Detailed_KMeans_Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                   # Numerical computation library\n",
    "import pandas as pa                  # Dataframe and Data manipulation Library\n",
    "import matplotlib.pyplot as plt      # Basic plotting functionality Library\n",
    "import seaborn as sn                 # Advanced Data visualization Library\n",
    "import numpy.random as rn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.colors as pltco # A package to help with coloring plots\n",
    "\n",
    "\n",
    "[x for x in dir(list) if not '__' in x]\n",
    "  \n",
    "# Curreny Exchange Rates compared to the US dollar from around the world 22 for 2008-2018\n",
    "gdp_per_industry = pa.read_csv('https://www.dropbox.com/s/emd7111t9g0rtd2/career_projections_group.csv?dl=1')\n",
    "workforce_demographic2018 = pa.read_csv('https://www.dropbox.com/s/w1zzumsadlgg9pn/Colorado_Current_Workforce_Demographic.csv?dl=1')\n",
    "\n",
    "gdp_per_industry = gdp_per_industry.drop(0)\n",
    "workforce_demographic2018 = workforce_demographic2018.drop(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.cm as cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "workforce_demographic2018['OCC_GROUP'] = workforce_demographic2018['OCC_GROUP'].astype(str)\n",
    "detailed_groups_df = workforce_demographic2018.loc[workforce_demographic2018.OCC_GROUP.apply(lambda cat: 'detailed' in cat)]\n",
    "detailed_groups_raw_data = detailed_groups_df.drop(['AREA','STATE','ST','OCC_GROUP','OCC_TITLE','ANNUAL','HOURLY'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OCC_CODE</th>\n",
       "      <th>TOT_EMP</th>\n",
       "      <th>EMP_PRSE</th>\n",
       "      <th>JOBS_1000</th>\n",
       "      <th>LOC_Q</th>\n",
       "      <th>H_MEAN</th>\n",
       "      <th>A_MEAN</th>\n",
       "      <th>MEAN_PRSE</th>\n",
       "      <th>H_PCT10</th>\n",
       "      <th>H_PCT25</th>\n",
       "      <th>H_MEDIAN</th>\n",
       "      <th>H_PCT75</th>\n",
       "      <th>H_PCT90</th>\n",
       "      <th>A_PCT10</th>\n",
       "      <th>A_PCT25</th>\n",
       "      <th>A_MEDIAN</th>\n",
       "      <th>A_PCT75</th>\n",
       "      <th>A_PCT90</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>112011</td>\n",
       "      <td>390.0</td>\n",
       "      <td>25.9</td>\n",
       "      <td>0.147</td>\n",
       "      <td>0.84</td>\n",
       "      <td>59.12</td>\n",
       "      <td>122970.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>27.10</td>\n",
       "      <td>45.76</td>\n",
       "      <td>58.58</td>\n",
       "      <td>68.07</td>\n",
       "      <td>87.05</td>\n",
       "      <td>56380.0</td>\n",
       "      <td>95180.0</td>\n",
       "      <td>121850.0</td>\n",
       "      <td>141580.0</td>\n",
       "      <td>181070.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>112031</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.474</td>\n",
       "      <td>0.95</td>\n",
       "      <td>65.86</td>\n",
       "      <td>136990.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>36.52</td>\n",
       "      <td>45.86</td>\n",
       "      <td>61.70</td>\n",
       "      <td>78.47</td>\n",
       "      <td>98.37</td>\n",
       "      <td>75970.0</td>\n",
       "      <td>95390.0</td>\n",
       "      <td>128330.0</td>\n",
       "      <td>163210.0</td>\n",
       "      <td>204600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>113011</td>\n",
       "      <td>2390.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.47</td>\n",
       "      <td>57.52</td>\n",
       "      <td>119650.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>30.26</td>\n",
       "      <td>41.16</td>\n",
       "      <td>54.48</td>\n",
       "      <td>70.10</td>\n",
       "      <td>87.39</td>\n",
       "      <td>62940.0</td>\n",
       "      <td>85620.0</td>\n",
       "      <td>113320.0</td>\n",
       "      <td>145800.0</td>\n",
       "      <td>181770.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>113051</td>\n",
       "      <td>1160.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0.35</td>\n",
       "      <td>61.72</td>\n",
       "      <td>128380.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>30.50</td>\n",
       "      <td>41.67</td>\n",
       "      <td>56.35</td>\n",
       "      <td>74.97</td>\n",
       "      <td>97.65</td>\n",
       "      <td>63430.0</td>\n",
       "      <td>86670.0</td>\n",
       "      <td>117200.0</td>\n",
       "      <td>155940.0</td>\n",
       "      <td>203100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>113061</td>\n",
       "      <td>800.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.307</td>\n",
       "      <td>0.64</td>\n",
       "      <td>69.27</td>\n",
       "      <td>144090.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>44.21</td>\n",
       "      <td>54.89</td>\n",
