Math437-Unsupervised_Learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "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>AREA</th>\n",
       "      <th>ST</th>\n",
       "      <th>STATE</th>\n",
       "      <th>OCC_CODE</th>\n",
       "      <th>OCC_TITLE</th>\n",
       "      <th>OCC_GROUP</th>\n",
       "      <th>TOT_EMP</th>\n",
       "      <th>EMP_PRSE</th>\n",
       "      <th>JOBS_1000</th>\n",
       "      <th>LOC_Q</th>\n",
       "      <th>...</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",
       "      <th>ANNUAL</th>\n",
       "      <th>HOURLY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>CO</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>00-0000</td>\n",
       "      <td>All Occupations</td>\n",
       "      <td>total</td>\n",
       "      <td>2,620,640</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1000.000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>...</td>\n",
       "      <td>20.34</td>\n",
       "      <td>32.49</td>\n",
       "      <td>50.05</td>\n",
       "      <td>22,480</td>\n",
       "      <td>28,110</td>\n",
       "      <td>42,310</td>\n",
       "      <td>67,570</td>\n",
       "      <td>104,110</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>CO</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>11-0000</td>\n",
       "      <td>Management Occupations</td>\n",
       "      <td>major</td>\n",
       "      <td>116,770</td>\n",
       "      <td>1.1</td>\n",
       "      <td>44.556</td>\n",
       "      <td>0.85</td>\n",
       "      <td>...</td>\n",
       "      <td>56.15</td>\n",
       "      <td>78.38</td>\n",
       "      <td>#</td>\n",
       "      <td>55,700</td>\n",
       "      <td>79,630</td>\n",
       "      <td>116,800</td>\n",
       "      <td>163,030</td>\n",
       "      <td>#</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>CO</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>11-1011</td>\n",
       "      <td>Chief Executives</td>\n",
       "      <td>detailed</td>\n",
       "      <td>570</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.216</td>\n",
       "      <td>0.16</td>\n",
       "      <td>...</td>\n",
       "      <td>86.63</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>85,110</td>\n",
       "      <td>118,210</td>\n",
       "      <td>180,190</td>\n",
       "      <td>#</td>\n",
       "      <td>#</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>CO</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>11-1021</td>\n",
       "      <td>General and Operations Managers</td>\n",
       "      <td>detailed</td>\n",
       "      <td>39,200</td>\n",
       "      <td>1.9</td>\n",
       "      <td>14.958</td>\n",
       "      <td>0.95</td>\n",
       "      <td>...</td>\n",
       "      <td>51.51</td>\n",
       "      <td>81.71</td>\n",
       "      <td>#</td>\n",
       "      <td>46,310</td>\n",
       "      <td>67,650</td>\n",
       "      <td>107,140</td>\n",
       "      <td>169,970</td>\n",
       "      <td>#</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>CO</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>11-1031</td>\n",
       "      <td>Legislators</td>\n",
       "      <td>detailed</td>\n",
       "      <td>630</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.69</td>\n",
       "      <td>...</td>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "      <td>21,230</td>\n",
       "      <td>23,970</td>\n",
       "      <td>48,260</td>\n",
       "      <td>68,270</td>\n",
       "      <td>100,430</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   AREA  ST     STATE OCC_CODE                        OCC_TITLE OCC_GROUP  \\\n",
       "0     8  CO  Colorado  00-0000                  All Occupations     total   \n",
       "1     8  CO  Colorado  11-0000           Management Occupations     major   \n",
       "2     8  CO  Colorado  11-1011                 Chief Executives  detailed   \n",
       "3     8  CO  Colorado  11-1021  General and Operations Managers  detailed   \n",
       "4     8  CO  Colorado  11-1031                      Legislators  detailed   \n",
       "\n",
       "     TOT_EMP EMP_PRSE JOBS_1000 LOC_Q  ... H_MEDIAN H_PCT75 H_PCT90 A_PCT10  \\\n",
       "0  2,620,640      0.3  1000.000  1.00  ...    20.34   32.49   50.05  22,480   \n",
       "1    116,770      1.1    44.556  0.85  ...    56.15   78.38       #  55,700   \n",
       "2        570      5.3     0.216  0.16  ...    86.63       #       #  85,110   \n",
