assignment.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "py_assign.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "-uTJs0vyIxR8",
        "colab_type": "code",
        "outputId": "101401ec-b048-4938-d75e-0d0662415c75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive \n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G3ydsfWJI6t_",
        "colab_type": "code",
        "outputId": "0872cd3a-ad4c-4f99-c259-de0964044f29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cd /content/drive/My Drive/Colab Notebooks/py_assign "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Colab Notebooks/py_assign\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w351O6gMHUgu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "from copulas.multivariate import GaussianMultivariate\n",
        "import sys\n",
        "import utils\n",
        "import numpy as np  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QjA5-KCFMQB4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def handle_non_numerical_data(data):\n",
        "    columns = data.columns.values\n",
        "\n",
        "    for column in columns:\n",
        "        text_digit_vals = {}\n",
        "\n",
        "        def convert_to_int(val):\n",
        "            return text_digit_vals[val]\n",
        "\n",
        "        if data[column].dtype != np.int64 and data[column].dtype != np.float64:\n",
        "            column_contents = data[column].values.tolist()\n",
        "            unique_elements = set(column_contents)\n",
        "            x = 0\n",
        "            for unique in unique_elements:\n",
        "                if unique not in text_digit_vals:\n",
        "                    text_digit_vals[unique] = x\n",
        "                    x = x + 1\n",
        "\n",
        "            data[column] = list(map(convert_to_int, data[column]))\n",
        "\n",
        "    return data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIidgFsxHPRb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "copula = GaussianMultivariate()\n",
        "data = pd.read_excel('data.xlsx')\n",
        "data = handle_non_numerical_data(data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NcbLiBVKOCw0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = data.replace(np.nan,0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rjRoA7ofNVd8",
        "colab_type": "code",
        "outputId": "f447517e-434e-463c-c631-bfba7c2cc47c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        }
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Unnamed: 1</th>\n",
              "      <th>Factor Loadings</th>\n",
              "      <th>Unnamed: 3</th>\n",
              "      <th>Unnamed: 4</th>\n",
              "      <th>Unnamed: 5</th>\n",
              "      <th>Unnamed: 6</th>\n",
              "      <th>Unnamed: 7</th>\n",
              "      <th>Unnamed: 8</th>\n",
              "      <th>Unnamed: 9</th>\n",
              "      <th>Unnamed: 10</th>\n",
              "      <th>Unnamed: 11</th>\n",
              "      <th>Unnamed: 12</th>\n",
              "      <th>Unnamed: 13</th>\n",
              "      <th>Unnamed: 14</th>\n",
              "      <th>Unnamed: 15</th>\n",
              "      <th>Unnamed: 16</th>\n",
              "      <th>Unnamed: 17</th>\n",
              "      <th>Unnamed: 18</th>\n",
              "      <th>Unnamed: 19</th>\n",
              "      <th>Unnamed: 20</th>\n",
              "      <th>Unnamed: 21</th>\n",
              "      <th>Unnamed: 22</th>\n",
              "      <th>Unnamed: 23</th>\n",
              "      <th>Unnamed: 24</th>\n",
              "      <th>Unnamed: 25</th>\n",
              "      <th>Unnamed: 26</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>101</td>\n",
              "      <td>1</td>\n",
              "      <td>73</td>\n",
              "      <td>20</td>\n",
              "      <td>26</td>\n",
              "      <td>50</td>\n",
              "      <td>42</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>57</td>\n",
              "      <td>0.0</td>\n",
              "      <td>82</td>\n",
              "      <td>73</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.055583</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>51</td>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>89</td>\n",
              "      <td>97</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>77</td>\n",
              "      <td>74</td>\n",
              "      <td>74</td>\n",
              "      <td>88</td>\n",
              "      <td>10</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100</td>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>52</td>\n",
              "      <td>13</td>\n",
              "      <td>61</td>\n",
              "      <td>46</td>\n",
              "      <td>98</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>64</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>28</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  Unnamed: 1  ...  Unnamed: 25  Unnamed: 26\n",
              "0         101           1  ...            1            6\n",
              "1           0           0  ...            0            0\n",
              "2           0           0  ...            0            0\n",
              "3           1           3  ...            7            7\n",
              "4           2           6  ...            2            4\n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BsIu_a_vIBAV",
        "colab_type": "code",
        "outputId": "a1ecae0d-79b4-4cdd-d4fb-cd542d414b2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "copula.fit(data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:56: FutureWarning: Series.nonzero() is deprecated and will be removed in a future version.Use Series.to_numpy().nonzero() instead\n",
            "  return getattr(obj, method)(*args, **kwds)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iFQiXNquMxSU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_samples = 1000\n",
        "samples = copula.sample(num_samples)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zhP9n23-IA8z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_path = 'mymodel.pkl'\n",
        "copula.save(model_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5HLsiIqoIA7Q",
        "colab_type": "code",
        "outputId": "4a7d6392-7f30-46ce-a660-ff66c0fe8fd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "copula_params = copula.to_dict()\n",
        "print(copula_params)\n",
