXgAAAACAFujUZO/KnrXSEQAAAGBh3H7oyUncT5fg6WRgFp1q9gIAAADNnbr6xtIRAJiCNQ4AAAAAAC2g2QsAAABMdHv/Hbm9/47SMQBoqFNrHOy7AbY4TRcYpy4Ao9QEGFrdeyBJ0u/4/bSaACwKk70AAAAAAC3Qqclep4cCAABAc2v7DyZxP32seTIZmJXJXgAAAACAFujUZC8AAADQ3M7lHykdAYApaPYCAAAAEz3nnN8oHQGAKXSq2WvnDbDFabrAOHUBGKUmAKPUBGBR2NkLAAAATHTxxRfn4osvLh0DgIY6Ndnr9FAAAABobu2zX07ifvrG1//npSMANGKyFwAAAACgBTR7AQAAAABaQLMXAAAAAKAFOrWz95pzd5WOAMwJp+kC49QFYJSaAEP//I6fSZK82f00wELoVLMXAAAAaO7Nb35z6QgATKFTzd6unx4KAAAAbeepXqDL7OwFAAAAJrrt/ZfmtvdfWjoGAA11arIXAAAAaO6hg/eVjgDAFEz2AgAAAAC0gGYvAAAAAEALdGqNgyXtwJZer5eNjY3SMYA5oi4Ao9QEGFrduzNJ0nc/DbAQOtXsXdmzVjoCAAAALIz1J56eZDHupw14AXSs2QsAAAA0t/yy80pHAGAKdvYCAAAAALSAZi8AAAAw0V++95L85XsvKR0DgIascQAAAAAmevh7D5aOAMAUOtXstawd2OKEbWCcugCMUhNgaHXvziRJ3/00wEKwxgEAAAAAoAU6Ndm7smetdAQAAABYGGv7DyYpdz/tCV2A6XSq2QsAAAA0d8oLXlI6AgBT0OwFAAAAJnr6z1alIwAwBTt7AQAAAABaoFOTvXb9AFucsA2MUxeAUWoCDK2uriZJ+v1+4SQANGGyFwAAAACgBTo12Vvq9FAAAABYRGv7DyaZ/X7aE7YAx5bJXgAAAACAFtDsBQAAAABogU6tcQAAAACae/ILf7Z0BACm0Klmr11BwBYnbAPj1AVglJoAm9xHAywUaxwAAACAie6///7cf//9pWMA0FCnJntnPT0UAAAA2m7S07DnnXdekqTf7x/rOADMwGQvAAAAAEALaPYCAAAAALSAZi8AAAAAQAto9gIAAAAAtECnDmibtGwe6KZer5eNjY3SMYA5oi4Ao9QEGHr1q19dOgIAU+hUs3dlz1rpCAAAALBtjvZQ02te85qj+v0AbC9rHAAAAICJDhw4kAMHDpSOAUBDnZrsBQAAAJq78MILkyT9fr9wEgCaMNkLAAAAANACmr0AAAAAAC3QqTUOR3txPbA4nLANjFMXgFFqAgCwiEz2AgAAAAC0QKcme1f2rJWOAAAAMHc8BcmjOe+880pHAGAKnWr2AgAAAM2trKyUjgDAFKxxAAAAACa6++67c/fdd5eOAUBDJnsBAACAiV7/+tcnSfr9fuEkADTRqWavPVTAFidsA+PUBWCUmgAALCJrHAAAAAAAWqBTk70re9ZKRwAAADhqPM0IAN1mshcAAAAAoAU6NdkLAAAANHfhhReWjgDAFDR7AQAAgIle/vKXl44AwBQ61ey1vwrY4oRtYJy6AIxSE2DotttuS5KcdtpphZMA0ESnmr0AAABAc5dcckmSpN/vF04CQBOdavau7FkrHQEAAHgMnsYDAJjdcaUDAAAAAABw5DR7AQAAAABaQLMXAAAAAKAFOrWzFwAAAGjuda97XekIAEyhU81ehz0AW3q9XjY2NkrHAOaIugAAP+jss88uHQGAKXSq2buyZ610BAAA6CzDF7B4vvCFLyRJzjzzzMJJAGiiU81eAAAAoLlLL700SdLv98sGAaARB7QBAAAAALSAZi8AAAAAQAto9gIAAAAAtECndvY6EALY0uv1srGxUToGMEfUBQAAYNF1qtkLAAAANPemN72pdAQAptCpZu/KnrXSEQAAYK54+g14LLt37y4dAYAp2NkLAAAATHTzzTfn5ptvLh0DgIY6NdkLAAAANHfZZZclSfr9fuEkADRhshcAAAAAoAU6NdlrHxmwpdfrZWNjo3QMYI6oCwAAwKIz2QsAAAAA0AKdmuxd2bNWOgIAwDHhiSYAAOieTjV7AQAAgOYuvfTS0hEAmIJmLwAAADDRmWeeWToCAFOwsxcAAACY6IYbbsgNN9xQOgYADXVqstfuOmBLr9fLxsZG6RjAHFEXAOAHvetd70qSnH322YWTANCEyV4AAAAAgBbo1GTvyp610hEAAI4aTzEBAEC3mewFAAAAAGgBzV4AAAAAgBbo1BoHAAAAoLm3ve1tpSMAMIVONXvtsQO29Hq9bGxslI4BzBF1AQB+0GmnnVY6AgBTsMYBAAAAmOj666/P9ddfXzoGAA11arJ3Zc9a6QgAdJSnSwCARfTud787SfLyl7+8cBIAmjDZCwAAAADQApq9AAAAAAAtoNkLAAAAANACmr0AAAAAAC3QqQPaHI4DbOn1etnY2CgdAwAA5trll19eOgIAU+hUsxcAAABo7pnPfGbpCABMoVPN3pU9a6UjANAxnioBABbZNddckyRZWVkpnASAJjrV7AUAAACa+8AHPpBEsxdgUTigDQAAAACgBTR7AQAAAABaoFNrHOxNBLb0er1sbGyUjgEAAACwbUz2AgAAAAC0wNJgMCid4VgZ3PmLZ5XOAAAAAAvjwIMPJUmefEKnHgwGFsCOK68tev3STwwvLy8nydL466o1AAAAMJEmL8BiscYBAAAAmOhDd23kQ3c56wJgUWj2AgAAABN96K5v5EN3faN0DAAa6tTzGKV3eQDzo/RuHWD+qAvAKDUBhpZWV5MkO67sF05SlpoALAqTvQAAAAAALdCpyd5DF5xTOgIwJ/aXDgDMHXUBGKUmwNBg374k7qfVBJg/nuCfzGQvAAAAAEALdGqyFwAAAGjufbufXzoCAFPQ7AUAAAAmevwODwQDLJJONXvt8gC2OE0XGKcuAKPUBBi66qqrkiTnn39+0RylqQnAovC/6AAAAICJrrvuulx33XWlYwDQUKcme7t+eijwCKfpAuPUBWCUmgBDg337krifVhNg/niCfzKTvQAAAAAALaDZCwAAAADQApq9AAAAAAAt0KmdvQAAAEBz9UtOLx0BgCl0qtlrcTOwpdfrZWNjo3QMYI6oC8AoNQEYpSYAi8IaBwAAAGCiK664IldccUXpGAA01KnJ3kMXnFM6AjAn9pcOAMwddQEYpSbA0P99074kyQWf+ZPCScpSE2D+eIJ/MpO9AAAAAAAtoNkLAAAAANACmr0AAAAAAC3QqZ29dnkAW5ymC4xTF4BRagIMPf61r02S7Ljy6sJJylITgEXRqWYvAAAA0NzVV3e7yQuwaDrV7D10wTmlIwBzwmm6wDh1ARilJgCj1ASYP57gn8zOXgAAAGCiy2/9ai6/9aulYwDQkGYvAAAAMNGN37gvN37jvtIxAGhIsxcAAAAAoAU6tbPXLg9gi9N0gXHqAjBKTYChpdXVJMmOK/uFk5SlJgCLwmQvAAAAAEALdGqy99AF55SOAMwJp+kC49QFYJSaAEOnfOXLSdxPqwkwfzzBP1mnmr0AAABAc7//4h8pHQGAKVjjAAAAAADQApq9AAAAwERvW7s7b1u7u3QMABrq1BoHuzyALU7TBcapC8AoNQGG/mJ1NUmy48p+4SRlqQnAojDZCwAAAADQAp2a7O366aHAI5ymC4xTF4BRagIMDfbtS+J+Wk2A+eMJ/slM9gIAAAAAtECnJnsBAACA5p5x4gmlIwAwBc1eAAAAYKLLX/S80hEAmII1DgAAAAAALdCpyV6Lm4EtvV4vGxsbpWMAc0RdAEapCTD0lre8JUny1re+tXCSstQEYFF0qtkLAAAANHfLLbeUjgDAFDrV7D10wTmlIwBzYn/pAMDcUReAUWoCDA327UviflpNgPnjCf7J7OwFAAAAAGgBzV4AAAAAgBbo1BoHAAAAoLnnPeHE0hEAmMLSYDAoneFYGayvr5fOAMwJp+kC49QFYJSaAIxSE4BxpevC8vJykiyNv26NAwAAAABAC3RqjUPXTw8FHuE0XWCcugCMUhNg6E3/8a+TJJf9+HMLJylLTYD5s+PKa0tHmEudavYCAAAAzf3Vdx8oHQGAKVjjAAAAAADQApq9AAAAAAAt0Kk1DnZ5AFtKn5oJzB91ARilJsDQ0upqkmTHlf3CScpSE4BF0almLwAAANDcGWecUToCAFNYGgwGpTNMVFXVE5O8NMnuJGdt/nzK5p9fUNf12pRfObjzF8/axoQAAAAAQAmln+AvPfG/vLycJEvjr8/zZO/PJ9lbOgQAAAAAwCKY52ZvktyT5NNJbk5yd5J3l40DAAAA3fH6z/5VkuTyFz2vcBIAmpjnZu+H67r+t1u/VFV1asEsAAAA0DlffeDB0hEAmMLcNnvruj603d9ZepcHMD9K79YB5o+6AIxSE2BoaXU1SbLjyn7hJGWpCcCiOK50AAAAAAAAjtzcTvYeDYcuOKd0BGBO7C8dAJg76gIwSk2AocG+fUncT6sJMH88wT9Zp5q9AAAAQHN/55STSkcAYAqtbvZWVXVhkguTpK7rwmkAAABgsVyy65mlIwBM1Ov1il7/+OOPL55hklY3e+u6fneSd2/+OiiZBQAAAADYHqUPTSx9cOPy8vLE1x3QBgAAAEz065/5cn79M18uHQOAhlo92TvO4mZgS+n/AwfMH3UBGKUmwNC9q6tJkh1X9gsnKUtNABZFp5q9XT89FHiE03SBceoCMEpNgKHBvn1J3E+rCTB/DHVOZo0DAAAAAEALaPYCAAAAALTAXK9xqKqqN/LrD4/8+5Sxvx2o6/rhYxTXTy+7AAAgAElEQVQLAAAAOuGnn3Jy6QgATGGum71Jvv4or//52O/PS3L70Y0CAAAA3fL65z+jdAQApjDvzd5tZXEzsMVpusA4dQEYpSYAo9QEYFHMdbO3ruul0hkAAACgq1772tcmSa6++urCSQBoYq6bvdvt0AXnlI4AzIn9pQMAc0ddAEapCTB0/+f2JXE/rSbA/PEE/2THlQ4AAAAAAMCR0+wFAAAAAGgBzV4AAAAAgBbo1M5euzyALU7TBcapC8AoNQGG/t4VVyRJdlx0UeEkZakJwKLoVLMXAAAAaO6ijjd5ARZNp5q9XT89FHiE03SBceoCMEpNAEapCTB/PME/mZ29AAAAwETVTftS3bSvdAwAGtLsBQAAAABoAc1eAAAAAIAW6NTOXrs8gC1O0wXGqQvAKDUBhpZWV5MkO67sF05SlpoALAqTvQAAAAAALdCpyd5DF5xTOgIwJ5ymC4xTF4BRagIM/eJ99yRxP60mwPzxBP9knWr2AgAAAM39ynOfVjoCAFOwxgEAAACY6P5DD+f+Qw+XjgFAQyZ7AQAAgIn+yc23Jknql5xeOAkATZjsBQAAAABogU5N9lrcDGzp9XrZ2NgoHQOYI+oCMEpNgKGl1dUkyY4r+4WTlKUmAIuiU83erp8eCjzCabrAOHUBGKUmwNBg374k7qfVBJg/hjons8YBAAAAAKAFOjXZCwAAADT36mc9pXQEAKag2QsAAABM9Opn9UpHAGAK1jgAAAAAEx148KEcePCh0jEAaKhTk70WNwNbnKYLjFMXgFFqAgz9N6urSZL++/qFk5SlJgCLwmQvAAAAAEALdGqy99AF55SOAMyJ/aUDAHNHXQBGqQkwNNi3L4n7aTUB5o8n+Ccz2QsAAAAA0AKavQAAAAAALdCpNQ4AAABAc699zlNLRwBgCp1q9trlAWxxmi4wTl0ARqkJMPSq0gHmhJoALAprHAAAAICJ7r777tx9992lYwDQUKcme7t+eijwCKfpAuPUBWCUmgBDr7tpX5KkfsnphZOUpSbA/PEE/2QmewEAAAAAWkCzFwAAAACgBTR7AQAAAABaoFM7e+3yALY4TRcYpy4Ao9QEGFpaXU2S7LiyXzhJWWoCsCg61ewFAAAAmrvwwgtLRwBgCkuDwaB0hmNlcOcvnlU6AwAAAABwhEo/wV964n95eTlJlsZft7MXAAAAmOjL33kgX/7OA6VjANCQNQ4AAADARG/+wl8nSeqXnF44CQBNmOwFAAAAAGgBzV4AAAAAgBbo1BqH0oubgflRepE6MH