       "3     39,200      1.9    14.958  0.95  ...    51.51   81.71       #  46,310   \n",
       "4        630      8.0     0.239  0.69  ...        *       *       *  21,230   \n",
       "\n",
       "   A_PCT25 A_MEDIAN  A_PCT75  A_PCT90 ANNUAL HOURLY  \n",
       "0   28,110   42,310   67,570  104,110    NaN    NaN  \n",
       "1   79,630  116,800  163,030        #    NaN    NaN  \n",
       "2  118,210  180,190        #        #    NaN    NaN  \n",
       "3   67,650  107,140  169,970        #    NaN    NaN  \n",
       "4   23,970   48,260   68,270  100,430   True    NaN  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "import matplotlib.cm as cm\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",
    "# Current Workforce demographic for the state of colorado input from dropbox\n",
    "workforce_demographic2018 = pa.read_csv('https://www.dropbox.com/s/w1zzumsadlgg9pn/Colorado_Current_Workforce_Demographic.csv?dl=1')\n",
    "\n",
    "\n",
    "workforce_demographic2018.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_strings (X):\n",
    "    try:\n",
    "        if '+' in X:\n",
    "            K = X.find('+')\n",
    "            return X[:K]\n",
    "        if '-' in X:\n",
    "            K = X.find('-')\n",
    "            return X[:K]\n",
    "        if '**' in X:\n",
    "            K = X.find('**')\n",
    "            return X[:K]\n",
    "        if '*' in X:\n",
    "            K = X.find('*')\n",
    "            return X[:K]\n",
    "        if '#' in X:\n",
    "            K = X.find('#')\n",
    "            return X[:K]\n",
    "        else:  \n",
    "            return X\n",
    "    except ValueError:\n",
    "        return X\n",
    "\n",
    "def convertFloat(dataframe, feature):\n",
    "    for K in dataframe.index: \n",
    "        dataframe.loc[K,feature] = remove_strings(dataframe.loc[K, feature])\n",
    "        \n",
    "    \n",
    "    dataframe.loc[:, feature] = dataframe.loc[:,feature].astype(\"float\")  \n",
    "    return dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "workforce_demographic2018 = convertFloat(workforce_demographic2018, \"OCC_CODE\")  ##this works to convert OCC_Code to float64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Luke/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py:543: 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"
     ]
    }
   ],
   "source": [
    "workforce_demographic2018['TOT_EMP'] = workforce_demographic2018['TOT_EMP'].str.replace(',','')\n",
    "workforce_demographic2018['TOT_EMP'].replace('', np.nan, inplace=True)\n",
    "workforce_demographic2018 = workforce_demographic2018[workforce_demographic2018['TOT_EMP'].notna()]\n",
    "workforce_demographic2018 = convertFloat(workforce_demographic2018, \"TOT_EMP\")  ##this works to convert OCC_Code to float64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Luke/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py:6586: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    }
   ],
   "source": [
    "workforce_demographic2018['EMP_PRSE'].replace('', np.nan, inplace=True)\n",
    "workforce_demographic2018 = workforce_demographic2018[workforce_demographic2018['EMP_PRSE'].notna()]\n",
    "workforce_demographic2018 = convertFloat(workforce_demographic2018, \"EMP_PRSE\")  ##this works to convert OCC_Code to float64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "workforce_demographic2018['JOBS_1000'].replace('', np.nan, inplace=True)\n",
    "workforce_demographic2018 = workforce_demographic2018[workforce_demographic2018['JOBS_1000'].notna()]\n",
    "workforce_demographic2018 = convertFloat(workforce_demographic2018, \"JOBS_1000\")  ##this works to convert OCC_Code to float64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "workforce_demographic2018['LOC_Q'].replace('', np.nan, inplace=True)\n",
    "workforce_demographic2018 = workforce_demographic2018[workforce_demographic2018['LOC_Q'].notna()]\n",
    "workforce_demographic2018 = convertFloat(workforce_demographic2018, \"LOC_Q\")  ##this works to convert OCC_Code to float64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "workforce_demographic2018['H_MEAN'].replace('', np.nan, inplace=True)  ##This one has some problems\n",
    "workforce_demographic2018 = workforce_demographic2018[workforce_demographic2018['H_MEAN'].notna()]\n",
    "workforce_demographic2018 = convertFloat(workforce_demographic2018, \"H_MEAN\")  ##this works to convert OCC_Code to float64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "workforce_demographic2018['A_MEAN'] = workforce_demographic2018['A_MEAN'].str.replace(',','') ##This one has some problems\n",
