CALDISS_EDA2.ipynb

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      "name": "CALDISS - EDA2",
      "version": "0.3.2",
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RJuro/CALDISS-EDA/blob/master/CALDISS_EDA2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "gnrQ-FwsBqJe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "outputId": "887b506c-b29d-4505-eb07-eef5f2967094"
      },
      "cell_type": "code",
      "source": [
        "!pip3 install seaborn==0.9.0"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting seaborn==0.9.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a8/76/220ba4420459d9c4c9c9587c6ce607bf56c25b3d3d2de62056efe482dadc/seaborn-0.9.0-py3-none-any.whl (208kB)\n",
            "\u001b[K    100% |████████████████████████████████| 215kB 24.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from seaborn==0.9.0) (1.1.0)\n",
            "Requirement already satisfied: numpy>=1.9.3 in /usr/local/lib/python3.6/dist-packages (from seaborn==0.9.0) (1.14.6)\n",
            "Requirement already satisfied: matplotlib>=1.4.3 in /usr/local/lib/python3.6/dist-packages (from seaborn==0.9.0) (3.0.3)\n",
            "Requirement already satisfied: pandas>=0.15.2 in /usr/local/lib/python3.6/dist-packages (from seaborn==0.9.0) (0.22.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn==0.9.0) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn==0.9.0) (2.3.1)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn==0.9.0) (2.5.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn==0.9.0) (1.0.1)\n",
            "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas>=0.15.2->seaborn==0.9.0) (2018.9)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib>=1.4.3->seaborn==0.9.0) (1.11.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib>=1.4.3->seaborn==0.9.0) (40.8.0)\n",
            "Installing collected packages: seaborn\n",
            "  Found existing installation: seaborn 0.7.1\n",
            "    Uninstalling seaborn-0.7.1:\n",
            "      Successfully uninstalled seaborn-0.7.1\n",
            "Successfully installed seaborn-0.9.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "seaborn"
                ]
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          "metadata": {
            "tags": []
          }
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      ]
    },
    {
      "metadata": {
        "id": "B6OcLMqIcJ0Y",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!pip3 install hdbscan"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "axhadLhBtP30",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# A quick dive into nomad geography and the gig-economy\n",
        "## CALDISS - EDA Workshop, 20/3 2019, Roman Jurowetzki\n",
        "\n",
        "In this workshop you will learn how to work with dimensionality reduction and clustering techniques. We will have a look at data that comes in slightly different shapes and how we can aggregate it to identify latent patterns in a population.\n",
        "\n",
        "We will use a dataset on cities worldwide to get familiar with dimensionality reduction.\n",
        "\n",
        "Then, we will use a dataset of jobs performed by 1000 freelance-workers.\n",
        "\n",
        "![](https://www.phocuswire.com/uploadedimages/uploads/2013/04/global-routes.jpg?width=800&height=400&scale=both&mode=crop)\n",
        "\n",
        "There are many many ways to do similar things in Python and despite that we are not working with Natural Language Processing, we will use some tools and packages from this domeain for our analysis."
      ]
    },
    {
      "metadata": {
        "id": "15jNW6FNtZzd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
        "import numpy as np\n",
        "\n",
        "import gensim\n",
        "from gensim.corpora.dictionary import Dictionary"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "el2mVQlWvljI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "cities = pd.read_csv('https://github.com/SDS-AAU/M1-2018/raw/master/data/nomad_cities.csv', sep='\\t')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aIf50m7-wifL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "vars_analysis = [\"cost_nomad\", \"cost_coworking\", \"cost_expat\", \"coffee_in_cafe\", \"cost_beer\", # costs\n",
        "          \"places_to_work\", \"free_wifi_available\", \"internet_speed\", # work\n",
        "          \"freedom_score\", \"peace_score\", \"safety\", \"fragile_states_index\", \"press_freedom_index\", # safety & freedom\n",
        "          \"female_friendly\", \"lgbt_friendly\", \"friendly_to_foreigners\", \"racism\", # friendly\n",
        "          \"leisure\",\"life_score\",\"nightlife\",\"weed\"] # fun \n",
        "\n",
        "vars_descr = [\"nomad_score\", \"cost_nomad\", \"places_to_work\", \"freedom_score\", \"friendly_to_foreigners\", \"life_score\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "R2PtH_vtwjHe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data = cities[vars_analysis]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1tuUt1Unw_UF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "descr = cities[vars_descr]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WfLSyC59yGql",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "d4720ff5-8f22-41c0-a299-42ad2a686aa4"
      },
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 781 entries, 0 to 780\n",
            "Data columns (total 21 columns):\n",
            "cost_nomad                781 non-null int64\n",
            "cost_coworking            781 non-null float64\n",
            "cost_expat                781 non-null int64\n",
            "coffee_in_cafe            781 non-null float64\n",
            "cost_beer                 781 non-null float64\n",
            "places_to_work            781 non-null float64\n",
            "free_wifi_available       781 non-null float64\n",
            "internet_speed            781 non-null int64\n",
            "freedom_score             781 non-null object\n",
            "peace_score               781 non-null object\n",
            "safety                    781 non-null float64\n",
            "fragile_states_index      781 non-null object\n",
            "press_freedom_index       781 non-null object\n",
            "female_friendly           781 non-null float64\n",
            "lgbt_friendly             781 non-null float64\n",
            "friendly_to_foreigners    781 non-null float64\n",
            "racism                    781 non-null float64\n",
            "leisure                   781 non-null float64\n",
            "life_score                781 non-null float64\n",
            "nightlife                 781 non-null float64\n",
            "weed                      781 non-null int64\n",
            "dtypes: float64(13), int64(4), object(4)\n",
            "memory usage: 128.2+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "rb2w3utrx2cI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "outputId": "8a262bb0-55dc-4eeb-df62-020d3f7ac645"
      },
      "cell_type": "code",
      "source": [
        "print(data['peace_score'].unique())\n",
        "print(data['freedom_score'].unique())\n",
        "print(data['fragile_states_index'].unique())\n",
        "print(data['press_freedom_index'].unique())"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['0.8' '0.4' 'DotMap(__next__=DotMap())' '1' '0.6' 'DotMap()' '0.2']\n",
            "['0.6' '0.2' '0.8' '1' '0.4' 'DotMap()']\n",
            "['52.7' '78.8' '40.8' 'DotMap(__next__=DotMap())' '34' '39.8' '29.2' '29'\n",
            " '21.3' '22.6' '74.9' '48.4' '23.8' '28.6' '70.7' 'DotMap()' '22.5' '36.1'\n",
            " '28.2' '21.8' '39.6' '65.3' '34.5' '43.1' '27.5' '32.4' '55.9' '53.2'\n",
            " '35.1' '52.9' '21.2' '70.4' '32.9' '40.7' '47.4' '43.4' '36.2' '75.5'\n",
            " '67' '73.9' '81' '79.6' '41.9' '72' '21.5' '69.6' '44.9' '77.3' '18.8'\n",
            " '64' '45.1' '52.4' '33.9' '53.7' '78.9' '78' '42.4' '66.1' '70.8' '65'\n",
            " '44.5' '51.6' '75.6' '74.2' '66.3' '80.2' '62' '24.1' '72.6' '87.7'\n",
            " '55.2' '83.6' '84.7' '43.2' '61.2' '63.4' '22.8' '72.2' '76.3' '66' '74'\n",
            " '73.2' '69.9' '90.7' '66.5' '87.4' '98.3' '89.6' '78.5' '101.7' '79'\n",
            " '72.5' '90.2' '56.6' '83.2' '96.3' '84.4' '103.5' '74.6' '71.1' '95.2'\n",
            " '91.2' '78.3' '91.3' '83.5' '87.8' '84.2' '81.8' '97.7' '71.2' '79.8'\n",
            " '83.8' '97.2' '86.9' '100.5' '103.8' '97.8' '90.5' '111.5' '110.8' '81.6'\n",
            " '86.2' '91' '96.4' '104.7' '93.9']\n",
            "['28.17' '44.53' '16.66' '24.37' '22.49' '19.92' '17.27' '14.18' '10.01'\n",
            " '12.33' '41.72' '25.09' '15.26' '14.8' '74.27' '28.5' '17.84' '28.58'\n",
            " '8.76' '11.76' '23.84' '32.62' '23.83' '28.93' '13.18' '32.58' '21.7'\n",
            " '30.35' '30.59' '28.67' '24.29' '8.79' '49.33' '52.96' '23.89' '17.38'\n",
            " '14.31' '15.88' '32.93' 'DotMap()' '36.09' '80.96' '12.4' '54.32' '49.03'\n",
            " '43.17' '19.23' '27.6' '8.89' '28.79' '13.26' '50.76' '8.59' '18.26'\n",
            " '11.1' '27.91' '22.26' '34.46' '27.96' '44.49' '19.95' '46.57' '27.9'\n",
            " '12.45' '28.45' '36.73' '40.43' '33.21' '29.99' '42.64' '70.23' '44.11'\n",
            " '53.85' '14.43' '33.63' '44.96' '32.79' '27.99' '44.66' '27.69' '29.92'\n",
            " '54.86' '15.3' '59.72' '57.89' '20.61' '34.17' '28.83' '21.92' '45.94'\n",
            " '35.97' '54.55' '40.7' '30.17' '31.16' '31.95' '31.78' '42.93' '48.52'\n",
            " '28.82' '54.45' '27.61' '38.03' '30.16' '71.58' '35.9' '31.6' '15.15'\n",
            " '39.83' '41.69' '54.61' '61.15' '30.25' '27.04' '28.65' '17.95' '44.62'\n",
            " '50.34' '45.13' '66.52' '40.41' '33.08' '40.53' '39.89' '72.53' '81.35'\n",
            " '44.77' '25.81' '29.94' '54.35' '83.76']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Nvu_3-PQy69_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def floater(x):\n",
        "  try: \n",
        "    return float(x)\n",
        "  except ValueError:\n",
        "    return np.nan"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VJIxog6kzYi5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "c0f0f2ed-29e5-4121-b52a-1c5390243132"
      },
      "cell_type": "code",
      "source": [
        "data.loc[:,'peace_score'] = data['peace_score'].map(floater)\n",
        "data.loc[:,'freedom_score'] = data['freedom_score'].map(floater)\n",
        "data.loc[:,'fragile_states_index'] = data['fragile_states_index'].map(floater)\n",
        "data.loc[:,'press_freedom_index'] = data['press_freedom_index'].map(floater)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:537: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  self.obj[item] = s\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "lmzRZp7Uz7_A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f083df70-953e-43f7-895a-084bfa4abce8"
      },
      "cell_type": "code",
      "source": [
        "from fancyimpute import MatrixFactorization, SimpleFill"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "NMIqDDhf0Uss",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data_imp = SimpleFill(fill_method='mean').fit_transform(data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YMIbt_pZ1rgI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data_imp = MatrixFactorization().fit_transform(data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1hP8PUgL3w8I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "outputId": "2ab2cbb1-eabe-4b2f-f8ac-134f2e4bb0e3"
      },
      "cell_type": "code",
      "source": [
        "pd.DataFrame(data_imp, columns = data.columns).describe()"
      ],
      "execution_count": 13,