        "print(type(copula_params))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'covariance': [[0.9914845315176679, 0.04184433658410097, 0.03288527696147693, -0.03529466000687392, -0.13247684498214315, -0.17927490822669157, -0.03782731381273307, -3.569644411775154e-33, 0.19750439439579887, 0.05770571899535679, 0.05770571899535679, 0.05770571899535679, 0.19750439439579887, 0.12996026556316373, -8.98207961390261e-32, 0.26424300922257865, 0.21062490389877606, 0.18947083696334682, 0.020204797895257497, -0.2415010838746474, -0.352049881780852, -0.3600386361669552, -0.24686898810454178, -0.24686898810454178, -0.33150978404477915, -0.35737224947091245, -0.21659814788837206], [0.04184433658410097, 0.9388149407801467, 0.09128325296594379, 0.07496238466645654, 0.10923090337112237, -0.06053353468554455, 0.09254754558069295, -2.8793606099254716e-33, 0.03934048789759487, 0.2697775529977754, 0.2697775529977754, 0.2697775529977754, 0.03934048789759487, 0.1498952111058496, -4.2713693816112953e-32, -0.09823832119879221, -0.014835545507334652, 0.2853347937268479, 0.33021592178874976, -0.04810457249656736, -0.037601041095161956, -0.008379660870484916, -0.1174386023763629, -0.1174386023763629, -0.07013855323404473, -0.09378857780636479, -0.1706511576629047], [0.03288527696147693, 0.09128325296594379, 1.010290252474808, 0.03477106760449798, -0.08441042068007705, -0.06971573854218002, 0.04723690694861341, -1.1288619079972709e-33, 0.04299688975596549, -0.02415107257426183, -0.02415107257426183, -0.02415107257426183, 0.04299688975596549, -0.00122710438823704, 9.763130015111533e-33, 0.0594445226246905, 0.011263243705259485, 0.1501488523375306, -0.09424450572011635, -0.0525749421966012, -0.10015627423564727, -0.0743886893844073, 0.03819623558123302, 0.03819623558123302, 0.005298633379213402, -0.02936134036930615, 0.04495199317659316], [-0.03529466000687392, 0.07496238466645654, 0.03477106760449798, 0.9903846153846152, 0.06243559876349623, -0.026359706106357277, -0.05684669145647595, -2.6238411915612242e-33, 0.0371336774271919, 0.1513220889388727, 0.1513220889388727, 0.1513220889388727, 0.0371336774271919, 0.17837839360514862, 5.076827607857997e-32, -0.0015632973713156435, -0.11348057882711349, -0.13023147825279896, 0.13343605896792482, -0.04540594903396988, 0.19833781599537334, 0.18817595183084262, 0.03231014499420897, 0.03231014499420897, 0.09281823475863037, 0.19092358418354208, 0.055514703690363], [-0.13247684498214315, 0.10923090337112237, -0.08441042068007705, 0.06243559876349623, 0.9903846153846154, -0.05797791317593294, 0.09815308453922314, 2.6238411915612242e-33, -0.06644973869943764, 0.009707454762116364, 0.009707454762116364, 0.009707454762116364, -0.06644973869943764, 0.02931042490807388, -7.810504012089226e-33, 0.05289996588742606, 0.06285560682765111, 0.01468629814501327, 0.0455345601819328, 0.08125223426218962, -0.02812658869795003, -0.04699862197601501, 0.01821117262259132, 0.01821117262259132, 0.04170945990974721, 0.009399314889652134, 0.0029372858854379005], [-0.17927490822669157, -0.06053353468554455, -0.06971573854218002, -0.026359706106357277, -0.05797791317593294, 0.9903846153846152, 0.012807445902961297, -1.922116221725083e-33, -0.07622175904709011, -0.08394093235477092, -0.08394093235477092, -0.08394093235477092, -0.07622175904709011, 0.025175251216206472, 3.319464205137921e-32, -0.09926097825397236, 0.009262577841494615, -0.1681629767730317, -0.07562696517173186, 0.09320126736169909, 0.11958336726401145, 0.16785222281329812, 0.10721093570687466, 0.10721093570687466, 0.11661025062172696, 0.1606695392859097, 0.12365973680793181], [-0.03782731381273307, 0.09254754558069295, 0.04723690694861341, -0.05684669145647595, 0.09815308453922314, 0.012807445902961297, 0.9903846153846151, -2.166194472102871e-33, -0.10749222431030397, -0.09307736036617448, -0.09307736036617448, -0.09307736036617448, -0.10749222431030397, -0.12854338708012303, 4.7839337074046507e-32, -0.14673142746467802, -0.08480802831414364, -0.03511095119436927, -0.040783127815122396, 0.13143763813933731, 0.04554692714994059, -0.08292807140946357, 0.003818471731115839, 0.003818471731115839, 0.01321778664596723, -0.03906590256799129, -0.013511515143969484], [-3.569644411775154e-33, -2.8793606099254716e-33, -1.1288619079972709e-33, -2.6238411915612242e-33, 2.6238411915612242e-33, -1.922116221725083e-33, -2.166194472102871e-33, 1.7504987019282007e-33, 4.385781061475884e-34, 5.98182399558689e-33, 5.98182399558689e-33, 5.98182399558689e-33, 4.385781061475884e-34, -9.152934389167061e-35, -3.734397230780161e-32, 1.8305868778334122e-34, 4.881565007555766e-34, -3.356075942694589e-34, -1.1608971783593557e-32, 2.153476808905063e-34, 1.2901823766063403e-32, 1.2901823766063403e-32, -4.118820475125178e-33, -4.118820475125178e-33, -2.166194472102871e-33, -4.118820475125178e-33, -2.166194472102871e-33], [0.19750439439579887, 0.03934048789759487, 0.04299688975596549, 0.0371336774271919, -0.06644973869943764, -0.07622175904709011, -0.10749222431030397, 4.385781061475884e-34, 0.6820753510124921, 0.19928492531497186, 0.19928492531497186, 0.19928492531497186, 0.6820753510124921, 0.1114628760060287, -1.1959834268511627e-32, 0.16035010236385264, 0.1426714977369436, 0.23760273095755507, -0.13818512684056317, -0.8340165648344741, -1.1857696567573996e-11, -1.1857696567573996e-11, 0.2562738101457314, 0.2562738101457314, 0.10250952405063167, 0.05125476201891806, 0.3075285721799559], [0.05770571899535679, 0.2697775529977754, -0.02415107257426183, 0.1513220889388727, 0.009707454762116364, -0.08394093235477092, -0.09307736036617448, 5.98182399558689e-33, 0.19928492531497186, 0.5719842787722059, 0.5719842787722059, 0.5719842787722059, 0.19928492531497186, 0.10798396920913313, -6.27281103470916e-32, 0.07941678158766928, 0.0927078724686545, 0.11375779663648286, 0.5486134524886073, -0.24367919797987983, 0.02285682097448604, 0.02285682097448604, -0.12508802568566202, -0.12508802568566202, -0.035236063575805505, -0.005285409539178733, -0.15503867972375587], [0.05770571899535679, 0.2697775529977754, -0.02415107257426183, 0.1513220889388727, 0.009707454762116364, -0.08394093235477092, -0.09307736036617448, 5.98182399558689e-33, 0.19928492531497186, 0.5719842787722059, 0.5719842787722059, 0.5719842787722059, 0.19928492531497186, 0.10798396920913313, -6.27281103470916e-32, 0.07941678158766928, 0.0927078724686545, 0.11375779663648286, 0.5486134524886073, -0.24367919797987983, 0.02285682097448604, 0.02285682097448604, -0.12508802568566202, -0.12508802568566202, -0.035236063575805505, -0.005285409539178733, -0.15503867972375587], [0.05770571899535679, 0.2697775529977754, -0.02415107257426183, 0.1513220889388727, 0.009707454762116364, -0.08394093235477092, -0.09307736036617448, 5.98182399558689e-33, 0.19928492531497186, 0.5719842787722059, 0.5719842787722059, 0.5719842787722059, 0.19928492531497186, 0.10798396920913313, -6.27281103470916e-32, 0.07941678158766928, 0.0927078724686545, 0.11375779663648286, 0.5486134524886073, -0.24367919797987983, 0.02285682097448604, 0.02285682097448604, -0.12508802568566202, -0.12508802568566202, -0.035236063575805505, -0.005285409539178733, -0.15503867972375587], [0.19750439439579887, 