       "      <td>64.82</td>\n",
       "      <td>78.24</td>\n",
       "      <td>96.96</td>\n",
       "      <td>91950.0</td>\n",
       "      <td>114160.0</td>\n",
       "      <td>134820.0</td>\n",
       "      <td>162730.0</td>\n",
       "      <td>201680.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    OCC_CODE  TOT_EMP  EMP_PRSE  JOBS_1000  LOC_Q  H_MEAN    A_MEAN  \\\n",
       "5     112011    390.0      25.9      0.147   0.84   59.12  122970.0   \n",
       "8     112031   1240.0       4.6      0.474   0.95   65.86  136990.0   \n",
       "9     113011   2390.0       3.2      0.913   0.47   57.52  119650.0   \n",
       "12    113051   1160.0       4.8      0.441   0.35   61.72  128380.0   \n",
       "13    113061    800.0       4.7      0.307   0.64   69.27  144090.0   \n",
       "\n",
       "    MEAN_PRSE  H_PCT10  H_PCT25  H_MEDIAN  H_PCT75  H_PCT90  A_PCT10  \\\n",
       "5         7.1    27.10    45.76     58.58    68.07    87.05  56380.0   \n",
       "8         2.1    36.52    45.86     61.70    78.47    98.37  75970.0   \n",
       "9         1.7    30.26    41.16     54.48    70.10    87.39  62940.0   \n",
       "12        2.4    30.50    41.67     56.35    74.97    97.65  63430.0   \n",
       "13        1.8    44.21    54.89     64.82    78.24    96.96  91950.0   \n",
       "\n",
       "     A_PCT25  A_MEDIAN   A_PCT75   A_PCT90  \n",
       "5    95180.0  121850.0  141580.0  181070.0  \n",
       "8    95390.0  128330.0  163210.0  204600.0  \n",
       "9    85620.0  113320.0  145800.0  181770.0  \n",
       "12   86670.0  117200.0  155940.0  203100.0  \n",
       "13  114160.0  134820.0  162730.0  201680.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detailed_groups_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "detailed_groups_raw_data = detailed_groups_raw_data.replace('-','',regex=True)\n",
    "gdp_per_industry = gdp_per_industry.replace('-','',regex=True)\n",
    "detailed_groups_raw_data = detailed_groups_raw_data.replace(',','',regex=True)\n",
    "detailed_groups_raw_data = detailed_groups_raw_data.replace('#',np.nan,regex=True)\n",
    "detailed_groups_raw_data = detailed_groups_raw_data.replace('*',np.nan)\n",
    "detailed_groups_raw_data = detailed_groups_raw_data.replace('**',np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "detailed_groups_final = detailed_groups_raw_data.apply(pa.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "detailed_groups_final = detailed_groups_final.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "X = np.array(detailed_groups_final.iloc[:,1:])\n",
    "y = np.array(detailed_groups_final.iloc[:,0:1])\n",
    "le = LabelEncoder()\n",
    "y = le.fit_transform(y.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cluster_plot(cluster, X):\n",
    "       \n",
    "    n_clusters = cluster.get_params()['n_clusters']\n",
    "    cmap_bold = pltco.ListedColormap([cm.hot(k/n_clusters) for k in range(n_clusters) ])  \n",
    "    # We need a color map that expands to match the number of clusters\n",
    "\n",
    "    plt.scatter(X[:, 0], X[:, 1], c=cluster.fit_predict(X), cmap=cmap_bold,marker='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OCC_CODE</th>\n",
       "      <th>TOT_EMP</th>\n",
       "      <th>EMP_PRSE</th>\n",
       "      <th>JOBS_1000</th>\n",
       "      <th>LOC_Q</th>\n",
       "      <th>H_MEAN</th>\n",
       "      <th>A_MEAN</th>\n",
       "      <th>MEAN_PRSE</th>\n",
       "      <th>H_PCT10</th>\n",
       "      <th>H_PCT25</th>\n",
       "      <th>H_MEDIAN</th>\n",
       "      <th>H_PCT75</th>\n",
       "      <th>H_PCT90</th>\n",
       "      <th>A_PCT10</th>\n",
       "      <th>A_PCT25</th>\n",
       "      <th>A_MEDIAN</th>\n",
       "      <th>A_PCT75</th>\n",
       "      <th>A_PCT90</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>112011</td>\n",
       "      <td>390.0</td>\n",
       "      <td>25.9</td>\n",
       "      <td>0.147</td>\n",
       "      <td>0.84</td>\n",
       "      <td>59.12</td>\n",
       "      <td>122970.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>27.10</td>\n",
       "      <td>45.76</td>\n",
       "      <td>58.58</td>\n",
       "      <td>68.07</td>\n",
       "      <td>87.05</td>\n",
       "      <td>56380.0</td>\n",
       "      <td>95180.0</td>\n",
       "      <td>121850.0</td>\n",
       "      <td>141580.0</td>\n",
       "      <td>181070.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>112031</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.474</td>\n",
       "      <td>0.95</td>\n",
       "      <td>65.86</td>\n",
       "      <td>136990.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>36.52</td>\n",
       "      <td>45.86</td>\n",
       "      <td>61.70</td>\n",
       "      <td>78.47</td>\n",
       "      <td>98.37</td>\n",
       "      <td>75970.0</td>\n",
       "      <td>95390.0</td>\n",