    "workforce_demographic2018['A_MEAN'].replace('', np.nan, inplace=True)\n",
    "workforce_demographic2018 = workforce_demographic2018[workforce_demographic2018['A_MEAN'].notna()]\n",
    "workforce_demographic2018 = convertFloat(workforce_demographic2018, \"A_MEAN\")  ##this works to convert OCC_Code to float64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "workforce_demographic2018 = convertFloat(workforce_demographic2018, \"MEAN_PRSE\")  ##this works to convert OCC_Code to float64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "workforce_demographic2018['H_PCT10'].replace('', np.nan, inplace=True)\n",
    "workforce_demographic2018 = workforce_demographic2018[workforce_demographic2018['H_PCT10'].notna()]\n",
    "workforce_demographic2018 = convertFloat(workforce_demographic2018, \"H_PCT10\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "workforce_demographic2018['H_PCT25'].replace('', np.nan, inplace=True)        ##This one has some problems\n",
    "workforce_demographic2018 = workforce_demographic2018[workforce_demographic2018['H_PCT25'].notna()]\n",
    "workforce_demographic2018 = convertFloat(workforce_demographic2018, \"H_PCT25\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "workforce_demographic2018['H_MEDIAN'].replace('', np.nan, inplace=True)      ##This one has some problems\n",
    "workforce_demographic2018 = workforce_demographic2018[workforce_demographic2018['H_MEDIAN'].notna()]\n",
    "workforce_demographic2018 = convertFloat(workforce_demographic2018, \"H_MEDIAN\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "workforce_demographic2018['H_PCT75'].replace('', np.nan, inplace=True)      ##This one has some problems\n",
    "workforce_demographic2018 = workforce_demographic2018[workforce_demographic2018['H_PCT75'].notna()]\n",
    "workforce_demographic2018 = convertFloat(workforce_demographic2018, \"H_PCT75\")  ##this works to convert OCC_Code to float64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "workforce_demographic2018['H_PCT90'].replace('', np.nan, inplace=True)     ##This one has some problems\n",
    "workforce_demographic2018 = workforce_demographic2018[workforce_demographic2018['H_PCT90'].notna()]\n",
    "workforce_demographic2018 = convertFloat(workforce_demographic2018, \"H_PCT90\")  ##this works to convert OCC_Code to float64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "workforce_demographic2018.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_employees_array = workforce_demographic2018[[\"TOT_EMP\"]].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [],
   "source": [
    "for l in range(total_employees_array.shape[1]):\n",
    "    total_employees_array[:, l] = ( total_employees_array[:, l] - total_employees_array[:, l].min())/(total_employees_array[:, l].max() - total_employees_array[:, l].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/keSyqvrfVa5ttXTp87XAp6vqH5LspfcUtMuq6v9Wv7w1MfL8Ws8j9HXzcOox6tJnkrwJ+ABwsKp+NabaVsuwPl8EXAbMJPkhvbnG6Q1+YrTr9/ZXqurZqvoB8Bi9gN+ouvT5euAegKqaBV5I75dYtarTz/u5WM+Bvq4eTj0mQ/vcn364k16Yb/R5VRjS56p6uqq2V9WuqtpF77zBwaqaW5tyR6LL9/aX6Z0AJ8l2elMwp8Za5Wh16fOPgasAkryaXqAvjLXK8ZoG/rx/tctrgaer6qcXdMS1PhM85CzxAeC/6J0d/0B/2230fqCh94F/ATgJ/CfwqrWueQx9/jfgv4Fv97+m17rm1e7zkrYzbPCrXDp+zgH+ETgBfBc4vNY1j6HPe4Bv0LsC5tvAn6x1zRfY388BPwWepTcavx54F/CuRZ/xkf7fx3dH8X3trf+S1Ij1POUiSToHBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqxP8DSqepg1x6N4QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(total_employees_array[:, 0], total_employees_array[:, 0], 'b.');\n",
    "plt.savefig('17.1.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {},
   "outputs": [],
   "source": [
    "jobs_per_1000 = workforce_demographic2018[[\"JOBS_1000\"]].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {},
   "outputs": [],
   "source": [
    "for l in range(jobs_per_1000.shape[1]):\n",
    "    jobs_per_1000[:, l] = (jobs_per_1000[:, l] - jobs_per_1000[:, l].min())/(jobs_per_1000[:, l].max() - jobs_per_1000[:, l].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(total_employees_array[:, 0], total_employees_array[:, 0], 'b.');\n",
    "plt.savefig('17.1.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {},
   "outputs": [],
   "source": [
    "workforce_demographic2018 = workforce_demographic2018.drop([0])   ## Remove the first row from the data frame becasue it contains all the jobs combined and is an \"outlier\"\n",