/UBWCUmgBDS6urSZIdV/YLJylLTQAWRaeavYcuOKd0BGBO7C8dAJg76gIwSk2AocG+fUncT6sJ881wHzyiU81eAAAAoLmLT3tG6QgATEGzFwAAAJjoZ3onl44AwBQc0AYAAABM9MX7DuaL9x0sHQOAhkz2AgAAABP99i13Jknql5xeOAkATXSq2WthN7DFabrAOHUBGKUmwNDS6mqSZMeV/cJJylITgEVhjQMAAAAAQAt0arL30AXnlI4AzIn9pQMAc0ddAEapCTA02LcvifvpeaoJnloGHovJXgAAAACAFujUZC8AAADQ3D89/ZmlIwAwBc1eAAAAYKKzfvik0hEAmEKnmr322gBbnKYLjFMXgFFqAgzdfPPNSZLdu3cXTlKWmgAsik41ewEAAIDmLrvssiRJv98vnASAJjrV7O366aHAI+bpNF1gPqgLwCg1AYYG+/YlcT89TzXBU8vAYzmudAAAAAAAAI6cZi8AAAAAQAto9gIAAADw/7d352FyVWXix7+BgJCFTZBERODnBoIrMjijAjoCjhuL+A7KjIio44aC+y64AOICKOoQHA2EkScvKIuMgxkFCY+CG+pP8CfqD3FFFgETMAiEzB/nFF0U1Z2u7rpd3ZXv53nuc+vWPffcc29XTqreOvUeSUNgncrZa14bSS3Opiupk/2CpHb2CVJxzFVXAbD+LrsMuCWDZZ8gaaZYp4K9kiRJkiRp/HZZx4O8kjTTmMZBkiRJkiR1tXz5cpYvXz7oZkiSxsmRvZIkSZIkqatPfepTAOyxxx4DbokkaTwc2StJkiRJkiRJQ8BgryRJkiRJkiQNAYO9kiRJkiRJkjQEDPZKkiRJkiRJ0hBwgjZJkiRJktTV8ccfP+gmSJJ6YLBXkiRJkiR19chHPnLQTZAk9cA0DpIkSZIkqatly5axbNmyQTdDkjROjuyVJEmSJEldLVq0CIB99tlnwC2RJI2HI3slSZIkSZIkaQgY7JUkSZIkSZKkIWCwV5IkSZIkSZKGgMFeSZIkSZIkSRoCTtAmSZIkSZK6OvnkkwfdBElSDwz2SpIkSZKkrrbZZptBN0GS1APTOEiSJEmSpK7OP/98zj///EE3Q5I0To7slSRJkiRJXS1ZsgSA/fbbb8AtkSSNhyN7JUmSJEmSJGkIGOyVJEmSJEmSpCFgsFeSJEmSJEmShoDBXkmSJEmSJEkaAk7QJkmSJEmSulq0aNGgmyBJ6oHBXkmSJEmS1NUWW2wx6CZIknpgGgdJkiRJktTV0qVLWbp06aCbIUkaJ4O9kiRJkiSpq7PPPpuzzz570M2QJI2TwV5JkiRJkiRJGgIGeyVJkiRJkiRpCBjslSRJkiRJkqQhYLBXkiRJkiRJkobA7EE3QJIkSZIkTU9LliwZdBMkST0w2CtJkiRJkrraeOONB90ESVIPTOMgSZIkSZK6Wrx4MYsXLx50MyRJ42SwV5IkSZIkdXXhhRdy4YUXDroZkqRxMtgrSZIkSZIkSUPAYK8kSZIkSZIkDQGDvZIkSZIkSZI0BAz2SpIkSZIkSdIQmLVmzZpBt2GqrDMXKkmSJEmSJGnozep8Yp0Z2RsRP6TcABcXF5dZ9gkuLi6di/2Ci4tL+2Kf4OLi0r7YJ7i4uHQu06RfeIB1JtgrSZIkSZIkScPMYK8kSZIkSZIkDYF1Kdi7aNANkDSt2CdI6mS/IKmdfYKkdvYJkjpNy35hXZqgTZIkSZIkSZKG1ro0sleSJEmSJEmShpbBXkmSJEmSJEkaArMH3YBeRMQC4F3A84FtgL8A3wNOysxvTqLeTYC3Ay8CtgNWAT8GPpeZ50y23ZKa0e8+ISK2Ag4Eng08uda5Gvgt8M1a76/603pJTWjqvULHOdYHvgvsWp86JjOP7kfdkvqryT4hIrYBjgCeR/kMsR5wPfB9YGlmnj+Z+iU1o8G4wgHA4ZT3B1sCdwL/H7gIODEzb5hk0yX1UUTMB54J7AY8pa4fXHfvlJk/n2T9A4s1zpicvRHxeOBiRm78CmAe5U3VGuDdmXn8BOp9GLAc2KE+dTuwESOB8M9l5usm0XRJDWiiT4iIu7n/l2C3AxvWBcobtldk5lmTaLqkhjT1XqHLeY4ETmx7ymCvNA012SdExIuB/wDm16f+WuucW7e/mZnPnmDTJTWkoc8Q6wFnAIe0Pb0SmAOsX7dvAZ6Tmd+feOsl9VNE7A+cO8ruSQV7Bx1rnBFpHCJiY+ACSof8I2CXzNwU2Bz4BDALODYi9umx3lnAOZSbfx3wtMycT3nT9nbgXuC1EfGqPl2KpD5oqk+gdLzLgUOBhbU/mAM8nfIN3EbAGfVNoqRppMF+ofM8DwM+BPwGcISONE012SdExHOBsyifGb4A7JiZczNzHmU034uAr/XlQiT1TYP9wqsYCfSeBGydmZtQPjs8h/IrwS2As2pgWNL0cSPl/+xjgFf3o8LpEGucKWkc/o0y5Pl24AWZ+QeAzFwBvDUiHgHsDxwHLOuh3v2A3Sk3+oDM/HGt907gYxHxUOBI4IMRcXpm3tWvC5I0KU31CXtm5vL2JzJzNfDt+qbvKuAhwFHAYZO+Ckn91FS/0OnTlBFAhwCfmlSLJTWpkT6h/iTz85TResdm5nva92fmn4Gv9OUKJPVbU+8VXlrXF2fmUa0nM/Me4OsRcShwCfAI4PGUQSSSBu+rmXleayMitu9TvQOPNc6Ub5Va35J9qdUhd/hYXT85Ih4zgXq/0br5HT5O+SnHAuBZPdQrqVmN9Amdgd6OfTcxMkpn19HKSRqYpt4r3CciXkj5EHhhZl4wkTokTZmm+oTDgIXA74GjJ948SQPQVL+wdV3/aJT9P2x7PHeUMpKmWB3Y1YSBxxqnfbC3JkxuBVa+PkqxKyhJ1QH+sYfqnzlWvfU/gKvrpsFeaRpouE9Ymz/X9fpjlpI0paaiX4iIucAplIkVjuj1eElTp+E+ofUB7pzMvHsCzZM0AA33C9fV9ZNG2d8679+An/VQr6SZaeCxxmkf7AV2ouTOgZGbcT+ZeS9wTd187HgqjYiHMJKUvWu9VaszHle9khrXSJ8wTnvW9VV9rFPS5E1Fv/AhYFvKz7avm8DxkqZOU58fNgKeWDd/FBE7RsRZEXFjRNwZEddGxOf6+DNQSf3T5HuF0+r6WRFxYo01EBGzI2Jf4PS6/4OZeWtvzZY0k0yXWONMCPYubHv8xzHKtfYtHKPMVNQrqVkD+bcbEfsBT6mbX+xHnZL6ptF+ISKeBLwR+AVwQm9NkzQATfUJ2wMb1MePBq4EDqb8LPtuykQsrwF+EhF7jbNOSVOjsfcKmfll4D3AakoezhsiYgVwJ3ARsBI4LDOP7anFkmaiaRFrnAnB3vacNqvGKPfXup434HolNWvK/+1GxDbAorp5QWZeNNk6JfVVY/1CnTX7VEr6ljc4Was0IzTVJ2zW9vhdwK3AvsC8Osv20yhfCm0CnB0RW4yzXknNa/ozxHHAy4E76vZ8RlK/zQW2rO8pJA23aRFrtLORpDFExDzgPOAhwG+AwwfbIklT7PXAbkBm5v8MujGSBmq9jscvy8xlmbkGIDO/AxxEmX17S+CVU99ESVOt5gP+KrAEuATYnfKlz/aU0f6bUCZ/+88BNVHSOmYmBHvvaHu88Rjl5tT17QOuV1Kzpuzfbs3Ndz4lfcNNwL6ZefNE65PUmEb6hYh4KPBhys8vj5pY0yQNQFPvFdrLXZ2Z3+wskJk/Bb5RN/s5SaykyWnyM8QngecB38jMF2Tm9zJzZWb+JjNPBQ4E1gAHR8Q/9dRqSTPNtIg1zm6i0j5rz3HxUEYSpnd6aF1fP8F6f9qneiU1q6k+4X4iYkPgHMrsmLcB+2TmaOeSNFhN9QvHUUbjvA9YUUf6t2tN9LJha19m+uWwNHhT8flhrPcE1wD7UCZ1lDQ9NNIvRMQmwGF186RuZTLz0oi4EtgV2A/47/HULWlGmhaxxpkwsvfnlG/BAHbuVqDmvnlM3fxZtzKdMvMmoDVCr2u9VWtmvHHVK6lxjfQJHcfPBs6ifEN/O/DczPxx702VNEWa6he2q+sPUUb3di4Pr/vf1facpMFr6vPDzcANPbRjzdqLSJoiTb1XeBQjuXl/PUa5a+t6+3HWK2kGmi6xxmkf7M3MlcAP6ubeoxTbHdi0Pn7Az6nGcMlY9dZJmVp/nF7qldSQhvuE1pu80yk/t1oFvDAzL59AUyVNkab7BUkzS8N9QitFw2PGKLNjXV/XQ72SGtRgv3Bv2+OHj1pq5AtkvxiWht/AY43TPthbfamuD4mIhV32v7Wuf9jjz6xb9e4TEU/osv/NlJ9oXs/IH0vS4DXSJ0TELGAR8FLgLuDAzPTfvjQz9L1fyMy9MnPWaAtl0kaAY9qekzQ9NPX54Yy63jkint25MyIex0iu3q/1UK+k5jXRL1wD/K0+flW3AhHxZODJdfO746xX0sw18FjjTAn2nkr5QDUfuDAiHgtl1suIOIEyAg/g3Z0HRsSauhzdpd7zKZ3tesC5EfHUesyDIuItwJG13Acy865+XpCkSWmqTzgROBy4B4jMvKiJxktqRFP9gqSZqZE+ITOXAf9TN0+PiL3rl8VExN9T8v2vR/k59xf7e0mSJqnv/UJm/pXyq0CAAyPitIjYth6zUUTsB5xHmS9pBbC471clacIiYsvWAmzetmuz9n31F8Dtx03rWONMmKCNzFxVO8lvUr4RuzoiVgDzKDdvDfDu+uarl3rXRMRBwHJgB+DyiLgd2IiRe/PvmXlany5FUh800SdExMOBN9XNNcCpEXHqGG1YMNH2S+q/pt4rSJqZGu4TXkIZifM4YBnw14hYTQkgQZmc5YU1CCRpmmiwX3gLJf/m04FXAq+MiDuAjRkZYLcSeHHN/S1p+rhplOc7UznuwDjTM02HWONMGdlLZv4E2AX4FCW5+YOAPwP/BeydmcdPsN7fA08EjqUkbZ9N6YgvoYzse+3kWy+p3xroE9r7ww2ArdeySJpmmnqvIGlmavDzw5+B3YB3AD+i5OycDVxN+Uzx+My8atIXIKnvmugXMvN2YC/gFZQvgG6q9a4CrqL8evBxfuEsrTsGHWuctWaNk8RKkiRJkiRJ0kw3Y0b2SpIkSZIkSZJGZ7BXkiRJkiRJkoaAwV5JkiRJkiRJGgIGeyVJkiRJkiRpCBjslSRJkiRJkqQhYLBXkiRJkiRJkoaAwV5JkiRJkiRJGgIGeyVJkiRJkiRpCMwedAMkSZL0QBHxcuCLwKWZuVfHvm8BewKHZebituf3Ai4BfpOZ209NSzVsImIf4B3ArsAmwCzaXmsRsQD4MLAvsIDymeLSzNwrIo4GPgCcnpkv70NbFgOHAsdk5tGTrU+SJGnYGeyVJElqUETMBv4FOBh4AvBg4A7gT8C1wGXAxZn5vYE1coaKiCcC+wPXtQe9u5Q7EtgMWJyZ101N62amiHgG8N+UXwCuBm4C1gCr6v7ZwMXATvWQW4G7gFumvLEDEBGbAUcCGHyWJEnTkcFeSZKkhkTEVsDXgKe0PX0nZaTkY4AdgecCf6EEI9v9BbgG+G3zLZ2xnkgZRXopsHiMckcC2wHfAq5rulEz3Bspgd4EXp6Zqzr270sJ9N4CPDUzf9mx/2bK6/b6PrXn+lrfzX2qb7I2o7zmAI4eYDskSZK6MtgrSZLUnDMpgd6VwIeAJZn5J4CImA/sDhwAPK/zwMw8Fzh36poqAbBzXS/pEuht339Jl0AvmXkKcEq/GpOZ7wLe1a/6JEmShp3BXkmSpAZExI7APnXzFZl5Tvv+zFwJfAP4RkS8ZarbJ41i47q+fYL7JUmSNEAGeyVJkprxuLbHF45VMDPv7HxurAnaxisinkYZFflUYC7wC+A04DOZuWaUYx4EvB74Z0qaiQ0pqST+CzihNTK545ijWcukXGubaCsi1gMOAV5GSc+wKeWn+5cBn8zM73aUb2//nh3bAM8E9mLkJ/cAl0REe5luk9/No6QyOAB4NPAg4HeUPLYfy8zfdbu+8YiI5wCvpPw9tqLku72O8vo4vVvdEXEg8GpGJku7iZK24hOZeeVazveCer7dgS2A24DvAqdk5tc7ynbev/Z7dWlt56Ft+w+NiPbtHTLzunG+Fp4KvA7YgzLB2+2U19jXKXmVr2kru5i1TNDWy3XW8i+n7d9WPf7NwJMon4+uAk7OzLM6jvsWZWLE1nbnPXMSOUmSNHDrDboBkiRJ64BtpvqENaB1KSUn8GxgI+DxwKeBE0c5ZivgcuATwN9RAp13U4KeRwE/q4G6frd1PiXQdwbwbMokdquAhUAA34mIN3QcdgOwoj6+u263L3dRgog3APfWcrd2lLnfpGIRsRMl0PcRSvqNOcA9wCOBI4Cf1AB6r9e3YUQsoQSMX0R5PawC5lEClB8CDu84Zr2IOB34MiVP7ubAX+uxLwW+HxGvHeV8G0TEmcAFwAuBrev5tgKeD1wUER/tOKx1T7rdq1soOaRvoEwuCCX3dPu9XD2O+zCrnvdy4F8peZT/RvlC4UnAO+khZcMEr7OzjvfV4/eoT82l/E2+VCf2a3cL988d3Pmac7SzJEkaOIO9kiRJzfhh2+PP1EDqVNkKOBX4HLAwMzejBAs/Xfe/MSJ27nLcGZSg262UIOvczNwE2A34aa3jvIjYss/tbQV5r6QENudk5qaUUZrvpQQST24PtGbmAuBNdfM7mbmgY/lOZn68lmuNmD2wo8yBrfoiYlPKZHrbAWcDTwA2ysx5wCOAL9Xr/3JEdE6mtzYnAv9Sr+MYYEFmblbr/j/A24A/dhzzdsoo5zXA+4DNM3Nz4GG1fesBp0TEHjzQCZRR0r+i/B3n1fu5CWVE7Urg7RHxktYBrXsyyr06MDPfVPd/vO5f2nEvxzPi+a31ugA+C2yfmZvW19hDgdcAD8gDPIaer7NDa4K/9wEPrv9OFgCtlCvHRcQWrcL19bJb23bna+7jSJIkDZhpHCRJkhqQmddGxBmUgN2+wO8j4jLgCuD7lADlTQ2dfg7w+cw8oq09t1GCvHtRUky8CLi6tT8ingE8p26+pP3n75n5g4jYG/h/lNGTbwTe34+GRsSzgf2Ba4BnZeZf2s57K/CRiFgNHEcZ9fn8fpy3i7cB2wNnZeZL23dk5rXAITXw10rFMK7AXg2qt0bgvi4zF3XU/evOumoqidYI149m5ofbyv+hBi8XAk8HPszIqFQi4lGUIPhNlPv5u7ZjVwKfi4hbgbOA99R14+oXBEfXzeMy893t+zPzesoXFOOtrx/XuSnw3sz8SNuxN0TEyyjpGlojhM8Yb7skSZIGzZG9kiRJzXkV8ElKSoENgX+kBJ7OA26MiO9FxCERMauBcx83yvPn1/UuHc8fVNc/6JbnNDNvAP69bkbn/klo5X09rT3Q2+E/6/qZEbF+H8/drR2fGKPMl+p67x7q/VdgFvDzzkDvGPamjE69izJ69X4yczUl9QPAMyJiQdvul9XzLR1jtO05lPQJO0fEwnG2abIOonwJcSsjbZ+MflznncBJnU9m5ipKWhF44L8TSZKkac2RvZIkSQ3JzLuAt9S8oQdQRgs+hZIDdhblJ+FnAvtFxMGZee+olfXmljoatZs/1PXmHc8/ua4vGaPeiykjTh8dEXMz844xyo7XP9T1eyPibWspO4eSz/fGPpz3PhGxLSU9AsDXuky81bJhXW/bQ/WtHMdf6+GY1t/iJ3V0czfLKWkh1q/lW/W37uehEfHiMc6xQV1vC1zfQ9smqnUfLqnB1Mnqx3X+bIzX8Gj/TiRJkqY1g72SJEkNy8wbKT9RPxUgIrYGXkBJhbAt8GLg28DJfTrlyjH23VnXG3Q838op/AdG9/u6ngVsychkXZPRGnE53jy4c/pwztHaAPCQPrdh67r+bQ/HrPVvkZl3RsTNtf72fNCta5lfl7Vp4n52M5H7MJZ+XOdE/p1IkiRNawZ7JUmSplhNifD5iDgPuIoSCHsF/Qv2TsZGU3y+VlqxAzLzvCk+d2cboEyEdtuA2tFpIn+L1rUclZkPSFEwRNaV65QkSeqJOXslSZIGJDNvZiSH7qMH2RbKRFcADx+jTCvVwRrg5rbn76nrsYKTm47y/A3jOG/Tbmh73O92tOrerodj1vq3iIiNKCkt2su3n2+Q97ObidyH8dQ33a5TkiRpoAz2SpIkDVYrFcJdA20FXFnXe44xYdyz6voXHblOWyNhH0YXtb5dR6nz8rr+p/E2tE0rx/HaJrgbs1xm/pqR4OFE2jGWKyZQb+tv8aiI2GaUMnsw8iu9K9ueb93P5/RwvqnQug97RcTGfahvUNd5X17thiZWlCRJmhSDvZIkSQ2IiB0i4hFrKTMH2L9u/rj5Vo3pnLreGdivc2fNM/yaupkdu39a17tFxEIe6BBGn9RscV3vGxFjBu4ionOyrBV1vbZ8v+Mp12rHW8cIsBIRsyJivPmFAZZQRkLvGBH/Ns5jllHavAHwgEnrImJ94H1187LM/FPb7jPq+XZa2/m63M8mnQOsokx49v4+1Deo61zR9riX14EkSdKUMNgrSZLUjJ2BayLiK1HcFwSNiLkR8QLgMmCH+vRA8/Vm5mXARXXzCxFxUA0qEhG7UgKQm1NGwHa29dvAH4ENgbMiYod63JwaiDsNuHWU814EfIUy6vbciHhbRNw34VhEbBER+0fEBcAnOw6/uq4fGxG7j3F5rXIvqekPujkeuJYy8dx36t/svhGoEfHwiHg1ZRTt/qPU0e36rqZOzAd8JiKOjoj7JoGrXwocHRGvaTvmDuDYuvnGiHhPRMyr5bcBzgKeThll+t6O8/0MOLFufjYijouI+0ZcR8T8iNgnIs4Ezh7vdUxWTVlyTN18Z0ScEhH3pWCIiIUR8eaIGFcgeFDXWfM5/7FuHtaveiVJkvrFCdokSZKacTewPnBAXYiIVZR0De35a1cD78/Mr0x5Cx/oZZSg7hMpAbI7I+JuYH7dfytlIrU/tx+UmfdExBsoozf3BK6NiBXAHMr7zS9Q7sWhY5x3PUoQ9QTgoxHxl3rM/LZyizvO+8uIWE5JaXBFRNwCrKy7D87MVuqA/wBeCrwY2C8ibqTc9ysy8+Ba120RsS9wAbATsBRYHRG31etoTz2wZpTrGM2RwBZAAB8APlDr3QCYW8sc03HMx4HHUu7Nh4Fj6j3djBIYvxc4IjOXdznf22t7Xwu8kxJcXVHbvQkj6Sy+1eN1TNYJwALK/Xg98Pr6d55V2wVweg/1Deo6P08ZnfyJiPggI/mrT3KyOEmSNGiO7JUkSWpAZn4deAzwVuA84Fd11zxKjtsrgZOAJ2TmsV0rmWKZeRPw95Q2/4ASsN4Q+CWlrTtn5uWjHHsusA9wCSXguj4lNcXhmXn4Ws57R2YeADyfMsr3j5QA6waU+5aUUZRHdDn8QOCzwK8p93a7utw3gjczL6YE3C+lpBLYppZZ0NGOXwFPAl5Xr+NWSmD+HuD/AouA5wFnjnU9Xa7vb5n5z5T0GF+ljI6eS7lPVwDvoYx+bj9mdWYeChxECcDfVq/vesrI3r/LzM+Ocr7Vmfk6yujfM4HfAA+q9+S3lID2G2rdUyYz12TmUZTg/FLgD5Rg7d8o/x6OBT7SQ32Dus4PAu+gvCZmMfKaM62DJEkauFlr1vQ6MEGSJEmSJN4jlE8AAACGSURBVEmSNN04sleSJEmSJEmShoDBXkmSJEmSJEkaAgZ7JUmSJEmSJGkIGOyVJEmSJEmSpCFgsFeSJEmSJEmShoDBXkmSJEmSJEkaAgZ7JUmSJEmSJGkIGOyVJEmSJEmSpCFgsFeSJEmSJEmShoDBXkmSJEmSJEkaAgZ7JUmSJEmSJGkI/C/ry/DbSSLdLgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1728x1152 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "n_clusters = 6\n",
        "\n",
        "# Create an instance of the class KMeans, fit the model, and predict cluster memberships\n",
        "kmeans_model = KMeans(n_clusters=n_clusters,\n",
        "                      init=init_type,\n",
        "                      n_init=n_init,\n",
        "                      max_iter=max_iter,\n",
        "                      random_state=1)\n",
        "y_pred = kmeans_model.fit_predict(df_final)\n",
        "\n",
        "y_pred_kmeans = y_pred\n",
        "\n",
        "# Retrieve and count unique clusters\n",
        "kmeans_clusters = np.unique(y_pred)\n",
        "n_clusters      = kmeans_clusters.shape[0]\n",
        "\n",
        "# Calculate silhouette values for all samples\n",
        "silhouette_vals = silhouette_samples(df_final, y_pred, metric='euclidean')\n",
        "\n",
        "# Create the silhouette plot\n",
        "plt.figure(figsize=(24,16)) \n",
        "\n",
        "y_ax_lower, y_ax_upper = 0, 0\n",
        "yticks = []\n",
        "\n",
        "for ix_clusters in kmeans_clusters:    \n",
        "    c_silhouette_vals = silhouette_vals[y_pred == ix_clusters]\n",
        "    c_silhouette_vals.sort()\n",
        "    y_ax_upper += len(c_silhouette_vals)\n",
        "    plt.barh(range(y_ax_lower, y_ax_upper),\n",
        "             c_silhouette_vals,\n",
        "             height=0.7,\n",
        "             edgecolor='none')\n",
        "    yticks.append((y_ax_lower + y_ax_upper) / 2)\n",
        "    y_ax_lower += len(c_silhouette_vals)\n",
        "    \n",
        "silhouette_avg = np.mean(silhouette_vals)\n",
        "plt.axvline(silhouette_avg,\n",
        "            color='k',\n",
        "            linestyle='--')\n",
        "plt.yticks(yticks, kmeans_clusters + 1)\n",
        "plt.xticks(size=24) \n",
        "plt.yticks(size=24)\n",
        "plt.title('K-Means Clustering: Silhouette Plot', size=24)\n",
        "plt.ylabel('Cluster', size=24)\n",
        "plt.xlabel('Silhouette coefficient', size=24)\n",
        "plt.show()    "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that majority of the observation have good Silhouette coefficient, however, majority of observations are within second cluster."
