Onet pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       O*NET-SOC Code                              Title   Element ID  \\\n",
      "0          11-1011.00                   Chief Executives  4.C.1.a.2.c   \n",
      "1          11-1011.00                   Chief Executives  4.C.1.a.2.c   \n",
      "2          11-1011.00                   Chief Executives  4.C.1.a.2.c   \n",
      "3          11-1011.00                   Chief Executives  4.C.1.a.2.c   \n",
      "4          11-1011.00                   Chief Executives  4.C.1.a.2.c   \n",
      "...               ...                                ...          ...   \n",
      "326879     53-7121.00  Tank Car, Truck, and Ship Loaders    4.C.3.d.4   \n",
      "326880     53-7121.00  Tank Car, Truck, and Ship Loaders    4.C.3.d.8   \n",
      "326881     53-7121.00  Tank Car, Truck, and Ship Loaders    4.C.3.d.8   \n",
      "326882     53-7121.00  Tank Car, Truck, and Ship Loaders    4.C.3.d.8   \n",
      "326883     53-7121.00  Tank Car, Truck, and Ship Loaders    4.C.3.d.8   \n",
      "\n",
      "                         Element Name Scale ID                Scale Name  \\\n",
      "0                     Public Speaking       CX                   Context   \n",
      "1                     Public Speaking      CXP  Context (Categories 1-5)   \n",
      "2                     Public Speaking      CXP  Context (Categories 1-5)   \n",
      "3                     Public Speaking      CXP  Context (Categories 1-5)   \n",
      "4                     Public Speaking      CXP  Context (Categories 1-5)   \n",
      "...                               ...      ...                       ...   \n",
      "326879                 Work Schedules      CTP  Context (Categories 1-3)   \n",
      "326880  Duration of Typical Work Week       CT                   Context   \n",
      "326881  Duration of Typical Work Week      CTP  Context (Categories 1-3)   \n",
      "326882  Duration of Typical Work Week      CTP  Context (Categories 1-3)   \n",
      "326883  Duration of Typical Work Week      CTP  Context (Categories 1-3)   \n",
      "\n",
      "        Category  Data Value     N  Standard Error  Lower CI Bound  \\\n",
      "0            NaN        3.39  25.0            0.22            2.93   \n",
      "1            1.0        6.02  25.0            5.14            0.97   \n",
      "2            2.0       12.40  25.0            7.33            3.39   \n",
      "3            3.0       23.36  25.0           10.36            8.45   \n",
      "4            4.0       52.55  25.0           12.13           28.87   \n",
      "...          ...         ...   ...             ...             ...   \n",
      "326879       3.0        0.89  32.0            0.98            0.09   \n",
      "326880       NaN        2.82  32.0            0.10            2.62   \n",
      "326881       1.0        0.00  32.0            0.00             NaN   \n",
      "326882       2.0       17.56  32.0            9.83            5.06   \n",
      "326883       3.0       82.44  32.0            9.83           54.01   \n",
      "\n",
      "        Upper CI Bound Recommend Suppress  Not Relevant     Date Domain Source  \n",
      "0                 3.86                  N           NaN  07/2014     Incumbent  \n",
      "1                29.49                  N           NaN  07/2014     Incumbent  \n",
      "2                36.32                  N           NaN  07/2014     Incumbent  \n",
      "3                50.16                  N           NaN  07/2014     Incumbent  \n",
      "4                75.14                  N           NaN  07/2014     Incumbent  \n",
      "...                ...                ...           ...      ...           ...  \n",
      "326879            7.87                  N           NaN  08/2019     Incumbent  \n",
      "326880            3.00                  N           NaN  08/2019     Incumbent  \n",
      "326881             NaN                  N           NaN  08/2019     Incumbent  \n",
      "326882           45.99                  N           NaN  08/2019     Incumbent  \n",
      "326883           94.94                  N           NaN  08/2019     Incumbent  \n",
      "\n",
      "[326884 rows x 16 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('Work Context.xlsx')\n",
    "print(df)\n",
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       O*NET-SOC Code   Element ID                   Element Name Scale ID  \\\n",
      "0          11-1011.00  4.C.1.a.2.c                Public Speaking       CX   \n",
      "1          11-1011.00  4.C.1.a.2.c                Public Speaking      CXP   \n",
      "2          11-1011.00  4.C.1.a.2.c                Public Speaking      CXP   \n",
      "3          11-1011.00  4.C.1.a.2.c                Public Speaking      CXP   \n",