       "      <td>128330.0</td>\n",
       "      <td>163210.0</td>\n",
       "      <td>204600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>113011</td>\n",
       "      <td>2390.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.47</td>\n",
       "      <td>57.52</td>\n",
       "      <td>119650.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>30.26</td>\n",
       "      <td>41.16</td>\n",
       "      <td>54.48</td>\n",
       "      <td>70.10</td>\n",
       "      <td>87.39</td>\n",
       "      <td>62940.0</td>\n",
       "      <td>85620.0</td>\n",
       "      <td>113320.0</td>\n",
       "      <td>145800.0</td>\n",
       "      <td>181770.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>113051</td>\n",
       "      <td>1160.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0.35</td>\n",
       "      <td>61.72</td>\n",
       "      <td>128380.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>30.50</td>\n",
       "      <td>41.67</td>\n",
       "      <td>56.35</td>\n",
       "      <td>74.97</td>\n",
       "      <td>97.65</td>\n",
       "      <td>63430.0</td>\n",
       "      <td>86670.0</td>\n",
       "      <td>117200.0</td>\n",
       "      <td>155940.0</td>\n",
       "      <td>203100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>113061</td>\n",
       "      <td>800.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.307</td>\n",
       "      <td>0.64</td>\n",
       "      <td>69.27</td>\n",
       "      <td>144090.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>44.21</td>\n",
       "      <td>54.89</td>\n",
       "      <td>64.82</td>\n",
       "      <td>78.24</td>\n",
       "      <td>96.96</td>\n",
       "      <td>91950.0</td>\n",
       "      <td>114160.0</td>\n",
       "      <td>134820.0</td>\n",
       "      <td>162730.0</td>\n",
       "      <td>201680.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    OCC_CODE  TOT_EMP  EMP_PRSE  JOBS_1000  LOC_Q  H_MEAN    A_MEAN  \\\n",
       "5     112011    390.0      25.9      0.147   0.84   59.12  122970.0   \n",
       "8     112031   1240.0       4.6      0.474   0.95   65.86  136990.0   \n",
       "9     113011   2390.0       3.2      0.913   0.47   57.52  119650.0   \n",
       "12    113051   1160.0       4.8      0.441   0.35   61.72  128380.0   \n",
       "13    113061    800.0       4.7      0.307   0.64   69.27  144090.0   \n",
       "\n",
       "    MEAN_PRSE  H_PCT10  H_PCT25  H_MEDIAN  H_PCT75  H_PCT90  A_PCT10  \\\n",
       "5         7.1    27.10    45.76     58.58    68.07    87.05  56380.0   \n",
       "8         2.1    36.52    45.86     61.70    78.47    98.37  75970.0   \n",
       "9         1.7    30.26    41.16     54.48    70.10    87.39  62940.0   \n",
       "12        2.4    30.50    41.67     56.35    74.97    97.65  63430.0   \n",
       "13        1.8    44.21    54.89     64.82    78.24    96.96  91950.0   \n",
       "\n",
       "     A_PCT25  A_MEDIAN   A_PCT75   A_PCT90  \n",
       "5    95180.0  121850.0  141580.0  181070.0  \n",
       "8    95390.0  128330.0  163210.0  204600.0  \n",
       "9    85620.0  113320.0  145800.0  181770.0  \n",
       "12   86670.0  117200.0  155940.0  203100.0  \n",
       "13  114160.0  134820.0  162730.0  201680.0  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detailed_groups_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "km = KMeans(n_clusters=10).fit(detailed_groups_final[['TOT_EMP', 'JOBS_1000','H_MEAN','MEAN_PRSE']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_map = pa.DataFrame()\n",
    "cluster_map['Cluster'] = km.labels_\n",
    "cluster_map['OCC CODE'] = detailed_groups_final.OCC_CODE.values\n",
    "cluster_map['JOBS PER 1000'] = detailed_groups_final.JOBS_1000.values\n",
    "cluster_map['MEAN_PRSE'] = detailed_groups_final.MEAN_PRSE.values\n",
    "cluster_map[\"Total Employees\"] = detailed_groups_final.TOT_EMP.values\n",
    "cluster_map[\"Hourly Mean\"] = detailed_groups_final.H_MEAN.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_plot(cluster_map, TOT_EMP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "detailed_cluster_example = detailed_groups_final[['JOBS_1000', 'H_MEAN','TOT_EMP','MEAN_PRSE']].to_numpy()\n",
    "cluster = KMeans(n_clusters=10)\n",
    "cluster_plot(km, detailed_cluster_example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for i in range (10):\n",
    " #   print(cluster_map[cluster_map.Cluster == i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "x = detailed_groups_final.values #returns a numpy array   .loc[:,'TOT_EMP':]\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(x)\n",
    "\n",
    "detailed_groups_normalized = pa.DataFrame(x_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "detailed_cluster_example = detailed_groups_normalized.loc[:,1:6].to_numpy()\n",
    "cluster = KMeans(n_clusters=10).fit(detailed_groups_final[['TOT_EMP', 'JOBS_1000','H_MEAN','MEAN_PRSE']])\n",