0.03934048789759487, 0.04299688975596549, 0.0371336774271919, -0.06644973869943764, -0.07622175904709011, -0.10749222431030397, 4.385781061475884e-34, 0.6820753510124921, 0.19928492531497186, 0.19928492531497186, 0.19928492531497186, 0.6820753510124921, 0.1114628760060287, -1.1959834268511627e-32, 0.16035010236385264, 0.1426714977369436, 0.23760273095755507, -0.13818512684056317, -0.8340165648344741, -1.1857696567573996e-11, -1.1857696567573996e-11, 0.2562738101457314, 0.2562738101457314, 0.10250952405063167, 0.05125476201891806, 0.3075285721799559], [0.12996026556316373, 0.1498952111058496, -0.00122710438823704, 0.17837839360514862, 0.02931042490807388, 0.025175251216206472, -0.12854338708012303, -9.152934389167061e-35, 0.1114628760060287, 0.10798396920913313, 0.10798396920913313, 0.10798396920913313, 0.1114628760060287, 0.991484531517668, 2.3431512036267676e-32, 0.19229140884996124, 0.14783526109245296, -0.13322320277494706, -0.014658382786755457, -0.13629282981032217, -0.09459411427941945, 0.041577835553438004, 0.07141567151953723, 0.07141567151953723, 0.04937379757677454, 0.012637341004578214, 0.050843255839527174], [-8.98207961390261e-32, -4.2713693816112953e-32, 9.763130015111533e-33, 5.076827607857997e-32, -7.810504012089226e-33, 3.319464205137921e-32, 4.7839337074046507e-32, -3.734397230780161e-32, -1.1959834268511627e-32, -6.27281103470916e-32, -6.27281103470916e-32, -6.27281103470916e-32, -1.1959834268511627e-32, 2.3431512036267676e-32, 7.96671409233101e-31, 6.834191010578073e-32, -4.295777206649074e-32, 3.905252006044613e-32, -9.177342214204841e-32, -4.594685262080849e-33, 3.4805558503872613e-31, 3.4805558503872613e-31, -2.9582283945787943e-31, -2.9582283945787943e-31, -2.333388073611656e-31, -2.333388073611656e-31, -2.333388073611656e-31], [0.26424300922257865, -0.09823832119879221, 0.0594445226246905, -0.0015632973713156435, 0.05289996588742606, -0.09926097825397236, -0.14673142746467802, 1.8305868778334122e-34, 0.16035010236385264, 0.07941678158766928, 0.07941678158766928, 0.07941678158766928, 0.16035010236385264, 0.19229140884996124, 6.834191010578073e-32, 0.991484531517668, 0.2319731583242704, 0.029875473522212415, -0.052690943530769675, -0.19607024689232508, -0.17085040649361005, -0.06554409821953577, -0.0076411830227834515, -0.0076411830227834515, -0.04878601438261327, -0.041732614720907814, 0.016751824141720665], [0.21062490389877606, -0.014835545507334652, 0.011263243705259485, -0.11348057882711349, 0.06285560682765111, 0.009262577841494615, -0.08480802831414364, 4.881565007555766e-34, 0.1426714977369436, 0.0927078724686545, 0.0927078724686545, 0.0927078724686545, 0.1426714977369436, 0.14783526109245296, -4.295777206649074e-32, 0.2319731583242704, 1.0017059464796632, 0.13814769999022916, 0.05048008700817167, -0.17445353565175542, -0.27155234047631976, -0.270826493002116, -0.17887495255914587, -0.17887495255914587, -0.22557779854235982, -0.26170641524672134, -0.1730003807367026], [0.18947083696334682, 0.2853347937268479, 0.1501488523375306, -0.13023147825279896, 0.01468629814501327, -0.1681629767730317, -0.03511095119436927, -3.356075942694589e-34, 0.23760273095755507, 0.11375779663648286, 0.11375779663648286, 0.11375779663648286, 0.23760273095755507, -0.13322320277494706, 3.905252006044613e-32, 0.029875473522212415, 0.13814769999022916, 1.002938858089952, 0.09236399919456553, -0.2905319315435815, -0.10177657131457439, -0.19002001003882163, -0.05541468105733129, -0.05541468105733129, -0.11152954767921279, -0.16764441430135665, -0.05031332954574617], [0.020204797895257497, 0.33021592178874976, -0.09424450572011635, 0.13343605896792482, 0.0455345601819328, -0.07562696517173186, -0.040783127815122396, -1.1608971783593557e-32, -0.13818512684056317, 0.5486134524886073, 0.5486134524886073, 0.5486134524886073, -0.13818512684056317, -0.014658382786755457, -9.177342214204841e-32, -0.052690943530769675, 0.05048008700817167, 0.09236399919456553, 0.8129711393941395, 0.16896658766774583, 0.01132073544131568, -0.09132059972139782, -0.4169874101627732, -0.4169874101627732, -0.2300760612385244, -0.1677722782637577, -0.47929119314059193], [-0.2415010838746474, -0.04810457249656736, -0.0525749421966012, -0.04540594903396988, 0.08125223426218962, 0.09320126736169909, 0.13143763813933731, 2.153476808905063e-34, -0.8340165648344741, -0.24367919797987983, -0.24367919797987983, -0.24367919797987983, -0.8340165648344741, -0.13629282981032217, -4.594685262080849e-33, -0.19607024689232508, -0.17445353565175542, -0.2905319315435815, 0.16896658766774583, 1.0198046731742143, -4.209982854997568e-08, -4.209982854997568e-08, -0.31336178792176755, -0.31336178792176755, -0.12534474242717758, -0.06267239392896481, -0.3760341364230471], [-0.352049881780852, -0.037601041095161956, -0.10015627423564727, 0.19833781599537334, -0.02812658869795003, 0.11958336726401145, 0.04554692714994059, 1.2901823766063403e-32, -1.1857696567573996e-11, 0.02285682097448604, 0.02285682097448604, 0.02285682097448604, -1.1857696567573996e-11, -0.09459411427941945, 3.4805558503872613e-31, -0.17085040649361005, -0.27155234047631976, -0.10177657131457439, 0.01132073544131568, -4.209982854997568e-08, 1.0193083414002075, 0.76059010760587, 0.6018590069280918, 0.6018590069280918, 0.7446255620317579, 0.8683565764172961, 0.6208945473516511], [-0.3600386361669552, -0.008379660870484916, -0.0743886893844073, 0.18817595183084262, -0.04699862197601501, 0.16785222281329812, -0.08292807140946357, 1.2901823766063403e-32, -1.1857696567573996e-11, 0.02285682097448604, 0.02285682097448604, 0.02285682097448604, -1.1857696567573996e-11, 0.041577835553438004, 3.4805558503872613e-31, -0.06554409821953577, -0.270826493002116, -0.19002001003882163, -0.09132059972139782, -4.209982854997568e-08, 0.76059010760587, 1.0193083414002078, 0.6399300891059382, 0.6399300891059382, 0.7255900223459878, 0.8873921178755695, 0.6399300891054643], [-0.24686898810454178, -0.1174386023763629, 0.03819623558123302, 0.03231014499420897, 0.01821117262259132, 0.10721093570687466, 0.003818471731115839, -4.118820475125178e-33, 0.2562738101457314, -0.12508802568566202, -0.12508802568566202, -0.12508802568566202, 0.2562738101457314, 0.07141567151953723, -2.9582283945787943e-31, -0.0076411830227834515, -0.17887495255914587, -0.05541468105733129, -0.4169874101627732, -0.31336178792176755, 0.6018590069280918, 0.6399300891059382, 1.0192271427622748, 1.0192271427622748, 0.9730084845221717, 0.8343525097995734, 0.9961178136410977], [-0.24686898810454178, -0.1174386023763629, 0.03819623558123302, 0.03231014499420897, 0.01821117262259132, 0.10721093570687466, 0.003818471731115839, -4.118820475125178e-33, 0.2562738101457314, -0.12508802568566202, -0.12508802568566202, -0.12508802568566202, 0.2562738101457314, 