      "4          11-1011.00  4.C.1.a.2.c                Public Speaking      CXP   \n",
      "...               ...          ...                            ...      ...   \n",
      "264433     53-7121.00    4.C.3.d.4                 Work Schedules      CTP   \n",
      "264434     53-7121.00    4.C.3.d.8  Duration of Typical Work Week       CT   \n",
      "264435     53-7121.00    4.C.3.d.8  Duration of Typical Work Week      CTP   \n",
      "264436     53-7121.00    4.C.3.d.8  Duration of Typical Work Week      CTP   \n",
      "264437     53-7121.00    4.C.3.d.8  Duration of Typical Work Week      CTP   \n",
      "\n",
      "        Category  Data Value     N  Standard Error  Lower CI Bound  \\\n",
      "0            NaN        3.47  27.0            0.55            2.33   \n",
      "1            1.0       14.55  27.0           13.42            1.82   \n",
      "2            2.0        2.39  27.0            1.82            0.49   \n",
      "3            3.0       31.56  27.0           15.59            9.47   \n",
      "4            4.0       24.71  27.0           15.87            5.38   \n",
      "...          ...         ...   ...             ...             ...   \n",
      "264433       3.0        1.28  16.0            1.43            0.12   \n",
      "264434       NaN        2.76  16.0            0.15            2.44   \n",
      "264435       1.0        0.00  16.0            0.00             NaN   \n",
      "264436       2.0       23.54  16.0           15.42            4.73   \n",
      "264437       3.0       76.46  16.0           15.42           34.35   \n",
      "\n",
      "        Upper CI Bound Recommend Suppress  Not Relevant     Date Domain Source  \n",
      "0                 4.61                  N           NaN  06/2006     Incumbent  \n",
      "1                61.04                  N           NaN  06/2006     Incumbent  \n",
      "2                10.86                  N           NaN  06/2006     Incumbent  \n",
      "3                67.03                  N           NaN  06/2006     Incumbent  \n",
      "4                65.47                  N           NaN  06/2006     Incumbent  \n",
      "...                ...                ...           ...      ...           ...  \n",
      "264433           12.69                  N           NaN  12/2006     Incumbent  \n",
      "264434            3.00                  N           NaN  12/2006     Incumbent  \n",
      "264435             NaN                  N           NaN  12/2006     Incumbent  \n",
      "264436           65.65                  N           NaN  12/2006     Incumbent  \n",
      "264437           95.27                  N           NaN  12/2006     Incumbent  \n",
      "\n",
      "[264438 rows x 14 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftxt = pd.read_csv('Work_Context.txt', sep='\\t')\n",
    "print(dftxt)\n",
    "type(dftxt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       O*NET-SOC Code                              Title   Element ID  \\\n",
      "0          11-1011.00                   Chief Executives  4.C.1.a.2.c   \n",
      "1          11-1011.00                   Chief Executives  4.C.1.a.2.c   \n",
      "2          11-1011.00                   Chief Executives  4.C.1.a.2.c   \n",
      "3          11-1011.00                   Chief Executives  4.C.1.a.2.c   \n",
      "4          11-1011.00                   Chief Executives  4.C.1.a.2.c   \n",
      "...               ...                                ...          ...   \n",
      "321585     53-7121.00  Tank Car, Truck, and Ship Loaders    4.C.3.d.4   \n",
      "321586     53-7121.00  Tank Car, Truck, and Ship Loaders    4.C.3.d.8   \n",
      "321587     53-7121.00  Tank Car, Truck, and Ship Loaders    4.C.3.d.8   \n",
      "321588     53-7121.00  Tank Car, Truck, and Ship Loaders    4.C.3.d.8   \n",
      "321589     53-7121.00  Tank Car, Truck, and Ship Loaders    4.C.3.d.8   \n",
      "\n",
      "                         Element Name Scale ID                Scale Name  \\\n",
      "0                     Public Speaking       CX                   Context   \n",
      "1                     Public Speaking      CXP  Context (Categories 1-5)   \n",
      "2                     Public Speaking      CXP  Context (Categories 1-5)   \n",
      "3                     Public Speaking      CXP  Context (Categories 1-5)   \n",
      "4                     Public Speaking      CXP  Context (Categories 1-5)   \n",
      "...                               ...      ...                       ...   \n",
      "321585                 Work Schedules      CTP  Context (Categories 1-3)   \n",
      "321586  Duration of Typical Work Week       CT                   Context   \n",
      "321587  Duration of Typical Work Week      CTP  Context (Categories 1-3)   \n",