      ],
      "metadata": {
        "id": "PxiGqsB2yuoq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Gaussian Mixture Models"
      ],
      "metadata": {
        "id": "PRzrmd4Ntufy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This algorithm requires setting the number of components, cluster in advance, so the first step is to calculate Average Silhouette Scores for model that has up to 30 clusters."
      ],
      "metadata": {
        "id": "yS0oYNW9zknM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the bounds for the number of clusters\n",
        "n_clusters_lb = 2\n",
        "n_clusters_ub = 30\n",
        "\n",
        "# Initiate the average silhouette score vector \n",
        "gmm_silhouette_avg = []\n",
        "\n",
        "# Fit K-means model for different numbers of clusters\n",
        "for ix_clusters in range(n_clusters_lb, n_clusters_ub):\n",
        "    gmm_model = GaussianMixture(n_components=ix_clusters,\n",
        "                                n_init=10,\n",
        "                                random_state=1)\n",
        "    y_pred = gmm_model.fit_predict(df_final)\n",
        "    silhouette_vals = silhouette_samples(df_final, y_pred, metric='euclidean')\n",
        "    gmm_silhouette_avg.append(np.mean(silhouette_vals))\n",
        "\n",
        "# Create the elbow plot\n",
        "plt.figure(figsize=(16,8))    \n",
        "plt.plot(range(n_clusters_lb,n_clusters_ub), gmm_silhouette_avg, marker='s')\n",
        "plt.title('GMM Clustering: Average Silhouette Score Plot', size=16)\n",
        "plt.xlabel('Number of clusters', size=16)\n",
        "plt.ylabel('Average silhouette score', size=16)\n",
        "plt.xticks(size=16) \n",
        "plt.yticks(size=16)  \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "27oI94-R0KGy",
        "outputId": "a1005379-4cb8-4d7d-d147-3f803111291d"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the graph above we can conclude that we get best score for 5 components/clusters. However, we also have good scores for 9 clusters, as well as 15 clusters.\n",
        "\n",
        "We will pick 5 as it is the maximum, so let's see how individual observations fit in the clusters:"
      ],
      "metadata": {
        "id": "KgGlE6oE1qKb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "8017de01",
        "outputId": "86605bec-ead1-4fa8-84e7-5f714f774f70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 919
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 5760x2880 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Define the key inputs for the GMM\n",
        "n_components = 5\n",
        "n_init       = 10\n",
        "random_state = 1\n",
        "\n",
        "# Create an instance of the class GaussianMixture, fit the model, and predict the cluster membership\n",
        "gmm_model = GaussianMixture(n_components=n_components,\n",
        "                            n_init=n_init,\n",
        "                            random_state=random_state)\n",
        "y_pred = gmm_model.fit_predict(df_final)\n",
        "\n",
        "y_pred_gmm = y_pred\n",
        "\n",
        "# Retrieve and count unique clusters\n",
        "gmm_clusters = np.unique(y_pred)\n",
        "n_clusters   = gmm_clusters.shape[0]\n",
        "\n",
        "# Calculate silhouette values for all samples\n",
        "silhouette_vals = silhouette_samples(df_final, y_pred, metric='euclidean')\n",
        "\n",
        "# Create the silhouette plot\n",
        "plt.figure(figsize=(80,40)) \n",
        "\n",
        "y_ax_lower, y_ax_upper = 0, 0\n",
        "yticks = []\n",
        "\n",
        "for ix_clusters in gmm_clusters:    \n",
        "    c_silhouette_vals = silhouette_vals[y_pred == ix_clusters]\n",
        "    c_silhouette_vals.sort()\n",
        "    y_ax_upper += len(c_silhouette_vals)\n",
        "    plt.barh(range(y_ax_lower, y_ax_upper),\n",
        "             c_silhouette_vals,\n",
        "             height=0.7,\n",
        "             edgecolor='none')\n",
        "    yticks.append((y_ax_lower + y_ax_upper) / 2)\n",
        "    y_ax_lower += len(c_silhouette_vals)\n",
        "    \n",
        "silhouette_avg = np.mean(silhouette_vals)\n",
        "plt.axvline(silhouette_avg,\n",
        "            color='k',\n",
        "            linestyle='--')\n",
        "plt.yticks(yticks, gmm_clusters + 1)\n",
        "plt.xticks(size=24) \n",
        "plt.yticks(size=24)\n",
        "plt.title('GMM Clustering: Silhouette Plot', size=24)\n",
        "plt.ylabel('Cluster', size=24)\n",
        "plt.xlabel('Silhouette coefficient', size=24)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Well, we see that average score is good, but we have some observations that have negative score, which means that these samples are probably assigned to wrong clusters."
      ],
      "metadata": {
        "id": "o-yIpims2tlj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, let's take a look at Bayesian information criterion (BIC).\n",
        "This criterion gives us an estimation on how much is good the GMM in terms of predicting the data we actually have. The lower is the BIC, the better is the model to actually predict the data we have, and by extension, the true, unknown, distribution. In order to avoid overfitting, this technique penalizes models with big number of clusters."
      ],
      "metadata": {
        "id": "8Q-zhWA-3Q27"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the bounds for the number of clusters\n",
        "n_clusters_lb = 1\n",
        "n_clusters_ub = 30\n",
        "\n",
        "# Initiate the vectors of BIC scores\n",
        "gmm_bic = []\n",
        "\n",
        "# Fit GMM model for different numbers of clusters\n",
        "for ix_clusters in range(n_clusters_lb, n_clusters_ub):\n",
        "    gmm_model = GaussianMixture(n_components=ix_clusters,\n",
        "                                n_init=10,\n",
        "                                random_state=1)\n",
        "    gmm_model.fit(df_final)\n",
        "    \n",
        "    gmm_bic.append(gmm_model.bic(df_final))\n",
        "\n",
        "# Display the key information       \n",
        "print(f'Best BIC score: {round(min(gmm_bic),2)}')\n",
        "print(f'Optimal number of clusters: {gmm_bic.index(min(gmm_bic))+1}') \n",
        "print(100*'-')\n",
        "    \n",
        "# Create the elbow plot\n",
        "plt.figure(figsize=(16,8))    \n",
        "plt.plot(range(n_clusters_lb,n_clusters_ub), gmm_bic, marker='o', label='BIC')\n",
        "plt.legend(fontsize=16, facecolor='white')\n",
        "plt.title('GMM Clustering: BIC Scores', size=16)\n",
        "plt.xlabel('Number of clusters', size=16)\n",
        "plt.ylabel('SCore', size=16)\n",
        "plt.xticks(size=16) \n",
        "plt.yticks(size=16)  \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "AfCrIhwVt2Td",
        "outputId": "c1068941-38be-4398-c7f3-6680493036bd"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best BIC score: -274.98\n",
            "Optimal number of clusters: 12\n",
            "----------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The BIC score shows us that the optimal number of clusters is 12. However, our Average Silhouette Score showed us that 5 clusters give the best result, so GMM algorithm is not good for this dataset."
      ],
      "metadata": {
        "id": "8SmKJKvx4VRz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Hierarchical Clustering Algorithm"
      ],
      "metadata": {
        "id": "khfXH59ZM5Au"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Like in the previous two alrgothims, the challenge is to find the number of clasters. Let's draw the Average Silhouette Plot:"
      ],
      "metadata": {
        "id": "Ai7NafjvPCN-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the bounds for the number of clusters\n",
        "n_clusters_lb = 2\n",
        "n_clusters_ub = 10\n",
        "\n",
        "# Initiate the average silhouette score vector \n",
        "hca_silhouette_avg = []\n",
        "\n",
        "# Fit AgglomerativeClustering model for different numbers of clusters\n",
        "for ix_clusters in range(n_clusters_lb, n_clusters_ub):\n",
        "    hca_model = AgglomerativeClustering(n_clusters=ix_clusters,\n",
        "                                        linkage='ward')\n",
        "    y_pred = hca_model.fit_predict(df_final)\n",
        "    silhouette_vals = silhouette_samples(df_final, y_pred, metric='euclidean')\n",
        "    hca_silhouette_avg.append(np.mean(silhouette_vals))\n",
        "\n",
        "# Create the elbow plot\n",
        "plt.figure(figsize=(16,8))    \n",
        "plt.plot(range(n_clusters_lb,n_clusters_ub), hca_silhouette_avg, marker='s')\n",
        "plt.title('Hierarchical Agglomerative Clustering: Average Silhouette Score Plot', size=16)\n",
        "plt.xlabel('Number of clusters', size=16)\n",
        "plt.ylabel('Average silhouette score', size=16)\n",
        "plt.xticks(size=16) \n",
        "plt.yticks(size=16)  \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "rVcyXVCUPUHO",
        "outputId": "5ff1222e-5642-4384-bf1d-572581b19b78"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The best Average Silhouette score will be if we pick 3 clusters."