    "X = np.array(workforce_demographic2018[['TOT_EMP']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X)\n",
    "plt.ylabel('Total Workers')\n",
    "plt.xlabel('Row #')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of [Index(['A_MEAN'], dtype='object')] are in the [index]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-48-49659d2f80d2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mY\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworkforce_demographic2018\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'A_MEAN'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m   \u001b[0;31m#try log mean salary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mZ\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworkforce_demographic2018\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'EMP_PRSE'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    909\u001b[0m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    910\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 911\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    912\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    913\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_with\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    949\u001b[0m             \u001b[0;31m# handle the dup indexing case (GH 4246)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    950\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 951\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    952\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    953\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1498\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1499\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1500\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1501\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1502\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1900\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Cannot index with multidimensional key'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1902\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_iterable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1903\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1904\u001b[0m             \u001b[0;31m# nested tuple slicing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_iterable\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1203\u001b[0m             \u001b[0;31m# A collection of keys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1204\u001b[0m             keyarr, indexer = self._get_listlike_indexer(key, axis,\n\u001b[0;32m-> 1205\u001b[0;31m                                                          raise_missing=False)\n\u001b[0m\u001b[1;32m   1206\u001b[0m             return self.obj._reindex_with_indexers({axis: [keyarr, indexer]},\n\u001b[1;32m   1207\u001b[0m                                                    copy=True, allow_dups=True)\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1159\u001b[0m         self._validate_read_indexer(keyarr, indexer,\n\u001b[1;32m   1160\u001b[0m                                     \u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1161\u001b[0;31m                                     raise_missing=raise_missing)\n\u001b[0m\u001b[1;32m   1162\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1244\u001b[0m                 raise KeyError(\n\u001b[1;32m   1245\u001b[0m                     u\"None of [{key}] are in the [{axis}]\".format(\n\u001b[0;32m-> 1246\u001b[0;31m                         key=key, axis=self.obj._get_axis_name(axis)))\n\u001b[0m\u001b[1;32m   1247\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1248\u001b[0m             \u001b[0;31m# We (temporarily) allow for some missing keys with .loc, except in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"None of [Index(['A_MEAN'], dtype='object')] are in the [index]\""
     ]
    }
   ],
   "source": [
    "Y = np.array(workforce_demographic2018[['A_MEAN']])   #try log mean salary\n",
    "Z = np.array(workforce_demographic2018[['EMP_PRSE']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('Mean Anual Salary')\n",
    "plt.ylabel('Total Workers')\n",
    "plt.plot(Y, X, 'o', color='black');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('Mean Anual Salary')\n",
    "plt.ylabel('Employement relative standard error')\n",
    "plt.plot(Y, Z, 'o', color='black');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 570,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 100\n",