0.07141567151953723, -2.9582283945787943e-31, -0.0076411830227834515, -0.17887495255914587, -0.05541468105733129, -0.4169874101627732, -0.31336178792176755, 0.6018590069280918, 0.6399300891059382, 1.0192271427622748, 1.0192271427622748, 0.9730084845221717, 0.8343525097995734, 0.9961178136410977], [-0.33150978404477915, -0.07013855323404473, 0.005298633379213402, 0.09281823475863037, 0.04170945990974721, 0.11661025062172696, 0.01321778664596723, -2.166194472102871e-33, 0.10250952405063167, -0.035236063575805505, -0.035236063575805505, -0.035236063575805505, 0.10250952405063167, 0.04937379757677454, -2.333388073611656e-31, -0.04878601438261327, -0.22557779854235982, -0.11152954767921279, -0.2300760612385244, -0.12534474242717758, 0.7446255620317579, 0.7255900223459878, 0.9730084845221717, 0.9730084845221717, 1.019227142762275, 0.9267898262801635, 0.9344929359864803], [-0.35737224947091245, -0.09378857780636479, -0.02936134036930615, 0.19092358418354208, 0.009399314889652134, 0.1606695392859097, -0.03906590256799129, -4.118820475125178e-33, 0.05125476201891806, -0.005285409539178733, -0.005285409539178733, -0.005285409539178733, 0.05125476201891806, 0.012637341004578214, -2.333388073611656e-31, -0.041732614720907814, -0.26170641524672134, -0.16764441430135665, -0.1677722782637577, -0.06267239392896481, 0.8683565764172961, 0.8873921178755695, 0.8343525097995734, 0.8343525097995734, 0.9267898262801635, 1.0192271427622748, 0.8574618389196067], [-0.21659814788837206, -0.1706511576629047, 0.04495199317659316, 0.055514703690363, 0.0029372858854379005, 0.12365973680793181, -0.013511515143969484, -2.166194472102871e-33, 0.3075285721799559, -0.15503867972375587, -0.15503867972375587, -0.15503867972375587, 0.3075285721799559, 0.050843255839527174, -2.333388073611656e-31, 0.016751824141720665, -0.1730003807367026, -0.05031332954574617, -0.47929119314059193, -0.3760341364230471, 0.6208945473516511, 0.6399300891054643, 0.9961178136410977, 0.9961178136410977, 0.9344929359864803, 0.8574618389196067, 1.0192271427622748]], 'distribs': {'Unnamed: 0': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 50.00970873786408, 'std': 29.71564105342736}, 'Unnamed: 1': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 4.203883495145631, 'std': 1.4770688850832199}, 'Factor Loadings': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 51.0, 'std': 29.732137494637012}, 'Unnamed: 3': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 51.0, 'std': 29.732137494637012}, 'Unnamed: 4': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 51.0, 'std': 29.732137494637012}, 'Unnamed: 5': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 51.0, 'std': 29.732137494637012}, 'Unnamed: 6': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 51.0, 'std': 29.732137494637012}, 'Unnamed: 7': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 0.0005396429594737321, 'std': 0.005450126734157277}, 'Unnamed: 8': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 0.9902912621359223, 'std': 0.1703876580076143}, 'Unnamed: 9': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 1.9514563106796117, 'std': 0.2915855809258489}, 'Unnamed: 10': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 1.9514563106796117, 'std': 0.2915855809258489}, 'Unnamed: 11': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 1.9514563106796117, 'std': 0.2915855809258489}, 'Unnamed: 12': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 0.9902912621359223, 'std': 0.1703876580076143}, 'Unnamed: 13': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 50.00970873786408, 'std': 29.715641053427355}, 'Unnamed: 14': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 0.0, 'std': 0}, 'Unnamed: 15': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 50.00970873786408, 'std': 29.71564105342736}, 'Unnamed: 16': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 51.0, 'std': 29.732137494637012}, 'Unnamed: 17': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 4.223300970873787, 'std': 1.7119343447336615}, 'Unnamed: 18': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 2.912621359223301, 'std': 0.42051277335354803}, 'Unnamed: 19': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 1.0, 'std': 0.13934660285832354}, 'Unnamed: 20': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 0.20388349514563106, 'std': 0.9175656194726719}, 'Unnamed: 21': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 0.20388349514563106, 'std': 0.9175656194726719}, 'Unnamed: 22': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 0.27184466019417475, 'std': 1.1337212098636915}, 'Unnamed: 23': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 0.27184466019417475, 'std': 1.1337212098636915}, 'Unnamed: 24': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 0.27184466019417475, 'std': 1.1337212098636915}, 'Unnamed: 25': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 0.27184466019417475, 'std': 1.1337212098636915}, 'Unnamed: 26': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 0.27184466019417475, 'std': 1.1337212098636915}}, 'type': 'copulas.multivariate.gaussian.GaussianMultivariate', 'fitted': True, 'distribution': 'copulas.univariate.gaussian.GaussianUnivariate'}\n",
            "<class 'dict'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RyY_JBexHwR2",
        "colab_type": "code",
        "outputId": "ff257d1f-203f-4996-b3fb-278abbb5d16e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(type(copula_params))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'dict'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TMOhVDErIAxY",
        "colab_type": "code",
        "outputId": "0fa883e9-6728-48ee-f88a-8ea6ccd26744",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "list(copula_params.keys())  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['covariance', 'distribs', 'type', 'fitted', 'distribution']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w3gS7pNjH-05",
        "colab_type": "code",