      ],
      "metadata": {
        "id": "xZ1nJ4jPaWUu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the key inputs for the AgglomerativeClustering model\n",
        "distance_threshold = 100\n",
        "linkage_type       = 'ward' \n",
        "\n",
        "# Create an instance of the class AgglomerativeClustering and fit the model\n",
        "hca_model = AgglomerativeClustering(linkage=linkage_type, \n",
        "                                    distance_threshold=distance_threshold, \n",
        "                                    n_clusters=None)\n",
        "\n",
        "y_pred_hier = hca_model.fit_predict(df_final)\n",
        "n_clusters = len(set(y_pred_hier))\n",
        "\n",
        "# Prepare inputs for the linkage matrix\n",
        "counts = np.zeros(hca_model.children_.shape[0])\n",
        "n_samples = len(hca_model.labels_)\n",
        "\n",
        "for i, children in enumerate(hca_model.children_):\n",
        "    current_count = 0\n",
        "    for ix_child in children:\n",
        "        if ix_child < n_samples:\n",
        "            current_count += 1  \n",
        "        else:\n",
        "            current_count += counts[ix_child - n_samples]\n",
        "    counts[i] = current_count\n",
        "\n",
        "# Compute the linkage matrix    \n",
        "linkage_matrix = np.column_stack([hca_model.children_, hca_model.distances_, counts]).astype(float)\n",
        "\n",
        "# Calculate the distance threshold (cut-off level) given the number of clusters\n",
        "distances_sorted = np.sort(linkage_matrix[:,2])\n",
        "distances_sorted = distances_sorted[::-1]\n",
        "n_color_threshold  = 0.5 * (distances_sorted[n_clusters-2] + distances_sorted[n_clusters-1])\n",
        "\n",
        "# Create a dendogram plot (and add the threshold line)\n",
        "plt.figure(figsize=(24,16))\n",
        "dendrogram(linkage_matrix, truncate_mode='level', p=4)\n",
        "#dendrogram(linkage_matrix, truncate_mode='level', p=4, color_threshold=n_color_threshold, above_threshold_color='k')\n",
        "plt.axhline(n_color_threshold, color='orange', linestyle='--')\n",
        "plt.title('Hierarchical Clustering Dendrogram (scikit-learn)', size=24)\n",
        "plt.xlabel('Number of points in node (or index of point if no parenthesis)', size=24)\n",
        "plt.ylabel('Dissimilarity measure', size=24)\n",
        "plt.xticks(size=24) \n",
        "plt.yticks(size=24)   \n",
        "plt.show()\n",
        "\n",
        "# Reset the color palette to the default values\n",
        "set_link_color_palette(None)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xizFeLMjNs9t",
        "outputId": "927dc53d-e4dc-4177-cb0b-d1896a2064e1"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Oy6VeI1fzuTt+zmxN94syADYRQTUALK5Rb6p9+seOTOvZ9pN5BfoeWyemG7dy5Px0/9m8PN2wAVvTjbU7y6x9XDPb/63yvXn1mKXWeu6MRRf2091mLF+zupRSXpHuJ+Ujl6QLaS4Z2/ZumX+MluP0se3uMmqtZ5ZSHp1umI5bJnl1kvTh5wnpfg6+lOBx0ug8L+l+fbAjs3pwzj3Xl2DWuZbMPt9Ggd7kF0TjVjXm7lKVLuHfv3863ht/dHx3zyqO7wrei9fqp/Pa/4M5y0bmXV9Gvy6dt53/nahPsv11O7vvMT/LqZkfVO+M6+usc+uyfnrajN6v4+ss9To5sns/vXjawhVeC0bn3oo+C+Y4OF1InSRPXM+QupTysHQ3ib2SesXx+GdZ8Wd6KWXvdL/s+NWx5RekOwdHXwSNjvFemR5UL+kaucrP3QvH/p72hS4AC87QHwCwuEaf839Uay1LeJw4ZRuXTZk37m/ShSjfTvKQJNeste5da712/x/vO84tvbR9bFillPukC6kvS3Jskpsk2b3WekDtb7SVrndh0gWta+Fr/fRWa7S9NVNr/UC6cbmPTtKmC/IOTPeT9hNLKSv5Sf/oPD97ief54TO2s7Dn4RL9fLYHjOPDkIyO73uWeHyP3am13rGlvK4rGVppLeyM6+sQRl907D9rhXW6FqzEj7K9Z/4LSik/t4772iOzh/daitV8pj8rXUg9uhHwdWqte46dS9cbW3fWZ9HOuEaO97Y+feZaACwsQTUALK7ReLvzevStWP/T+Qf0Tx9Za31nrXXyp95L/Q/4NGckubT/+4ar2M5aWGldRj/Pfl2t9Tm11m9N6b24mmM0zWgYg+uUUm6/xttOth+HqeFeKWW/afNHaq1n11pfW2t9WK31eulukvbafvETSim/vsz6jM7zfXe0713QqNfivLFklzPO7GrcZ+zvT479va7XkTlGvTfnDWuxmuFyzsj2nqTz2nb9ifok21+3/fqhOWZZ8Wu3E66v62l0fOYOGbLMa8HoPFzrz4KLktwv3Q0gr5fko6WUdfm8qbUeNytUXuImVvNeHH0W/d9a65tqrT+eWL6rnEujc+biWus5c9cEYCEJqgFgcY3Gk7z3Om1/a7b3wPzijHXusdKN11ovSfKF/ul9V7qdtbCKuoxCrqnHpw9EbrKKqk3zrmwP1Z651EJlzuDOE0Y33Lv+jOW3W+o+k6TW+tVa69HpbsaXJL82uUo/nVW/z6cLz0vW71xfL6Pz4s5z1rnLeleiHxbgmP7p52qt/zO2eHQduWUp5XrZef6zn955zrk5ea4sWe1usDm6yeK8McDvNlGfJPlSP71KrjiUws+UUg7O6kLVdb2+rrNv9NMbLafQDq4Fo3n3yRqrtZ6X7rr+2XQh8EdLKbOub/OMvvhYq1/HTFrNZ/rcz6LsOufSIf30G/NWAmBxCaoBYHG9KV3Id2gp5XfmrVhKWe7NspJuTN5RiHiLKdu8bq44NvNKvKmfHlVKueUqt7VaK6nL2f30Ssen9/yscajRj5n77P7pkaWUZ81bv5SypZTy/Cw9EB3dnO0Bkwv6QPGpM/YzedOuSaOxfnefmD/qVTd1GIF+PNR/6Z8+t5Qyc+zWvq1776AeO9O7+ulDSimHTC4spdwu63wjxf54vDVdQFST/PnEKh9J8v104yH/1Q62tZLryCzvTBf8XS/Jo2bs63dXuY939NOj+uvV5D6OSHcjxaQbniJJ0t8AcdTr/E9mbPtPV1m3nXF9XS//0U9vO23hCq8Fb053PG66o8+zleh7794r3RcSN04XVi+3R/zca9UaWM1n+szPov4a8Iw1qeHqjb7onHeDWQAWmKAaABZUrfWrSV7WP31lKeUF473ESin7lFKOKKW8Jck/r2D752Z7L7fXl1Ju3W/3KqWUu6cb93O1Iew/pOu9uHuSj5RSHl1K2bPfz1VLKbctpby2lHKHVe5nveryoX76O6WU3xoFNKWUg0spb0zy8HQ3RltTtdZXZXuw/txSygdLKfcspfxsuI5SyvVLKb+bbkzrp2fp/y4cBXa/Xkp5aillr357hyR5W5LbzCj3xFLK8aWUR4wHQKWU/Uspf5bk8H7W8RPlTu6njymlXHXGtp+WbiiHX0jyqVLKvUspu/XbL6WUny+lPDnJ1zMjPBvIW5N8M93YtR8spfxK8rM63zvJu7M9YFoz/fYPLaUck+6LhyP7RX9ea/3g+Lr9rwn+IF1A9vBSyrtH7/V+W7v15/6Lk3xnrepYa/1uktf3T/++lPKYsdf0Fkk+mNWPLf3/pbvZ4Oj437bf/lVLKQ9J8vZ+vQ/XWj86Ufa5/fTepZTXlVKu3Zfdt5TynCS/n1W8djvp+rpeRiHjL814zy77WlBrPTn9TReT/F0p5djRMe/L3qift+IvL2qtZyW5Z5KvpBuz/SOllGvNL3UFo2vVg8s6DEO0ys/00WfRS0spvzb6lUL/ZdhHkhyw1vVdoVFQ/YlBawHAYATVALDYnpLkVek+85+W5PullLNLKWelC1GOT/LIdL0lV+KP0vV+u0WSL5ZSzktyXpIPp/uP72+vpvK11ouS3D/dT/S3pgtfzymlbEvy0ySfS/L4dEHTulphXY5LFzZtSRd0/7SUcmaS76a7adiz04Ui6+GodDdwvChdT8ET+v2fUUq5IF0v2VelG3rkP5L8z/TNXFGt9d/S9XYtSV6Y7hicmS6kvH+S35xRtCQ5Isk/Jjm1lHJeX+7MJM/rl7+mv8nauNf102OSnFdK+W4p5ZRSyl+P1emUdD+HPzXJzZP8W5Lz+9fmwr5tL0nyc9neS3VwtdYL040de1aSX0wXsp+b5Px0bTgvyV/0q1+0il29s5Tyo/5xRpJLkoxCr0PShbVNrfUvZ9Tzveneyxen60n/xVLKT0spp6d7/38uXQ/itQ7n/ijdzUb3TPLGJOf2166vpBvP+Imr2Xg/5vMD052Dt0zyuVLKOemO+zvSjZf7lXTXyMmyH073/kq6YzM6tmek65X+kiRf7pev9LVb1+vrOvp8uhtA7pXtofO4lV4Ljkn3RdlV0107TyulnNkfl2/38w5cTcVrrWekGwbjq0kOTfLhUspSQ9w3p3uP3DnJtlLKD/pr1Vr2Dl7pZ/oz040dfoMkJ6b7LDgv3XAnt0jyiDWs44qUUm7Q1+X8JO8fuDoADERQDQALrNZ6Wa3199L9x/kt6QLS3dP1RPxekvem6y350BVu/6R0P41/d7qQYbckP07X8+3W2R7UrFit9fvpesH+Ybqeeucm2TtduHZ8unD4s6vdz3rUpR8H9x7pAt1vpxvK4NJ0vduOrLX+RdZJ7TwnXRD9nCSfSjd29d7pgsqT09247K611jvXWk9dxuYfnu6n4t9I155L0g2/ccda6wkzyrw1yROS/FO6XtyXZPuxe2+S+9dar/Rz9lrrG/pyn+33dYN0Y/9unVjvc0lumm7okU+lC/T2T/clwueTvCLJr9VaP76Mdq67WuuXktwqyRuS/Cjde+hHSV6a5PbZPu7tWVM3sDTXSHeztOuke+//JF24/PdJHpzk4Frr3F9V9K/DLyb5m3TnzmVJ9k1yerrg69n98jXTjx18eLrgd/RFyoXpzqHbZ/uYvavZx2eTHJYutP+fdMf/0nTnzJ8mucOUG8+Nyj4nXXD/iXTh2pZ0x/VRtdbx4H5Fr93OuL6uh/6GsaPe8NO+uFrpteCiWuvD0h3z96W7ueBe6a7Dn0l3TXrtZLkV1P8nSe6e7vp2yyQfKqXscDiPWuvX0/XI/mC60PjAdNeqlYx3PWsfK/pMr7V+O9175i3pzqGrpjsv/zHJ7eZct3emh6X7kqLtf1EAwCZUrnzjeQAAYFdQSnlzujGan1NrPXbg6rBE/ZA4p6cLEW/U9/rfNEopByU5JV2IfFD/ixSYqZTyhSS/nORXa62r/hIKgI1Jj2oAANgFlVJunOQh/dMPzVuXXc4fpgup/99mC6mTpP+FxquTXDPJ4wauDru4Uspd04XUxwupATY3QTUAAAyklPKAUsrzSyk3G7tZ4O6llAck+Wi6Mc8/U2v9j0ErypWUUl5aSjmqlHKdsXkHllKem+1ji79kmNrtEv4i3RA8Ty2lbBm6MuzSnpnu/gFPH7oiAAzL0B8AADCQUsrjs31c3cvTjRu7b7rxjpNuDNq711q/NUD1mKO/Sd6d+qcX9o/xsYzfnOSxdRP/h6uU8qB0Y7Aftxl7lrNjpZS9k/