      "321588  Duration of Typical Work Week      CTP  Context (Categories 1-3)   \n",
      "321589  Duration of Typical Work Week      CTP  Context (Categories 1-3)   \n",
      "\n",
      "        Category  Data Value     N  Standard Error  Lower CI Bound  \\\n",
      "0            NaN        3.39  25.0            0.22            2.93   \n",
      "1            1.0        6.02  25.0            5.14            0.97   \n",
      "2            2.0       12.40  25.0            7.33            3.39   \n",
      "3            3.0       23.36  25.0           10.36            8.45   \n",
      "4            4.0       52.55  25.0           12.13           28.87   \n",
      "...          ...         ...   ...             ...             ...   \n",
      "321585       3.0        1.28  16.0            1.43            0.12   \n",
      "321586       NaN        2.76  16.0            0.15            2.44   \n",
      "321587       1.0        0.00  16.0            0.00             NaN   \n",
      "321588       2.0       23.54  16.0           15.42            4.73   \n",
      "321589       3.0       76.46  16.0           15.42           34.35   \n",
      "\n",
      "        Upper CI Bound Recommend Suppress  Not Relevant     Date Domain Source  \n",
      "0                 3.86                  N           NaN  07/2014     Incumbent  \n",
      "1                29.49                  N           NaN  07/2014     Incumbent  \n",
      "2                36.32                  N           NaN  07/2014     Incumbent  \n",
      "3                50.16                  N           NaN  07/2014     Incumbent  \n",
      "4                75.14                  N           NaN  07/2014     Incumbent  \n",
      "...                ...                ...           ...      ...           ...  \n",
      "321585           12.69                  N           NaN  12/2006     Incumbent  \n",
      "321586            3.00                  N           NaN  12/2006     Incumbent  \n",
      "321587             NaN                  N           NaN  12/2006     Incumbent  \n",
      "321588           65.65                  N           NaN  12/2006     Incumbent  \n",
      "321589           95.27                  N           NaN  12/2006     Incumbent  \n",
      "\n",
      "[321590 rows x 16 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('Work Contextmid.xlsx')\n",
    "print(df)\n",
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>O*NET-SOC Code</th>\n",
       "      <th>Title</th>\n",
       "      <th>Element Name</th>\n",
       "      <th>Category</th>\n",
       "      <th>Data Value</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11-1011.00</td>\n",
       "      <td>Chief Executives</td>\n",
       "      <td>Public Speaking</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.39</td>\n",
       "      <td>07/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11-1011.00</td>\n",
       "      <td>Chief Executives</td>\n",
       "      <td>Public Speaking</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.02</td>\n",
       "      <td>07/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11-1011.00</td>\n",
       "      <td>Chief Executives</td>\n",
       "      <td>Public Speaking</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.40</td>\n",
       "      <td>07/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11-1011.00</td>\n",
       "      <td>Chief Executives</td>\n",
       "      <td>Public Speaking</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.36</td>\n",
       "      <td>07/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11-1011.00</td>\n",
       "      <td>Chief Executives</td>\n",
       "      <td>Public Speaking</td>\n",
       "      <td>4.0</td>\n",
       "      <td>52.55</td>\n",
       "      <td>07/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326879</th>\n",
       "      <td>53-7121.00</td>\n",
       "      <td>Tank Car, Truck, and Ship Loaders</td>\n",
       "      <td>Work Schedules</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.89</td>\n",
       "      <td>08/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326880</th>\n",
       "      <td>53-7121.00</td>\n",
       "      <td>Tank Car, Truck, and Ship Loaders</td>\n",
       "      <td>Duration of Typical Work Week</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.82</td>\n",
       "      <td>08/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326881</th>\n",
       "      <td>53-7121.00</td>\n",
       "      <td>Tank Car, Truck, and Ship Loaders</td>\n",
       "      <td>Duration of Typical Work Week</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>08/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326882</th>\n",
       "      <td>53-7121.00</td>\n",
       "      <td>Tank Car, Truck, and Ship Loaders</td>\n",