    "cluster_plot(km, detailed_cluster_example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_map = pa.DataFrame()\n",
    "cluster_map['Cluster'] = cluster.labels_\n",
    "cluster_map['OCC CODE'] = detailed_groups_final.OCC_CODE.values\n",
    "cluster_map['JOBS PER 1000'] = detailed_groups_final.JOBS_1000.values\n",
    "cluster_map['MEAN_PRSE'] = detailed_groups_final.MEAN_PRSE.values\n",
    "cluster_map[\"Total Employees\"] = detailed_groups_final.TOT_EMP.values\n",
    "cluster_map[\"Hourly Mean\"] = detailed_groups_final.H_MEAN.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Cluster  OCC CODE  JOBS PER 1000  MEAN_PRSE  Total Employees  Hourly Mean\n",
      "0          0    112011          0.147        7.1            390.0        59.12\n",
      "1          0    112031          0.474        2.1           1240.0        65.86\n",
      "3          0    113051          0.441        2.4           1160.0        61.72\n",
      "4          0    113061          0.307        1.8            800.0        69.27\n",
      "5          0    113071          0.508        3.4           1330.0        57.01\n",
      "6          0    113131          0.178        4.4            470.0        58.80\n",
      "8          0    119031          0.346        3.2            910.0        25.08\n",
      "10         0    119039          0.207        2.7            540.0        49.69\n",
      "11         0    119061          0.023        9.3             60.0        37.19\n",
      "12         0    119071          0.029        7.2             80.0        38.38\n",
      "13         0    119081          0.308        4.9            810.0        39.67\n",
      "15         0    119131          0.077        0.7            200.0        34.94\n",
      "17         0    119151          0.584        2.5           1530.0        40.56\n",
      "18         0    119161          0.053        1.7            140.0        41.86\n",
      "22         0    131032          0.061        4.3            160.0        31.38\n",
      "26         0    131075          0.211       10.0            550.0        36.84\n",
      "31         0    131141          0.544        2.3           1430.0        34.78\n",
      "36         0    132021          0.606       12.9           1590.0        38.17\n",
      "37         0    132031          0.390        0.9           1020.0        39.93\n",
      "38         0    132041          0.523        3.0           1370.0        36.96\n",
      "41         0    132053          0.507        3.9           1330.0        37.10\n",
      "42         0    132061          0.504        3.1           1320.0        39.61\n",
      "44         0    132081          0.232        1.4            610.0        31.24\n",
      "45         0    132082          0.430        8.0           1130.0        27.72\n",
      "47         0    151111          0.182        4.8            480.0        58.26\n",
      "60         0    152011          0.081        9.2            210.0        57.53\n",
      "61         0    152021          0.030        9.2             80.0        59.06\n",
      "62         0    152031          0.338        2.8            890.0        43.40\n",
      "63         0    152041          0.413        1.2           1080.0        40.57\n",
      "64         0    152090          0.018        8.8             50.0        39.28\n",
      "..       ...       ...            ...        ...              ...          ...\n",
      "553        0    519021          0.217        3.8            570.0        20.08\n",
      "554        0    519023          0.286        4.5            750.0        18.35\n",
      "555        0    519031          0.086        2.6            230.0        14.87\n",
      "556        0    519032          0.309        3.9            810.0        17.91\n",
      "557        0    519041          0.280        3.0            730.0        17.54\n",
      "558        0    519051          0.053        3.7            140.0        19.28\n",
      "560        0    519071          0.111       13.7            290.0        20.43\n",
      "561        0    519081          0.210        4.5            550.0        22.70\n",
      "562        0    519083          0.192        6.7            500.0        20.22\n",
      "564        0    519121          0.357        2.3            940.0        16.69\n",
      "565        0    519122          0.436       10.4           1140.0        26.32\n",
      "566        0    519141          0.523        5.2           1370.0        16.15\n",
      "567        0    519151          0.066        7.1            170.0        17.87\n",
      "568        0    519192          0.019        8.8             50.0        16.89\n",
      "569        0    519193          0.043        5.1            110.0        17.46\n",
      "570        0    519195          0.214        3.0            560.0        17.83\n",
      "571        0    519196          0.061        6.5            160.0        15.77\n",
      "573        0    519199          0.563        3.2           1470.0        17.03\n",
      "574        0    531011          0.100        4.2            260.0        26.94\n",
      "576        0    532021          0.272        3.2            710.0        61.64\n",
      "586        0    536051          0.206        5.6            540.0        37.34\n",
      "588        0    537011          0.091        4.2            240.0        16.78\n",
      "589        0    537021          0.132        5.5            350.0        28.20\n",
      "590        0    537032          0.323        3.3            850.0        25.63\n",
      "591        0    537033          0.033        3.8             90.0        28.58\n",
      "596        0    537071          0.050        2.4            130.0        28.11\n",
      "597        0    537072          0.157        8.5            410.0        22.39\n",
      "598        0    537073          0.343        2.8            900.0        33.24\n",
      "600        0    537121          0.067        4.8            170.0        22.32\n",
      "601        0    537199          0.088        5.9            230.0        17.93\n",
      "\n",
      "[347 rows x 6 columns]\n",
      "     Cluster  OCC CODE  JOBS PER 1000  MEAN_PRSE  Total Employees  Hourly Mean\n",
      "33         1    131161          7.178        1.1          18810.0        35.32\n",
      "59         1    151199          6.273        1.8          16440.0        49.27\n",
      "245        1    311014          8.038        0.9          21070.0        15.68\n",
      "272        1    339032          6.407        3.4          16790.0        16.77\n",
      "275        1    351012          6.936        1.5          18180.0        19.54\n",
      "294        1    372012          7.037        1.1          18440.0        12.81\n",
      "296        1    373011          8.460        1.2          22170.0        16.32\n",
      "323        1    411011          7.293        1.6          19110.0        24.45\n",
      "343        1    431011          7.874        0.9          20630.0        30.10\n",
      "367        1    434171          6.961        0.9          18240.0        15.56\n",
      "402        1    471011          6.167        1.3          16160.0        35.38\n",
      "411        1    472061          7.980        1.3          20910.0        17.56\n",
      "489        1    499071          8.939        0.8          23430.0        19.95\n",
      "580        1    533032          8.730        1.5          22880.0        23.48\n",
      "581        1    533033          6.719        3.1          17610.0        18.74\n",
      "     Cluster  OCC CODE  JOBS PER 1000  MEAN_PRSE  Total Employees  Hourly Mean\n",
      "216        2    291141         19.752        0.6          51760.0        35.69\n",
      "285        2    353031         19.229        2.1          50390.0        12.26\n",
      "325        2    412011         21.638        1.0          56710.0        12.44\n",
      "357        2    434051         18.362        1.2          48120.0        17.98\n",
      "     Cluster  OCC CODE  JOBS PER 1000  MEAN_PRSE  Total Employees  Hourly Mean\n",
      "14         3    119111          2.084        1.9           5460.0        56.99\n",
      "21         3    131031          1.985        2.0           5200.0        34.84\n",
      "23         3    131041          2.454        1.2           6430.0        35.44\n",
      "24         3    131051          2.105        2.7           5520.0        35.15\n",
      "32         3    131151          2.387        2.0           6260.0        34.72\n",
      "43         3    132072          1.974        3.3           5170.0        34.14\n",
      "56         3    151143          2.306        1.2           6040.0        55.44\n",
      "75         3    172072          2.146        1.8           5630.0        50.75\n",
      "80         3    172141          1.811        4.2           4750.0        50.10\n",
      "133        3    211012          1.787        2.1           4680.0        25.53\n",
      "136        3    211018          2.967        1.5           7770.0        23.31\n",
      "138        3    211021          2.557        1.6           6700.0        24.55\n",
      "154        3    232011          2.002        2.0           5250.0        28.05\n",
      "178        3    271024          1.804        3.8           4730.0        27.25\n",
      "189        3    273031          1.976        2.7           5180.0        31.98\n",
      "204        3    291051          1.976        1.9           5180.0        58.16\n",
      "208        3    291123          2.001        1.6           5250.0        39.69\n",
      "220        3    292010          1.787        1.5           4680.0        27.72\n",