xxklNrraseYxyAjU1QDQAAAymlHJLuJpx3y/abRF6Y5Jvpboz28lrram6kyDoppdwnSZPkDulunLd3upsefj7J62ut/zJg9QAANhxBNQAAAAAAgzJW2K7PNwkAAAAAwKIo02YKqjeAU089dd33sXXr1mzbtm3d97OzadfGsYhtShazXYvYpmQx27WIbUq0ayNZxDYli9muRWxTspjtWsQ2Jdq1kSxim5LFbNcitilZzHYtYpsS7dpIdmabDjrooJnLrrJTagAAAAAAADMIqgEAAAAAGJSgGgAAAACAQQmqAQAAAAAYlKAaAAAAAIBBCaoBAAAAABiUoBoAAAAAgEEJqgEAAAAAGJSgGgAAAACAQQmqAQAAAAAYlKAaAAAAAIBBCaoBAAAAABiUoBoAAAAAgEEJqgEAAAAAGJSgGgAAAACAQQmqAQAAAAAYlKAaAAAAAIBBCaoBAAAAABiUoBoAAAAAgEEJqgEAAAAAGJSgGgAAAACAQQmqAQAAAAAYlKAaAAAAAIBBCaoBAAAAABiUoBoAAAAAgEEJqgEAAAAAGJSgGgAAAACAQQmqAQAAAAAYlKAaAAAAAIBBCaoBAAAAABiUoBoAAAAAgEEJqgEAAAAAGJSgGgAAAACAQQmqAQAAAAAYlKAaAAAAAIBBCaoBAAAAABiUoBoAAAAAgEEJqgEAAAAAGJSgGgAAAACAQW0ZugIALJ63vGXPvPvdewxdjWXZbbctueSSA4auxppaxDYl2rWRLGKbksVs1yK2KVnMdi1imxLtWmsPfOAFedSjfrrT9wsAq6FHNQBr7t3v3iMnn7zb0NUAANh0Tj55tw3XYQAAEj2qAVgnN7vZJXnHO04fuhpLtnXr1mzbtnHquxSL2KZEuzaSRWxTspjtWsQ2JYvZrkVsU6Jda+mhD128nukAbA56VAMAAAAAMChBNQAAAAAAgxJUAwAAAAAwKEE1AAAAAACDElQDAAAAADAoQTUAAAAAAIPaMnQFZmmaZp8kd01yuyS37acH9IsPbdv26zsof5Ukj0/yuCSHJrlqkm8meWuSl7dte/EOyt82yZ8k+T9Jrpnkx0lOSPLCtm2/uYOy+yZ5SpKHJLlhkguSfCnJq9q2fce8sgAAAAAAm82u3KP67knek+SZSe6d7SH1DjVNs1uS9yV5dZI7JtkjXVB96yQvTvLvTdPsPaf8Y5N8OsnDkhyY5MIkN0jy20m+1DTN3eaUvX66UPoZSW6a5LIk+ya5W5J/bprmlUttBwAAAADAZrArB9VJ14v5A0mek+ToZZT7yyT3TRcwH5VkzyR7JTkyyRnpeme/elrBpmlumeS16Xqb/2OS67Rtu3+SQ5J8qN/OvzRNc60pZUuSdyS5UZJTktypbdt9kuyTrof15Ume2DTNE5bRFgAAAACAhbYrB9Xva9v2Om3b/nrbtsemC4l3qGmaA5M8qX/61LZt39i27WVt29a2bf81yW/1yx7eh9KTnptktySfT/LYtm1/kiRt2343yYOTfD/J/kmeNqXsA5LcIV0g/aC2bT/Vl72wbdu/SvKK0T6aprnaUtoDAAAAALDodtmgum3by1ZY9CFJdk9ydpLXTNnue5L8T5KS5BHjy5qm2T9dT+wkeelkHdq2PS/J3/dPH973oB73yH764bZtvzSlbn+dpKYbTmTm8CEAAAAAAJvJLhtUr8Jd++kn2ra9cMY6J/TTybD4zul6U4+vM+n4fnrddDdpnLbv4zNF27Y/SHLyjH0DAAAAAGxKixhUH9ZPT56zzlf76aETvaJHZX/Utu3pOyg7vn6aprl2tt/wcSn7PmzOOgAAAAAAm8YiBtXX7aenzllntGzv/rHksm3bXpDkrIn1J/9eyr6vO2cdAAAAAIBNYxZyBXAAACAASURBVBGD6r366QVz1vnp2N/jQfVSyo6Xn1Z2qfvee846AAAAAACbxpahK8CVNU1zdJKjk6Rt22zdunXd97lly5adsp+dTbs2jkVsU7KY7VpKm3bbrft42Uht36yv1UakXRvHIrYpWcx2LWKbksVs1yK2KdGutbTe/w7zWm0ci9imZDHbtYhtSrRrI9lV2rSIQfX5SfZPssecdfYc+/u8ibLZQdnx8tPK7qj8tLJX0Lbta5K8pn9at23btoPqrN7WrVuzM/azs2nXxrGIbUoWs11LadMll3RD9m/bNmu4/13PZn2tNiLt2jgWsU3JYrZrEduULGa7FrFNiXatpfX+d5jXauNYxDYli9muRWxTol0byc5s00EHHTRz2SIO/TEaA3p2q7cvO69t23OXU7Zpmj3SBeFJ8sMpZZe67x/OWQcAAAAAYNNYxKD6q/30ZnPWOayffm1G2QObpjlgB2XH10/btj9JMvrqYSn7/uqcdQAAAAAANo1FDKo/1k/v0jTN1Wesc89++pGJ+f+e5JL+73vMKHtEPz01Vw66R/u+Z6ZomuZ62R5iT+4bAAAAAGBTWsSg+p1JLko3PMfjJxc2TXNkkl9MUpO8bXxZ27ZnJ/lA//TJTdNcZaLsXkl+t3/6trZt68Tm39pPj2ia5lZT6vbkJCXdsB8fm7IcAAAAAGDT2aVvptg0zfjtJq8x9vf+E8vOaNv28iRp2/ZHTdO8PMlTkry4aZqzk7y1bdvLmqa5b5I39GXe1rbtV6bs9tlJ7pvk9kmOa5rmyW3bbmua5uAkr01ycJKzkrxoStn3JDkpyR2SvKtpmke0bfuZpml2T/IHSY4Z7aNt24uXfCAAAAAAABbYrt6j+idjj/8cm//piWUHT5R7Zrqe0XskeVOS85umOT/J+5MckORz2d4z+gratv1ykickuTTJo5P8uGmas5J8N92wH+cneUg/JvVk2ZrkoUm+k+RGST7dNM25Sc5L8tfpjvfft2372mUdBQAAAACABbarB9Ur0rbtJUmOTBdGfybdUCA1yZeSPDXJndu2PXdO+Tcm+ZUkbZLT0gXe30/y+iS3btv2o3PK/m+SWyd5fpKvp+u1fm66oT6atm2fuNr2AQAAAAAskl166I+2bcsqyl6e5NX9YyXlP5/kYSsse06SZ/QPAAAAAADmWMge1QAAAAAAbByCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQW4auwHppmuYqSR6b5JFJbpVk/yTnJ/lGkvcmeUXbtufOKHu1JMckeUSSmyS5NMnXkrwhyWvbtq072Pc9kjwpyR2S7JvkB0n+Ncnz27Y9bdWNAwAAAABYIAvZo7ppmj2TfCjJ65PcPcnWdCH1vklun+Qvk/xX0zQ3nlJ23ySfSvKidAF3SbJHkjsmeXWS9zZNMzPgb5rmGf2+75fkgCQXJblxkj/s93nztWklAAAAAMBiWMigOsmzktwtSU3y9CT7t227f5KrJ3l4krOS3DDJ66aUfW2S2yQ5I8mRSfZOsmeSo5JcmC6Afs60nTZNc990IXiSvKTf735Jbp7kS0muleQ9TdPsvuoWAgAAAAAsiEUd+uMR/fQNbdu+cDSzbduLk7y9aZqrpxvG465N01yjbdszk6Rpml9K0vSrP65t23/t/74syRubptk/yd8k+aOmaV7etu2PJ/b7/H76rrZt/2Rsvyc3TXNkuuFDbpzk6CR/u1aNBQAAAADYyBa1R/V1+ukXZyz/wtjfe479PQq4v9G27XunlHtNkrPTDQXy4PEFTdPcLN1QIUnyV5MF27b93yRv658+cmbNAQAAAAA2mUUNqk/pp780Y/lt+ulpbdv+YGz+XfvpCdMKtW17QZJP9k/vNrF4VPbsJCfN2O/x/fT2TdPsPWMdAAAAAIBNZVGD6tf208c1TfO0pmn2S5Kmaa7WNM3Dkrws3fjVPxueo2makuSm/dOT52z7q/30sIn5o+dfa9v28h2UHd8XAAAAAMCmtqhB9d8k+bt0gfALkpzVNM1ZSS5I8vYkX09y/7Zt3zJWZt8ke/V/nzpn26Nl152Yf92J5fPKTisPAAAAALApLWRQ3bbtZUmOSfLHSS7tZ++X7e3dJ8m1JortNfb3BXM2/9N+Ojl0x6j8UspOKw8AAAAAsCltGboC66FpmgOTvCfJ7ZO8MclLk3wrXS/mhyb58ySvb5rmF9q2ffpgFZ2haZqjkxydJG3bZuvWreu+zy1btuyU/exs2rVxLGKbksVs11LatNtu3cfLRmr7Zn2tNiLt2jgWsU3JYrZrEduULGa7FrFNiXatpfX+d5jXauNYxDYli9muRWxTol0bya7SpoUMqpO8KV1I/Q9t2z5+bP43k7ywaZof9Os8pWmat7Rte3KS88fW22POtvfsp+dNzB+VX0rZaeV/pm3b1yR5Tf+0btu2bc4m18bWrVuzM/azs2nXxrGIbUoWs11LadMllxyQJNm27fSdUaU1sVlfq41IuzaORWxTspjtWsQ2JYvZrkVsU6Jda2m9/x3mtdo4FrFNyWK2axHblGjXRrIz23TQQQfNXLZwQ380TXNYknv2T182bZ22bd+c5PR07T+yn31OtofNs4/Y9mU/nJh/6sTyeWWnlQcAAAAA2JQWLqhOcujY39+Zs963++khSdK2bU3ytX7ezeaUO6yffnVi/uj5oU3TzDquo7Lj+wIAAAAA2NQWMai+fOzvg+esd8N+eu7YvI/103tmiqZprp7kLv3Tj0wsHpXdL8ntZuzziH56Utu2589YBwAAAABgU1nEoPrLY38/YdoKTdMcmeTa/dOTxha9rZ/etGma+00p+oR0QfQFSd41vqBt26+O7ftPp+zzoCQP75/+45z6AwAAAABsKgsXVLdt++0kJ/RPj2ma5gVN01w7SZqm2btpmqOSHNcvPyXJe8fKfjFJ2z89rmma+/blrto0zWOSvKhf9rK2bX88Zfd/1k8f0jTNi5um2acvf1iS9yXZJ92QI69dbTsBAAAAABbFwgXVvaPSjQF9lSRPS3Ja0zTnpBvm4w1JrpnktCQPbtv24omyT0jyhSQHJHl/0zTnp7vJ4huT7JHkX5M8e9pO27b9QJJn9U//NMmZTdOcneTkJL+cZFuSB7Rte9HaNBMAAAAAYONbyKC6bdsfJrlNkmOSfCLJGUn2THJOkv9M8hdJbtH3oJ4se06SX00XcH853Y0PL0rymSS/k+T+bdteOmfff5lujOv3Jzkzye7pelG/IsnN27b977VpJQAAAADAYtgydAXWS9u2FyR5ef9YbtmL0w3z8aIdrTuj/IeTfHglZQEAAAAANpuF7FENAAAAAMDGIagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAYlqAYAAAAAYFCCagAAAAAABiWoBgAAAABgUIJqAAAAAAAGJagGAAAAAGBQgmoAAAAAAAa1ZTWFm6YpSQ5Ismfbtt9bmyoBAAAAALCZrCiobprmV5I8Pcldk+yZpI5vq2ma/ZO8pJ//h23b/nT1VQUAAAAAYBEte+iPpml+P8knktwvyV5JSv/4mbZtz0qyNcnjkjxk9dUEAAAAAGBRLSuobprm9klenuTyJE9LcnCS02as/oZ0AfZ9V1NBAAAAAAAW23KH/nhyuvD52LZtX5wkTdPMWvfj/fSXV1Y1AAAAAAA2g+UO/XGXfvrKHa3Ytu2ZSc5Ncv3lVgoAAAAAgM1juUH11iTntG179hLXv2wF+wAAAAAAYBNZboh8dpJ9mqa52o5WbJpma5L9kvxkJRUDAAAAAGBzWG5Q/eV0Y1TfZUcrJjmqX/ekZe4DAAAAAIBNZLlB9ZvShc8vaJpm71krNU1zRJLnJqlJXr/y6gEAAAAAsOi2LHP9tyR5TJK7JzmpaZrXJdk9SZqmOTLJDZPcJ8m90oXg72rb9t/WrroAAAAAACyaZfWobtu2JnlQkvckOTTJX6cbhzpJ3p3k5emC6qskeWeSR69ZTQEAAAAAWEjL7VGdtm3PS/Kgpmnunm4c6l9Jct104fRpST6d5Li2bY9fw3oCAAAAALCglhVUN02zb//n+W3bfiTJR9a+SgAAAAAAbCbLvZniWUnOSHLQOtQFAAAAAIBNaLlDf5yX5NK2bb+/HpUBAAAAAGDzWW6P6u8k2bNpmmWPbQ0AAAAAANMsN6huk+yW5IHrUBcAAAAAADah5QbVf5Xk80le3TTN3dehPgAAAAAAbDLLHcLjaUk+muTQJCc0TfOVJJ9O8pMkl80q1Lbtc1dcQwAAAAAAFtpyg+pjk9QkpX9+qyS3nLN+6dcXVAMAAAAAMNVyg+o3pQueAQAAAABgTSwrqG7b9qh1qgcAAAAAAJvUcm+mCAAAAAAAa0pQDQAAAADAoJY19EfTNAevZCdt235vJeUAAAAAAFh8y72Z4ndWsI+6gv0AAAAAALBJLDdALivYx0rKAAAAAACwSSwrqG7/f/buPcyyrK7v/7uZHmAGGW4DygQQQQPCeMVbwCuiIoIowspPIEaioDFE1GhEjZookgxKEDVeIEZA+ZEsE0HkotxESbhEIxKcIUR/yG1AcEAGgRkYZvr3xzktZVlV3ae7qs/0qdfreerZ56y91t7fb9cfNc9n9rP2nHvuaT3GuKD63Oqx1WdV/8+c8yWnXh4AAAAAAJtuX1+mOOd8/5zzpXPOr6heUj1njHH3/bwHAAAAAACbZV+D6m0eW51f/cgB3gMAAAAAgLPcgQXVc843V++rvuSg7gEAAAAAwNlv1ZcpnrQxxvnVBdU1B3UPAAAAAADOfge59cejl9f/8wO8BwAAAAAAZ7mVnqgeY3zxCabcuLpd9cDqa6pj1TNOrTQAAAAAAA6DVbf+eHmL8PlEjiyPv1H91Ir3AAAAAADgEFk1qH5rewfVH23xAsXXV3PO+dunWhgAAAAAAIfDSkH1nPOOB1QHAAAAAACH1EG+TBEAAAAAAE5IUA0AAAAAwFqttPXHGOOW1edUV845X7Pt3EXVk6ovqW5U/Xb1L+ac79inWgEAAAAA2ECrPlH9qOqF1dg6OMa4cfX71YOr21Q3W855+RjjJvtQJwAAAAAAG2rVoPqrlsdnbhv/5upO1Xurb6/+cXV5defq0adRHwAAAAAAG27VoPqTlsfLto0/pDpW/cCc8ylzzl+tHlEdqb7+9EoEAAAAAGCTrRpU37p635zz6uMDY4yj1T+orqt+fcvcl1XXVnc53SIBAAAAANhcqwbVR6rte07fo7px9bo555XHB+ecx6orq/NOq0IAAAAAADbaqkH126pzxxifvmXs65bHV2ydOMa4QXXT6i9PvTwAAAAAADbd0RXnv6z6lOoXxhjfVd22+o4W+1P/1ra5d6vOrd5+ukUCAAAAALC5Vg2qL6keWn1B9erl2JHqf8w5X7Zt7te2CLBfeVoVAgAAAACw0Vba+mPO+ebqy6rfq66u3l39SvXArfPGGOdUj2wRYr9kPwoFAAAAAGAzrfpEdXPOP6rufYJp11Wfufz8/lXvAQAAAADA4bFyUH0y5pzHqisP4toAAAAAAGyWlbb+AAAAAACA/XbKT1SPMe5Q3bO6qLpJi/2odzTn/LFTvQ8AAAAAAJtt5aB6jHFR9UvVV7dHOL10pDpWCaoBAAAAANjRSkH1GONm1e9Vd6quqF5ZPbC6qvpv1cdXX1DddHn++ftZLAAAAAAAm2fVPaq/u7pz9QfVXeacX78cv3LO+U1zzq+qblv9u+rC6po55yP2rVoAAAAAADbOqkH117bYyuP75pzv22nCnPNDc84frJ5YfcsY42GnWSMAAAAAABts1aD6ztV1Lbb82OqGO8y9ZHl85KpFAQAAAABweKwaVB+t3jfnvHbL2AerC8YYf+vFinPOK6r3VZ92eiUCAAAAALDJVg2qL68+btvY26tzqrtsHRxjnFfdvDr/lKsDAAAAAGDjrRpU/3/VDccYd94y9qrl8du3zX1MdWS5BgAAAAAAdnR0xfkvrb6qum/1H5Zjv1D94+qfjzE+pfrjFtt9fE2LFy8+fX9KBQAAAABgE636RPWzqv9W3eb4wJzzD6rvbxFKf3X12Or+LZ6mfnb1xH2pFAAAAACAjbTSE9Vzzsurh+ww/lNjjBdU31DdrrqyevGc88X7UiUAAAAAABtr1a0/djXnvKy6bL+uBwAAAADA4bDq1h8AAAAAALCvTvmJ6jHGZ1dfUd2+Om/O+S1bzt2w+oTq2JzzbaddJQAAAAAAG2vloHqMcevqGdVXLoeOtHiR4rdsmXaD6tXVbcYYnzPn/OPTLRQAAAAAgM20UlA9xji/ekn1adU7qxdW/7A6f+u8OefVY4xfrP51i5cvri2oHmPcpXp0i2D9dtVHq3dUr6qePuf8vR3W3LD6ruqh1Scv17yh+pXqqXPOYye4532qx1SfX11QXV49r3r8nPNd+9MZAAAAAMBmWHWP6ke3CKn/oLr7nPNbqw/sMvc3lscvPsXaTtsY4zur/92i7r9fXVfdsLpr9YjqH+2w5oLqldUl1We0eGL8vOoLql+qnjvG2DXgH2P8UPXi6v7VraoPV3eqvrN6/Rjj4n1qDwAAAABgI6waVI8W23x855zzfSeYe1l1TXWXUynsdI0xvq16counxi+pPnHOedM553nVbatvahFIb/fU6h7Ve6sHVB/X4onxb66ubhFA/5td7nm/6nHLr0+sbj7nvFl1cYunym9d/eYY40b70CIAAAAAwEZYdY/qv98ifP6fJ5o457xujPH+6uanUtjpGGPcsfr3y6/fPud86rba/qL61R3WfVaLML7qEXPO5y0/X1s9fYxx8+qnq+8eYzx5zvnubZd4/PL47Dnn926536VjjAe02D7kTtWjqp891f4AAAAAADbJqk9Un1Ndc6I9mqvGGEdaPI38wVMp7DQ9psVT0K/ZHlKfwEOXxzfOOZ+7w/mnVFe22ArkQVtPjDHu3mKrkKqf3L5wzvn26lnLrw9boSYAAAAAgI22alD9tur8McZtT2LuPasbVX+2clWn73jg/Kw9Z/1dX7Y8vmink3POq6pXLL/ee5e1V1av2eX6v7M8ft4Y4+NWrA0AAAAAYCOtuvXHi6tPqb69+tHdJo0xzmmxDcax6gWnXN0pGGPcubrN8utrxxhfUP1Qi+D8/Oot1W9VP7l1647lE+B3XX69dI9bXNZin+q7bRs//v0Nc87r9lhbixc03rX6wxM2BAAAAACw4VZ9ovqnqg9Xjx1jfOsY4++sH2N8bvWS6otaPF18pvdi/pQtn7+0+u8tguVzWwTnd6m+t/rj5XYdx11Q3WT5+R17XP/4ue1Pld922/m91u60HgAAAADgUFrpieo551vGGA9vsaXGL1WXtNiHujHGH1W3r27Z4onhD1ffOOe8Yl8rPrGtL2/80eqNLV6M+JplsP5V1dNaBMX/bYxx8Zzzo30spK66ao/rf2h53L51x/H1J7N2p/V/Y4zxqBYvXGzO2YUXXrjHJffH0aNHz8h9zjR9nT02safazL5Opqdzz138eTmbej+sv6uzkb7OHpvYU21mX5vYU21mX5vYU+lrPx30f4f5XZ09NrGn2sy+NrGn0tfZ5PrS06pbfzTn/I0xxhdWT2qxncZxn7nl86ur75xzrmNri61PeR+rvn7O+caq5ZYcLxxj/JPqeS2ern5QNc94lXuYcz6lxYsbq45dccXBZ/0XXnhhZ+I+Z5q+zh6b2FNtZl8n09M119yqqiuueM+ZKGlfHNbf1dlIX2ePTeypNrOvTeypNrOvTeyp9LWfDvq/w/yuzh6b2FNtZl+b2FPp62xyJnu66KKLdj23clBdNef8g+oLxxh3ahFW37ZFQPyu6lXHg+E1+cCWz7+9Uy1zzuePMf5v9ferL28RVH9wy5Tz9rj++Tvcpy3rT2btTusBAAAAAA6lUwqqj5tzvql60z7Vsl+27gO9V2D+xhZB9e2X39/fImy+SbV7tP+xc+/c5b4ns3an9QAAAAAAh9KqL1M8G1xWXbfC/GNVc85j1RuWY3fffXp323Kf7fet+tSdXjK5be3WewEAAAAAHGqn9UT1GOO8Fi8vPHeveXPOt57OfVYx5/zQGONV1b1a7EG9m+Pn3rxl7Herz6m+YqcFY4wbV1+0/PrSbad/d3m8WfW51Wt2uMRXLo+vmXN+cIfzAAAAAACHzspPVI8xbjHG+HdjjD9rsc/y26s/3+NnHVuDPGN5vO8Y4++E1WOMr2mx7UfVC7acetbyeNcxxv13uO4jWwTRV1XP3npiznlZ9brl1+/b4Z4XVd+4/PrMk+gBAAAAAOBQWCmoHmPcvnptiyD2TtWRk/hZx/Yi/6nFVhznVL8xxvi8qjHGDcYY961+eTnv1W0Jquecr23xYsWqp40x7rdcd84Y45uqS5bnnjTnfPcO9/3B5fEbxhhPGGPcdLn+btVvVTdtEdw/dX/aBAAAAAA4+6269ccTqjtUf1H9yxbbX7xrzrnKntAHbs750THGA6qXt9gX+jVjjL9uEVyfv5x2WfXg5d7UWz2yunN1j+r5Y4wPLdfdaHn+edWP7nLfF4wxfrj68RZh/veMMT5YXbCcckX1wDnnh0+/SwAAAACAzbDq085f2eJFgA+ec/7anPOd17eQ+rg555uqT6t+BaF2GwAAIABJREFUokUofbRF7X9U/UD1eXPOy3dY9/7qntVjW2zlcaz6cIunr7+t+to550f3uO/jWuxx/fzqr1oE3G+qfqa6eM75J/vUIgAAAADARlj1iepzqw/OOV95EMXstznnldW/Wv6ssu4jLbb5uOREc3dZ/5LqJaeyFgAAAADgsFn1ier/W91wjLFqwA0AAAAAADtaNah+SnXD6iEHUAsAAAAAAIfQSkH1nPMp1X+ufnGM8bCDKQkAAAAAgMNk5S085pwPHWP8WPWMMcbjW7yo8J17LDk25/yWUy0QAAAAAIDNtnJQPcb47uq7qyPV7Zc/ezlWCaoBAAAAANjRSkH1GOPh1ROXX/+seln17urafa4LAAAAAIBDYtUnqr+nxRPSv1g9es55bP9LAgAAAADgMFnpZYrVXVoE1d8vpAYAAAAAYD+s+kT1ldWN55wfOIhiAAAAAAA4fFZ9ovp3q5uNMe5wEMUAAAAAAHD4rBpU/1j1gepnxhirrgUAAAAAgL9j1a0/rqq+tXpKdekY44nV66t37rVozvnWUysPAAAAAIBNt2pQ/edbPl9Q/dJJrDl2CvcBAAAAAOCQWDVAPnIK9ziVNQAAAAAAHBIrBdVzTvtSAwAAAACwrwTPAAAAAACslaAaAAAAAIC1ElQDAAAAALBWgmoAAAAAANZKUA0AAAAAwFoJqgEAAAAAWCtBNQAAAAAAayWoBgAAAABgrQTVAAAAAACs1UpB9RjjPgdVCAAAAAAAh9PRFee/aIzx5urp1dPmnG/Z/5IAAAAAADhMVg2qP1TdsfqR6ofHGL9b/XL17Dnnh/e5NgAAAAAADoFV96j++OqR1auWa7+8emb1zjHGz40x7rHP9QEAAAAAsOFWeqJ6zvnBFk9Q//IY41Oqf1L9o+qi6p9W/3SM8SfVf6yeOed87z7XCwAAAADAhln1ieq/Mef80znnD1R3qO5fPbu6pvq06qery8cY/2WMcd8xxpF9qRYAAAAAgI2z6h7Vf8ec87rqBdULxhi3qh7WYnuQu1cPXv5cPsb45eqX5px/cbr3BAAAAABgc5zyE9W7uGN1lxZbgRyrjix/btfiBYxvGmP86D7fEwAAAACAs9hpP1E9xrh19fDqES2eoq5FOP3HLfaq/o0WL1389upe1Y+MMa6acz7hdO8NAAAAAMDZ75SC6jHGDaqvafEyxfstr3Oken/1rOqpc84/2rLkmdUzxxjfUj21elQlqAYAAAAAYLWgeoxxtxZPTj+8uk2LcLrqlS0C6DnnvGq39XPOXx5jXFJ94qmVCwAAAADApln1ieo/6WN7T19RPaP6j3PO/7PCNT5Q3WLF+wIAAAAAsKFOZeuPl7TYe/o5c85rTmH9vU7xvgAAAAAAbKBVA+NPmnO+5XRuOOe8/HTWAwAAAACwWW6w4vwvGWM85GQnjzEeNMb4phXvAQAAAADAIbJqUP206qdXmP/E6j+teA8AAAAAAA6RVYPqWrxI8SDnAwAAAABwiJxKUL2Km1dXH/A9AAAAAAA4ix1YUD3GeFB1s+rNB3UPAAAAAADOfkf3OjnGeEz1mG3Dtx5jvGmPZUdaBNQ3q45Vzz6tCgEAAAAA2Gh7BtUttu6445bvx6pzto3t5prqWdWPn0phAAAAAAAcDicKqp9WvXz5+Uj1suq91Tfssea66v3Vn845P3Sa9QEAAAAAsOH2DKrnnG+p3nL8+xjjrdW75py/d9CFAQAAAABwOJzoieq/Zc55xwOqAwAAAACAQ+oG6y4AAAAAAIDDbdcnqscYX7z8+KE55x9uG1vJnPP3T2UdAAAAAACbb6+tP15eHaveWN1t29gqjp3gPgAAAAAAHGJ7BchvbREyv2OHMQAAAAAA2Be7BtU7vTjRyxQBAAAAANhvXqYIAAAAAMBarbR39BjjtdV11UPmnG86mJIAAAAAADhMVn3J4adWHxFSAwAAAACwX1bd+uPy6shBFAIAAAAAwOG0alD9O9X5Y4zPP4hiAAAAAAA4fFYNqh9Xvaf6xTHGhQdQDwAAAAAAh8yqe1R/cvVD1ROrN44xnlG9qvrL6trdFs05f/+UKwQAAAAAYKOtGlS/vDq2/Hyk+s7lz16OncJ9AAAAAAA4JFYNkN/ax4JqAAAAAAA4bSsF1XPOOx5QHQAAAAAAHFKrvkwRAAAAAAD2laAaAAAAAIC1ElQDAAAAALBWq75Msaoxxu2qR1T3qi6qblId2WX6sTnnnU+tPAAAAAAANt3KQfUY42HVU6obt0c4veXcsVMrDQAAAACAw2ClrT/GGJ9d/Up13vL49ctT763uUz1sOf6R6orq4dW996tYAAAAAAA2z6pPVH/Pcs2T5pz/omqMUfWROefLlnOeNcb46epF1eOqz96nWgEAAAAA2ECrvkzxC1ts5fGkbeN/awuQOefrq39W3bF67KkWBwAAAADA5ls1qP746uo559u3jF3bYiuQ7Z7bYguQrzvF2gAAAAAAOARWDao/UF21bezK6qZjjPO3Ds45P1p9uLr9qZcHAAAAAMCmWzWovry6+RjjxlvG3rg83nPrxDHGnaubVtecenkAAAAAAGy6VYPq17XYj3rrCxJ/ezn2+DHGJ1SNMS6sntpiP+tX70OdAAAAAABsqFWD6ue1CKUfsmXs56p3V/eo3jrGuLz6i+pLq+uqnzj9MgEAAAAA2FSrBtW/WT2ges7xgTnnX1X3rv6wOlrddnndt1cPmXO+Yn9KBQAAAABgEx1dZfKc8yPV83cYv6z6/DHG7avbtXjB4hvmnMf2pUoAAAAAADbWSkH1icw531a9bT+vCQAAAADAZlt16w8AAAAAANhXgmoAAAAAANZq160/xhjX7tM9js0593WLEQAAAAAANsdeAfKRfbrHfl0HAAAAAIANtFdQ/UlnrAoAAAAAAA6tXYPqOedbzmQhAAAAAAAcTl6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\n",
            "text/plain": [
              "<Figure size 1728x1152 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### DBSCAN algorithm"
      ],
      "metadata": {
        "id": "wrac2zgadfkW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The biggest challenge for DBSCAN algorithm is to find *eps* and *minPoints*. \n",
        "\n",
        "We will take minimum number of points (samples) twice as the dimension of our final dataset. The dimension is 4, therore the *minPoints* will be 8."