       "      <td>Duration of Typical Work Week</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.56</td>\n",
       "      <td>08/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326883</th>\n",
       "      <td>53-7121.00</td>\n",
       "      <td>Tank Car, Truck, and Ship Loaders</td>\n",
       "      <td>Duration of Typical Work Week</td>\n",
       "      <td>3.0</td>\n",
       "      <td>82.44</td>\n",
       "      <td>08/2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>326884 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       O*NET-SOC Code                              Title  \\\n",
       "0          11-1011.00                   Chief Executives   \n",
       "1          11-1011.00                   Chief Executives   \n",
       "2          11-1011.00                   Chief Executives   \n",
       "3          11-1011.00                   Chief Executives   \n",
       "4          11-1011.00                   Chief Executives   \n",
       "...               ...                                ...   \n",
       "326879     53-7121.00  Tank Car, Truck, and Ship Loaders   \n",
       "326880     53-7121.00  Tank Car, Truck, and Ship Loaders   \n",
       "326881     53-7121.00  Tank Car, Truck, and Ship Loaders   \n",
       "326882     53-7121.00  Tank Car, Truck, and Ship Loaders   \n",
       "326883     53-7121.00  Tank Car, Truck, and Ship Loaders   \n",
       "\n",
       "                         Element Name  Category  Data Value     Date  \n",
       "0                     Public Speaking       NaN        3.39  07/2014  \n",
       "1                     Public Speaking       1.0        6.02  07/2014  \n",
       "2                     Public Speaking       2.0       12.40  07/2014  \n",
       "3                     Public Speaking       3.0       23.36  07/2014  \n",
       "4                     Public Speaking       4.0       52.55  07/2014  \n",
       "...                               ...       ...         ...      ...  \n",
       "326879                 Work Schedules       3.0        0.89  08/2019  \n",
       "326880  Duration of Typical Work Week       NaN        2.82  08/2019  \n",
       "326881  Duration of Typical Work Week       1.0        0.00  08/2019  \n",
       "326882  Duration of Typical Work Week       2.0       17.56  08/2019  \n",
       "326883  Duration of Typical Work Week       3.0       82.44  08/2019  \n",
       "\n",
       "[326884 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true = pd.DataFrame(df, columns = [\"O*NET-SOC Code\", 'Title', 'Element Name', 'Category', 'Data Value', 'Date'])\n",
    "true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>O*NET-SOC Code</th>\n",
       "      <th>Element Name</th>\n",
       "      <th>Category</th>\n",
       "      <th>Data Value</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>952</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>954 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     O*NET-SOC Code  Element Name  Category  Data Value  Date\n",
       "0               NaN           NaN       NaN         NaN   NaN\n",
       "1               NaN           NaN       NaN         NaN   NaN\n",
       "2               NaN           NaN       NaN         NaN   NaN\n",
       "3               NaN           NaN       NaN         NaN   NaN\n",
       "4               NaN           NaN       NaN         NaN   NaN\n",
       "..              ...           ...       ...         ...   ...\n",
       "949             NaN           NaN       NaN         NaN   NaN\n",
       "950             NaN           NaN       NaN         NaN   NaN\n",
       "951             NaN           NaN       NaN         NaN   NaN\n",
       "952             NaN           NaN       NaN         NaN   NaN\n",
       "953             NaN           NaN       NaN         NaN   NaN\n",
       "\n",
       "[954 rows x 5 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftxt= pd.DataFrame(df, columns = ['O*NET-SOC Code', 'Element Name', 'Category', 'Data Value', 'Date'])\n",
    "dftxt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ajj4p\\Documents\\anaconda3\\lib\\site-packages\\pandas\\core\\ops\\array_ops.py:253: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  res_values = method(rvalues)\n"
     ]
    },
    {
     "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>Title</th>\n",
       "      <th>Element Name</th>\n",
       "      <th>Category</th>\n",
       "      <th>Data Value</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chief Executives</td>\n",
       "      <td>Public Speaking</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.39</td>\n",
       "      <td>07/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chief Executives</td>\n",
       "      <td>Public Speaking</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.02</td>\n",