      "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>cost_nomad</th>\n",
              "      <th>cost_coworking</th>\n",
              "      <th>cost_expat</th>\n",
              "      <th>coffee_in_cafe</th>\n",
              "      <th>cost_beer</th>\n",
              "      <th>places_to_work</th>\n",
              "      <th>free_wifi_available</th>\n",
              "      <th>internet_speed</th>\n",
              "      <th>freedom_score</th>\n",
              "      <th>peace_score</th>\n",
              "      <th>...</th>\n",
              "      <th>fragile_states_index</th>\n",
              "      <th>press_freedom_index</th>\n",
              "      <th>female_friendly</th>\n",
              "      <th>lgbt_friendly</th>\n",
              "      <th>friendly_to_foreigners</th>\n",
              "      <th>racism</th>\n",
              "      <th>leisure</th>\n",
              "      <th>life_score</th>\n",
              "      <th>nightlife</th>\n",
              "      <th>weed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>...</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2332.348</td>\n",
              "      <td>210.360</td>\n",
              "      <td>1880.431</td>\n",
              "      <td>3.296</td>\n",
              "      <td>3.296</td>\n",
              "      <td>0.571</td>\n",
              "      <td>0.561</td>\n",
              "      <td>13.589</td>\n",
              "      <td>0.478</td>\n",
              "      <td>0.674</td>\n",
              "      <td>...</td>\n",
              "      <td>55.563</td>\n",
              "      <td>35.086</td>\n",
              "      <td>0.645</td>\n",
              "      <td>0.545</td>\n",
              "      <td>0.737</td>\n",
              "      <td>23280176929344430622638080.000</td>\n",
              "      <td>0.631</td>\n",
              "      <td>0.700</td>\n",
              "      <td>0.605</td>\n",
              "      <td>0.117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1117.532</td>\n",
              "      <td>173.979</td>\n",
              "      <td>1265.411</td>\n",
              "      <td>1.982</td>\n",
              "      <td>1.982</td>\n",
              "      <td>0.220</td>\n",
              "      <td>0.240</td>\n",
              "      <td>18.199</td>\n",
              "      <td>0.247</td>\n",
              "      <td>0.223</td>\n",
              "      <td>...</td>\n",
              "      <td>23.329</td>\n",
              "      <td>19.462</td>\n",
              "      <td>0.247</td>\n",
              "      <td>0.244</td>\n",
              "      <td>0.184</td>\n",
              "      <td>650596606331366367658246144.000</td>\n",
              "      <td>0.201</td>\n",
              "      <td>0.148</td>\n",
              "      <td>0.234</td>\n",
              "      <td>0.321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>387.000</td>\n",
              "      <td>0.010</td>\n",
              "      <td>296.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.178</td>\n",
              "      <td>0.200</td>\n",
              "      <td>...</td>\n",
              "      <td>18.800</td>\n",
              "      <td>8.590</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.180</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1486.000</td>\n",
              "      <td>88.960</td>\n",
              "      <td>1010.000</td>\n",
              "      <td>1.600</td>\n",
              "      <td>1.600</td>\n",
              "      <td>0.400</td>\n",
              "      <td>0.400</td>\n",
              "      <td>2.000</td>\n",
              "      <td>0.200</td>\n",
              "      <td>0.568</td>\n",
              "      <td>...</td>\n",
              "      <td>34.000</td>\n",
              "      <td>22.490</td>\n",
              "      <td>0.440</td>\n",
              "      <td>0.400</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.400</td>\n",
              "      <td>0.450</td>\n",
              "      <td>0.620</td>\n",
              "      <td>0.400</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2209.000</td>\n",
              "      <td>172.050</td>\n",
              "      <td>1646.000</td>\n",
              "      <td>3.030</td>\n",
              "      <td>3.030</td>\n",
              "      <td>0.400</td>\n",
              "      <td>0.600</td>\n",
              "      <td>6.000</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.800</td>\n",
              "      <td>...</td>\n",
              "      <td>53.700</td>\n",
              "      <td>28.930</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.800</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.720</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3056.000</td>\n",
              "      <td>277.350</td>\n",
              "      <td>2408.000</td>\n",
              "      <td>4.620</td>\n",
              "      <td>4.620</td>\n",
              "      <td>0.800</td>\n",
              "      <td>0.800</td>\n",
              "      <td>19.000</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.800</td>\n",
              "      <td>...</td>\n",
              "      <td>74.900</td>\n",
              "      <td>44.530</td>\n",
              "      <td>0.800</td>\n",
              "      <td>0.800</td>\n",
              "      <td>0.800</td>\n",
              "      <td>0.800</td>\n",
              "      <td>0.800</td>\n",
              "      <td>0.810</td>\n",
              "      <td>0.800</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>10121.000</td>\n",
              "      <td>1524.460</td>\n",
              "      <td>13406.000</td>\n",
              "      <td>13.720</td>\n",
              "      <td>13.720</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>197.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>...</td>\n",
              "      <td>111.500</td>\n",
              "      <td>83.760</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>18181818181818001939777978368.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 21 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       cost_nomad  cost_coworking  cost_expat  coffee_in_cafe  cost_beer  \\\n",
              "count     781.000         781.000     781.000         781.000    781.000   \n",
              "mean     2332.348         210.360    1880.431           3.296      3.296   \n",
              "std      1117.532         173.979    1265.411           1.982      1.982   \n",
              "min       387.000           0.010     296.000           0.000      0.000   \n",
              "25%      1486.000          88.960    1010.000           1.600      1.600   \n",
              "50%      2209.000         172.050    1646.000           3.030      3.030   \n",
              "75%      3056.000         277.350    2408.000           4.620      4.620   \n",
              "max     10121.000        1524.460   13406.000          13.720     13.720   \n",
              "\n",
              "       places_to_work  free_wifi_available  internet_speed  freedom_score  \\\n",
              "count         781.000              781.000         781.000        781.000   \n",
              "mean            0.571                0.561          13.589          0.478   \n",
              "std             0.220                0.240          18.199          0.247   \n",
              "min             0.000                0.000           0.000          0.178   \n",
              "25%             0.400                0.400           2.000          0.200   \n",
              "50%             0.400                0.600           6.000          0.600   \n",
              "75%             0.800                0.800          19.000          0.600   \n",
              "max             1.000                1.000         197.000          1.000   \n",
              "\n",
              "       peace_score   ...    fragile_states_index  press_freedom_index  \\\n",
              "count      781.000   ...                 781.000              781.000   \n",
              "mean         0.674   ...                  55.563               35.086   \n",
              "std          0.223   ...                  23.329               19.462   \n",
              "min          0.200   ...                  18.800                8.590   \n",
              "25%          0.568   ...                  34.000               22.490   \n",
              "50%          0.800   ...                  53.700               28.930   \n",
              "75%          0.800   ...                  74.900               44.530   \n",
              "max          1.000   ...                 111.500               83.760   \n",
              "\n",
              "       female_friendly  lgbt_friendly  friendly_to_foreigners  \\\n",
              "count          781.000        781.000                 781.000   \n",
              "mean             0.645          0.545                   0.737   \n",
              "std              0.247          0.244                   0.184   \n",
              "min              0.000          0.180                   0.000   \n",
              "25%              0.440          0.400                   0.600   \n",
              "50%              0.600          0.600                   0.800   \n",
              "75%              0.800          0.800                   0.800   \n",
              "max              1.000          1.000                   1.000   \n",
              "\n",
              "                                 racism  leisure  life_score  nightlife  \\\n",
              "count                           781.000  781.000     781.000    781.000   \n",
              "mean     23280176929344430622638080.000    0.631       0.700      0.605   \n",
              "std     650596606331366367658246144.000    0.201       0.148      0.234   \n",
              "min                               0.000    0.000       0.010      0.000   \n",
              "25%                               0.400    0.450       0.620      0.400   \n",
              "50%                               0.600    0.600       0.720      0.600   \n",
              "75%                               0.800    0.800       0.810      0.800   \n",
              "max   18181818181818001939777978368.000    1.000       1.000      1.000   \n",
              "\n",
              "         weed  \n",
              "count 781.000  \n",
              "mean    0.117  \n",
              "std     0.321  \n",
              "min     0.000  \n",
              "25%     0.000  \n",
              "50%     0.000  \n",
              "75%     0.000  \n",
              "max     1.000  \n",
              "\n",
              "[8 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "muuGk6V33kIm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "\n",
        "data_scaled = scaler.fit_transform(data_imp)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "y2cAn_IE4GQ3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "outputId": "16669219-f2f7-4340-9d24-72422457e5d4"
      },
      "cell_type": "code",
      "source": [
        "pd.DataFrame(data_scaled, columns=data.columns).describe()"
      ],
      "execution_count": 15,
      "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>cost_nomad</th>\n",
              "      <th>cost_coworking</th>\n",
              "      <th>cost_expat</th>\n",
              "      <th>coffee_in_cafe</th>\n",
              "      <th>cost_beer</th>\n",
              "      <th>places_to_work</th>\n",
              "      <th>free_wifi_available</th>\n",
              "      <th>internet_speed</th>\n",
              "      <th>freedom_score</th>\n",
              "      <th>peace_score</th>\n",
              "      <th>...</th>\n",
              "      <th>fragile_states_index</th>\n",