      "221        3    292021          1.704        1.6           4470.0        40.63\n",
      "229        3    292052          2.141        1.3           5610.0        17.73\n",
      "235        3    292061          2.118        1.0           5550.0        24.62\n",
      "251        3    319011          2.010        3.5           5270.0        22.21\n",
      "252        3    319091          3.016        1.7           7910.0        19.42\n",
      "263        3    332011          2.120        2.3           5560.0        29.40\n",
      "266        3    333012          2.667        1.5           6990.0        24.34\n",
      "273        3    339092          1.692        1.7           4430.0        12.18\n",
      "276        3    352011          2.217        1.6           5810.0        11.60\n",
      "284        3    353022          1.958        1.9           5130.0        11.68\n",
      "286        3    353041          2.803        1.8           7340.0        12.85\n",
      "301        3    391021          1.671        1.6           4380.0        20.74\n",
      "303        3    392021          1.881        2.8           4930.0        13.23\n",
      "321        3    399032          2.326        2.4           6090.0        14.96\n",
      "324        3    411012          2.232        3.0           5850.0        44.47\n",
      "334        3    414011          2.028        3.6           5320.0        47.69\n",
      "340        3    419041          1.667        3.8           4370.0        14.41\n",
      "342        3    419099          1.583        2.5           4150.0        20.29\n",
      "346        3    433021          2.870        1.8           7520.0        19.74\n",
      "351        3    433071          2.530        1.3           6630.0        14.81\n",
      "355        3    434031          2.030        0.8           5320.0        19.94\n",
      "360        3    434081          2.309        1.3           6050.0        13.46\n",
      "361        3    434111          1.870        2.1           4900.0        18.22\n",
      "376        3    435052          2.071        0.9           5430.0        24.56\n",
      "383        3    436013          2.459        1.5           6450.0        18.70\n",
      "410        3    472051          1.974        2.1           5170.0        21.53\n",
      "419        3    472141          1.682        2.1           4410.0        20.00\n",
      "455        3    492022          1.839        2.3           4820.0        30.79\n",
      "467        3    493031          1.761        1.6           4610.0        25.17\n",
      "479        3    499021          2.314        3.3           6060.0        25.70\n",
      "481        3    499041          1.851        1.1           4850.0        28.86\n",
      "496        3    511011          2.578        1.2           6760.0        33.38\n",
      "497        3    512028          2.101        1.5           5500.0        17.23\n",
      "500        3    512098          2.635        1.5           6910.0        16.42\n",
      "523        3    514121          1.866        2.2           4890.0        24.18\n",
      "559        3    519061          2.184        2.1           5720.0        22.54\n",
      "563        3    519111          1.764        3.1           4620.0        16.71\n",
      "572        3    519198          1.907        4.1           5000.0        15.00\n",
      "575        3    531048          2.321        1.2           6080.0        29.43\n",
      "578        3    533022          2.390        1.5           6260.0        16.83\n",
      "593        3    537061          2.469        1.7           6470.0        13.37\n",
      "     Cluster  OCC CODE  JOBS PER 1000  MEAN_PRSE  Total Employees  Hourly Mean\n",
      "34         4    131199         16.714        1.4          43800.0        39.48\n",
      "35         4    132011         13.847        1.5          36290.0        39.58\n",
      "293        4    372011         13.108        1.8          34350.0        13.93\n",
      "380        4    435081         13.941        0.8          36530.0        15.01\n",
      "384        4    436014         14.732        0.5          38610.0        18.66\n",
      "390        4    439061         15.756        0.7          41290.0        19.64\n",
      "     Cluster  OCC CODE  JOBS PER 1000  MEAN_PRSE  Total Employees  Hourly Mean\n",
      "328        5    412031         33.049        1.5          86610.0        14.84\n",
      "     Cluster  OCC CODE  JOBS PER 1000  MEAN_PRSE  Total Employees  Hourly Mean\n",
      "2          6    113011          0.913        1.7           2390.0        57.52\n",
      "9          6    119033          0.779        3.5           2040.0        53.89\n",
      "16         6    119141          0.731        5.8           1920.0        44.66\n",
      "27         6    131081          1.288        2.5           3370.0        36.40\n",