        "outputId": "5068fc31-c5f3-4e15-8f58-034d6fdebdb1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print(copula_params.get(\"covariance\"))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.9914845315176679, 0.04184433658410097, 0.03288527696147693, -0.03529466000687392, -0.13247684498214315, -0.17927490822669157, -0.03782731381273307, -3.569644411775154e-33, 0.19750439439579887, 0.05770571899535679, 0.05770571899535679, 0.05770571899535679, 0.19750439439579887, 0.12996026556316373, -8.98207961390261e-32, 0.26424300922257865, 0.21062490389877606, 0.18947083696334682, 0.020204797895257497, -0.2415010838746474, -0.352049881780852, -0.3600386361669552, -0.24686898810454178, -0.24686898810454178, -0.33150978404477915, -0.35737224947091245, -0.21659814788837206], [0.04184433658410097, 0.9388149407801467, 0.09128325296594379, 0.07496238466645654, 0.10923090337112237, -0.06053353468554455, 0.09254754558069295, -2.8793606099254716e-33, 0.03934048789759487, 0.2697775529977754, 0.2697775529977754, 0.2697775529977754, 0.03934048789759487, 0.1498952111058496, -4.2713693816112953e-32, -0.09823832119879221, -0.014835545507334652, 0.2853347937268479, 0.33021592178874976, -0.04810457249656736, -0.037601041095161956, -0.008379660870484916, -0.1174386023763629, -0.1174386023763629, -0.07013855323404473, -0.09378857780636479, -0.1706511576629047], [0.03288527696147693, 0.09128325296594379, 1.010290252474808, 0.03477106760449798, -0.08441042068007705, -0.06971573854218002, 0.04723690694861341, -1.1288619079972709e-33, 0.04299688975596549, -0.02415107257426183, -0.02415107257426183, -0.02415107257426183, 0.04299688975596549, -0.00122710438823704, 9.763130015111533e-33, 0.0594445226246905, 0.011263243705259485, 0.1501488523375306, -0.09424450572011635, -0.0525749421966012, -0.10015627423564727, -0.0743886893844073, 0.03819623558123302, 0.03819623558123302, 0.005298633379213402, -0.02936134036930615, 0.04495199317659316], [-0.03529466000687392, 0.07496238466645654, 0.03477106760449798, 0.9903846153846152, 0.06243559876349623, -0.026359706106357277, -0.05684669145647595, -2.6238411915612242e-33, 0.0371336774271919, 0.1513220889388727, 0.1513220889388727, 0.1513220889388727, 0.0371336774271919, 0.17837839360514862, 5.076827607857997e-32, -0.0015632973713156435, -0.11348057882711349, -0.13023147825279896, 0.13343605896792482, -0.04540594903396988, 0.19833781599537334, 0.18817595183084262, 0.03231014499420897, 0.03231014499420897, 0.09281823475863037, 0.19092358418354208, 0.055514703690363], [-0.13247684498214315, 0.10923090337112237, -0.08441042068007705, 0.06243559876349623, 0.9903846153846154, -0.05797791317593294, 0.09815308453922314, 2.6238411915612242e-33, -0.06644973869943764, 0.009707454762116364, 0.009707454762116364, 0.009707454762116364, -0.06644973869943764, 0.02931042490807388, -7.810504012089226e-33, 0.05289996588742606, 0.06285560682765111, 0.01468629814501327, 0.0455345601819328, 0.08125223426218962, -0.02812658869795003, -0.04699862197601501, 0.01821117262259132, 0.01821117262259132, 0.04170945990974721, 0.009399314889652134, 0.0029372858854379005], [-0.17927490822669157, -0.06053353468554455, -0.06971573854218002, -0.026359706106357277, -0.05797791317593294, 0.9903846153846152, 0.012807445902961297, -1.922116221725083e-33, -0.07622175904709011, -0.08394093235477092, -0.08394093235477092, -0.08394093235477092, -0.07622175904709011, 0.025175251216206472, 3.319464205137921e-32, -0.09926097825397236, 0.009262577841494615, -0.1681629767730317, -0.07562696517173186, 0.09320126736169909, 0.11958336726401145, 0.16785222281329812, 0.10721093570687466, 0.10721093570687466, 0.11661025062172696, 0.1606695392859097, 0.12365973680793181], [-0.03782731381273307, 0.09254754558069295, 0.04723690694861341, -0.05684669145647595, 0.09815308453922314, 0.012807445902961297, 0.9903846153846151, -2.166194472102871e-33, -0.10749222431030397, -0.09307736036617448, -0.09307736036617448, -0.09307736036617448, -0.10749222431030397, -0.12854338708012303, 4.7839337074046507e-32, -0.14673142746467802, -0.08480802831414364, -0.03511095119436927, -0.040783127815122396, 0.13143763813933731, 0.04554692714994059, -0.08292807140946357, 0.003818471731115839, 0.003818471731115839, 0.01321778664596723, -0.03906590256799129, -0.013511515143969484], [-3.569644411775154e-33, -2.8793606099254716e-33, -1.1288619079972709e-33, -2.6238411915612242e-33, 2.6238411915612242e-33, -1.922116221725083e-33, -2.166194472102871e-33, 1.7504987019282007e-33, 4.385781061475884e-34, 5.98182399558689e-33, 5.98182399558689e-33, 5.98182399558689e-33, 4.385781061475884e-34, -9.152934389167061e-35, -3.734397230780161e-32, 1.8305868778334122e-34, 4.881565007555766e-34, -3.356075942694589e-34, -1.1608971783593557e-32, 2.153476808905063e-34, 1.2901823766063403e-32, 1.2901823766063403e-32, -4.118820475125178e-33, -4.118820475125178e-33, -2.166194472102871e-33, -4.118820475125178e-33, -2.166194472102871e-33], [0.19750439439579887, 0.03934048789759487, 0.04299688975596549, 0.0371336774271919, -0.06644973869943764, -0.07622175904709011, -0.10749222431030397, 4.385781061475884e-34, 0.6820753510124921, 0.19928492531497186, 0.19928492531497186, 0.19928492531497186, 0.6820753510124921, 0.1114628760060287, -1.1959834268511627e-32, 0.16035010236385264, 0.1426714977369436, 0.23760273095755507, -0.13818512684056317, -0.8340165648344741, -1.1857696567573996e-11, -1.1857696567573996e-11, 0.2562738101457314, 0.2562738101457314, 0.10250952405063167, 0.05125476201891806, 0.3075285721799559], [0.05770571899535679, 0.2697775529977754, -0.02415107257426183, 0.1513220889388727, 0.009707454762116364, -0.08394093235477092, -0.09307736036617448, 5.98182399558689e-33, 0.19928492531497186, 0.5719842787722059, 0.5719842787722059, 0.5719842787722059, 0.19928492531497186, 0.10798396920913313, -6.27281103470916e-32, 0.07941678158766928, 0.0927078724686545, 0.11375779663648286, 0.5486134524886073, -0.24367919797987983, 0.02285682097448604, 0.02285682097448604, -0.12508802568566202, -0.12508802568566202, -0.035236063575805505, -0.005285409539178733, -0.15503867972375587], [0.05770571899535679, 0.2697775529977754, -0.02415107257426183, 0.1513220889388727, 0.009707454762116364, -0.08394093235477092, -0.09307736036617448, 5.98182399558689e-33, 0.19928492531497186, 0.5719842787722059, 0.5719842787722059, 0.5719842787722059, 0.19928492531497186, 0.10798396920913313, -6.27281103470916e-32, 0.07941678158766928, 0.0927078724686545, 0.11375779663648286, 0.5486134524886073, -0.24367919797987983, 0.02285682097448604, 0.02285682097448604, -0.12508802568566202, -0.12508802568566202, -0.035236063575805505, -0.005285409539178733, -0.15503867972375587], [0.05770571899535679, 0.2697775529977754, -0.02415107257426183, 0.1513220889388727, 0.009707454762116364, -0.08394093235477092, -0.09307736036617448, 5.98182399558689e-33, 0.19928492531497186, 0.5719842787722059, 0.5719842787722059, 0.5719842787722059, 0.19928492531497186, 0.10798396920913313, -6.27281103470916e-32, 0.07941678158766928, 0.0927078724686545, 0.11375779663648286, 0.5486134524886073, -0.24367919797987983, 0.02285682097448604, 0.02285682097448604, -0.12508802568566202, -0.12508802568566202, -0.035236063575805505, -0.005285409539178733, -0.15503867972375587], [0.19750439439579887, 0.03934048789759487, 0.04299688975596549, 0.0371336774271919, -0.06644973869943764, -0.07622175904709011, -0.10749222431030397, 4.385781061475884e-34, 0.6820753510124921, 0.19928492531497186, 0.19928492531497186, 0.19928492531497186, 0.6820753510124921, 0.1114628760060287, -1.1959834268511627e-32, 0.16035010236385264, 0.1426714977369436, 0.23760273095755507, -0.13818512684056317, -0.8340165648344741, -1.1857696567573996e-11, -1.1857696567573996e-11, 0.2562738101457314, 0.2562738101457314, 