       "      <td>07/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chief Executives</td>\n",
       "      <td>Public Speaking</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.40</td>\n",
       "      <td>07/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chief Executives</td>\n",
       "      <td>Public Speaking</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.36</td>\n",
       "      <td>07/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chief Executives</td>\n",
       "      <td>Public Speaking</td>\n",
       "      <td>4.0</td>\n",
       "      <td>52.55</td>\n",
       "      <td>07/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321585</th>\n",
       "      <td>Tank Car, Truck, and Ship Loaders</td>\n",
       "      <td>Work Schedules</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.28</td>\n",
       "      <td>12/2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321586</th>\n",
       "      <td>Tank Car, Truck, and Ship Loaders</td>\n",
       "      <td>Duration of Typical Work Week</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.76</td>\n",
       "      <td>12/2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321587</th>\n",
       "      <td>Tank Car, Truck, and Ship Loaders</td>\n",
       "      <td>Duration of Typical Work Week</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12/2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321588</th>\n",
       "      <td>Tank Car, Truck, and Ship Loaders</td>\n",
       "      <td>Duration of Typical Work Week</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.54</td>\n",
       "      <td>12/2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321589</th>\n",
       "      <td>Tank Car, Truck, and Ship Loaders</td>\n",
       "      <td>Duration of Typical Work Week</td>\n",
       "      <td>3.0</td>\n",
       "      <td>76.46</td>\n",
       "      <td>12/2006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>321590 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Title                   Element Name  \\\n",
       "0                        Chief Executives                Public Speaking   \n",
       "1                        Chief Executives                Public Speaking   \n",
       "2                        Chief Executives                Public Speaking   \n",
       "3                        Chief Executives                Public Speaking   \n",
       "4                        Chief Executives                Public Speaking   \n",
       "...                                   ...                            ...   \n",
       "321585  Tank Car, Truck, and Ship Loaders                 Work Schedules   \n",
       "321586  Tank Car, Truck, and Ship Loaders  Duration of Typical Work Week   \n",
       "321587  Tank Car, Truck, and Ship Loaders  Duration of Typical Work Week   \n",
       "321588  Tank Car, Truck, and Ship Loaders  Duration of Typical Work Week   \n",
       "321589  Tank Car, Truck, and Ship Loaders  Duration of Typical Work Week   \n",
       "\n",
       "        Category  Data Value     Date  \n",
       "0            NaN        3.39  07/2014  \n",
       "1            1.0        6.02  07/2014  \n",
       "2            2.0       12.40  07/2014  \n",
       "3            3.0       23.36  07/2014  \n",
       "4            4.0       52.55  07/2014  \n",
       "...          ...         ...      ...  \n",
       "321585       3.0        1.28  12/2006  \n",
       "321586       NaN        2.76  12/2006  \n",
       "321587       1.0        0.00  12/2006  \n",
       "321588       2.0       23.54  12/2006  \n",
       "321589       3.0       76.46  12/2006  \n",
       "\n",
       "[321590 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true[true.Category != 'NaN']\n",
    "#true['Category'] = true['Category'].fillna(0)\n",
    "# converted NaN to 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ajj4p\\Documents\\anaconda3\\lib\\site-packages\\pandas\\core\\ops\\array_ops.py:253: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  res_values = method(rvalues)\n"
     ]
    },
    {
     "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>O*NET-SOC Code</th>\n",
       "      <th>Element Name</th>\n",
       "      <th>Category</th>\n",
       "      <th>Data Value</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>952</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>954 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     O*NET-SOC Code  Element Name  Category  Data Value  Date\n",
       "0               NaN           NaN       NaN         NaN   NaN\n",
       "1               NaN           NaN       NaN         NaN   NaN\n",
       "2               NaN           NaN       NaN         NaN   NaN\n",
       "3               NaN           NaN       NaN         NaN   NaN\n",