              "      <th>press_freedom_index</th>\n",
              "      <th>female_friendly</th>\n",
              "      <th>lgbt_friendly</th>\n",
              "      <th>friendly_to_foreigners</th>\n",
              "      <th>racism</th>\n",
              "      <th>leisure</th>\n",
              "      <th>life_score</th>\n",
              "      <th>nightlife</th>\n",
              "      <th>weed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>...</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "      <td>781.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.001</td>\n",
              "      <td>1.001</td>\n",
              "      <td>1.001</td>\n",
              "      <td>1.001</td>\n",
              "      <td>1.001</td>\n",
              "      <td>1.001</td>\n",
              "      <td>1.001</td>\n",
              "      <td>1.001</td>\n",
              "      <td>1.001</td>\n",
              "      <td>1.001</td>\n",
              "      <td>...</td>\n",
              "      <td>1.001</td>\n",
              "      <td>1.001</td>\n",
              "      <td>1.001</td>\n",
              "      <td>1.001</td>\n",
              "      <td>1.001</td>\n",
              "      <td>1.001</td>\n",
              "      <td>1.001</td>\n",
              "      <td>1.001</td>\n",
              "      <td>1.001</td>\n",
              "      <td>1.001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-1.742</td>\n",
              "      <td>-1.210</td>\n",
              "      <td>-1.253</td>\n",
              "      <td>-1.664</td>\n",
              "      <td>-1.664</td>\n",
              "      <td>-2.597</td>\n",
              "      <td>-2.344</td>\n",
              "      <td>-0.747</td>\n",
              "      <td>-1.216</td>\n",
              "      <td>-2.126</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.577</td>\n",
              "      <td>-1.362</td>\n",
              "      <td>-2.610</td>\n",
              "      <td>-1.498</td>\n",
              "      <td>-4.009</td>\n",
              "      <td>-0.036</td>\n",
              "      <td>-3.142</td>\n",
              "      <td>-4.674</td>\n",
              "      <td>-2.587</td>\n",
              "      <td>-0.363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.758</td>\n",
              "      <td>-0.698</td>\n",
              "      <td>-0.688</td>\n",
              "      <td>-0.856</td>\n",
              "      <td>-0.856</td>\n",
              "      <td>-0.779</td>\n",
              "      <td>-0.673</td>\n",
              "      <td>-0.637</td>\n",
              "      <td>-1.129</td>\n",
              "      <td>-0.475</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.925</td>\n",
              "      <td>-0.648</td>\n",
              "      <td>-0.831</td>\n",
              "      <td>-0.596</td>\n",
              "      <td>-0.744</td>\n",
              "      <td>-0.036</td>\n",
              "      <td>-0.900</td>\n",
              "      <td>-0.544</td>\n",
              "      <td>-0.877</td>\n",
              "      <td>-0.363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-0.110</td>\n",
              "      <td>-0.220</td>\n",
              "      <td>-0.185</td>\n",
              "      <td>-0.134</td>\n",
              "      <td>-0.134</td>\n",
              "      <td>-0.779</td>\n",
              "      <td>0.162</td>\n",
              "      <td>-0.417</td>\n",
              "      <td>0.493</td>\n",
              "      <td>0.566</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.080</td>\n",
              "      <td>-0.317</td>\n",
              "      <td>-0.184</td>\n",
              "      <td>0.224</td>\n",
              "      <td>0.345</td>\n",
              "      <td>-0.036</td>\n",
              "      <td>-0.153</td>\n",
              "      <td>0.133</td>\n",
              "      <td>-0.022</td>\n",
              "      <td>-0.363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.648</td>\n",
              "      <td>0.385</td>\n",
              "      <td>0.417</td>\n",
              "      <td>0.668</td>\n",
              "      <td>0.668</td>\n",
              "      <td>1.039</td>\n",
              "      <td>0.998</td>\n",
              "      <td>0.298</td>\n",
              "      <td>0.493</td>\n",
              "      <td>0.566</td>\n",
              "      <td>...</td>\n",
              "      <td>0.829</td>\n",
              "      <td>0.486</td>\n",
              "      <td>0.625</td>\n",
              "      <td>1.044</td>\n",
              "      <td>0.345</td>\n",
              "      <td>-0.036</td>\n",
              "      <td>0.843</td>\n",
              "      <td>0.742</td>\n",
              "      <td>0.832</td>\n",
              "      <td>-0.363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6.974</td>\n",
              "      <td>7.558</td>\n",
              "      <td>9.114</td>\n",
              "      <td>5.263</td>\n",
              "      <td>5.263</td>\n",
              "      <td>1.948</td>\n",
              "      <td>1.833</td>\n",
              "      <td>10.084</td>\n",
              "      <td>2.114</td>\n",
              "      <td>1.464</td>\n",
              "      <td>...</td>\n",
              "      <td>2.399</td>\n",
              "      <td>2.503</td>\n",
              "      <td>1.434</td>\n",
              "      <td>1.864</td>\n",
              "      <td>1.433</td>\n",
              "      <td>27.928</td>\n",
              "      <td>1.840</td>\n",
              "      <td>2.028</td>\n",
              "      <td>1.687</td>\n",
              "      <td>2.754</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 21 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       cost_nomad  cost_coworking  cost_expat  coffee_in_cafe  cost_beer  \\\n",
              "count     781.000         781.000     781.000         781.000    781.000   \n",
              "mean        0.000          -0.000      -0.000          -0.000     -0.000   \n",
              "std         1.001           1.001       1.001           1.001      1.001   \n",
              "min        -1.742          -1.210      -1.253          -1.664     -1.664   \n",
              "25%        -0.758          -0.698      -0.688          -0.856     -0.856   \n",
              "50%        -0.110          -0.220      -0.185          -0.134     -0.134   \n",
              "75%         0.648           0.385       0.417           0.668      0.668   \n",
              "max         6.974           7.558       9.114           5.263      5.263   \n",
              "\n",
              "       places_to_work  free_wifi_available  internet_speed  freedom_score  \\\n",
              "count         781.000              781.000         781.000        781.000   \n",
              "mean            0.000                0.000          -0.000          0.000   \n",
              "std             1.001                1.001           1.001          1.001   \n",
              "min            -2.597               -2.344          -0.747         -1.216   \n",
              "25%            -0.779               -0.673          -0.637         -1.129   \n",
              "50%            -0.779                0.162          -0.417          0.493   \n",
              "75%             1.039                0.998           0.298          0.493   \n",
              "max             1.948                1.833          10.084          2.114   \n",
              "\n",
              "       peace_score   ...    fragile_states_index  press_freedom_index  \\\n",
              "count      781.000   ...                 781.000              781.000   \n",
              "mean        -0.000   ...                   0.000                0.000   \n",
              "std          1.001   ...                   1.001                1.001   \n",
              "min         -2.126   ...                  -1.577               -1.362   \n",
              "25%         -0.475   ...                  -0.925               -0.648   \n",
              "50%          0.566   ...                  -0.080               -0.317   \n",
              "75%          0.566   ...                   0.829                0.486   \n",
              "max          1.464   ...                   2.399                2.503   \n",
              "\n",
              "       female_friendly  lgbt_friendly  friendly_to_foreigners  racism  \\\n",
              "count          781.000        781.000                 781.000 781.000   \n",
              "mean            -0.000          0.000                  -0.000   0.000   \n",
              "std              1.001          1.001                   1.001   1.001   \n",
              "min             -2.610         -1.498                  -4.009  -0.036   \n",
              "25%             -0.831         -0.596                  -0.744  -0.036   \n",
              "50%             -0.184          0.224                   0.345  -0.036   \n",
              "75%              0.625          1.044                   0.345  -0.036   \n",
              "max              1.434          1.864                   1.433  27.928   \n",
              "\n",
              "       leisure  life_score  nightlife    weed  \n",
              "count  781.000     781.000    781.000 781.000  \n",
              "mean    -0.000       0.000      0.000  -0.000  \n",
              "std      1.001       1.001      1.001   1.001  \n",
              "min     -3.142      -4.674     -2.587  -0.363  \n",
              "25%     -0.900      -0.544     -0.877  -0.363  \n",
              "50%     -0.153       0.133     -0.022  -0.363  \n",
              "75%      0.843       0.742      0.832  -0.363  \n",
              "max      1.840       2.028      1.687   2.754  \n",
              "\n",
              "[8 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "y9GtwCtv4gNM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA(n_components=7)\n",
        "\n",
        "data_reduced = pca.fit_transform(data_scaled)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "52y7Z-Vh49us",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "e78ac866-14f2-4750-b58a-eb643dac65f8"
      },
      "cell_type": "code",
      "source": [
        "data_reduced"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.59161946, -1.97280039,  0.41413782, ..., -0.36593139,\n",
              "         1.08463654,  0.82403567],\n",
              "       [ 2.00744539, -1.88395696, -0.50121689, ..., -0.26932597,\n",
              "         1.36993593,  0.05541092],\n",
              "       [-2.42510441, -2.8355557 ,  1.43543611, ..., -0.33528915,\n",
              "        -0.50913345, -0.32155467],\n",
              "       ...,\n",
              "       [ 3.14849187, -1.21045675,  2.6997298 , ..., -0.1113557 ,\n",
              "        -1.1964548 , -1.44628704],\n",
              "       [ 5.11342156,  5.77321705,  0.54783562, ..., -0.27515873,\n",
              "         0.38557406, -1.06267979],\n",
              "       [ 4.6043324 ,  5.33209691,  2.05049228, ...,  0.21381022,\n",
              "        -1.31366253,  1.51600103]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "ClO7TBNL5ODu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "fba3bed8-a2aa-4104-9f79-1f9758de3492"
      },
      "cell_type": "code",
      "source": [
        "pca.explained_variance_ratio_"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.41389151, 0.09407141, 0.06133784, 0.05362708, 0.04770246,\n",
              "       0.04185117, 0.03694504])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "id": "MqdX6ngX5wyU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "27cdcf41-63c1-40a3-b85c-d058c808c681"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "\n",
        "clusterer = KMeans(n_clusters=3)\n",