    "colors = np.random.rand(N)\n",
    "area = (30 * np.random.rand(N))**2  # 0 to 15 point radii\n",
    "plt.xlabel('Mean Anual Salary')\n",
    "plt.ylabel('Total Workers')\n",
    "plt.xlim(20000, 160000)\n",
    "plt.ylim(0, 120000)\n",
    "plt.scatter(Y, X, s=area, alpha=0.5)   # s = Z \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A = workforce_demographic2018.iloc[:, 6]\n",
    "B = workforce_demographic2018.iloc[:, 11]\n",
    "# Fitting K-Means to the dataset\n",
    "kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = 42)\n",
    "y_kmeans = kmeans.fit_predict(workforce_demographic2018)\n",
    "#beginning of  the cluster numbering with 1 instead of 0\n",
    "y_kmeans1=y_kmeans\n",
    "y_kmeans1=y_kmeans+1\n",
    "# New Dataframe called cluster\n",
    "cluster = pd.DataFrame(y_kmeans1)\n",
    "# Adding cluster to the Dataset1\n",
    "workforce_demographic2018['cluster'] = cluster\n",
    "#Mean of clusters\n",
    "kmeans_mean_cluster = pd.DataFrame(round(workforce_demographic2018.groupby('cluster').mean(),1))\n",
    "kmeans_mean_cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 590,
   "metadata": {},
   "outputs": [],
   "source": [
    "nueral_network_input1 = np.array(workforce_demographic2018[['TOT_EMP', 'EMP_PRSE']])\n",
    "nueral_network_input2 = np.array(workforce_demographic2018['OCC_CODE'])\n",
    "nueral_network_input3 = np.array(workforce_demographic2018['A_MEAN'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 593,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_reg_model(reg, X, y):\n",
    "\n",
    "    # We shuffle the data using a random permutation\n",
    "\n",
    "    n = X.shape[0]\n",
    "    test = int(0.20*n)            # We will use a test set made up of 20% of the data from our sample\n",
    "    perm = rn.permutation(n)   \n",
    "    X = X[perm]\n",
    "    y = y[perm]\n",
    "    X_test = X[:test]       # Then create the test\n",
    "    y_test = y[:test]\n",
    "    X_train = X[test:]     # and train sets\n",
    "    y_train = y[test:]\n",
    "    \n",
    "    reg.fit(X_train, y_train) # Fit the model\n",
    "     \n",
    "    nn = 200\n",
    "    # Plot the decision boundary. For that, we will assign a color to each\n",
    "    # point in the mesh [x_min, m_max]x[y_min, y_max].\n",
    "    x_min, x_max = X[:, 0].min()-0.1, X[:, 0].max()+0.1\n",
    "    y_min, y_max = X[:, 1].min()-0.1, X[:, 1].max()+0.1\n",
    "    xx, yy = np.meshgrid(np.linspace(x_min, x_max, nn),   # Changed the function to use linspace rather than arrange\n",
    "        np.linspace(y_min, y_max, nn))                    # This keeps us from having to adjust h.\n",
    "    Z = reg.predict(np.c_[xx.ravel(), yy.ravel()]) # predict\n",
    "\n",
    "    # Put the result into a color plot\n",
    "    Z = Z.reshape(xx.shape)\n",
    "\n",
    "    #plt.figure(figsize=(8, 6))\n",
    "    plt.contour(xx, yy, Z)\n",
    "    plt.xlim(0, 7500)\n",
    "    plt.ylim(0, 55)\n",
    "    \n",
    "    plt.scatter(X_train[:, 0], X_train[:, 1], c=\"r\", marker='.')\n",
    "    plt.scatter(X_test[:, 0], X_test[:, 1], c=\"b\", marker='.');\n",
    "    \n",
    "    print('Training R2: {}'.format(reg.score(X_train, y_train)))\n",
    "    print('Testing R2: {}'.format(reg.score(X_test, y_test)))\n",
    "    \n",
    "    return reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 594,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training R2: 0.08047978197653327\n",
      "Testing R2: 0.02088499247932951\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = LinearRegression()\n",
    "plot_reg_model(reg, nueral_network_input1, nueral_network_input2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 592,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training R2: 0.0490647970645709\n",
      "Testing R2: 0.08546716893269402\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = LinearRegression()\n",
    "plot_reg_model(reg, nueral_network_input1, nueral_network_input3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_employees_anuanl_mean = workforce_demographic2018[['TOT_EMP', 'A_MEAN']].to_numpy()\n",
    "cluster = KMeans(n_clusters=4)\n",
    "cluster_plot(cluster, total_employees_anuanl_mean)\n",
    "plt.xlim(-1000, 400000);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jobs_1000_anual_mean = workforce_demographic2018[['JOBS_1000', 'A_MEAN']].to_numpy()\n",
    "cluster = KMeans(n_clusters=4)\n",
    "cluster_plot(cluster, jobs_1000_anual_mean)\n",