      "29         6    131121          1.157        1.6           3030.0        24.55\n",
      "30         6    131131          0.830        2.6           2170.0        28.07\n",
      "39         6    132051          1.439        6.6           3770.0        52.46\n",
      "40         6    132052          1.530        7.6           4010.0        49.78\n",
      "46         6    132099          1.392        4.0           3650.0        40.29\n",
      "49         6    151122          1.369        2.9           3590.0        49.43\n",
      "50         6    151131          1.133        2.1           2970.0        45.55\n",
      "53         6    151134          0.949        5.2           2490.0        37.88\n",
      "54         6    151141          1.032        1.6           2710.0        43.95\n",
      "58         6    151152          1.397        2.5           3660.0        35.55\n",
      "65         6    171011          1.451        2.4           3800.0        35.14\n",
      "69         6    172011          0.720        6.4           1890.0        59.65\n",
      "73         6    172061          1.481        1.9           3880.0        56.31\n",
      "74         6    172071          1.289        1.7           3380.0        48.34\n",
      "78         6    172112          1.241        1.6           3250.0        48.30\n",
      "82         6    172199          0.954        1.6           2500.0        53.66\n",
      "83         6    173011          1.005        1.7           2630.0        28.63\n",
      "89         6    173023          0.687        2.2           1800.0        34.55\n",
      "95         6    173031          0.897        2.4           2350.0        25.17\n",
      "110        6    192031          0.649        3.1           1700.0        37.14\n",
      "112        6    192041          1.117        4.2           2930.0        44.72\n",
      "113        6    192042          0.768        8.0           2010.0        53.43\n",
      "118        6    193031          1.020        2.6           2670.0        41.09\n",
      "126        6    194021          1.087        1.8           2850.0        21.41\n",
      "135        6    211015          0.628        7.9           1650.0        25.30\n",
      "139        6    211022          1.195        2.1           3130.0        25.07\n",
      "..       ...       ...            ...        ...              ...          ...\n",
      "425        6    472211          1.096        4.2           2870.0        22.64\n",
      "426        6    472221          0.641        3.4           1680.0        25.30\n",
      "433        6    474011          1.063        4.0           2780.0        32.02\n",
      "437        6    474051          1.085        0.6           2840.0        20.52\n",
      "442        6    475013          0.967        6.6           2530.0        27.07\n",
      "449        6    475071          1.107        2.4           2900.0        21.52\n",
      "458        6    492094          0.805        1.9           2110.0        28.82\n",
      "463        6    493011          0.722        4.1           1890.0        35.16\n",
      "464        6    493021          1.239        3.2           3250.0        23.75\n",
      "469        6    493042          1.268        1.6           3320.0        26.06\n",
      "476        6    493093          1.004        2.5           2630.0        14.73\n",
      "484        6    499051          0.931        1.8           2440.0        37.63\n",
      "485        6    499052          0.886        3.1           2320.0        24.90\n",
      "495        6    499099          1.430        1.8           3750.0        19.22\n",
      "501        6    513011          1.381        3.4           3620.0        14.98\n",
      "502        6    513021          0.870        1.4           2280.0        16.87\n",
      "504        6    513092          0.865        2.0           2270.0        14.63\n",
      "517        6    514041          1.504        1.8           3940.0        23.18\n",
      "529        6    515112          1.006        4.1           2640.0        18.93\n",
      "531        6    516011          1.249        1.3           3270.0        12.18\n",
      "539        6    517011          0.945        1.6           2480.0        17.71\n",
      "546        6    518031          0.746        1.4           1960.0        26.87\n",
      "552        6    519012          0.820        5.4           2150.0        21.40\n",
      "577        6    533021          1.373        1.6           3600.0        18.54\n",
      "582        6    533041          0.977        3.1           2560.0        14.74\n",
      "583        6    533099          1.162        2.4           3050.0        14.11\n",
      "584        6    536021          0.779        1.9           2040.0        12.61\n",
      "585        6    536031          0.835        1.6           2190.0        13.23\n",