        "\n",
        "clusterer.fit(data_reduced)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
              "    n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\n",
              "    random_state=None, tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "id": "YtEQJfnQ6Iqs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "outputId": "7a27c2f2-6eeb-4f38-a886-8627eb81dda3"
      },
      "cell_type": "code",
      "source": [
        "clusterer.labels_"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
              "       0, 1, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1,\n",
              "       1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
              "       1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 2, 0, 0,\n",
              "       1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
              "       0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1,\n",
              "       0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
              "       1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 1, 0,\n",
              "       0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 1, 0, 1, 0, 1, 0,\n",
              "       1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 1, 0, 1, 0, 2, 1, 0,\n",
              "       0, 1, 0, 0, 2, 0, 1, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 2, 1, 0, 1, 2,\n",
              "       0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1,\n",
              "       1, 2, 2, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1,\n",
              "       0, 1, 2, 0, 1, 1, 0, 1, 2, 2, 0, 0, 0, 2, 2, 0, 2, 0, 0, 0, 1, 0,\n",
              "       1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 2, 0, 0, 1, 0, 0, 2, 1, 0, 1, 0,\n",
              "       0, 0, 0, 1, 2, 2, 0, 0, 1, 0, 0, 0, 2, 1, 1, 1, 1, 0, 2, 1, 2, 1,\n",
              "       1, 1, 1, 2, 0, 2, 0, 1, 2, 2, 2, 0, 0, 1, 0, 2, 0, 1, 1, 0, 1, 1,\n",
              "       2, 1, 0, 2, 2, 1, 2, 0, 0, 0, 2, 2, 2, 2, 1, 2, 2, 1, 1, 0, 1, 1,\n",
              "       2, 0, 0, 2, 1, 0, 1, 0, 2, 2, 2, 0, 0, 2, 2, 2, 1, 0, 2, 2, 1, 2,\n",
              "       2, 2, 0, 0, 2, 0, 1, 2, 2, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 1, 2, 1,\n",
              "       1, 1, 2, 2, 2, 1, 0, 1, 2, 1, 2, 2, 2, 1, 0, 1, 1, 2, 1, 2, 1, 2,\n",
              "       2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 2,\n",
              "       1, 0, 2, 1, 2, 1, 2, 0, 1, 2, 1, 2, 2, 2, 1, 1, 2, 1, 0, 2, 1, 2,\n",
              "       1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2,\n",
              "       2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2,\n",
              "       1, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 0, 2, 2, 1, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2, 2, 1,\n",
              "       2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "metadata": {
        "id": "8zHyrHkY6adh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1w9zyKypCCtz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 704
        },
        "outputId": "474cb23b-00b6-4dcd-ef46-efc6291a9a3e"
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,12))\n",
        "g = sns.scatterplot(data_reduced[:,0], data_reduced[:,1], hue=clusterer.labels_,\n",
        "               legend='full', palette='viridis')\n",
        "\n",
        "legend = g.get_legend()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SW1HndGuSJtvstXmn2sT9t8bn9zrQjYiI9CUKxVli0iHbE8wJdKgddcEhBEOB\ntXyFbKj6qgYeve5fHWrzZs2nqbbZoY4k3UZvPYJDz94fjze1NWaPY3Zih8nbOt2WiIj0AdpTnCWK\nyiP85cPrefCaJ2iub+bYXx3BsLGDnW6rnzH4Ap2/5T1evTfsLyKlBZx6zVSOvfgIsJCTHyS/SAeV\niIjI+ikUZwl/wM+wsYM5/69nkExacvJCTrfU70TKwpzyu+P4/YnT22vb7bsVuQUai9Of5BXkkleg\nw0lERGTDKBRnmWBO0OkW+i2v18sOB23DXz78A2898R5jth3J5jtvRqS0wOnWRMRBNlEJba9h4/Mw\nOYeDd7huhhJxIYVicZVwYT7hrfMZs/VIp1sRkSxgE1XYmtMg/nnqcfO9mMJbscF9NbJRxGW0mVJE\nRNwrWdkeiFexjdMhWe1QQyLiFK0Ui4iIi9lu1qQ7rI2l3lBEZ4O3FLwjdXKb9BkKxSIi4l6eMvBt\nCvF57SWTdw54ih1sqg9LLMJWHQl25ahL/zZQ+OesDcbW2tSnBck68OSCycd4dJ+JWykUi6RBIp46\nMc3r0yERIn2J8ZZA0d+h9SVsfC4m5yjwjdB+4o1gk03YhhtXB2KA2GxILIQsDcUkFmGrp0JyRepx\n7k8g/wyMJ+JsX+IIV4Ti2op6vpr9DXNmzOWHB23HoFEDCRdrdqn0XFtLlBXfVvLETc/g9Xs56vxD\nKB1ajD/gd7o1Eekm4y2FvONRDO6peNd7sbN0f7ZNNmIbrl0diAGa/wa5PwKFYlfq96G4obqR2y+8\nl1ceeAOA+696jLOnn8pBZ+yj4CI9VrG4kjO2vJB4LA7AS3e/xl1zbmbgJmUbdJ1oa5TG2mYCOX7y\nI3m90aqISK8yngjknoitm/m9Yh74t3CuqXWxLRD/unM9sQx8IzLejjiv30+faGlsbQ/Eq9x7xT9o\nqG50qCPpL5LJJP+65cX2QAypleP/rPH9tj61K+q457KHOW+ny7h26p9Y+tUykslkutsVEel9wZ0w\nhdPBvz0EJ2NKngRPlm6d8EQgtN8axSD4NLLTrfr9SnFX4SLWFtPNxdJjxhiCOZ0/bQjmBLp9jdbm\nVv5+xSM8d8d/AFi2YAVfzf41f5n1B4oHFaWtVxGRTDCeCIQOgMAkwI/xZO/pksYEIPcUrG2GlmfA\nOxhTcBV4Cp1uTRzS71eKc/JDbLPX5h1qR5x3EHmF2fuDKn2DMYZDzzqA3PDqY6ILSsLscczO3b5G\nc10L//3HOx1q1ctqaaxtSlufIiKZZjyRrA7EqxhvCSb/Ikzpc5jiuzGBrTFGJ8u6Vb9fKY6UFnDJ\ngz/n9cfe4X9vfs5uP5rE1nuOC1+kAAAgAElEQVRM0HHKkhalQ4q587ObeOPxGfj8PnY+fAeKB3V/\nlcHj81A+YgBff7ygvWaMIZQf6oVuRaQvsDaBMZpkkynGEwL0O1fAWJv5fQQTJ060M2fOXP8T08ha\nSywaJxDUzXWSXb54/0t+seeVtLVEATj+8in86MJDyNMNdyKuYhOVqdFwsY8wOYeDfwJGH+WL9Jgx\nZpa1duL6ntfvV4pXMcYoEEtWGr3VCO798ha+m7+CksFFhIvzFYhFXMYmqrG1Z6fm+gK29SlM+FJs\n7vEYo79d6WITVak5ysa/8qAOjWeV1VwTikWylT/op2RwMSWDnTlBq7mxhcbqJpZ+vYxBowcSLsrv\nsE9apC+zduXsXNsKJgSeouwMmbaxPRC3l5r+igkdBN4NG/EoXbOJ5dia0yE+F/BB3lmQd6IO6pB2\nCsUi/UDN8joS8QS+gJfCsu7/go+2xXjv2Vn8/oRbSCaTGGO4+L5z2XXKjgRC3Z+iIZKNrLUQn4et\n+QkkK8BEMEW3Yf3bZGEw7uq+d/2JThebbMU2TF8ZiAHi0DQdcibroA5p1++nT4j0Z4lEgvmfLOTC\nPa7guGE/5dIDp7H062Xd/vqGqkb+dObf2kcXWmuZfvadmuMt/UOyClv7s1QgBrB12JpzIVnjbF9d\n8eRBYNcOJZN/Hng0mjEtbBPEP+1cj3+T+V4ka+ltqEgaJeIJ6iobwEBhWQEeT+p9Z1NdM/VVDSye\nt5Th44YSLk7PFoW6inounTyNqqWpY1S//HA+Vx99I9e+cBmFA9a/+pFMJGiqa+5Qa65vJpHQ4SH9\nWXM0yneNjTz5xRyGFhSw98jRlOX1x33scUh827Fka8C2OdPOOhhPEUT+ANEPsLFPMKEDwLcJxujP\ndFp4whDcA+Kff78Ivs2c6kiykH7aRNKkvrqB/z7yNv/4w1P4Az5OuWYqE/fbCl/AyysPvcktZ98J\npG76vPSh89j5yB3x+3v2I9jWHG0PxKt8NfubDqfsrUsgJ8Dmu2zGp2990V6bsNPYDTqARPqez6sq\nOebxR0iunD40snAm/zjqGEpz+1sw9oNvXMcg5BmU2luchYy3BHIOwOQc4HQr/U7qoI6TsIkl0Poc\neEowBVfqoA7pQNsnRNJk3sz53HLOXaz4tpIlXy3jd8fcyPKFFTTWNnPHL+5rf177FoXKhh6/ZiAn\nQLi4493TQ34wCK+vezNOI6UFXP7I+ex/8h4MGjWQfX+8B79+9AIipQU97k2yU31rKzfNeLs9EAN8\nU1vD19XV6/iqvsl4S1JHDvvGpwre0Ziiv2XvscPSq4w3FYRN2eup46eDe2I8PXsjaG0Cm6jAJlZg\nkz3/nS7O0kqxSBrEojFe/vtrnepv/fM9Dj1r//YZxKs0VDeSTPZ8RnhBaZjfPPELrjryjzTUNFIy\nqIhfP3oBRQO7v/pRMriYc245jeaGVnLCIXLysnMVTdLDAnHbeXtMPNk/t8wY3yZQfDfYGOBLrcaK\na6VGsKVnDJtNNkP0fWz9r1P71oP7QMGVGG9pWq4vmadQLJIGPr+PMduM5LVH3u5QH7XVJnj9XsZu\nP4a5H3zVXt/+gK3TskXB7/cxfqex/O3TG4m2RAnmBomUhTs9L9YWo60lSl4kF2NMp/8+lBcipDAM\nQO2KOr6du4TG6ibGbj+awoERvN7+c7pYJBTiZzv8kBOefLy9Njg/zKYl/fcPufE4M+5Q+jlbh609\nE0ikHre9jG0qh/xfrDwlT/oahWKRNDDGsM+Ju/PKg28y/5OFAGyz1xZM2HkzwkX5XPXkL7nvqsf4\n9K0v2HafLTjukiM7bXvYWH6/j5JBa79DvXJJNY/98Wm+/ngBex23CzsfsYO2R6xFzfI6Lj94GvNm\nzQcgL5LLn2dex+DR5Q53ll5bDCjn6WNP4IFPPmJYJMKPxm/eT2+0Sy+brIZkKxgvmDDGk+t0S9JN\nNpHaHmS8aXyDFP+G9kC8StsbkPd/6Njovsk1xzyLZELN8loaaprweD3kF+ZRWLY6fLY2t9HS0EJu\nQW7GbmSrXlbLhXv8hsXzlrbXjr98ClMvm6ITHrvw4X8+4eL9ru5Q2/ek3TnvLz8hmBN0qKvek0wm\n2yekyLrZxAps7bkrD9gIYMIXQs4UjEdvMLOZTTZAdDa2aTrYJCb/XAhMxHg6f6K2wdeOL8JW7t2x\nGDoYU3B1j/cqS3p195hn/TYUSaOigYUM32wIQ38wqEMgBgjlBikaWJjRyQ5NdU0dAjHAs7f/m8Ya\nzSHuSvWy2k61mmW1xKOJLp7d9ykQd49NtmKb/vq9E+ei2IZrIVnlaF/SDYkl2NrTIfYJxD/F1v60\n85i+jeWJYMKXACsXGHxjMOFfKhD3YfqNKNKPBYKdA3i4OL/LfcUCW+0+vtObliN+Npm8SHZ9TJ66\n470Km+wc4qUX2GaIzupcj3/Zuy9rLbaLmyKl+2zLPzvXmh9Ny7WNpwByjsaUvYopfQVTdB/GOygt\n1xZnKBSL9GM5BSH2Pn71KVkej4czbzq5Wwd7pFttRR0rvq2gamkNsWgs46/fHYUDI9w28zp2PnwH\nttxtPFc9eRHjJm3qdFsd2GQttDyBrfkxtuZsbPR/2GSr0231b558COzSue4b1ysvZ20MG1+MbbgO\nWz8NG1+IzcIDR/oC4xvVrdpGX9+Th/EOxPiGaepEP6A9xSL9XF1FPUvnL+fbOYvYYrcJFA4oSMtp\nehtixaJKrjzier78cD7h4nx+cfdZbLvPloRys3OfbktDC/FYIm03Q6aTbXkOW3f+9yp+TNm/Md7B\njvXkBjZRmRq91fYqmAJMwWUQ3Dste1M7v9YSbOVksC0rKwFM6fMY3/C0v1Z/ZxOV2JqTIP4VeIdj\n8s5IvcHxFOpGSRfp7p5ihWKRLFVbUc9385ez7JvlTNhpMwoHFBAI9b2T5prqmrjux7cy4+nVP/M+\nv4/7599G6RCNytoQNlmfutkrOqND3UT+gMk53KGu3MMm61cGVQOeIozpnZtVkw23QdOfOhZzT8aE\nL3HF1idrY5CsJfX/c0mP/zfbRCU2UY0xBtvw29S2l8CemPAFGG9ZepqWrNbdUKyRbCJZqK6ynht/\n8pf2IOnz+7jpzd+y2Q4/cLizDdfaHOWzt+d2qMVjcWqW1yoUb7AgeId2LnuHZL4VF0pNmsjAtImu\nwrbxuyMQJ2qwLY9C8wNg8jHhS7CB7Xp081pqW4PFVh2eOmQDoPUJLG1Q8NuVB3qIaE+xSFaqq6jv\nsLIaj8X5y/l/p76q7x0jGsoNMmHnsR1qPr9vg07dW5dEIkH1shqqvquhub45LdfMVsYTxOSd1fGY\n4sAk8KZvj6Q4z+QcCuZ74dvkYXKnOtdQJkXfgMYbILkcEl9ja38CycqeX9c2rg7Eq7S+/L0tKiJa\nKRbJSk31nX9R11U2EI/1vdFgeZFczr3lNKqW1jBv5tcUlIT5xd1nkV/U87FFLY0tzH71U6af+Tdq\nVtSx+9E7ceZNJ1PkwI2EGeMdjCl5GhILwYTBU5beAwnEeZ5STOmz2JZngTgm5zDw9P+buGyyEdvy\n1JpViL4Dvk16dnGTQyryxFfXfCPQ2qB8n0KxSBYauEkpxeWFHebmHnLmfkRK039TTyaUDSvlmucu\nJdoSxev3UlAaxu/v+a+f+qpGrjryjySTqbFVrz38FuWblHHib36Ev58eTmKMAW9Z6l/SLxnjA285\nJv90p1vJLBME31iIvtWx7h2dhmuHMeGLU/OlSaZW3wuuxXhL1vul4h4KxSJZqGhgIdNnTOP+qx5j\nyVffccApezHp0Il4fV6nW9toax5mkg7z/7ewPRCv8u5zszjy5wc5MnZORDaeMX7IOxnb9p/UJyEA\nwX3BP6bn1/bkYXOOxIT2g2Q1eMrAU9Tj60r/olAskoWMMQzcpIxzbz2NaGuM/KI8V9xks6GGbdp5\nDNlmO4whlB9yoBsR51mbSJ20F/0QTC74x/ep+bnGOxCKH05NnzAB8IQxaQqvqfF5YdABG7IW2kwj\nksWCuUGdQLcOkbICTvndse0r6CM2H85JVx6TtfOPRXpdcjm28mBs3c+wtadjq4/HJtJwo1oGGW8p\nxj8G4xuetkAs0h1aKRaRPitclM/h505mvx/vQSwaJycvpG0T4lrWxrFNd4P93vHfiW8g+i7kHOxc\nYyJ9hEJxFmlrbqOlsZXcSC6BfnqTkEi65YZzMn5Cn0hWsomux5elY6SZiAsoFGeJqqXV3HfVY8yZ\nMY+J+23J0b88LG1zXEU2RmNdE62NbRiTGqsWytM+XZFsZjxByD0Z2/r896qB1M1qIrJeCsVZoLai\njisO/wPzZn4NwIJPv2XpV8v45d/PIb+w57NcRb4vFo3RXNdCKD9EMKfrY6NrK+q4/YJ7ee3ht/EF\nfBx3yREcetb+FJT0zZFwIq7hG40pegDb9FcwuZj8n7lixrFIOigUZ4HWprb2QLzKjGdm0drcplAs\naVWzvI4n//QcH7z0EeN32pSpl06hZFDHG1mstbz5xHu88uCbAERbo9z7m38wcf+tFYpFspzxhCG4\nA/jHAV6MJ9fplkT6DIXiLODz+/AH/cTaYu21gpKwJg70A82NLTTXtZBIJAjlBomUpn9Wb3c11TUz\n/ey/8dY/3wPgq9nfMO+Dr7n6mUs6zBBua27jgxdmd/r6T17/jM126Pm8UBHpfanxYyKyIRSKs0Be\nYS6nXTuV2y+4F0jNqD33ttP67OllklJf3cC/bnmBh6c9STwWZ6s9JnDZwz93bK94a1Mr7/zrgw61\nL97/itamVvheKA7kBNhuv62oq6zngFP3Iic/h/df+JAtdhuX6ZZFREQyRqE4C+Tkhdj/5D354cET\nWTR3CSMmDKOgJIwvDcfginMqF1dz/1WPtT/++L+f8dRtL3LC5UfhC2T+n60xhvyiPOqrGtpr/oCv\n0/eZx+Nht6N+yMDhpdx9+cM0VDdywKl7MXATHSssIpLtbLIVbB3Y1tQBLp5ijOm7p6Fmkg7vyBL5\nhXkMGVPODw/ajvIRAzRiaiPUrqijelktjbVNTrcCwJcfzu9U++T1ObQ0tTrQDYRLw5xzy6kdtuWc\ndNUx5EU67zlsa4nymyOv55v/fUvlkmoeuPpx3n32w05HKouISPawyRZoew1bsS+2cl9s1eEQ7/y3\nSLqmpUjp8xLxBAs+W8R1J93Cws8WM/GArTj/jv+jdHCxo32N++GmnWo7HrStY294/H4fO07elnu/\nvIWvP17AJuOHUjggQk4XRyJ/+tYXJBMdA/ArD7zBrlN2JFyU3+X1rbUZ2wffVNdES2MbAHkFOeTo\nTaRkidQxy9VAEkwQ49FoTckg24CtuwhI/X4kWYGt+yUU343xOPs3sS/QSrH0ebUV9Vy0z2/55n/f\nkkwmef/52Uw/8w6a6pod7au4vJDzbj+DvEguHo+HvY7bhf1P2bP9SGIn5BbkMmjUQHY5YkeGjR2y\n1oA75AeDOtWGjxvSaYSbtbZ9JfnWc+9iwWeLaGns3ZXw2op6bjvvHo7f5ExOHHU29131KHWV9b36\nmiLdYZOtEH0XW3UktmJXbO3PsYkVTrclbmKbaQ/Eq8Tnpg52kfXSSrH0ec31LR32yQLMfOlj2prb\nutwakCn5hXns9+M9mHTIRKy15OSFHO1nQwwePZA9jtmJ//7jHQAGblLG1EunEAh1DMXVy2o5a+LF\n1CxPHSv77O3/5pZ3p7HpxNG91tvMF2fz7/teByCZTPL4jc+y40HbsfWem/faa4p0i63D1vwUiKYe\nR9/BNlwHBb/FeDReUzLA5IIp7HjUd2AnMEHneupDtFIsfV5Ofgj/Gjeujdh8OB4HV2RXCQT9lAwq\nonRwcZ8JxACR0gLOvfV07v3yFu745Aamz5hG2bCSTs/77O0v2gMxpELqg9c8QXMvrRbHY3He72Jc\n3If/+aRXXk9kgySW0x6IV4nOAJsd9zmIC3iKMcV/B9/K8ZmBnTCRazAe58aB9iUKxdLn5RfmcsFd\nZ+IP+gEoHBDhonvP6TB7VzZcQUmYwaPLGbn5cIrLu94X6fF0/hXi8Xrora3FPr+PHQ/arlN92323\n7J0XFFexyUZsogKbqMRau+EX8A6g059V/xZgdES6ZIYxPox/PKboPkzZW5jCmzHecqfb6jO0faKP\ni7XFaKhpBCBclN8eDN0klBdilyN2ZOs9Nqe1uY3ccIiIAnFGjN9pU0qHFFO5pBoAr8/LCb8+ipy8\n3gsBE/fbiv1P2ZN/3/c6Xq+HI86bzMjNh/fa64k72EQltv530PYSeAdjItdifVtiPBvwvWzCmIJp\n2IarwLaAdzQmfIVW6STjjFdHe28Ms1Hvhnto4sSJdubMmRl/3f6mvrqBF+56lYen/RNjDFMvPZL9\nT9lTR/FK2jTWNtHS0EIiniCUn9Pl6nvV0mreeHwG1cvrOODkPSkdUkwwt3f3rzXVN9PS0AIYciM5\n5OZr+kRfkckpJd1lk63Yht9Dy0PfqwYwZa9gvAM3+FqpGbFRMDkKJ32ITVRDcgUkG8A3AjwlGKMP\n1PsDY8wsa+3E9T1PK8V92PyPF3LnxQ+0P77jovvZdOJottpjgoNdSX9RV1nPfVc+yjN/eRlrLWO3\nH8Nvn7q401aKksHFHPGzgzLaW15BLnkFfWePtoBNNkBiAbb5H+AbBaFDMN4sORDGNkDbf9coRiGx\nGDYwFKdWlrVdoq+xiarU6LLoW6mCKcKUPAG+oc42Jhmlt0B92KrJAOuriWyMZd+s4Ok/v9S+t3Lu\nB1/x9J9fJB6LO9yZ9EnRD7BVU6DlUWzD77HVx2MTlU53lWJC4N+sc92jvZiukVi4OhAD2Bps4y2p\nlX9xjR6HYmPMMGPMa8aYOcaYz4wx56WjMVm/LXcb16m2RRc1kY3x1exvOtXmzJhHa3NbF88WWTub\nqMY23tqxmFgAiaWO9LMm4wljwpeBd9WqoA8T/hVoL7B7dPW9mFhMp5m/0q+lY/tEHLjQWvuhMSYM\nzDLG/NtaOycN15Z12HafLdnhwG3aR1TteNB2bLv3Fg53Jf3FFruN71Tb9cgddQS5bJwu92Zmz95i\n4xsGxf9IHX5ggqmb5jRb2D0C2wEBvj9Sz+Qeg/FEHGtJMq/Hodha+x3w3cr/3GCM+RwYAigU97LC\nAREuuu9cWhtawaTm9eomO0mXkkFFXHzfufz1wntpqm9h8ul7s+tRk7ocwyayLsZbDPnnYWtOW130\n/QC8nU9OdFLW7HGWzDPFmJLU1h6SNZic4yG4m9NdSYaldfqEMWYE8AawubW2fo3/7gzgDIDhw4dv\nt3DhwrS9roj0jngsTn1VI9Za8gpyCPXiqDXp32yyERJLsC3/wvhGQnAvTWaQrGOTdWDj4CnEGOcP\ngJL06O70ibSFYmNMPvA6cI219p/req5GsomIiIhIJnQ3FKflc1BjjB94AnhwfYFYRERERCTbpGP6\nhAHuAj631t7Y85ZERERERDIrHSvFOwMnAnsZYz5a+a/JabiuiIiIiEhGpGP6xFtk01wdERER6VU2\n2QDJSoh9DL5x4B2I8RSu/wtFspiOeRYREddJHTu9CNvyLMY/FgI7axpGN1kbg9aXsfWXrC7mnQV5\np2M8+c41JtJDCsUiDoi1xWhpbCU3koPPpx9DkUyy1kL0HWztuanHAP4tofCvGG+Jo731Ccma1Dzf\n72u6A3KPAxSKpe/SX2ORDKteVstjNzzNZ2/PZYcDt+Hgn+5L4QCdmiSSMckqbMPNHWuxTyBZAwrF\n3WMb1yjEU/N9RfowhWKRDKqrrOeqKdczZ8Y8AD5/dx4L5yzi57efQV5ER8qKZE6ymzXpxORA6GBo\nfWp1zb8NeHQEvPRtOq9V1slaS11lPY01TU630i+0NrW1B+JV3njsXVqb2hzqSMSFPEWY/DM71nxj\nwVOcsRZssg6bWI5NrMAm+9bPv/GEMeFLIP8X4J8IuWdgCm/FZPD/P5HeoJViWauGmkZmvvwxj9/w\nDHkFOZx+3QlsMn4YwZyA0631WV6fF5/fRzy2+mPGvEguqXHfmVFXWY/xGAqKwxl7TZFsYowXG9wT\nU/wQtuUJjG8zCB2UsRvtbKIKW38ltL0MJheTfxE25yCMpyAjr58OxlsMeadC7jFgcjBGfxek71Mo\n7sdisTj1FQ18N385ReURwsVhCoq7fxPEnBnzmHbc6n135+10OX+fN52Bm5T1RruukFuQw/GXT+He\n3/yjvfZ/N/6YgpLeD6iNtU188sYcHp72T/xBP6decxyjtx5JTn6o119bJNsYTwQCE8G/LcZk7kNT\na+PYlkeg7aWVhSZsw28wwR2hD4ViAGN8YHQ/hPQfCsX92KI5i/n5rr+mpbEVgCN+NpkTr/gR4W4E\n45aGFp7+80sdavFYnJkvfcRBZ+zbK/1mq5bGVqqW1vDmEzMYNGogW+0xgaKBGzePMzecw2FnH8Au\nR+7I/I8XsOn2YygaEMEX6P0fxQWfLeI3h/+h/fGFe1zJnZ/dxLCxg3v9tXtDY20TrU1tGGPIL84j\nGNJKlWy4TAZiIHWDWtubneux/4FvVGZ7EUfYRBXEZmHjX2KC+4O3XKPssoRCcT9VX9XA9LPvbA/E\nAE9Of54jzpvcrVDsC/goH9F5RXjAcPfN8fz64wVcuPtvSCZTN+GM3moE1750OUUbOTEiXJxPuDif\nEROGpbPNdYpFYzzzl45vcpLJJG88NoPjL5+SsT7WxlpL7fI62lqi+IN+Ckrz8Qf8a31+7Yo6bj7z\nDt751weE8oKces1x7HPC7uQX6WZFyXImDwI7QOzDjnXfeGf6kYyyiWps7TkQm5V63PgnTOEdENrD\n2cYE0I12/VYsGmfFt5Wd6g3Va47R6Zo/6OeYiw7vMCps3I4/YNPtRtNY20TN8lpi0Vja+s1W9dUN\n3H3pQ+2BGFIhedk3KxzsasN5fV4GjynvVB80eqAD3XS29Ktl/HzXX3Pi6LM5ffPz+ejVT4m2Rrt8\nbiKe4IW7XuHtJ9/HWktLYyu3nXcPVd9VZ7hrkQ1njB+TexIEJq2sBCD/QvAOcLQvyZBkTXsgXsU2\n3pBaPRbHaaW4nwoX5bHn1F149A9Pfa+WT8ng7t8dXDashL9+dD3ffr6EnPwQ5SMH0FDdyF/O/ztL\nvlrGnsfuzGHnHEhhWd/aB7chbNISbe0c/uurGmhrifaZmw49Hg8Hn7EvL93zXyoWpd4sjdxiONvs\ntbnDnaVu/LvupFtY+vUyILUt4rdH3cDf503v8vu1uaGFmS993Kn++Xtfssn4zK2+S8/FV77Z9Hnc\ntT5jvKUQuRloAbxgwhhPrtNtSUZ0sZhk21h5hIw4TKG4nwqEAvzowkPw+by89sjbDB49kLP+dCqF\nA7ofYI0xFJcXUVxeBED1dzWct/Pl1Fc1APDA1Y8TbYvx46uOIRBc+0fdfVmktICjf3EoVx9zY3ut\nbGgJueEcmuqaugzFTXXN+IM+Alm2x7VkcDG3vnctS75cii/gp3zkgI3eApJOiXiCuTO/7lBrbW7r\nsPXn+3LyQ2y15wQ+eWNOh/rYiaN7rUdJr1giwbLGRu6aPYu2RJzTttmOIeECcvz98/dIV4y3CChy\nug3JNE8peEdBYn57yeT9BDz6XsgGCsX9WGFZhBN+fRSHnXMggZCf/MKe7bes+q6mPRCv8uqDbzLl\nvIMoHrRhP9DRthix1hh5kexfHRn3wx9wxWMX8sbjMygdWsLuP9qJR69/ip/9+fQOz2uobuSTN+bw\nzF9eYuAmZUy9dAplw0vwZNEqWHF5IcXlG3eTYG/xBXxsvstmfPL66pCbF8klJ9z1QQA+v49D/m9/\nPn93HjNf+hh/0M+JVxxF6RCdRNZXVDQ3ceBD99IcS62aPfH5Z7ww9SRGF+ufofRvxlsKxfdjW56A\n+FxMzlHg3xxjvE63JigU93v+oD9tIairm5gGDC/Fu4GTEyoWVfLkLS+weO4S9j5hd7bZa/OMjCTb\nWKH8EN9+sYRIaQGVi6v41X6/5ZrnL6OwbPUqq7WW91/4kN+feEt77e1/fcAdH/9xg98wuE1BcZiL\n7jmH3x59A/Nmfs2A4aVc+tDPKShd+/dE0cAIlz1yPm3NbXh9XkK5QUJ5Gz9arqGmkYbqRqqWVjNo\nVDkFpeF+++nHmuKJBNWtLTRGo+T6/RQEg+T6e/dTjufmzW0PxJDaRnH37Flctec+rttKIe5jvGWQ\ndwYQ13znLKNQLN2WH8njsHMO5KlbXwAglBvk3FtPJ7IBgba2oo5lC1awxa7j2HHytixfUMHb/3qP\n/U7eE683O98ph4vyOeT/9qNmRR3LF1bw0+tP6hTY6iob+OfNz61Rq2fh54tdE4qjbTEaqhqx1hLK\nC27QJxMDR5RxzXOXEmuL4fV5KBwQWecKe21FPe889T7vPjuL7fbdit1/NGmjQ3FjXRP/vPk5Hrj6\ncSC19ej6V3/D+B9uulHX62u+rK7mhCcfo6a1hYDXy9V77MM+/8/eeYc5UXZ9+H4y6ckm2cbSO4gK\nAgJiQV8FBSzwivXFgr1iF3tXsKHYsAsq9t5QURQrCgjCZ0MBUTpsTdn0zDzfH7NkCbvALmxn7uvy\nkpxMebKbTX5z5pzf6dqNbEf9jey1mat+9djMFhpuhI2BQeOiWwEagripYYhigxqTleNm3O0ncuwl\nIynZUEb7Hm22m82rjnAgyqT/PUzJhjIA9jtqX0ZdOJxgcWinvX8bAk9uFp7cLDrt2b7a580WBVc1\nIrA5lIcAxKMJQiUhVi1dS0GnfLx5nhpZ922m3B/mq9fn8tx1LxMLxxly/GAufeycDPeSHVHThs1w\nMMJz173MZy98BcCPHy5k8Ze/cvW0i8jKrr3XZzQY5ZWJ76QfJ2IJHjr/aSZ/cWut1r+rlEajaFKS\n43BgaqAJhyWRCBNmf0pZLApAQlW55esv2K9d+3oVxSO69WDqgnkURyMAOC0Wzuq/L0oLyRJLqYFW\nCkgQHoTJ1thLMjAwqNtp8XsAACAASURBVAGGKDaoFZ6cLDw5WbTvWfuBD/FonNfvfS8tiAEWfPIz\noy8ajmJp3m9Ft8/F+fefzmUH3EgyoY9w3vugXrTq0Dx8nZct/Jvrjrgzvfax14/h5Ov+i8tbs2xv\n6QY/j178bPrxt2/9SPd+nTnpmv+imHf+DkA4ECFaHtPHUufq3sXRUIzZL32Tsd3c9xcw/pGzdkoU\nxyJxpMzs/C5cVYSqatvYo24JJxL8WriRe+d+RzyV4rx9BzKsSze89vqfNKhJyfLSTCuohKpSGovg\ni9l3eQ3+WBRNQs5WAruVy8VHY09n9soVxNUUR3bvSb6z8TympVRBKwF1I5h8YPLqE+925lhaGBI/\nIUN3geYHx0ngOk8fi9xMkVopaGFAgMmFMJrCDFoozVuJGDQrErEk65ZvqBIPFIdqNX66qdJxz3Y8\nv+xR/m/Ob+R3zKNz744NmmncWcoK/Tx84TNpQQzw+n3vM+qi4TUWxX8uWF4l9tOsJRxzwXCyctzE\nInHK/WFS8SQ2p61GdwX8hQEeu+Q5vn93AY4sOxc8MI6DT9gfIQQWq5l4tNLHWDEriJ3MMrq8TnLb\n5lCyvtLn+NCTD8Tlqb9M6ZYUhsOc9t7baBXCfMLsWcw49niGdOxc7+e2mRUO6diZOf9WdsLnOhwI\nBIqp9tlqqZVCai1SLSSp7Mlzi5Yxd+16xg8azH7t2uOx6SJbCEGB281p+/Srs9eyS6j/IktOBhnU\nHzvPAPclOyeMtVKk/wLSFluRaaC0RTpPaZbNVFItQQaugsSPesB6EHgfQChGU6RBy6Nl3KsyaBa4\nfS5GnHlYRsxsMdP30L1rfawth2k0Fax2KwUd8xl+5mH0H9qnSdid1QSpSorXZmYLpZTEItUPz6iO\nHvt2JSvHjdjitv8+/9kLh9tOJBjhq1e/58wel3J6t0uYMPQONq0q2u7xUqkUM5+ezbdvz0PTNMKB\nCFPOewr/pgDubCen3JQ5hW/MZUditu6c4Mgu8PHQt3dywKgBtOlawAlXHcOZE8fuUuNebZi9ckVa\nEG/mtd9+IZZKbWOPusNjs3PXYYczrEs3rIpC7/xWPDryGDxWK25r7W7565O6rkGWngCBi7GUHcWZ\nfXLwx2KcP/MDVpQ2zeEqUgsgg3dWCmKAyIt6lndnSP7M1p6zMvYJyFD12zd14t9UCmKAxFxIfN94\n6zEwqEeMTLFBgyGE4IDRA7ngwTP46InP8OZlceFDZ9YqmxoqK2flL6v47Pmv6LFvFw49+aAmXYvc\nHHB6HAw9ZQgzn56djrXqmFfjeuhwMIIjy85Nr12B0+Pk/cc+oWyjn9HjR2K2mindqGeiN1/IrF66\nlieueJ5rX7wEl6f6c0SCURZ8urhKfNnCv2nfsy3HXDic/sP6sHDWYrrs04lIIMpTV73IhQ+eUevs\nvBCCNl0LuG7GZcSjCdw+Z4N6THfJrnorumt2ToO5MLTJymLiYYdTnogTiMVp68nCZ9+JLLm2ERLf\nVT6WUbJTjzGuz+nc9f1CXv5lCX1aFWBpag21MgHq2qpxrQToVPvjmbtXjVn2AnbuzoOUKUAihGWL\nmAStGJK/gbCDuYdu9VUPyOSSqrHEEoTjv/VyPgODxsQQxQYNiic3izGXHsnQsUNQzCa8eTUfJqKq\nKt+/O58p5z0FwOwZ3/DZC19z76ybm0WZQlPF7rJzxh0n48nN4vt359O5T0fOu++0Gln5xcIxvnpt\nLo9e/CxSSixWM3d9dD17DOqG26eXxBSvK6mS2V+28G/i4fg2RbHDbafPwXuydN6yjHjXvp0B8OS4\nWaNq/P7DX3z2wtfpzHP/YX2q3I2oKS6vs1EaI/u3bkv/1m1YvFEvLWqX5eG0Pv0a1JqswO2mgF0s\nYdKqZoKFVkyeUxdznX2+ptlIZ/KBcxyEJlXGhAuU6ptqd4jSFhz/g+jrFY+7Ilzn17rZTsokqBuQ\n4edBRsF1NigdECYHaJuQJWMqhLt+DnJerhdhLBzHIDe/lnTs6Do/j4FBU8AQxQYNjmJWdso7OVAU\n4tW7382I/b3kX4IlIUMU7yK+Vl5Ou/UEjr30SGxOG85tDM7YmnJ/hCeueD7dqJZMpHjw3KeYOv+e\n9DatOuZhsZozapb3PXwfHNup2bVYLRx/5TH8OX85v3z7BxarmdNuOYHcLeztfvp0MYtm/5Kx30+z\nljD0lCFYrM3HYzjP6eSZY46lKBImnkrRNstDvqvxms52GnNPEFkZZQIh5XjeX7aRtllZnLz3Pg3m\nqlETpOoHdSUy/jXCsi/kvI30TwAlH+G5bacnjAlTNmRdDe4L9Sy0cO+cWNWKkCWjdEEMyNgHiLyP\nkKILMvxSpSAGfTpa4gdwjN6pNW8XpSci62Zk+AlAIFzjwdyj7s9jYNAEMESxQbNBCKp1MhA70RBk\nUBWL1VLrUpRkPEkynsyIFa8tQWqVNZVZOW7u+uh6Hjj7CUrWlzFwRF/OufsUHDuo2c1p7ePWtycQ\ni8RQFEWfcueu3GfA8L68MumdjH0OGDWgWQnizeQ6neQ6m4d93zYx5SJy30GGpoC2AWk/GUxDuHBA\nis6+7CYl9KUWR0bfgvLJ+mMA+xhE7ksgbAhTzf4OpFRBxkE4Murp9Qa9XbtQl9FZaUGso+pZ46xb\n9FKVrbdXN9WLz7NQfEjnWIT9SD1gykYIQzoYtEyMd7ZBs8Gb7+Gsif9jyrlPEQnpXxZ7H9SrVn66\nNaHcHyYeTSAEOxwisbtjd9lo16NNhqvI4KP7Y3NW1uTaHDb6De3N4z/di9QkVoe1xtZp3rwsvOhe\n2IlYgtINZSgWBW+eh057tefUm47nzQc+RE2pHHXuMAaN6F+3L9CgxgihgLkzeO8BEgjhwysEg9o1\n9sqqQQYh/ERmLPY+ZF1Rc0GsliCj70FyAcI2FGkfjjDVne2aMDm3atcDcOiC1Hk6MvbRFnEzwj6y\nzs5dZS3CAkp+vR3fYPtIrRxkOaACjmZt79fUEVv7czYEAwcOlAsXLmzw8xrUH+FAhFgkjt1l22ad\n6K4SLA0RKi0nVFKO1WGldEMZ3ft3qdPSidKNZTx84TPM+2gR+R1ymTB9PHsd0BObo/rGK1VVKS8N\nY3VYcLgbxsKrqbHp3yIev2I6yxetZMDwvpw9aSw5revWxzRQFOTth2Yye8Y35LfP4dKp59KlT0fU\nlEp5IILUJIloAn9RgNadC/DkZ2Fp5t7XBvWHVAuRRcOAeEZc5H+NUHbswS5VPzIwARLfVgbtJyA8\nNyJMdXORLtXCirrhCqcW4ULkfoQwt0dqIUj+hgw/CcKOcF+h1y6bGsYxxaDhkKofGX5Ot/ZDBUtf\nhO8JfVR0Y6xHi+h3giJvIZRWYD9G/38TRwixSEo5cIfbGaK4eROPJgiVlpNKprA7bY1SW1u0ppjH\nr3ieP35YRp+De3HhlDPJb1+3HpbBkhDPXfcyn06fA+gZ3EfmTqRtt9Z1do5YOMbjlz/PrIpzAFis\nZmb8/Th57apemQeKg8x+6RvmvPI97Xu24ey7T6GgU37GbdTdhXAgQjwSx+lx1LmVWSqZ4o373ueF\nW9/A7rKzz3/2QmoaVz17EXntcgiVlfPKxHd456GZgN6k9/B3d6Wb8gwMtkZqUWT5YxB5rjJoPRTh\nvR+h7DhTLNX1yKJDt4qaK0R13QgE3WGiCOJfg4yB/Qgw5WW6UGgBwIQw1W6yaEtCqqWACiZfxs+m\npSCTS5ElWzl9uC5AuC9rlNcrE78iS08EKpqnTQWI3HcbTaTXlJqKYuO+cDMmEory7Vs/clavyzm9\n63iuG34XhauLG3QN/qIAt42ZzNz3FlC2yc+3b8/jrpOmECgO7njnWlC60Z8WxKAPdnj2upfTZRR1\nQSQUZeFnmfZDyUSKwtVVPXWT8STvP/YpT189g+U/r+Sr1+dyxUE3U7ZxJ71Nmzkur5OcNtn14u0b\nKg0z57W5DBmzH/d9fgvd+3WmzyF7pWuZy/3htCAGiJbHeHT8cwRLmqkvbAMRiMVY5fezYN1aNpaX\nE28AX+SmgjA5EO7zEL6pYD8a4ZmI8N5TI0GsYwK26m8QdTvKWQiBUFohnCchXOMQSpsqIkiYvC1S\nEEutHKkWI7XMTL5US/T/pNTrwhO/IP3nI0tORJY/jVTLtnHEZkxqadVYYhHISIMvRWphZPgx0oIY\nQNsEyV8bfC31hXF/sRlTXhbmgbOfSNtdrfxlFU9d/SJXT7uo3koYtiYeSbD855UZsaXzlmVMG6sL\nCtdUFfvrlm8gEU3U2ClhR9gcVnrs24XidZXWUkKIarPEwdLyjIwyQMmGMko3BchpU7PSgXAwQjQU\nQwhwep07bDzbXbE6LPQa1I2R5wxjwtDb02L4g6mzeOKnewmVhqvss/HfIlKJ3Ufk1ZZgPM6zPy/k\niYXzAbCbzbx23En0bd2mkVfWcAhTNtiHg21o7RvHhFufeheZvkXochCGC86uIlPrkKH7IPUX2IaC\n61wQVkgsQJY/ClT8rC29kKVjgYpG3/CjYMpCOk9rlpMDt4mlmj4J2zD9PdjgbKuyoOErDuoLI1Pc\njKnO/3XpvGXEw/Ft7FH3mC3mKt6untysal0idoWuvTtWGagw7NSD67TJzuV1cfHDZ9Ouu16SYbFZ\nuOSxs3H5qnbNm81KteK3pgI9UBTk6atf5NTOF3F61/G8dve7dZ5dbym4PE7OmjiWT579IsPpomR9\nKT/P+ZWCTnlV3gdDxw7Bnd103A6aGuWJOE9WCGKAWCrFTV99QUmk4bNPDYlUS/UMpKz83NwZJwVh\nciPcFyBy3gT3DYjcD8AxptZexAaZSLUIWTYO4rNA/Qci05ChyUitBOm/SM+appYiQ5Mh+QtpQbx5\n/+iHoAUaZ/H1hSkP4b0PRDZgBvvxCMexjSL8hcmNcF1ChnQ0FYBlnwZfS31hZIqbMa065qOYFdSU\nmo71O6w3jqyGyzh68txc/dxFTBr7MGpKRTErXD3tIjx5dXtLz5PvYco3dzD10mmUrC9j+JmHMvLs\noXUuvlt3acWUb+8iFo5htVur2IBtxpvv4ZLHzubq/9yW9t8dfsahuH36BUKwNEQ8HEeYTDi9Dpxb\nNeH99NkSPp2mZ5o1VeO1e95j0Mh+9Dl4rzp9PXWNqqoEikOgSexZDpzV/GzqA3eOO+N9vplkLIk7\n28Uj30/k8cuns27FRg49+SCOv/LoBp1K19yIJJNVcjtrgwFU2fTGp9cFUiuH5BJk6AEggXCdj7Qd\nVmGdtnMIUzZYsxHWfnW30N0dGQF1TWYsNhPhPD3z/aqVgqmavhVzZ33CXwtCmLKQ9lEI6xBA6g2X\npka84Dd3Q+TNRIZfB6UA4Rjd5OuJa4Mhipsx7mwXd7x3DQ+c8yT+wgD9DuvNufee2qAuCBarhYEj\n+/HSyscp3VBGbpts3DnuOu/8t9os7DGoOxNn3oCaUsnKdmOx1U+TQU0Hi3Tr25kXV0xl+c8rad25\nFbltc/DkZuEvDPDAOU8w/+OfMVvMnDhhFCdcNQpPrn6hkEwkmf/xoirHW/jZ/9W7KI6EoqSSKp6d\nyLBHy2P88s3vPHLxc5Rt9DPstEM4955TGqS50+60cfK1x/LNmz+m7464fS4GjeyPoih06NWOm16/\nkmQ8RVa2q97eGy0Fr81OvtNFUaSy9GRUz15kWVtoplMrRJadw+bbvDJwLSL7RbAd0Ljr2s2RWhmo\nRaBuBMseepkEJjJqVpU2VDFglmUgHGAfDbEP9ZgpH+G+EmFq5n7f1SCEuclY4gmTC0zdEd6bG3sp\n9YLhPtHMUVMq/qIgUtWwOqxp4WXQOKiqyjsPzeTZa1/OiE+dfw97DOqefvz5i18z+azHM7aZ/OVt\n9Dusd72sKxFPsm75Bqbf+CrhQIQTrx5N74N71dgvGGDTqiLGdbsko2Rn3O0nMvaG4zA3gP1ZNBxj\n079FvPvwx7izXRx7yZHkts2u87sFuwNSSlYHAtz57Vf8XVbCyG49OG/fQc1/gMg20MqfhPKHMoP2\noxCe+0GGIfU7MvY1wjYErPvUqd+wQfVIrQwZnFQpaoUDcj6A2EcQfqxiKwsiezpS2QP8p0JquR42\nd0dkzwDMIAP6FEVTQYU7x+7n/mOwY2rqPmFkips5ilnJGH1r0LjEIwkWf/lblfgf85ZliOL9jtqX\nYacezJxXv8ekmDj20iPp0qdjva3Lv8nP+EHXp2tyf/1uKffMupmBw/vW+Bh/L/m3Sg373Pd/YtRF\nI/Hle+p0vdXhcNnpvHcHLn/yPIRJGENVdgEhBJ18Ph4acRTxVAqPzYbN3HK/DoTStWorkNIdSQLC\nz1R4wIKMzgDHWMi6ps78hg22geavFMSgT+8L3ozwPQSO/4K6FsxdQfgwmezI7Bd1pwMAU2uEsrl8\nonZTOA0MtkfL/RQ0MGgE7C4bg4/at4q129ZlEb58D5c8dg7n3HOq7j6R5cTpqb+yl0Vf/FplHPN7\nj37C3gf0xFHD5sB2Pas6E/Qc0BWHq2FvubeUzLAmJSWRCKrUsClmsh0NP/zFY7OBrYWWTGyJdSBY\n+kGy4u9S6YhwngQyjIzMyNw2+ia4LwIMUVyvaNXYp2mbAIEwdwRzZpJAKHmg5DXM2gx2WwxRbGBQ\nh5hMJg7930H8uWA5X702F6vDyrjbTqSgY9UPc7fPhbsaZ4ttEY/GUSwK5p3I6FV3NyG/fQ6KpeYC\nM7vAx9gbxvDGfR+gaRode7Xj9NtOwubcDURVHZNQU/zfpo1cOesT1peH2Ld1Wx498mjaZtV/xn13\nRCi54HsStGIgpQ8cUPKQ6iaq2knVT0mh1Px6NhQBwokw7ea/a6UDCB/ILbzdHSdADcdsGxjUB0ZN\nsYFBPRAORIiWxxBC4M52bXNMdE0IlZbz54LlfPzMF3TYoy3/vfRI8trWruaxbFOAG4+axIrF/wCQ\nle3miUX30bpz7aZvhQMRIqEoyXgSh9tOdoHxBbYzFIbLGTZjOuFkZfb+kI6deGTkMXjtLat7vikj\ntTCyfApEXqoM2scgPDfX6VAMqZYgg7dCfDZgAseJelOY0rxql6VaAomfkKllCPuRoLTe6Z+TlCqo\na5Ch+0Fdi3Acq//sFaMc0KDuMcY8Gxi0ADRN44uXv2XymZVNeQWd8nn0x7tr7JKxmbJNAdYsW0fE\nH6HHgG74CjwoSssoRWhu/F1ayhEvP58RsylmvjnzHFq5jNv2DYnUyiCxEBn/AmE9FKyD61ysysib\nyGBmt77Ifh5hO6hOz1OfSLUU6R8PyUrnHOF7CmyH7VJzm9TKQcYrxjQbn0cG9YPRaGdg0AIIFId4\na/KHGbFNq4ooXF1Ua1GcXeAlu8CYuNUUcFut2BQzcbVy6t4+BQVYTYYoaGj0yXZHgO3wenEukDKJ\nTPxYNZ5Y0KxEMVpphiAGkOVTEJa+oFTjGVxD9IZG40LQoGlgtG8bGNQzZYUBViz+hxWL/6FsU+2m\nLZlMAns1jWzGYIrmjcdm49EjjybLqv8eO3l93H/4SHyN0GxnoFNfVl5CWBC2I6rGbYfUy/nqj2rG\npssELWnEr4GBkSk22C0IByLEInFsDmutmtt2lbJNfq494i7+/W01AJ337sB9X9xKTg1rcb15Hs69\n7zSuHXZn2g5t7wP3IKeNUcvbnHFYLPynY2c+P+0sEqqK3Wwmr4V6BBsA1v3BOQ4ir4Ewg/MCMHdr\n7FXVDlMeKN1A/TsdEq7zwWTUABu0HAxRbNDiKVpbwtRLp/Hrd0vZc/8eXPb4eRR0apjpQHPfX5AW\nxAD//r6Gue/OZ9RFI2p8jJ4DujF96cP8+NFC2vVsQ6/9euDLN8ogmjtWs5kCt3HbuKkgtRiQqhd/\nYqHkgPsqcJ1fEfAgTM2roVIoeZAzAxl9D1J/IhwngmVPow7YoEVhiGKDFk2gOMRdJ01h6bxlACz4\nZDG3jL6X+2ff2iDjif/9bU2V2D9biOSa4HDbadejDSdcNaqultXsCRQHUZMqikXBm7ebW1sZ7BJS\nJkFdhyyfqnvnus4GS586t0zTxw/X/G6AlKpexysjIOwVQrpxy2uEkg+ucwEVIYxR6gYtD6Om2KBF\nk4gm0oJ4M//8upp4NNEg5x9x1mFVYiPPGtog526prP97I7eMupeT253Pzcfcw7oVGxt7SQbNGa0E\nWXKsPl0t8R2y7CxI/p6xidRiSLUQmVyOVDdVZJW3j1T9+vaxL5DqeqQWqd261H+QJf9FFh+BLBoG\n8dm1P0Y9IITJEMQGLRZDFBvUC1JKAiUhwsHG/RBXzKYqGWGX14m5FkMrdoW23Qq4471r6da3M133\n6cRt70ygbffWDXLulkhZYYDbjr2fpfOXA/DnghXcMuqeWjcwGhikSczXs7FbIMPTdaswKjLJyZ+Q\nRcOQJUcjiw6H5AI9vg2kFkSGn9S391+si9rkb2haWPcsltU0rW25v1qK9F9XMWwEIIEM3AAytEsv\n1cDAYPsY5RMGdU6wtJyFny3h/cc+xZfv4Zx7TqFtjzZYLA3/dvPme7j2hUu4bcz9JONJzBYzE6Zf\njCev7oz5t4fL6+KA0QPZ64Ce6fXUV5f77kAyluTf3zNLUtb8tZ5ErGEy/wYtEFFN06opm/TXo1aG\n9E8A4hVPxpH+axB5H4GyjeE3MgyRFyofWwfoxwzegkwtB9sIcI7VJ+1ViwqpP7eKJauIdwMDg7rF\nEMUGdc7iL3/lnlMfyXg8/c9HyG+/816WO4tiVuhzyJ7MWDEVf1EAX74Xt8+Jxdpwt/+EEA1Sv7w7\noFgUcttkU7KhLB3LLvBhsRofZQY7iWVvMPeEVEWZlXAh3OO3aIRLgSzL3EeWUa1FWfr5LazKzL0Q\nnruRpeNAW19xyL+Qsgzc11TfcCdsYDsY4nO2iHlBNJxzjoHB7ohRPmFQp4TKyvnw8VkZsVgkztIf\n/2qkFYHdaSOvXQ7d+3Uhr10OdlfDd31rmkYquf1bpgY7xtfKw01vXInLqzcsOT1Obnr9Crz5RrOd\nwc4hlDxE9guI7GkI7wOIvFmgtN1iCxuY98zcybynHt/mQV2gdNL/mTUBtMJKQbyZ6PvbLIcQJg/C\ncwdYDwEEmLsjcl4EU/MaC21g0Nww0isGdYrFaia3XdUP7uw2u6+XZfG6Uj5+ZjYb/ylk1MUj6LRn\nO1ze3TPjEwvHMJkVrLady9QrikKv/boz7feHiYVj2N12PLluFLNhC2Ww8wglD5SDt/FcLvieQAZv\ngeTPYOmP8EzcTunDFvZloakV2d1q3u+mVsC2S6mEUgC+KfoIZEzbPZ+BgUHdIKRs+Gk0AwcOlAsX\nLmzw8xo0DOtWbGT8oOsIB/T6t95DenHr2xPI3g1LCEo3+hk/6DqK15WmY5M+uZH9RvZvxFU1POFA\nhJW/ruKdKTPx5nsYe/0Y8jvmoiiGmDVoHkgtCDIGwoYw1eyzTGpR0EogNhOpboToqxXPWBDZ0xG2\nwfW3YAMDgzRCiEVSyoE72s7IFBvUOa275DPt94dZvngl3jwPbbq02m1ralf9sSZDEAO8OvEdeg3q\njie3YZr9mgLLf17JNcPuSD/+5s0feO63h8ir5q6CgUFTRPctrl2ZjjA5kGQhtWKEuStkvwjqerAO\nRJIF6gbADKZchGgZ1YxSLQUZAKmCKdvIcBs0KwxRbFDnKIpCbttsctsOaOylNDrVNYBZbBZMpob5\nAkzEk4RKQkTLYziyHHjysnbKBURVVfybgqxaugZvnoe8djk1HpoRDkZ44/4PMmOBCL99v5RDTz6o\n1msxMGhOCJMXXJfodcXqBrAdrFuy+c9Epv4E63CE53pk8jcwOcG8l15+0QyRagkycCUk5ukB8x6Q\n/XyzfT0Gux+GKDYwqEfa9WhDp73as+qPtQCYFBNnTxqLO7v+a4qTyRS/fbeU24+bTLQ8hsvrZNLH\nN9JrcPdaly1s+HsT4/e7gUiF7/T+xwzg6mkX46tBg5uimNKNcVtSXawuSSVTBEvKMVuU3Sorb9D0\nEIoPFB9YeiK1UvBfrFuuiSyE+zxkyRg9uwqgdIWclxtFSEqZ0qf6gZ7lFbWUCMlFlYIYdJeN6Pvg\nOsewojRoFhii2KDeiUfihENRnG57ozg/NCbZBT4mf3kbi2b/wqZ/C/nPSQeR265hmg6DxSEm/u8h\nouX69K1wIMKksQ8zdf495LTWvVnLNgX49/fVhErL2euAPcgu8FZpWouEojx33ctpQQwwb+YiClcX\nYXNYcbi3/zu1u+yMu/0k5n20MD1JsGOvdnTv36UuX24GgeIgn06bwyfPfkFOax8XP3wWnft03OkG\nPwODOkOmIPmL/m/7CGT0bUCA82yEubvuY5z4GRzDG3ZZql+vfQ4/CQiEazzScVSN66cBZHJrb2Ug\n9Tu6fV3z+9tLj9pGA2z6xY1Bi8YQxQb1SumGMmbc8SZL5vzGXgf14uyJY3e7OtLsAh+Hnnwg0fIY\nqUSqwTx1k/EkodLyjFjRmmLUCmu4sk0BbjhyIn8v+RfQ7c2eXHQfbbtlTtxLxpIUrS2pcvw1f67j\n02lzOPq8w2nfs812L3jadG3F9D8f4adZS/DmZVUI8Pr5glFVla9fn8u0G14BYMPKTVxx8C3MWP4Y\neY3glW1gkImiW7qllurOFDKByH4GGXkJGf4CLPuCpXvDL0tdhgzdmX4oQ7chLL3AWvOmYGEfgQxP\nzYw5jm+WY6GlFoPkYmTgWtA2gWUg+KYgFGMiaUumZVT2GzRJgqUh7h33GB8/8wXrVmxk9otfc/tx\nk/EXBRt7aQ1KsDTEe49+whk9LuW8Plfz8dOzCW4lVusDq8NKu61GSnfv3wWLXf+CWvnLv2lBDBAJ\nRnj5zreIR+MZ+2Tlujnq3MMzYnaXnTZdW/PJM18wfr/rqxXNW2KxWmjVIY+jzzucIWMGpzPV9UF5\nWZjPZ3yTEUvGkyz7eWW9ndPAoKYIJRfhewiUDhD/FuE8FRmaBLGPQF0NsfeRwVuRatmOD1aHyOgH\n1cRm1u4gSluEN8onKwAAIABJREFU7zFQuoDSDpF1O1h6180CGxoZRJadrwtigORCZHBSevy3QcvE\nEMUG9UYikmDxl79mxP76aQWJrURXSyAciOAvCqJpWpXnViz+h2eueYlQaTmB4iBTL5vOmj/X1vua\nslt5mfjxjfTarztCCHoP6cVtb0/Al6/fDi3bFKiyT9mmAKmkmhEzmUwcfML+XDr1HLru04mBI/px\n14fX8crEt9E0DU3V+Oz5r+r99dQUq91C224FVeKtOuZXu33pxjLmvj+fr9+YS8n60mp/hwYGdYrS\nBZHzOiJ7GggHJP8v8/nEAirHSjcMwtKvasxaNbbdY5iywHYEIucVRM6b4DwJYWqmJQdaCVV+B4l5\ndTZqW2oBpFpKY9jiGmwbo3zCoN4QiomsHHfGLXy7y96iBi0kkynWL9/AM9e+jH+Tn1EXj+DA0YMy\nGru+em1ulf2+efMH9j6wV72uTQhB+x5tmDjzBtSUhmJR8G6xrr6H7o3daSMWqfzgP+6Ko3F5Khvg\nkvEkwZIQyUSKIccN5tCTD2LF4n946PynWf/3xvR2OyqFCAcjREN6bbPL58RRj7XlDreDsyedwpI5\nv6XvSvznpAPJb1+1bKd0QxmXHnAjhauLAfDmeXhy0f3kdzDKLFoqUmqg+UFYdBHXEOfUykFYEcIK\n6H+bKPpFmlQLK8oowpU7CA91lbOSmh9kUh9fbdpOc6ttqF4ikKyYIWA9AKwH1vp8QpigJbhNmHLQ\nJdIWk0gt++gjuHcBqcUgtQxZPhm0CMJ1NtI2pFa12wb1hyGKDeoNT66by588j7vHPoKmaQghuPih\nMxvEeaGhCBQGuWS/G9LC8sFznkR5UeHw0w5Jd1vvfeAezJo+J2O/vWopiFVVpdwfweGyYbVba7Xv\ntqzTfAVenlh4Hy/c9gahkhDHX3kMvQb3SD8fj8ZZNPsX7j9jKuFAhA57tGXSxzfSuU9H4lsI6bx2\nOdu1VvMXBXnu+peZPeMbFMXECVeP4oSrRu2UI0S5P0y0PIYQArfPuc065oLO+Ty95AE2rS7G7XPh\nycvKuCDYzI8fLUwLYtAb9D58chZnTRzbYLZ5Bg2H1PwQ+wIZeQVMuZB1LZi71FvNq9T8kJiPjLwJ\n5k7gOg+htMncSHgRnluRgesBCZj0Ec+7mGGVUgN1VcUkvuVgOxSyJiCU6u+Y6JP7plaKc5MLsTuP\nlRZZCO8DyOBN+s9E6Ybw3LHr4lUrRpb+j81iWwau1O8Y2KqfqGjQsBii2KDesFgtDBrZn5dWTmXd\n8o206VZAVo4bm2PXrrSbEn/9tCIj0wow86nPGXzUvmnRt/8xA+g/tDeL5/wGwMAR/eh36N41Poe/\nKMjsGV/z3Tvz6TGgC2OvP65OmhUtFjMderVjwvSLUZMqbl/mxUqoLMzEkx8iGU8CsOav9Tx47pPc\n+vYEnlh4H3/MW4bJJOg1uOd2a4R/mrU4XV6hqRqv3fMeA4b3xaSY8ORmkd3KWyOB7C8K8Pil0/nm\nrR8xW8387/pjOfaSI6vd12QykdMmm5wdjBevrr7dXxhEatIoLmthSCkh/g0yeGNlrGQBIv9zqIfm\nKSlViH2ODN6sBxLfIWOfQ+57GcJUmGxI23BE/n6QWgPmjrpQFrW7+M04t1oEJJGlp+v+yACx95Ay\nDt67tpkhF0oOsBsL4S0QJifSNgyRN6Ai026vG5u8+NdkZJ8BGXkVLAO2n8k3aBAMUWxQrzizHDiz\nHNus52zuVCe68trnYt7C+svXystNr1+plw8I/fa+N69mWdJYOMbLd77FB4/PAmDpvGX831e/M/nL\n2+rMvWFbpQxhfzgtiDfz109/k4wnyW2TzZAxOx5Rm0wkmTdzUZX4vJmLWPvXeubNXMRR5x3OOfec\ngidn2z8TTdP49s0f+frNH/TjxpO8dMdb7Hdk/13yIB46dggv3/k2qQpHDiEEYy47qkWV+OyIcCJB\nOJlAIMhxOFBaaoZcBpCR17cKxvR63vpwFNDKkJEXt4oVgroWlHz9NroMAlIvbVDagdJOX6rUdGEr\nIyDsIDwIk6NGp5XqemTpGQjv3ZWCeDPx2SBvAgzf7pogTDagan/CLlHxO86MdYBm6NDREmmhn34G\nBg1Dm64F9B/WJ/3Y5XVy9sSxOLfy7vXmeWjdpRWtO7eqsSAG3SN469KLVX+sJVpe/004bp8LuzMz\nq7/PIb2wOWqewbJYLex3ZFVLpz0GdmfNn+sA+OTZLygv237zSjyaYMGsxVXiv367tMZrqY7cttk8\nueg+Djlhfw4YPZDH5t1N666tdumYzYmSaISJ333NIS88x+jXX2bOvysJJxKNvax6wgqmasSvaddF\nj1RLkGqRXi6xGWEGUc3funAiNb9uwVY8HFl0GDJ4tz7UYzPqKmTJ8cjiI5BFh0PsU6QWrnqsrdeh\nRZChKaCuQvcF3irvZe6M8bVfe6RahlSL9ez/rmLZR/9vM6Z8hOusZmlb1xIxMsUGBrUkHonjLwry\n2/dLab9HO6594RJKN/oJFAfp2qcjvoK6bJgQePI8FK2prHsVQtSJ13GwtJwNf29k3sxF7H1QL7r3\n75IxoS4rN4u7Prqee09/jJL1pew5uAeXP3lBlTKLHbH/0QMYdsoQ5rw2F8WscNR5h5NKpFi3orJR\nr9y//S98m8PKfiP7M//jnzPifQ7Zs1Zr2Rqr3Urn3h255vnxSMkOB5HUhkAsRkk0wppAgJ65efjs\ndhyWpvPFl9I0Xv/1F974XXeI2RQu58KZH/D1GediUUz4YzHKEwlcFis+ux2buXl/XQiTE7KuQCa+\nq8jQojeTKR13+phSqpD6GxmYoE+osw4G7/0IpY3uupB1HbL0VNK3y637g6kVqOugfHLlgWJvg7Uv\n0nGSntEO3ATa5r+PODJ4M8J2ELCDvz0Z0f2PARn7BOG+DFn+MKDp2WjPvXrtsEGNkFoUUkuRoXtB\nCyGcZyAdI3fJUUOv3X5afw/IKJi7gqkFNCa2EJr3p5yBQSOw/OeVTBh6B2pKzxocfNxgrnj6QnoO\n6Frn5/LmZ3Hp1HO47dj709Y9Yy47EkfWrom3ZCLJFy9/y5NXPJ+ODTvtEMY/chZZ2W4ArDYLfQ7Z\nkyd+uhdV1bDaLdts2tv+a/BwydRzOefe0wD4e8m/3HH8A+nnffke8tpuv/bXZDJxyIkHEAlFcflc\nxCNxLHZLlUEjO0tdT1oMxuM88/NPPLlwAQBmk4kZxx7P4HYdmsy422A8zux//s6ISeDXwo34Yx5O\nfe9tyhMJnBYLTxw1mgPad8BSy/HgTQ6lPSLvE0gt0xvZlLa71kymlSLLzgKtSH+cmI/0T4Dsx3Xh\nZO6FyPscEj/ot8jNPRFKNlr0nSqHkvE5CPsokIm0sK0kBVoQlB1ktU1esA2D1HKIvIB0nYvIeQOE\nootxU8NM02wxaCUVFzX6Z70M3apPtbOP3KXDCiUXjIuTJolxH8XAoBb4i4I8ccULaUEM8N278wmV\nhurlfIqi0PewvXlxxWPc+OrlPLV4MqfefAJun4tAcYjSjWUktqr7rQmhknJeuv3NjNiXL3+btk0r\nD4Qp3VBGuT9CTpts8tvn1loQJ+JJSjaUUby+FCEE+e1zyW+fS6/BPRh3+4l02qs9B/13EA/PnVij\n7LrJbMKb5+GjJz/jxw8XssfA7tictW/aLNsU4Pv35vPuIx+z/u9NGU4adUU4keCpCkEMelb2pjlf\nUBKtG4/TusBpNtOnVVWR1TU7h6s+/5TyijKKSDLJlZ99Qlks2tBLrHOEUBBKK4RtCMLSe9fdFWSk\nUhBvJvmTLmwBYXIgzO3BNkIfaIGKlAmEdd+qa7MeWFE/7ATrkK2edOmCdwcIYUE4zwD7CYAVYp+B\nLNedE5RWxi362pL4gc2CeDMy8qYxwKMFY2SKDQxqgZZSCZZUFcDRcKxOzxMsCREJRpFS4shy0KZL\nAW266AImmUiyYsk/PDb+OYrWlDDstEM4/spjMkofasLWTXQAQkDxulIeHf8si7/4lW79uzBh2kW0\n69GmVhnOcn+Yr16fy3PXvUwsHOfgE/bnkkfPxtfKiy/fw4nXjGbkOcOw2i0ZvsjbY8mc35hy/lPp\nx1cefAsvLHuUgk41b+LcerT1M9e8xCNzJ7LHoLodqxtTU2xtyb8pXE5T8um3WyxcMmh/flq3lmWl\nJZiE4Lx9B+Kx2fi7LHOaWlksSkKtg3rKlsZmEbvlQAelG1CZUZepdUj/+Xr2VrgRnrvBOhCcZ0Pk\nRUAF62FgP0b3+BVu8NyCDMQg8S0onRDeyTXO8golFzw3QdaVgABTjn7cLZBSglZcMTREAUvvbVq1\n7dYoHarGzF1gF5xBDJo2RqbYYLfGXxhg47+FFK8rJRLacSbMk5fFqIuGZ8Ty2+eS07rubkv6CwPc\nc9qjnN5tPOO6X8Jtx95H2abKBp5gcYgrh9zCHz8uo2htCa/f+x4fPfkZqURqO0fNxOV1MuayozJi\n+x7eB5Ni4v4zp/LjhwuJReL8PvdPrht+V7XT77ZH6YYyHr34WSKhKJqm8c2bPzBr+px0ht1sNpPd\nyltjQVweCPPxM19kxFLJFP/31W+1WteGfzZljLZWUyrTbniF8rIdNzHVBrfFSgdPZmbvv3vsicva\ntL5MC9xuXj7uJL4+4xy+O/M8Lh44GJtiZq/8zGbDrtk52Jt5TXG9ILwI7wP6VDrQBajvwXTdrtSC\nyOBtuiAGkOXIwNWAinBfgsj/CpH/DcJ3X0atr1Ba6cfJ/x6R8yrC2rdWWV5hciGUfN1CTCtFxucj\no7OQ6iakTIFWiCw5Fum/GOm/AFlyYoWNm0EG5p563flmTAUI17m7ZJdn0LQxPuUMdltK1pdy63/v\nY9milZgtZsbdfiLHXDg8XVNbHWaLmZFnD8Ob52H2jG/o0Kstp9x4/HZ9emvLb9//ycLPlqQf//Hj\nMr57ex6jx48kWh5l1dK1VbyRv3rte4654Iga27TZnDZOuHoUPQZ05du357HPwXtyyIkHoKa0KqO5\nC1cXEy2PUbqhDGES+Fp5d5g1Xjp/eZXYgk8Xc/QFR2z357strDYLrbtUdYUo6Fw7p4jqSiVi4Tiq\nWrejnfNdLl497iQm//AdfxYXMbxbD87o2x9nE2q020ye0wlkXpw8dfRorpk9i0Ub1tO3oDUPHHEk\nec6WM3SnrhAmO9I6RK8bltGKMoctSjJkDJK/bLVXErQShKUAqP5vQUoVZAqEA2Gq/d9L+jhqCbLs\nPEj9XrFgNyL3PWRsTmbZh7ZeL7VwnZaxL4kfkclfEfYj9SEnjTh1TWqlIOOAuSL7Xf/17ULJBe9D\nelZdRvUadCOj3qIxRLHBbkk8muDVSe+ybNFKQM86Tr/pNYaMGbxD0ebNy2LEWYdx0Jj9sNqttbIo\nqwl//rSiSmzp/OUcctIBvHn/B+x/zMAqz7ft3hqrvXaCy5vn4ZATDuCA0QMxW8wIISjb5Kdtt9YZ\nI5ztThvxaJxLB9+AJ9fN+EfPpveQXjjc2/ZN7VFN02HfQ/feaXcHq93KqTcdxw8f/IS/UM9a9zts\nbzrt1b5Wx+m0V3tyWvso3ViZeT/5umNrZZNXU9p5PEwaegTRVAqvzdasmtTae7w8efRoEqqGRTGR\nba+ZR259UBQO8+Pa1RRHIgzv1p18p6tJOWEIkx3YxvtaOMA6SPcHTmPVp+ltA6mVQfQDZPQ9UNpD\n1jWgdNg5EZj6q1IQg56pLn8cbIdUc95CNl/qSrUUGbgKEj/qjyPPIzyTkI4xCNHwP3uprkf6L9Mv\nMEwFCN+DSEtfxC6OXK4JQskBxRhosrtglE8Y7JbEymP8MW9ZlfiqpWtrtL8Qgqxsd50LYtDdLLbm\nmIuG8+f85bz14Ef889tqRpx1WPo5T24WFzwwDpd35zJ5Fqslnfn1tfJy/UuX4sxyVDxn5tLHz+Xt\nBz8iVFbOuhUbufmYe/EXVp0EtyV57XI5886T09Zx/Yf1YfTFIzBbdv4LNb9DHk8vnsyUb+7kqcWT\nuen1q/C1ql3mKrvAx9QF93Li1aMZctxgJs+5jb61mC5YW1xWK3lOZ7MSxJvx2R20crnqTBAXR8Ks\nDwUpCodRtZpl5ovCYf73zhtc8dknTPzuaw5/6XlWB/w73nEXkGopMjYHLXgfMvFTpvdwLRGmLITn\nFrBUNNaZ8hDZT4Oo/n0rZQoZeQcZult3oIjPRpacBFt6GNfkNWhBpFqmZzi3RitCWPYBtrzboyAc\nY7Y4QDgtiNOh8sdgF34WO4vUgsjAzZUZd20Tsux80GpX0mVgUBOazuW2gUED4vQ62O+o/qxY/E86\nJoSge78ujbgqnbbdW3PVsxfy4m1voiZTnHz9sXTt3ZGnJswA4OmrX+Sce0/l4e/uIplIkdcuh4+e\n+pwe+3bd5QlvQgi679uF6X8+QtgfxpHl4MtXvmP2jG/S22iaxu9z/6RN123bQ3ly3Bx3xdGMOGso\nmqphd9l2aV2b11aT0c07OkZ++1zOvmcsalJtUSPHmzKr/H4u/PgD/ioppq07i8eOGkXv/FY7vFj4\nvWgT//grm/4SqsrD839k8hEjcFrq/oJUagFdkMY+1B9HpoH7KnCeWZERrj1CaQ2+J4AEYNLLK2QU\nqQZB2DJLEjQ/RN/aalF+UFdDDW/by9QaZPAuSC1DZD+jZ6tlZb+EcJ6hD4zIeUPPGgsF4b50q8Em\n1XWEaiATSJlsWBcLGYfEwq1iEdBCoOw+g3YMGgZDFBvsllisFo699CjWr9jIt2/N08sCHjsbTz3c\nRq8tWdluhp9xKIOPHgBIPDlZmK1mBh3Zn0+e/YJkIsVTV72I2WJmwrQLeXrCjLS4P/vuUzjxqlGY\nd2G4h8VqIbdNNlabhXAwQuHqqtmmjnvuuGzB4XZst8SiMTGbzZhrcAs+EU+A1Ms3DHaO0miEy2fN\n5K8S/X20vjzEOR++y6xTz6CVa/ulSpFkVYeUSDKBptWTjYeMpAVxmvDT4DiebZZI1ACxxe13qW5A\nBu+ExE9g6QueOxHmitG/wqyLX/WfzAPUcFiEVIuRZWfoo6QBWf4AInsGMjwNZBnCeRZY++sjo639\nwPcQIBCmre4yCbee3U5WDssRztOR5Y8i3BfoDgy1RGoRvcba5K1dKYiwgKWPbnWXxga7UGttYLAt\njPIJg92W7FZernjqfF5d/SRPL3mAIWMGp8sGGhvFrJDT2kdO6+y0wO1zUC+OPHcYJpMJk8nEEeMO\nweZyZGS7P5g6i2AdeCbHwjFmPjObC/pO4Ihx/6F9z7YAtOvemmtfuIQ23Xd9NG5TJh5NsGrpWh46\n/2mmnP80q/5YQzxa/6O1WyJJVeOXwk0ZMX8sRjS5Y7eUAW3b4bVlitGLBw7GbauvDH81YluqSGnS\nxzinViHVjTUauVzt0bVSvTY2/qU+VS/xne7+oJYAIEw+RNbNZAhw21GZzXvbPUEkLYgBiH+N9F8O\nnlsQvqkI+9CMzLQwuasKYnQRL3xTEVm3geM4hPdhMHkg9h4y9IgucGvzulPrkMHbkGXnQuRFpFrz\nchBh8iG891b4PAPCg/A9ss0SFAODXcHIFBvs1ri8rp2uxW1ovPkeLpg8jnG3nghCoKkaZ/W6PGMb\nl9dZxZMU9JIHf2GAeCSB1WHFk+vGYt32LdDyQISX7niLZDzJ/WdM5fz7T6dNtwLCgQgfPD6Lf35d\nzZjLjiSvfW6TmdBWl5SsK+XCfteQqhBuX78+l+d+m5K+ODCoOWaTib3yW/FHUWE65rHZcNSgvjzP\n4WTm2NOZvmQRReEw5/QfQNfsemx6Ek59Ilz8y8qY+zIEQWTJONA2ARZE1o1Ix39r7wwhExXewFuQ\nWqZnUDdj7obInw3JP0FpDUo+wpSN1EKgrkfGPkdY9gJLv6ojm4UNsABbZNiFgsBUa+cIoeQhzT0g\n9ateT6xWTD+UJZnH3wFSLUKWjk2PrZah3/QyEdd4hKlmFzfC3AFyXtF/TsIKJp9hi2ZQLxii2MCg\nCRMoDlK4upg1f61nr/174s