0.10250952405063167, 0.05125476201891806, 0.3075285721799559], [0.12996026556316373, 0.1498952111058496, -0.00122710438823704, 0.17837839360514862, 0.02931042490807388, 0.025175251216206472, -0.12854338708012303, -9.152934389167061e-35, 0.1114628760060287, 0.10798396920913313, 0.10798396920913313, 0.10798396920913313, 0.1114628760060287, 0.991484531517668, 2.3431512036267676e-32, 0.19229140884996124, 0.14783526109245296, -0.13322320277494706, -0.014658382786755457, -0.13629282981032217, -0.09459411427941945, 0.041577835553438004, 0.07141567151953723, 0.07141567151953723, 0.04937379757677454, 0.012637341004578214, 0.050843255839527174], [-8.98207961390261e-32, -4.2713693816112953e-32, 9.763130015111533e-33, 5.076827607857997e-32, -7.810504012089226e-33, 3.319464205137921e-32, 4.7839337074046507e-32, -3.734397230780161e-32, -1.1959834268511627e-32, -6.27281103470916e-32, -6.27281103470916e-32, -6.27281103470916e-32, -1.1959834268511627e-32, 2.3431512036267676e-32, 7.96671409233101e-31, 6.834191010578073e-32, -4.295777206649074e-32, 3.905252006044613e-32, -9.177342214204841e-32, -4.594685262080849e-33, 3.4805558503872613e-31, 3.4805558503872613e-31, -2.9582283945787943e-31, -2.9582283945787943e-31, -2.333388073611656e-31, -2.333388073611656e-31, -2.333388073611656e-31], [0.26424300922257865, -0.09823832119879221, 0.0594445226246905, -0.0015632973713156435, 0.05289996588742606, -0.09926097825397236, -0.14673142746467802, 1.8305868778334122e-34, 0.16035010236385264, 0.07941678158766928, 0.07941678158766928, 0.07941678158766928, 0.16035010236385264, 0.19229140884996124, 6.834191010578073e-32, 0.991484531517668, 0.2319731583242704, 0.029875473522212415, -0.052690943530769675, -0.19607024689232508, -0.17085040649361005, -0.06554409821953577, -0.0076411830227834515, -0.0076411830227834515, -0.04878601438261327, -0.041732614720907814, 0.016751824141720665], [0.21062490389877606, -0.014835545507334652, 0.011263243705259485, -0.11348057882711349, 0.06285560682765111, 0.009262577841494615, -0.08480802831414364, 4.881565007555766e-34, 0.1426714977369436, 0.0927078724686545, 0.0927078724686545, 0.0927078724686545, 0.1426714977369436, 0.14783526109245296, -4.295777206649074e-32, 0.2319731583242704, 1.0017059464796632, 0.13814769999022916, 0.05048008700817167, -0.17445353565175542, -0.27155234047631976, -0.270826493002116, -0.17887495255914587, -0.17887495255914587, -0.22557779854235982, -0.26170641524672134, -0.1730003807367026], [0.18947083696334682, 0.2853347937268479, 0.1501488523375306, -0.13023147825279896, 0.01468629814501327, -0.1681629767730317, -0.03511095119436927, -3.356075942694589e-34, 0.23760273095755507, 0.11375779663648286, 0.11375779663648286, 0.11375779663648286, 0.23760273095755507, -0.13322320277494706, 3.905252006044613e-32, 0.029875473522212415, 0.13814769999022916, 1.002938858089952, 0.09236399919456553, -0.2905319315435815, -0.10177657131457439, -0.19002001003882163, -0.05541468105733129, -0.05541468105733129, -0.11152954767921279, -0.16764441430135665, -0.05031332954574617], [0.020204797895257497, 0.33021592178874976, -0.09424450572011635, 0.13343605896792482, 0.0455345601819328, -0.07562696517173186, -0.040783127815122396, -1.1608971783593557e-32, -0.13818512684056317, 0.5486134524886073, 0.5486134524886073, 0.5486134524886073, -0.13818512684056317, -0.014658382786755457, -9.177342214204841e-32, -0.052690943530769675, 0.05048008700817167, 0.09236399919456553, 0.8129711393941395, 0.16896658766774583, 0.01132073544131568, -0.09132059972139782, -0.4169874101627732, -0.4169874101627732, -0.2300760612385244, -0.1677722782637577, -0.47929119314059193], [-0.2415010838746474, -0.04810457249656736, -0.0525749421966012, -0.04540594903396988, 0.08125223426218962, 0.09320126736169909, 0.13143763813933731, 2.153476808905063e-34, -0.8340165648344741, -0.24367919797987983, -0.24367919797987983, -0.24367919797987983, -0.8340165648344741, -0.13629282981032217, -4.594685262080849e-33, -0.19607024689232508, -0.17445353565175542, -0.2905319315435815, 0.16896658766774583, 1.0198046731742143, -4.209982854997568e-08, -4.209982854997568e-08, -0.31336178792176755, -0.31336178792176755, -0.12534474242717758, -0.06267239392896481, -0.3760341364230471], [-0.352049881780852, -0.037601041095161956, -0.10015627423564727, 0.19833781599537334, -0.02812658869795003, 0.11958336726401145, 0.04554692714994059, 1.2901823766063403e-32, -1.1857696567573996e-11, 0.02285682097448604, 0.02285682097448604, 0.02285682097448604, -1.1857696567573996e-11, -0.09459411427941945, 3.4805558503872613e-31, -0.17085040649361005, -0.27155234047631976, -0.10177657131457439, 0.01132073544131568, -4.209982854997568e-08, 1.0193083414002075, 0.76059010760587, 0.6018590069280918, 0.6018590069280918, 0.7446255620317579, 0.8683565764172961, 0.6208945473516511], [-0.3600386361669552, -0.008379660870484916, -0.0743886893844073, 0.18817595183084262, -0.04699862197601501, 0.16785222281329812, -0.08292807140946357, 1.2901823766063403e-32, -1.1857696567573996e-11, 0.02285682097448604, 0.02285682097448604, 0.02285682097448604, -1.1857696567573996e-11, 0.041577835553438004, 3.4805558503872613e-31, -0.06554409821953577, -0.270826493002116, -0.19002001003882163, -0.09132059972139782, -4.209982854997568e-08, 0.76059010760587, 1.0193083414002078, 0.6399300891059382, 0.6399300891059382, 0.7255900223459878, 0.8873921178755695, 0.6399300891054643], [-0.24686898810454178, -0.1174386023763629, 0.03819623558123302, 0.03231014499420897, 0.01821117262259132, 0.10721093570687466, 0.003818471731115839, -4.118820475125178e-33, 0.2562738101457314, -0.12508802568566202, -0.12508802568566202, -0.12508802568566202, 0.2562738101457314, 0.07141567151953723, -2.9582283945787943e-31, -0.0076411830227834515, -0.17887495255914587, -0.05541468105733129, -0.4169874101627732, -0.31336178792176755, 0.6018590069280918, 0.6399300891059382, 1.0192271427622748, 1.0192271427622748, 0.9730084845221717, 0.8343525097995734, 0.9961178136410977], [-0.24686898810454178, -0.1174386023763629, 0.03819623558123302, 0.03231014499420897, 0.01821117262259132, 0.10721093570687466, 0.003818471731115839, -4.118820475125178e-33, 0.2562738101457314, -0.12508802568566202, -0.12508802568566202, -0.12508802568566202, 0.2562738101457314, 0.07141567151953723, -2.9582283945787943e-31, -0.0076411830227834515, -0.17887495255914587, -0.05541468105733129, -0.4169874101627732, -0.31336178792176755, 0.6018590069280918, 0.6399300891059382, 1.0192271427622748, 1.0192271427622748, 0.9730084845221717, 0.8343525097995734, 0.9961178136410977], [-0.33150978404477915, -0.07013855323404473, 0.005298633379213402, 0.09281823475863037, 0.04170945990974721, 0.11661025062172696, 0.01321778664596723, -2.166194472102871e-33, 0.10250952405063167, -0.035236063575805505, -0.035236063575805505, -0.035236063575805505, 0.10250952405063167, 