      "587        6    536099          0.726        7.7           1900.0        24.56\n",
      "599        6    537081          0.894        2.7           2340.0        19.61\n",
      "\n",
      "[123 rows x 6 columns]\n",
      "     Cluster  OCC CODE  JOBS PER 1000  MEAN_PRSE  Total Employees  Hourly Mean\n",
      "7          7    119021          3.301        2.1           8650.0        46.72\n",
      "19         7    119199          3.412        1.3           8940.0        66.58\n",
      "20         7    131020          3.334        1.1           8740.0        32.21\n",
      "25         7    131071          4.789        1.0          12550.0        33.38\n",
      "28         7    131111          4.160        2.1          10900.0        45.24\n",
      "48         7    151121          3.310        0.9           8680.0        46.17\n",
      "52         7    151133          4.262        3.4          11170.0        59.81\n",
      "55         7    151142          4.431        0.8          11610.0        40.46\n",
      "57         7    151151          5.492        1.4          14390.0        29.06\n",
      "72         7    172051          4.076        1.7          10680.0        44.98\n",
      "159        7    252011          3.320        2.2           8700.0        15.93\n",
      "243        7    311011          3.628        1.3           9510.0        12.97\n",
      "253        7    319092          4.255        1.1          11150.0        17.40\n",
      "268        7    333051          3.756        1.0           9840.0        35.07\n",
      "277        7    352012          3.571        1.0           9360.0        14.25\n",
      "281        7    352021          4.878        1.6          12780.0        13.22\n",
      "282        7    353011          4.345        3.1          11390.0        13.23\n",
      "287        7    359011          3.354        1.6           8790.0        11.47\n",
      "288        7    359021          3.777        0.8           9900.0        11.59\n",
      "289        7    359031          4.091        2.0          10720.0        12.24\n",
      "307        7    393091          3.492        1.5           9150.0        12.25\n",
      "312        7    395012          3.105        4.4           8140.0        16.75\n",
      "318        7    399011          3.289        1.4           8620.0        13.83\n",
      "320        7    399031          3.449        2.3           9040.0        22.27\n",
      "326        7    412021          5.109        1.8          13390.0        18.26\n",
      "330        7    413021          3.513        5.1           9210.0        32.96\n",
      "331        7    413031          4.472        4.8          11720.0        36.62\n",
      "369        7    434199          4.559        0.9          11950.0        20.83\n",
      "379        7    435071          3.222        1.1           8440.0        16.82\n",
      "382        7    436011          4.076        1.4          10680.0        27.32\n",
      "406        7    472031          5.408        1.6          14170.0        22.90\n",
      "413        7    472073          4.307        1.0          11290.0        23.92\n",
      "416        7    472111          5.666        1.5          14850.0        26.68\n",
      "422        7    472152          4.046        2.8          10600.0        25.65\n",
      "452        7    491011          3.277        1.0           8590.0        34.34\n",
      "466        7    493023          4.477        1.4          11730.0        23.10\n",
      "579        7    533031          3.984        4.8          10440.0        14.97\n",
      "592        7    537051          3.970        1.7          10400.0        17.63\n",
      "595        7    537064          3.129        1.4           8200.0        12.81\n",
      "     Cluster  OCC CODE  JOBS PER 1000  MEAN_PRSE  Total Employees  Hourly Mean\n",
      "51         8    151132         11.602        1.1          30400.0        51.31\n",
      "278        8    352014         12.560        1.2          32910.0        14.18\n",
      "319        8    399021         10.420        1.7          27310.0        12.70\n",
      "333        8    413099         11.351        2.5          29750.0        34.85\n",
      "335        8    414012         10.184        2.0          26690.0        37.18\n",
      "347        8    433031         10.516        0.9          27560.0        20.59\n",
      "594        8    537062         11.173        1.1          29280.0        15.39\n",
      "     Cluster  OCC CODE  JOBS PER 1000  MEAN_PRSE  Total Employees  Hourly Mean\n",
      "283        9    353021         25.058        0.6          65670.0        11.68\n"
     ]
    }
   ],
   "source": [
    "for i in range (10):\n",
    "    print(cluster_map[cluster_map.Cluster == i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
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