    "plt.xlim(-1, 40);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "major_groups = pa.np.where(workforce_demographic2018.OCC_TITLE.str.contains(\"major\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'OCC_GROUP'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-61-21bd0d29d43f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mworkforce_demographic2018\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'OCC_GROUP'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    866\u001b[0m         \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    867\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 868\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    869\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    870\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m   4373\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4374\u001b[0m             return self._engine.get_value(s, k,\n\u001b[0;32m-> 4375\u001b[0;31m                                           tz=getattr(series.dtype, 'tz', None))\n\u001b[0m\u001b[1;32m   4376\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4377\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mholds_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_boolean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.index.Int64Engine._check_type\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'OCC_GROUP'"
     ]
    }
   ],
   "source": [
    "workforce_demographic2018['OCC_GROUP'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Series' object has no attribute 'OCC_GROUP'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-60-e5f456e76b9c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmajor_groups_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mworkforce_demographic2018\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mworkforce_demographic2018\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOCC_GROUP\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mcat\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'major'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdetailed_groups_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mworkforce_demographic2018\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mworkforce_demographic2018\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOCC_GROUP\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mcat\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'detailed'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5065\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5066\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5067\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5068\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5069\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Series' object has no attribute 'OCC_GROUP'"
     ]
    }
   ],
   "source": [
    "major_groups_df = workforce_demographic2018.loc[workforce_demographic2018.OCC_GROUP.apply(lambda cat: 'major' in cat)]\n",
    "detailed_groups_df = workforce_demographic2018.loc[workforce_demographic2018.OCC_GROUP.apply(lambda cat: 'detailed' in cat)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 711,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_employees_anuanl_mean_major = major_groups_df[['TOT_EMP', 'A_MEAN']].to_numpy()\n",
    "cluster = KMeans(n_clusters=4)\n",
    "cluster_plot(cluster, total_employees_anuanl_mean_major)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 716,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_employees_anuanl_mean_detailed = detailed_groups_df[['TOT_EMP', 'A_MEAN']].to_numpy()\n",
    "cluster = KMeans(n_clusters=4)\n",
    "cluster_plot(cluster, total_employees_anuanl_mean_detailed)\n",
    "plt.xlim(-50,20000);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'major_groups_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-63-04a2edc49cba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mkm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKMeans\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_clusters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajor_groups_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'TOT_EMP'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'A_MEAN'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'major_groups_df' is not defined"
     ]
    }
   ],
   "source": [
    "km = KMeans(n_clusters=5).fit(major_groups_df[['TOT_EMP', 'A_MEAN']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}