33kNde/yIMloQYfFR/vnt3PqDXy1445Qx8raoO8Vjz5zpuOPJuitYU\nk5Xt5uY3r6TPkD2x2KoXxpqqpYd7rF22npfufIuTr/0vE//3UHqb2TO+5qklD5C7CzW+TZVPp32Z\nFsT9DuvN0FOGULKhjDbdClCaYdNcY5LrdPLIiKM596P3WBXwk+dwMvWoY2rUwKeYTLTzeLhhyH9Q\nNa3eXSeEyQeeiRA/Apn4scKKrDcyeHWFIAZIIkN3IexD2Zal2rZRwJSX2QAnMku2hLDo45y3GOks\npQbx75EB/SJYAthPAs81mWJXuBFZ1yNDEyu2sur1zLIMLTQdobQH2+E1txUzd6qYileZGRfOM2on\nsLWytCBOE30bnKcDNbc3E0pezc9pYLCTGKLYwKCJEiorZ9oNr/LpND1rZTKZmPTJjQw4Yh+EEHhy\ns7jsyfMZc/nRrF66lv5D++ArqOoh7C8MMGnswxStKU4f947jH2T6Hw+T27Z6QWt32ug/tDeL5+jD\nMQ48dhAfPzM7Yxt/UZDlC/8md1RVi7jmjsun+/aectNxtOvWhrce/JBUUmXsjWM4YNTAnfJa3p3p\nmp3Nmyf8j7iawqoo5NgdmGtxcWE2mTCbTEgpKQyHmfPPSqKpJCO79yDf6apTdw+h5ILzOHAcixAm\npFoIya2dajRd7CltandwUzbCey+y7GL0xruKrHPkBXBdWDXzmz5dKbL8Qf3fSnt9Kp66DmJzkLYD\ndFcLYUaYXEjHsQj7MFALQekI6j/IklMAqYtpZTrkvFYzkWnKQeS+iww9ArJEb9KzDqrdaxbVDOgx\n5UM1d7QMDBobQxQbGOwiakolVFqOxWbB5a3ZhLaaEAlG04IY9BKIxy+bzpRv7kgP6fDle/Dle+hz\n8J7bWZ/GP7+u3urYkfQAkEgoSjKRwpPjTgtqT24WN7xyOe89+gm/fPMHewzqzl8Lqvon25y1q+0M\nByNEQzGEELiznU3W/eHw0/7Dly99yz6H7M31I+5Kxyef+TgPfn0H+xyyVyOurvkhhCDftetlSkWR\nMKNff5miiJ65nDJvLp+cMo6O3robnrOZdBmScIPtPxB7f4snnUhTa1CLAU2fbFeDUgAhzGhKT0TO\nSyDLQWQho2/qmVPXWdvZU1YMrgDhvQcZuL2ynEG4ELkfgLmj/tCUBWSB0lZ30yh/mIxaaXUVpFZC\nDUSxEBa9qc57N5AE4dG9jmWq5n7FJjc4TtzCVcOC8NyK2I5Xs4FBY2GIYgODXSBQHGT2jG/47Pmv\naNUpnwsfHEfb7q3r5BZ7Ila1bi9QHERKWVEjHCQRTWC1W/DkZW3TA9hsVdhzcI+MKXO+fA8uj4O1\nyzcw/cZXKV5XyjEXHsH+Rw9IW6dlF/gYd9tJRMpjOD12cgp8LPzs/9JlBR32aEvn3h1q/Hr8RUGm\n3/gKn7/4DRarmVNuPp6jzz8cT07jO35sTU4bH1O+vYs37n+/ynOfTvuSvQ/cA8VslFE0NF+uXJkW\nxKC7U0xbvIhbDjkMs6l+Mo/C5ISsa5AyAYkFuhOE7zFEciEydC9oQXCeCq4zEaYdlxIJoSD9F+uW\nYiT0oKkAvRZ4G5iyEa7zkNG3ILWqUhADyDAy/DRk3Vq9MJfVeUKrO1xnxppNTt33OPYOMjYTzL3B\nNQ5RA0s0YfJB1gRwjgN1DVj2BtHySq4MWgaGKDZokSSTKYLFIeKROHanDW8rT53XgqZSKT57/iue\nve5lAP79fQ2/z/2Tab9vuyyhNrh9Ttp1b826FZX1eEeeO4ysbDer/1jLTcfcQ+HqYrx5Hm5752q6\n9OlItDyOpmos+eo3OvfuSJsurfDmebjx1Su466QHWbZoJW27team168glVIZP+h6IkG9k3zpvGVM\nmHYRw888LJ0xNlvNeHLcRMMx8tpnM33pw3z/7nxy2+XQ77DeNR4rLaVk3kcL+XTaHEDPrk+/8VX2\nHdanSYpik8lEVo6brn07V3mu6z6dDUHcSERTVS8UY6kUUtaTRdtmhB2RNUGvK1ba6T7D/ksqnw8/\nCUpHpOO4HTeemrIRvkf0EgqZ0LPMvke26zAhhBnpGI2w9KlmbDQVwz2qCl1h8kLWTcjgtXpDH4Cp\nLZi71+BFVyK1GDLyPISf0gOJH5CJryH7xW2XfGSsIxtM2WDZo1bnNTBoaAxRbNDiSCVTLP1xGbeP\nmUyorBxfKy+TPr6BHvt2rVOnhFBJOZ89/1VGLByIsG7FhjoRxdkFPh6Yczuv3v0uK39ZxX9OPpCh\nY4dQHohw18kPpf2DA8VBbhszmUkf38Ck/z3MwBF9GXbqIVx3xJ0MP+NQxt1+Eq27tGLSJzeSjKdQ\nzArZBV4WfrYkLYg38+ETn7H/MQPx5uvNeqqqsumfIp6/5TU2rSpm9EUjOOr8w3F5alcmkogl+PGj\nhVXiP3/5K3sMqt0XdEPSf2gf9ty/J0srph922qs9w049uJFXtfsysnsPpsybm/YvVoTgnP4D6nVi\noNTCEH0PGZoESLAMRtiHV90uNlOPi+1f5AlhRlr6I/I+0S3UhFMXyjvw7hUmH1j7I5U2oK5FWPqB\nVoiMvIFwna1ntDPWHQOtCJKLEO7L9XKKxCKEfWTNG+3SBwtB5JXMWGpZRQOeUQZh0HIwRLFBi2Fz\nvaqmatx5woOEysoBvdHsrpOm8MjcSeS0rrvaQ4vVQm67HFb/uS4jvllQ1pZUUs94bWmVltc+lwun\nnEEsEsfldaIoCsXrSlm91TjqUGk5yViKTauK+PiZL4iWxxh98QjeuO8DTrx6NG6fC19+Zse4N79q\nB3l2a1/G4A9/YZBLBt+Q/lkunbeMy544l6POO7zazHugOIS/KECopJy23QrSGXqLzcK+h+/DDx/8\nlLF9nyG9av+DakCyC7zc+f61+IuCaKpGdoGP7ALDH7WxyHe6+PSUM5i+ZBGRZJJz+g+gg6eefx+y\nHBm6j3QuME82AAAgAElEQVRdrrqm+gY789769LgaoDtM7Ow0NjNIDRm6B5SOCN8UpKkDVS73U8uR\npScDFX7Q5n0Q2U/XKLNbzYp1n2J1Kw/0mtYVGxg0E4z2T4MWgb8oyNNXv8ipnS9i47+FBIqDGc9v\n/KcQNVX19qKUklgktlO3X93ZLi5++Cwc7kqj/f9n77zDmyrfN/55c7JHk6YtLWVP2cgWAZElIiJD\nUFEUFRcq3x84cC8cgOAAcU/EgRs3ICqigCCKLBWQXaClMx3Zyfv745S0oYMWyjSf6/K6zNuz0pbm\nPs95nvs+99Kzia9V9Q9pT5GXvAMusvfl8vIdb/PUuBf5988duAtKYln1Rj1xTltEhGr1Ck07RCdK\nxSc78Hn8kde/fLqa9r1bEw6HCQXLD0moVT+RDv3aRl4bLUaunz4malgwbfO+iCA+yJcvLmbtkg1s\n/WM7ha6S/k5XVj6zb3mV61pPYtI5D3Bdm9tI365602o0GnqP6s7ZQ9XJdY2i4eJJF1KvxeGT8U40\njlp2GrauR+N2DWKCuBiX10uOx33s2xYOQaco1LPbua/XuTzWpz/NExIx6Y5x5LD0EeXLG96n+uUa\nh5asaZshLFeVGT6T4Vyk/09k0dvIwGZk2HV0lxL2IoteBM88CB+AwBpkzlUIPIds50IWziQiiAGC\n61XHiiNBE4+w3RO9ZhwG4tTweI8Ro6rEbvNinBb8vnhdpF+1KM9NrfqJUfHEjds1QHdI9HFeZj6/\nfPorq79dS5eBZ9Jr5FllqqmHo27z2rz5zyx2/Z2GM8VBfHJ8ZFCtMqSUZOzK5K3732fP5n10v6gL\nnQa0Z8rImfzw3i88/9s0mnVsXO6+jiQ7939wG49cPIMdG3ZTu3Ey//fC9bw39dPINikNk8jNcHFG\nl6YYK4hadiTFce97/0f6jgPkpOfRvFMT7Id4HNucZa3H4pMdbP1jO2/c9z5TvriL7heqlmxZe3NY\n9tHKyHYFuYW8evc7TH7rVsw2E45adu54/Wa8s70IjQaTzVjtNowYJxZPIMC/OdlMW76MIr+f6zt2\noWf9BtiNRx6BfCQcq6G6chFmUOqpFeJipPcHRNy9YJsEMqD6Ax9SgZXhAmThHHDPKzlU3JTivuMj\nDJ6Q+eBdAobzQVsXAhvAv0ptkyhdeZahKG/hkvUjS+ITQkHqz0YkLlLPpz0DlAbVDgSJEeNkRxzv\nO32Azp07yzVryvYXxohxJAT8aura0g9WANCia1OumzaGORNeZ+emPTTr2Jj7P5hEapOUyD5FLjez\nb36VH97/JbLWe1R3Jr58I1bHsa9+5KTncnPnu8neVxJ3OuqOi8jPymfRW0vpfcnZ3PnWLRiMFX94\n5h5wEfQHEQL+WLKBGdc8D6iV5Yc+uYPMPVl0v6jLUbWMuLLymX7VHH5buBYAg0nPlM/v4rlbXydt\nyz6admjE1IX34Uiys/rbP7hv8NSo/Ut/PUYJ+T4feV4Pu10umsQ7sRuNmKtQ8Szy+3H5vOzNz6du\nnJ04gwGL/vgle6Xlu+j79hsEwyWOBm8NvZhzGjQ8btdwIpDBPWpPceAvMJyDsE5ADeJwVGhNJkPp\nyMxzgVLuDyIOkfht9Xt6I8fMg/BepPdbCG5B6Huog3/alghtnZLtpATfQjXi+SCaRETC50d87hgx\nTmWEEL9LKQ9rqh+rFMc45dHpdXQ5v0NEFP+z+l9enPQW9394G1a7GUWnxXFIn6+n0BvZ/iDLPv6V\nG58ae1xEcW6GK0oQA/zw3i/c9NRYFr21FLPNiEZT+VBg6TaNs4d2Yd6258nal0NKo1roDTo69m9b\noU1bVbEnxjF57i2k7zhAxq4satVLYP70BaRt2Vdm28btGmIw6aPaOAZe3afcavPxIOALUJBbiNBo\nqtXScqwp8vv5YNMGpv7yE6AOi706ZBi96jdEqaQC6gsGWbJ9G7d/9y1hKVGE4LlBF9KvUZNjOmhW\nmh937IgSxADz1v9J59RUzLrTN3ZXaOuBfQbIAmRgEzJ7JBBC2O5EGvohNOXNEYSIEsQA0kOUZ3C1\nCSHzH4o4UEjfUjBfC7rOUT3FQgi1shv/JtL9Dij1EJZr1DS9GDFiVEispzjGaUHXCzrQ7/KeCCFQ\ntArtz22FIymOhFRnGUEMIAQoumghodUpNepOURlmW9mWhvhkO4V5hRhMei6dPCxq4O5wWB0WUhrV\nok2PFiSmOolLqNi3+FAO97TIkWSnRddmnNmnDc/c+HLUsNxVj1wSqQLbk2zMXvkEZ/ZpQ70zUrlh\nxpX0Gd3jhMQiu7LyeX/aZ4zvOJnJ/aewbunGSFjJiabA72Pmip8jr0NScs/335Hj8VSyF+T5vDyw\ndAnh4p/Xwf1yvZXvV5PUiSvbGlQvLg7d8WxnOEEIjQ38v0PeLao1WzgL6boLQukV7GAG3SHpb6ZR\nahjIkSLdZS3ZPPMRlLWqExo7wtADYX8KYbsTodQG6VKr3oEtyFDmkV9HjBinKbFKcYzTAkeSnVvn\nXMe4aWMQAsxx5nKF50EsdjOX3T2MeY98FFkbdcdFGC0GMnZlsuLz33DUstP+3NY16lgROb/DzMCx\n57Jo7lIAtDotN868ityMPN74exbxKce+sunKymfTis0s/2w1nc5rT8f+7cq9gTiIPdHGtEUPsPyz\nVezalMbAa/tQu0ly5Os6vY7G7Rrw4Ee3EwwEiUuwnRA/33A4zLKPVkZ+tjnpeUwe8Chv/zsHY4MT\n/+jYHwoROKTamukuOhjCWyHBUJhCvz9qzeXzEgqXF85QM6jDdOA0mRBC0C45hfbJKazLUIVgotnM\n9R27oFNO/48SGXYjvV+XXff9jNA1L7MuNPHgmIX0fAz+NQjjADAOKGOdVrVzFwEKCEUdbpMhwFt8\nosqPd/B8MpyLzJ8G3s/UL2hSIeE9hJJa7euJEeN05fT/SxbjP4PVYaly64PRYmTYrYPoen4H/ly6\nifa9W5HarDbZ+3IZ32kyAZ9aeanTrDbPLJsSCanIy8zHU+BBaARmm6lKQ3XlEee0cf2Mqxg+6ULS\nd2TQvGNj4hLjMJiOzyNoT5GX+VM/4+NnvgJg8dyl9B3dkwlzxmGNr7iS5UxxMGT8wEqPXVG7RG6G\ni5Vf/saWNdvoP6Y3DVrVPSatFYV5br5/9+eotXAozKYVm0k+CUSxWaejqdPJvzkl7TMDmzTFpK38\nyYBJq6VdcgrrM0oqk11S62DU1vyf8UK/jz/T05m58hcCoRC3dOlGj3oNSDRbeG3IcPYW5FPk99PU\nmUCi+T8yLCkMoDsTfD9EL+vaVLyLkgiW68E8BoS5JDq6isiwCwJ/qcEZSkuE+TKE42UgDOFMZP4U\nhO1ONRjjcIT2lQhigPA+ZMEciHsQoTm+g5IxYpysxERxjP8scQk24hJstOjWDABvkZd5j3wYEcQA\ne7fuZ8ua7XQb3JHcjDweHjGDv1aqQQ7dh3bh1lnXojfqcBymZ9Xn9uEu8GCymTCa1ShWe6INe6KN\nJu0aHKN3WDFul5vPn18Ytfbj/OVcN+2KSkVxdSnILcTv8RMOhflgxud8Pkc959evLOH2125iwNhz\na7y9wmDS06BVXTat2By1XrdZOd6yJ4BEs4W5Q0cyffkyNhzI4NwGjRjfpSs2QzkRvaVwms28NPgi\npv2yjDX799KtTj0mn92LeFPNi9L9hYWMXfBxpHZ967df8eHIy+icWocEs5mEwwjhQChESMpjIthP\nFEIoYBqJ9C0paWEwXgQ69e+HDGUVW55JUOqqgvjgfkfaMhFYh8y9DjAinLcgc8eUuGDouyESFoDG\nVuGwXxTB3WXXQv8CHuDUEMUylA3hdLVSrqRGvscxYtQUp89frBgxqkA4HEaGZbmP9cNhiafQW2bd\nU+hBSsmP7y+PCGKAlZ//Ro+LurDqmz+49blxFbZZ5OzPZd6Uj1i3dBNtz2nFVQ+NIiG14kjXyggF\nQ+RmuFi3dCM2p5VmHRtXOWo5GoE4pA9UCKE2W9cQOftzmXHtC6xZ9CdJdRO46emryc8u5Mdix493\nHv2Erhd0qvH2FINJz5gHRvL7d+vJ2KX2Tfa7ohe1ToIq8UFq22w83ncAnmAAq96AUaulwOfDEwxg\nULQVWpylWG081ncAnkAAs053zJwnvtryT5lmjvc2ruPMlNqV2qGFpSS9sIDX1/5OemEBY9t3pEVi\nInGGU0N0HQ6hJILjFdXarLiVQWjsyFAmMucqCG1TN1QagvPdo3J6kOF8ZNHr6gvjAKT3qyhbOPyr\nILQNoT2nagfUt0f9yC/xLhbGISCOLGzoeCNDWcjccRD8W11Q6oPzPcQRh6DEiFGWmCiOccqSn11A\nwB9EoxzeXSAcDpO9L5evXlpMTnoewyYMonbj5Ki+Y7PNxGV3D2f1t2sja7Z4K217tVSjo1dtKXPc\nnX/twea08vviP+l7Ra8yVc/87AKmjpnNnz9uBGDP5n0c2J3Jve9NxHYEFdmMXZmM7zg5Eu7RoFVd\nZnz/ULWFscVhZuRtF/LeEyXexuddfW5UEMnR4C708Nrd77Bm0Z8AZKZlM23MLJ5a+ghL5y9HSnlM\nhxqT6iXy3K9P4MoqwGDSY7Gbj7jV5Vhh0esjojajsJApy35gxZ49tKlViyf6DqCevfyfqVWvx3qM\nbdgaO8retDWNT0A5zM8sy13ERfPfiQwNfvvvVt68aAS9GzaqdL9TCaE4gUO+P77vSgQxQGgneL8G\ny9VHcSZtiWDVJEFoV9lNgtvBUEVRLOIRzreQ+Y9BOBtMl4Bx8GHjpU8a/MtLBDFAaDfS8ylYbjxu\nA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QuIewTQRFevg9uRob3qew2siz62tknVrgHUVgTzcDAOAGGsWoz08UBYEcbzkf5fo9f1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k988I8ixQ9Ypsj/DvKhWjpa0iqr5gerrs8I7zHXoTM0GeA/5ThQIRQX9fuJIuiEnh9ggpmIiQ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\nJCkWQgghhBBxT5JiIYQQQggR9/4fGnFpOOo4iVsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "L1_hSh8l-xGN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "73125169-63bb-47e5-9ed8-0a76a033d1dd"
      },
      "cell_type": "code",
      "source": [
        "descr['cluster'] = clusterer.labels_\n",
        "cities['cluster'] = clusterer.labels_"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "XHP3y1dE-17_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "2bad066f-3fa3-4955-8b60-d428936a8f96"
      },
      "cell_type": "code",
      "source": [
        "descr.groupby('cluster').mean()"
      ],
      "execution_count": 37,
      "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>nomad_score</th>\n",
              "      <th>cost_nomad</th>\n",
              "      <th>places_to_work</th>\n",
              "      <th>friendly_to_foreigners</th>\n",
              "      <th>life_score</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cluster</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>0</th>\n",
              "      <td>0.739</td>\n",
              "      <td>3102.791</td>\n",
              "      <td>0.665</td>\n",
              "      <td>0.806</td>\n",
              "      <td>0.818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.650</td>\n",
              "      <td>1871.141</td>\n",
              "      <td>0.580</td>\n",
              "      <td>0.800</td>\n",
              "      <td>0.680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.515</td>\n",
              "      <td>1597.500</td>\n",
              "      <td>0.425</td>\n",
              "      <td>0.577</td>\n",
              "      <td>0.544</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         nomad_score  cost_nomad  places_to_work  friendly_to_foreigners  \\\n",
              "cluster                                                                    \n",
              "0              0.739    3102.791           0.665                   0.806   \n",
              "1              0.650    1871.141           0.580                   0.800   \n",
              "2              0.515    1597.500           0.425                   0.577   \n",
              "\n",
              "         life_score  \n",
              "cluster              \n",
              "0             0.818  \n",
              "1             0.680  \n",
              "2             0.544  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "metadata": {
        "id": "KQjNTsH7_KgB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "f99d7c1f-f8ea-4df1-dc1c-c9f9f9f5a5f7"
      },
      "cell_type": "code",
      "source": [
        "cities[cities.cluster == 1].sort_values('nomad_score')['place'][:10]"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "778            Cali\n",
              "759    Johannesburg\n",
              "756          Luanda\n",
              "738           Accra\n",
              "733           Davao\n",
              "725     George Town\n",
              "722    Sulaymaniyah\n",
              "720             Goa\n",
              "719       San Pedro\n",
              "714           Batam\n",
              "Name: place, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "metadata": {
        "id": "x4SC0Pz1coqn",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Gig portfolios of online freelancers\n",
        "\n",
        "![alt text](http://sds-datacrunch/public/adult-alone-bar-1308625.jpg)\n",
        "\n",
        "You get gig-portfolio data for 1000 online freelancers – overall ~35k gigs. Given this data can you identify professional patterns?"