0.04937379757677454, -2.333388073611656e-31, -0.04878601438261327, -0.22557779854235982, -0.11152954767921279, -0.2300760612385244, -0.12534474242717758, 0.7446255620317579, 0.7255900223459878, 0.9730084845221717, 0.9730084845221717, 1.019227142762275, 0.9267898262801635, 0.9344929359864803], [-0.35737224947091245, -0.09378857780636479, -0.02936134036930615, 0.19092358418354208, 0.009399314889652134, 0.1606695392859097, -0.03906590256799129, -4.118820475125178e-33, 0.05125476201891806, -0.005285409539178733, -0.005285409539178733, -0.005285409539178733, 0.05125476201891806, 0.012637341004578214, -2.333388073611656e-31, -0.041732614720907814, -0.26170641524672134, -0.16764441430135665, -0.1677722782637577, -0.06267239392896481, 0.8683565764172961, 0.8873921178755695, 0.8343525097995734, 0.8343525097995734, 0.9267898262801635, 1.0192271427622748, 0.8574618389196067], [-0.21659814788837206, -0.1706511576629047, 0.04495199317659316, 0.055514703690363, 0.0029372858854379005, 0.12365973680793181, -0.013511515143969484, -2.166194472102871e-33, 0.3075285721799559, -0.15503867972375587, -0.15503867972375587, -0.15503867972375587, 0.3075285721799559, 0.050843255839527174, -2.333388073611656e-31, 0.016751824141720665, -0.1730003807367026, -0.05031332954574617, -0.47929119314059193, -0.3760341364230471, 0.6208945473516511, 0.6399300891054643, 0.9961178136410977, 0.9961178136410977, 0.9344929359864803, 0.8574618389196067, 1.0192271427622748]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jV5OHGTfNdPK",
        "colab_type": "code",
        "outputId": "bc191acf-9c47-4b0b-ebce-5efcfc464e72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print(copula_params.get(\"distribs\"))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'Unnamed: 0': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 50.00970873786408, 'std': 29.71564105342736}, 'Unnamed: 1': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 4.203883495145631, 'std': 1.4770688850832199}, 'Factor Loadings': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 51.0, 'std': 29.732137494637012}, 'Unnamed: 3': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 51.0, 'std': 29.732137494637012}, 'Unnamed: 4': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 51.0, 'std': 29.732137494637012}, 'Unnamed: 5': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 51.0, 'std': 29.732137494637012}, 'Unnamed: 6': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 51.0, 'std': 29.732137494637012}, 'Unnamed: 7': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 0.0005396429594737321, 'std': 0.005450126734157277}, 'Unnamed: 8': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 0.9902912621359223, 'std': 0.1703876580076143}, 'Unnamed: 9': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 1.9514563106796117, 'std': 0.2915855809258489}, 'Unnamed: 10': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 1.9514563106796117, 'std': 0.2915855809258489}, 'Unnamed: 11': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 1.9514563106796117, 'std': 0.2915855809258489}, 'Unnamed: 12': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 0.9902912621359223, 'std': 0.1703876580076143}, 'Unnamed: 13': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 50.00970873786408, 'std': 29.715641053427355}, 'Unnamed: 14': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 0.0, 'std': 0}, 'Unnamed: 15': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 50.00970873786408, 'std': 29.71564105342736}, 'Unnamed: 16': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 51.0, 'std': 29.732137494637012}, 'Unnamed: 17': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 4.223300970873787, 'std': 1.7119343447336615}, 'Unnamed: 18': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 2.912621359223301, 'std': 0.42051277335354803}, 'Unnamed: 19': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 1.0, 'std': 0.13934660285832354}, 'Unnamed: 20': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 0.20388349514563106, 'std': 0.9175656194726719}, 'Unnamed: 21': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 0.20388349514563106, 'std': 0.9175656194726719}, 'Unnamed: 22': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 0.27184466019417475, 'std': 1.1337212098636915}, 'Unnamed: 23': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 0.27184466019417475, 'std': 1.1337212098636915}, 'Unnamed: 24': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 0.27184466019417475, 'std': 1.1337212098636915}, 'Unnamed: 25': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 0.27184466019417475, 'std': 1.1337212098636915}, 'Unnamed: 26': {'type': 'copulas.univariate.gaussian.GaussianUnivariate', 'fitted': True, 'mean': 0.27184466019417475, 'std': 1.1337212098636915}}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_LTs7T9QNqmd",
        "colab_type": "code",
        "outputId": "2e9605c4-ce9e-4c4e-b1b1-f1fb75ac9d97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(copula_params.get(\"type\"))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "copulas.multivariate.gaussian.GaussianMultivariate\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qLN3JPaRNqZ-",
        "colab_type": "code",
        "outputId": "c1b79a4c-d866-44b5-bfdc-c12648065768",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(copula_params.get(\"fitted\"))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q09tUrf9NqYI",
        "colab_type": "code",
        "outputId": "ec69e9f0-bfd6-4d11-f1f9-39c18bb6cd47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(copula_params.get(\"distribution\"))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "copulas.univariate.gaussian.GaussianUnivariate\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "krZVcVTTNqXB",
        "colab_type": "code",
        "outputId": "b6988b7e-d557-407a-ce0d-1ffede7ca2e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "# samples.corr()\n",
        "a = samples\n",
        "# list(a.columns) \n",
        "tmp = a.drop(['Unnamed: 0',\n",
        " 'Unnamed: 1',\n",
        " 'Unnamed: 7',\n",
        " 'Unnamed: 8',\n",
        " 'Unnamed: 9',\n",
        " 'Unnamed: 10',\n",
        " 'Unnamed: 11',\n",
        " 'Unnamed: 12',\n",
        " 'Unnamed: 13',\n",
        " 'Unnamed: 14',\n",
        " 'Unnamed: 15',\n",
        " 'Unnamed: 16',\n",
        " 'Unnamed: 17',\n",
        " 'Unnamed: 18',\n",
        " 'Unnamed: 19',\n",
        " 'Unnamed: 20',\n",
        " 'Unnamed: 21',\n",
        " 'Unnamed: 22',\n",
        " 'Unnamed: 23',\n",
        " 'Unnamed: 24',\n",
        " 'Unnamed: 25',\n",
        " 'Unnamed: 26'], axis=1)\n",
        "tmp.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Factor Loadings</th>\n",
              "      <th>Unnamed: 3</th>\n",
              "      <th>Unnamed: 4</th>\n",