      ],
      "metadata": {
        "id": "LzJhzqY7Ik2E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "minPoints=8"
      ],
      "metadata": {
        "id": "AzfeQudFJnhF"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create an instance of the class NearestNeighbors and fit the model\n",
        "nn_model  = NearestNeighbors(n_neighbors=minPoints, metric='euclidean')\n",
        "neighbors = nn_model.fit(df_final)\n",
        "distances, indices = neighbors.kneighbors(df_final)\n",
        "\n",
        "# Plot the distribution of distances\n",
        "plt.figure(figsize=(16,12)) \n",
        "plt.hist(distances.flatten(), bins=30, color='b', edgecolor='black', linewidth=1.0)\n",
        "plt.title('Distribution of Pairwise Distances', size=16)\n",
        "plt.xlabel('Distance', size=16)\n",
        "plt.ylabel('Count', size=16)\n",
        "plt.xticks(size=16) \n",
        "plt.yticks(size=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        },
        "id": "FTgWhpv57Ahk",
        "outputId": "648ffe47-b91c-4399-9111-c3732cfbc1d1"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The idea is to calculate, the average of the distances of every point to its 8 nearest neighbors.\n",
        "\n",
        "Next, these distances are plotted in ascending order. The aim is to determine the \"knee\", which corresponds to the optimal eps parameter.\n",
        "\n",
        "A knee corresponds to a threshold where a sharp change occurs along the distance curve."
      ],
      "metadata": {
        "id": "-ZWGFw-AFs3t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create an instance of the class NearestNeighbors and fit the model\n",
        "n_neighbors = 8\n",
        "nn_model    = NearestNeighbors(n_neighbors=n_neighbors, metric='euclidean')\n",
        "neighbors   = nn_model.fit(df_final)\n",
        "\n",
        "# Calcuate average distances to n_neighbors\n",
        "distances, indices = neighbors.kneighbors(df_final)\n",
        "distances_avg = distances[:, 1:].mean(axis=1)\n",
        "distances_avg = np.sort(distances_avg, axis=0)\n",
        "\n",
        "# Plot distances\n",
        "plt.figure(figsize=(16,12))\n",
        "plt.plot(distances_avg, color='b')\n",
        "plt.title('Average distance to ' + str(n_neighbors) + ' Nearest Neighbors', size=16)\n",
        "plt.xlabel('Samples', size=16)\n",
        "plt.ylabel('Distance', size=16)\n",
        "plt.xticks(size=16) \n",
        "plt.yticks(size=16)  \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        },
        "id": "_UA1guOg-DLU",
        "outputId": "81599029-d83b-45c3-8d40-8694fa54dbaa"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Based on the plot above, we can see that we should pick between 5 and 15 for the *eps* value. \n",
        "\n",
        "Let's start with *eps*=5."
      ],
      "metadata": {
        "id": "1peT0aCXH1bt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute DBSCAN\n",
        "db = DBSCAN(eps=5, min_samples=8).fit(df_final)\n",
        "core_samples_mask = np.zeros_like(db.labels_, dtype=bool)\n",
        "core_samples_mask[db.core_sample_indices_] = True\n",
        "labels = db.labels_\n",
        "\n",
        "# Number of clusters in labels, ignoring noise if present.\n",
        "n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n",
        "n_noise_ = list(labels).count(-1)\n",
        "\n",
        "print(\"Estimated number of clusters: %d\" % n_clusters_)\n",
        "print(\"Estimated number of noise points: %d\" % n_noise_)\n",
        "print(\"Silhouette Coefficient: %0.3f\" % metrics.silhouette_score(df_final, labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EM6y33Nt-_bL",
        "outputId": "3a61ed88-82f9-4aaa-ccb5-8d2440502979"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Estimated number of clusters: 3\n",
            "Estimated number of noise points: 25\n",
            "Silhouette Coefficient: 0.620\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The result shows us that Silhouette Coefficient is 0.62 which is ok. Algorithm has identified 3 clusters and 25/127 outliers.\n",
        "\n",
        "Let's try to find optimal *eps* and *min_samples*."
      ],
      "metadata": {
        "id": "ACxNMFUXLgj9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the bounds for the number of clusters\n",
        "eps_lb   = 5\n",
        "eps_ub   = 15\n",
        "eps_step = 0.1\n",
        "\n",
        "min_samples_lb   = 5\n",
        "min_samples_ub   = 25\n",
        "min_samples_step = 1\n",
        "\n",
        "# Fit DBSCAN model for different eps and min_samples values\n",
        "dbscan_output = []\n",
        "\n",
        "for ix_eps in np.arange(eps_lb, eps_ub, eps_step):\n",
        "    dbscan_silhouette_avg_vec = []\n",
        "    \n",
        "    for ix_min_samples in np.arange(min_samples_lb, min_samples_ub, min_samples_step):\n",
        "        dbscan_model = DBSCAN(eps=ix_eps,\n",
        "                              min_samples=ix_min_samples)\n",
        "        y_pred = dbscan_model.fit_predict(df_final)\n",
        "        \n",
        "        dbscan_silhouette_vals = silhouette_samples(df_final, y_pred, metric='euclidean')\n",
        "        dbscan_silhouette_avg_vec.append(np.mean(dbscan_silhouette_vals))\n",
        "        \n",
        "        dbscan_output.append((ix_min_samples, ix_eps, np.mean(dbscan_silhouette_vals)))\n",
        "        \n",
        "    dbscan_silhouette_avg_vec = np.array(dbscan_silhouette_avg_vec)\n",
        "    \n",
        "    if ix_eps == eps_lb:\n",
        "        dbscan_silhouette_avg_mat = dbscan_silhouette_avg_vec\n",
        "    else:\n",
        "        dbscan_silhouette_avg_mat = np.vstack((dbscan_silhouette_avg_mat, dbscan_silhouette_avg_vec))\n",
        "\n",
        "# Display the key information       \n",
        "optimal_min_samples, optimal_eps, max_silhouette_avg = sorted(dbscan_output, key=lambda x:x[-1])[-1]\n",
        "print(f'Best silhouette_score: {round(max_silhouette_avg,2)}')\n",
        "print(f'Optimal min_samples: {optimal_min_samples}')\n",
        "print(f'Optimal eps: {round(optimal_eps,2)}')  \n",
        "\n",
        "labels = DBSCAN(min_samples=optimal_min_samples, eps = optimal_eps).fit(df_final).labels_\n",
        "clusters = len(Counter(labels))\n",
        "print(f'Number of clusters: {clusters}')\n",
        "print(f'Number of outliers: {Counter(labels)[-1]}')\n",
        "print(100*'-')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zJtt8vAiHDz5",
        "outputId": "e84f06bf-a658-4bef-bfef-94e9acd26541"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best silhouette_score: 0.79\n",
            "Optimal min_samples: 18\n",
            "Optimal eps: 14.9\n",
            "Number of clusters: 3\n",
            "Number of outliers: 17\n",
            "----------------------------------------------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we have the optimal values for *eps* and *min_samples*. The Silhouette score is 0.79 which is a really good score."
      ],
      "metadata": {
        "id": "_vdB6xUTMTtF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dbscan_model = DBSCAN(eps=14.9,\n",
        "                      min_samples=18)\n",
        "y_pred_dbscan = dbscan_model.fit_predict(df_final)\n"
      ],
      "metadata": {
        "id": "jTlh5j74Wvln"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Discuss your modeling choices and findings."
      ],
      "metadata": {
        "id": "Gu3Pxlf9cft_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**NOTE**: All choices and findings are explained above, each algorithm separately."
      ],
      "metadata": {
        "id": "xIbhdjJk1G1U"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## (c) Final output\n",
        "\n",
        "Try to consolidate (quantitatively or qualitatively) the results you\n",
        "have obtained using different algorithms and to come up with a final clustering proposal. Discuss the similarities and differences between your clustering results and the classification of the macroeconomic variables provided in McCracken and Ng (2016).\n",
        "(5 pts)"
      ],
      "metadata": {
        "id": "UTI5d0cOclfZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this section, we will provide two summary table. The first one will be the Average Silhouette Scores and number of clusters. Let's take a look:"
      ],
      "metadata": {
        "id": "JLqscT8YQqql"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "| Algorithm | Number of Clusters | Average Silhouette Score |\n",
        "| --- | --- | --- |\n",
        "| K-means | 6 | 0.75 |\n",
        "| Gaussian Mixture Models | 5 | 0.71 |\n",
        "| Hiearchical | 3 | 0.79 |\n",
        "| DBSCAN | 3 | 0.79 |"
      ],
      "metadata": {
        "id": "4yuWWCKvRAgl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Second table will be all timeseries (observations) and what cluster they belong to in every output that we got in the previous sections.\n"
      ],
      "metadata": {
        "id": "2h-015GsStZ1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cluster_table = pd.DataFrame(index=df_final.index)\n",
        "cluster_table.insert(0, \"K-means\", y_pred_kmeans, True)\n",
        "cluster_table.insert(1, \"GMM\", y_pred_gmm, True)\n",
        "cluster_table.insert(2, \"Hierarchical\", y_pred_hier, True)\n",
        "cluster_table.insert(3, \"DBSCAN\", y_pred_dbscan, True)\n",
        "cluster_table"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "kE1m_bflYemO",
        "outputId": "661fe73e-1912-4fd7-d1f7-c688b47d7b99"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "...                  ...  ...           ...     ...\n",
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              "\n",
              "[127 rows x 4 columns]"
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          "metadata": {},
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    }
  ]
}