      ]
    },
    {
      "metadata": {
        "id": "5YlCnHBnvNmK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# loading the data?\n",
        "data = pd.read_csv('http://sds-datacrunch.aau.dk/public/feelance_eda.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "X-06kXjWDi4X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "cda67f1f-984d-494c-fd56-57e86dfd0c7c"
      },
      "cell_type": "code",
      "source": [
        "# Quick data exploration\n",
        "data.info()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 34606 entries, 0 to 34605\n",
            "Data columns (total 13 columns):\n",
            "Unnamed: 0          34606 non-null int64\n",
            "as_job_type         34606 non-null object\n",
            "as_opening_title    34606 non-null object\n",
            "as_total_charge     25211 non-null float64\n",
            "as_total_hours      14029 non-null float64\n",
            "dev_country         34606 non-null object\n",
            "econ_sit            34606 non-null object\n",
            "fr_continent        34606 non-null object\n",
            "skls_required_no    5659 non-null float64\n",
            "sub_category        34606 non-null object\n",
            "tenure_d            34374 non-null float64\n",
            "hour_percent        34606 non-null float64\n",
            "f_id                34606 non-null int64\n",
            "dtypes: float64(5), int64(2), object(6)\n",
            "memory usage: 3.4+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "LPP8UOEfERbz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "f4e0ab95-7c31-415d-cf41-cbc7adcb9798"
      },
      "cell_type": "code",
      "source": [
        "# How does one portfolio look like?\n",
        "data[data.f_id == 0].sub_category"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     SEO - Search Engine Optimization\n",
              "1                           Data Entry\n",
              "2     SEO - Search Engine Optimization\n",
              "3     SEO - Search Engine Optimization\n",
              "4        SEM - Search Engine Marketing\n",
              "5        SEM - Search Engine Marketing\n",
              "6        SEM - Search Engine Marketing\n",
              "7        SEM - Search Engine Marketing\n",
              "8                  Display Advertising\n",
              "9        SEM - Search Engine Marketing\n",
              "10    SEO - Search Engine Optimization\n",
              "11    SEO - Search Engine Optimization\n",
              "12              Article & Blog Writing\n",
              "13       SEM - Search Engine Marketing\n",
              "Name: sub_category, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "_BgYMZ5pfSJT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Assembling gig-portfolios from a gig-list"
      ]
    },
    {
      "metadata": {
        "id": "qi5lJk7NDuHl",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# individual freelancers\n",
        "workers = data.f_id.unique()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZmKU47WygbJS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "A bit some thing on loops and slightly more advanced  stuff.\n",
        "\n",
        "the next couple of cells will show how you can \n",
        "- add stuff to lists and how to \n",
        "- work with loops"
      ]
    },
    {
      "metadata": {
        "id": "V_2IsbVYmtNw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "8af75fb5-17c3-41bb-e5fe-b26534097f66"
      },
      "cell_type": "code",
      "source": [
        "empty_list = []\n",
        "\n",
        "print(empty_list)\n",
        "\n",
        "empty_list.append(1)\n",
        "\n",
        "print(empty_list)\n",
        "\n",
        "empty_list.append(\"i don't want to be in that list\")\n",
        "\n",
        "print(empty_list)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[]\n",
            "[1]\n",
            "[1, \"i don't want to be in that list\"]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "QRDtXXxznqhD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "177b2244-3e2d-4f11-e186-6d0e2716be64"
      },
      "cell_type": "code",
      "source": [
        "empty_list.append(['🙉','👽','🐼'])\n",
        "\n",
        "print(empty_list)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1, \"i don't want to be in that list\", ['🙉', '👽', '🐼']]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Lag_wjvBn4ip",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Let's get the panda out! --> '🐼'\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eaLh4Xm3norE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "empty_list.extend(['🐧','🍅','🤘'])\n",
        "\n",
        "print(empty_list)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "X36e713QoF7j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "f44da991-9a7d-43cd-8298-98542faa717b"
      },
      "cell_type": "code",
      "source": [
        "# LOOPS\n",
        "\n",
        "emoji_list = ['🙉', '👽', '🐼','🐧','🍅','🤘']\n",
        "\n",
        "for some_emoji in emoji_list:\n",
        "  print(50 * some_emoji)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉\n",
            "👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽\n",
            "🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼\n",
            "🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧\n",
            "🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅\n",
            "🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "zDtA9s3uolC2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "df0a6847-4325-4a6b-d1b9-aeae6bcc38d1"
      },
      "cell_type": "code",
      "source": [
        "# LIST COMPREHENSIONS (mini-loops in Python)\n",
        "\n",
        "[50*x for x in emoji_list]"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉🙉',\n",
              " '👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽👽',\n",
              " '🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼🐼',\n",
              " '🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧🐧',\n",
              " '🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅🍅',\n",
              " '🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘🤘']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "metadata": {
        "id": "1HT8VMu1D0mL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#create empty list\n",
        "stuff_people_do = []\n",
        "\n",
        "for some_worker_id in workers: #initiate loop\n",
        "  stuff = list(data[data.f_id == some_worker_id].sub_category) # extract portfolio for a single worker\n",
        "  stuff_people_do.append((some_worker_id, stuff)) # append portfolio to the list of portfolios"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZD7-GNhvEHee",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#use pandas to make it into a datafrmae\n",
        "portfolios = pd.DataFrame(stuff_people_do, columns = ['f_id', 'gig_portfolio'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "H-jI8iPqqFMd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Now we are going to use Gensim (a library for Natural Language Processing) to transform these protfolio lists into BoW representations"
      ]
    },
    {
      "metadata": {
        "id": "_yEMm55tENaJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# First we ceate a dicitonary - an index-subcategory mapping object\n",
        "\n",
        "dictionary = Dictionary(portfolios['gig_portfolio'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HRBvzaZHEuz6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2a38c73b-9ded-4e54-8be7-007b6c4577a7"
      },
      "cell_type": "code",
      "source": [
        "# quick check: it's as long as the number of uniques subcategories\n",
        "len(dictionary)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "86"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "id": "hWashIuEFeP4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612
        },
        "outputId": "782e1af2-f78f-471d-ec0b-b940b8faee06"
      },
      "cell_type": "code",
      "source": [
        "data.sub_category.unique()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['SEO - Search Engine Optimization', 'Data Entry',\n",
              "       'SEM - Search Engine Marketing', 'Display Advertising',\n",
              "       'Article & Blog Writing', '3D Modeling & CAD', 'Web Development',\n",
              "       'Web & Mobile Design', '.', 'General Translation', 'Presentations',\n",
              "       'Ecommerce Development', 'Graphic Design',\n",
              "       'Email & Marketing Automation', 'Other - Writing',\n",
              "       'Editing & Proofreading', 'Creative Writing', 'Web Content',\n",
              "       'QA & Testing', 'Web Research', 'Market & Customer Research',\n",
              "       'Other - Sales & Marketing', 'Marketing Strategy', 'Copywriting',\n",
              "       'Personal / Virtual Assistant', 'Other - Design & Creative',\n",
              "       'Lead Generation', 'Logo Design & Branding',\n",
              "       'SMM - Social Media Marketing', 'Voice Talent', 'Public Relations',\n",
              "       'Photography', 'Audio Production', 'Animation',\n",
              "       'Other - Accounting & Consulting', 'Other - Admin Support',\n",
              "       'Academic Writing & Research', 'Mobile Development',\n",
              "       'Video Production', 'Telemarketing & Telesales', 'Illustration',\n",
              "       'Transcription', 'Contract Law', 'Financial Planning',\n",
              "       'Paralegal Services', 'Desktop Software Development',\n",
              "       'Project Management', 'Other - Software Development',\n",
              "       'ERP / CRM Software', 'Data Mining & Management',\n",
              "       'Network & System Administration', 'Game Development',\n",
              "       'Quantitative Analysis', 'Other - Legal', 'Technical Writing',\n",
              "       'Data Visualization', 'Other - Data Science & Analytics',\n",
              "       'Scripts & Utilities', 'Technical Support', 'Product Design',\n",
              "       'Intellectual Property Law', 'Legal Translation',\n",
              "       'Product Management', 'Customer Service', 'Accounting',\n",
              "       'Other - IT & Networking', 'Database Administration',\n",
              "       'Data Extraction / ETL', 'Other - Engineering & Architecture',\n",
              "       'Human Resources', 'Other - Customer Service',\n",
              "       'Technical Translation', 'Machine Learning',\n",
              "       'Management Consulting', 'Civil & Structural Engineering',\n",
              "       'Information Security', 'Architecture', 'Grant Writing',\n",
              "       'Resumes & Cover Letters', 'Corporate Law', 'Medical Translation',\n",
              "       'Electrical Engineering', 'Interior Design', 'Family Law',\n",
              "       'Contract Manufacturing', 'Mechanical Engineering'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "metadata": {
        "id": "srmR54I6FMhq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4d98e497-6b4f-452b-e79a-5bdd434b2cb5"
      },
      "cell_type": "code",
      "source": [
        "len(set(data.sub_category))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "86"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "metadata": {
        "id": "mFKCkOL3Fb3J",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Create a \"corpus of portfolios\" in BoW format\n",
        "corpus = [dictionary.doc2bow(sequence) for sequence in portfolios['gig_portfolio']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "g9SGtE34FqEr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Transform corpus into a matrix \n",
        "portfolio_matrix = gensim.matutils.corpus2dense(corpus=corpus, num_terms=len(dictionary))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WIBG8eSUrGck",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "460ce6bd-3a45-439f-c36d-f88248cd7ea1"
      },
      "cell_type": "code",
      "source": [
        "portfolio_matrix.shape"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(86, 1000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "metadata": {
        "id": "vTQkA7xNrMA7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# swap rows and columns with a transponse\n",
        "portfolio_matrix = portfolio_matrix.T"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gT5VA5PZr2Og",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Why should I trust this? Can we check if all the transformations didn't mess up things?"
      ]
    },
    {
      "metadata": {
        "id": "ipR4NJnyrS2R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "4a71df55-944d-4536-ba41-bbe6dd816c4c"
      },
      "cell_type": "code",
      "source": [
        "# portfolio of worker 0\n",
        "portfolio_matrix[0]"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 1., 1., 6., 5., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0.], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "metadata": {
        "id": "0ZTcwURUrX8P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fd3e5a03-b86c-46bd-ced8-2798c5b6caa6"
      },
      "cell_type": "code",
      "source": [
        "# what's the taks sub-category of index 3?\n",
        "dictionary.get(3)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'SEM - Search Engine Marketing'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "metadata": {
        "id": "yJie4th4rrqp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "c3491967-eaff-4891-dab0-35cc2b7a9118"
      },
      "cell_type": "code",
      "source": [
        "# How many times did the worker perform this gig?\n",
        "data[data.f_id == 0].sub_category"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     SEO - Search Engine Optimization\n",
              "1                           Data Entry\n",
              "2     SEO - Search Engine Optimization\n",
              "3     SEO - Search Engine Optimization\n",
              "4        SEM - Search Engine Marketing\n",
              "5        SEM - Search Engine Marketing\n",
              "6        SEM - Search Engine Marketing\n",
              "7        SEM - Search Engine Marketing\n",
              "8                  Display Advertising\n",
              "9        SEM - Search Engine Marketing\n",
              "10    SEO - Search Engine Optimization\n",
              "11    SEO - Search Engine Optimization\n",
              "12              Article & Blog Writing\n",
              "13       SEM - Search Engine Marketing\n",
              "Name: sub_category, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "metadata": {
        "id": "mTKYQtXtsRRh",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Now we sucessfully created \"Bag-of-Gigs\" representations of individual workers' portfolios\n",
        "The matrix is however very sparse, as we can see (many zeros and  only some non-0 values) as one would expect that.\n",
        "PCA is not a good choice here. Instead we will use Non-negative Matrix Factorization (NMF), which has the tendency to very well \"squash\" the data into interpretable latent themes:\n",
        "\n",
        "In Natural Language Processing, such themes are called topics and what we are going to do now is referred to as Vector Space Modelling or Topic Modelling\n",
        "\n",
        "The coolest thing about that identified components are very interopretable. This is because the model is build on assumptions about the world related to co-occurence."
      ]
    },
    {
      "metadata": {
        "id": "NylUni31F0Mj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Let's try to bring it all the way down to 5 dimensions\n",
        "\n",
        "from sklearn.decomposition import NMF\n",
        "model = NMF(n_components=5, init='random', random_state=0)\n",
        "portfolio_matrix_reduced = model.fit_transform(portfolio_matrix)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "REzpvIj8v2jG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "aae30f0b-5a2a-4e20-eda6-000409d9a85b"
      },
      "cell_type": "code",
      "source": [
        "# what are these components?\n",
        "model.components_.shape"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5, 86)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "metadata": {
        "id": "BmUWA_l_tRKU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Make a dataframe\n",
        "components_df = pd.DataFrame(model.components_, columns=list(dictionary.values()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qOMmC4X5uHRS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "0e798033-2af8-4f4a-f059-0155fcb388a3"
      },
      "cell_type": "code",
      "source": [
        "# Select a component\n",
        "component = components_df.iloc[4,:]\n",
        "\n",
        "# Print result of nlargest\n",
        "print(component.nlargest())"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "General Translation      13.173\n",
            "Voice Talent              1.739\n",
            "Article & Blog Writing    1.365\n",
            "Other - Writing           0.616\n",
            "Video Production          0.387\n",
            "Name: 4, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "5IXzZQoCGHIB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Import clustering and dimensionality reduction\n",
        "# HDBSCAN won't work with numpy < 1.16\n",
        "\n",
        "import hdbscan\n",
        "import umap"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "whHcbboJGgUk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "embedding = umap.UMAP(n_neighbors=15, metric='cosine').fit_transform(portfolio_matrix_reduced)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3xu8gU4eGvR6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "030d85ab-3683-488b-b619-87e5875be724"
      },
      "cell_type": "code",
      "source": [
        "# Warning can be ignored for now\n",
        "\n",
        "clusterer = hdbscan.HDBSCAN(min_cluster_size=50, \n",
        "                            min_samples=50, \n",
        "                            leaf_size=40, \n",
        "                            core_dist_n_jobs=16, \n",
        "                            prediction_data=True)\n",
        "clusterer.fit(embedding)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "HDBSCAN(algorithm='best', allow_single_cluster=False, alpha=1.0,\n",
              "    approx_min_span_tree=True, cluster_selection_method='eom',\n",
              "    core_dist_n_jobs=16, gen_min_span_tree=False, leaf_size=40,\n",
              "    match_reference_implementation=False, memory=Memory(location=None),\n",
              "    metric='euclidean', min_cluster_size=50, min_samples=50, p=None,\n",
              "    prediction_data=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "id": "zxxuBFk0HopB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        },
        "outputId": "62e7c41a-2f0d-4d13-84f5-68097a063f5c"
      },
      "cell_type": "code",
      "source": [
        "pal = sns.color_palette(\"Paired\", n_colors = len(set(clusterer.labels_)))[1:]\n",
        "plt.figure(figsize=(12,12))\n",
        "plt.rcParams.update({'font.size': 15})\n",
        "clusterer.condensed_tree_.plot(select_clusters=True,\n",
        "                               selection_palette=pal, label_clusters=True)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fca0529c438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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s9/5KREREOmfUUr7IytLIbE8C48tcPxHftiqLxiF+FHg28APOOdUoxHy5xvPOnUhtauRS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\nspVj3oZmAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Tg9vPbJbHqPK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "outputId": "530f7f12-2975-4077-89da-73c6b9cc3798"
      },
      "cell_type": "code",
      "source": [
        "# Scatterplor of the UMAP embeddings with cluster-coloring\n",
        "plt.rcParams.update({'font.size': 12})\n",
        "plt.figure(figsize=(10,10))\n",
        "g = sns.scatterplot(*embedding.T, \n",
        "                hue=clusterer.labels_, \n",
        "                legend='full',\n",
        "                palette = 'Paired')\n",
        "legend = g.get_legend()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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L4a+/mMu135/C+V8bQzyapPeQAlxemXoUQnRvSz7Yyht/XEpTbZCcQg+zbhvPhBkDOrSN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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "xzZDw9ToIJ4r",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "portfolios['cluster'] = clusterer.labels_"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "91rsYFfzJqbF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import itertools"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9gg8A9TlJ_OW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from collections import Counter"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JW0uwlnVJsmu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "counter = Counter(list(itertools.chain(*portfolios[portfolios.cluster == 4]['gig_portfolio'])))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0iJQm9MVJ2KY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "5f97da72-e0cd-45e8-bb2d-e0b6029cd1eb"
      },
      "cell_type": "code",
      "source": [
        "counter.most_common(20)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Graphic Design', 3266),\n",
              " ('Logo Design & Branding', 1291),\n",
              " ('Illustration', 619),\n",
              " ('Web Development', 420),\n",
              " ('Web & Mobile Design', 294),\n",
              " ('Photography', 150),\n",
              " ('Presentations', 131),\n",
              " ('Other - Design & Creative', 112),\n",
              " ('Animation', 102),\n",
              " ('Article & Blog Writing', 72),\n",
              " ('Data Entry', 69),\n",
              " ('Other - Writing', 51),\n",
              " ('Video Production', 46),\n",
              " ('Mobile Development', 46),\n",
              " ('3D Modeling & CAD', 46),\n",
              " ('.', 37),\n",
              " ('Game Development', 33),\n",
              " ('General Translation', 31),\n",
              " ('Web Content', 31),\n",
              " ('Web Research', 31)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    }
  ]
}