              "      <th>Unnamed: 5</th>\n",
              "      <th>Unnamed: 6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.023098</td>\n",
              "      <td>40.524861</td>\n",
              "      <td>33.179460</td>\n",
              "      <td>2.101020</td>\n",
              "      <td>39.014180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>54.022903</td>\n",
              "      <td>13.820276</td>\n",
              "      <td>83.632520</td>\n",
              "      <td>48.513248</td>\n",
              "      <td>53.266422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.446692</td>\n",
              "      <td>117.610072</td>\n",
              "      <td>50.018579</td>\n",
              "      <td>45.590367</td>\n",
              "      <td>34.537907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>73.765380</td>\n",
              "      <td>70.574786</td>\n",
              "      <td>70.366731</td>\n",
              "      <td>80.274378</td>\n",
              "      <td>17.915009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-32.079105</td>\n",
              "      <td>44.919402</td>\n",
              "      <td>56.363157</td>\n",
              "      <td>60.158135</td>\n",
              "      <td>33.734217</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Factor Loadings  Unnamed: 3  Unnamed: 4  Unnamed: 5  Unnamed: 6\n",
              "0         8.023098   40.524861   33.179460    2.101020   39.014180\n",
              "1        54.022903   13.820276   83.632520   48.513248   53.266422\n",
              "2        17.446692  117.610072   50.018579   45.590367   34.537907\n",
              "3        73.765380   70.574786   70.366731   80.274378   17.915009\n",
              "4       -32.079105   44.919402   56.363157   60.158135   33.734217"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8X9O_2ajXbsY",
        "colab_type": "code",
        "outputId": "e5335d8b-4fdc-45d9-8405-f1e614c17f5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "tmp = tmp.rename(columns={\"Factor Loadings\": \"60%\", \"Unnamed: 3\": \"25%\", \"Unnamed: 4\": \"10%\", \"Unnamed: 5\": \"3%\", \"Unnamed: 6\": \"2%\"})\n",
        "tmp.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>60%</th>\n",
              "      <th>25%</th>\n",
              "      <th>10%</th>\n",
              "      <th>3%</th>\n",
              "      <th>2%</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.023098</td>\n",
              "      <td>40.524861</td>\n",
              "      <td>33.179460</td>\n",
              "      <td>2.101020</td>\n",
              "      <td>39.014180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>54.022903</td>\n",
              "      <td>13.820276</td>\n",
              "      <td>83.632520</td>\n",
              "      <td>48.513248</td>\n",
              "      <td>53.266422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.446692</td>\n",
              "      <td>117.610072</td>\n",
              "      <td>50.018579</td>\n",
              "      <td>45.590367</td>\n",
              "      <td>34.537907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>73.765380</td>\n",
              "      <td>70.574786</td>\n",
              "      <td>70.366731</td>\n",
              "      <td>80.274378</td>\n",
              "      <td>17.915009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-32.079105</td>\n",
              "      <td>44.919402</td>\n",
              "      <td>56.363157</td>\n",
              "      <td>60.158135</td>\n",
              "      <td>33.734217</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         60%         25%        10%         3%         2%\n",
              "0   8.023098   40.524861  33.179460   2.101020  39.014180\n",
              "1  54.022903   13.820276  83.632520  48.513248  53.266422\n",
              "2  17.446692  117.610072  50.018579  45.590367  34.537907\n",
              "3  73.765380   70.574786  70.366731  80.274378  17.915009\n",
              "4 -32.079105   44.919402  56.363157  60.158135  33.734217"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MVaECsDwaG6y",
        "colab_type": "code",
        "outputId": "82fbdfe8-868e-405e-ad5b-4f45293e661b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "tmp.tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>60%</th>\n",
              "      <th>25%</th>\n",
              "      <th>10%</th>\n",
              "      <th>3%</th>\n",
              "      <th>2%</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>55.234662</td>\n",
              "      <td>19.021138</td>\n",
              "      <td>59.062753</td>\n",
              "      <td>34.479663</td>\n",
              "      <td>46.045075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>52.837032</td>\n",
              "      <td>38.660250</td>\n",
              "      <td>74.539660</td>\n",
              "      <td>52.731803</td>\n",
              "      <td>56.152247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>29.275184</td>\n",
              "      <td>61.589292</td>\n",
              "      <td>82.424742</td>\n",
              "      <td>83.753700</td>\n",
              "      <td>67.182363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>8.705218</td>\n",
              "      <td>41.131489</td>\n",
              "      <td>66.433303</td>\n",
              "      <td>109.561717</td>\n",
              "      <td>63.966428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>86.393988</td>\n",
              "      <td>79.389986</td>\n",
              "      <td>42.100877</td>\n",
              "      <td>48.085146</td>\n",
              "      <td>31.974789</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           60%        25%        10%          3%         2%\n",
              "995  55.234662  19.021138  59.062753   34.479663  46.045075\n",
              "996  52.837032  38.660250  74.539660   52.731803  56.152247\n",
              "997  29.275184  61.589292  82.424742   83.753700  67.182363\n",
              "998   8.705218  41.131489  66.433303  109.561717  63.966428\n",
              "999  86.393988  79.389986  42.100877   48.085146  31.974789"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mk8C2WOvNqUk",
        "colab_type": "code",
        "outputId": "dcb05fae-5d2d-4b4e-adfc-799461c52146",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "import seaborn as sns\n",
        "sns.heatmap(tmp)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd7e1a44898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAD8CAYAAABTjp5OAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qoyl1AWaVT1n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FByVvCxS2_Rl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9_K7L_Xq3Vlp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}