       "4               NaN           NaN       NaN         NaN   NaN\n",
       "..              ...           ...       ...         ...   ...\n",
       "949             NaN           NaN       NaN         NaN   NaN\n",
       "950             NaN           NaN       NaN         NaN   NaN\n",
       "951             NaN           NaN       NaN         NaN   NaN\n",
       "952             NaN           NaN       NaN         NaN   NaN\n",
       "953             NaN           NaN       NaN         NaN   NaN\n",
       "\n",
       "[954 rows x 5 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftxt[dftxt.Category != 'NaN']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Chief Executives', 'Chief Sustainability Officers',\n",
       "       'General and Operations Managers',\n",
       "       'Advertising and Promotions Managers', 'Marketing Managers',\n",
       "       'Sales Managers', 'Public Relations and Fundraising Managers',\n",
       "       'Administrative Services Managers',\n",
       "       'Computer and Information Systems Managers',\n",
       "       'Treasurers and Controllers',\n",
       "       'Financial Managers, Branch or Department',\n",
       "       'Industrial Production Managers',\n",
       "       'Quality Control Systems Managers',\n",
       "       'Geothermal Production Managers', 'Biomass Power Plant Managers',\n",
       "       'Purchasing Managers', 'Transportation Managers',\n",
       "       'Storage and Distribution Managers', 'Logistics Managers',\n",
       "       'Compensation and Benefits Managers', 'Human Resources Managers',\n",
       "       'Training and Development Managers',\n",
       "       'Nursery and Greenhouse Managers', 'Farm and Ranch Managers',\n",
       "       'Aquacultural Managers', 'Construction Managers',\n",
       "       'Education Administrators, Preschool and Childcare Center/Program',\n",
       "       'Education Administrators, Elementary and Secondary School',\n",
       "       'Education Administrators, Postsecondary',\n",
       "       'Distance Learning Coordinators',\n",
       "       'Fitness and Wellness Coordinators',\n",
       "       'Architectural and Engineering Managers',\n",
       "       'Biofuels/Biodiesel Technology and Product Development Managers',\n",
       "       'Food Service Managers', 'Funeral Service Managers',\n",
       "       'Gaming Managers', 'Lodging Managers',\n",
       "       'Medical and Health Services Managers',\n",
       "       'Natural Sciences Managers', 'Clinical Research Coordinators',\n",
       "       'Water Resource Specialists',\n",
       "       'Postmasters and Mail Superintendents',\n",
       "       'Property, Real Estate, and Community Association Managers',\n",
       "       'Social and Community Service Managers',\n",
       "       'Emergency Management Directors', 'Regulatory Affairs Managers',\n",
       "       'Compliance Managers', 'Investment Fund Managers',\n",
       "       'Supply Chain Managers', 'Security Managers',\n",
       "       'Loss Prevention Managers',\n",
       "       'Brownfield Redevelopment Specialists and Site Managers',\n",
       "       'Agents and Business Managers of Artists, Performers, and Athletes',\n",
       "       'Buyers and Purchasing Agents, Farm Products',\n",
       "       'Wholesale and Retail Buyers, Except Farm Products',\n",
       "       'Purchasing Agents, Except Wholesale, Retail, and Farm Products',\n",
       "       'Claims Examiners, Property and Casualty Insurance',\n",
       "       'Insurance Adjusters, Examiners, and Investigators',\n",
       "       'Insurance Appraisers, Auto Damage',\n",
       "       'Environmental Compliance Inspectors',\n",
       "       'Licensing Examiners and Inspectors',\n",
       "       'Equal Opportunity Representatives and Officers',\n",
       "       'Government Property Inspectors and Investigators', 'Coroners',\n",
       "       'Regulatory Affairs Specialists', 'Cost Estimators',\n",
       "       'Human Resources Specialists', 'Farm Labor Contractors',\n",
       "       'Labor Relations Specialists', 'Logisticians',\n",
       "       'Logistics Engineers', 'Logistics Analysts', 'Management Analysts',\n",
       "       'Meeting, Convention, and Event Planners', 'Fundraisers',\n",
       "       'Compensation, Benefits, and Job Analysis Specialists',\n",
       "       'Training and Development Specialists',\n",
       "       'Market Research Analysts and Marketing Specialists',\n",
       "       'Energy Auditors', 'Security Management Specialists',\n",
       "       'Customs Brokers', 'Business Continuity Planners',\n",
       "       'Sustainability Specialists', 'Online Merchants', 'Accountants',\n",
       "       'Auditors', 'Assessors', 'Appraisers, Real Estate',\n",
       "       'Budget Analysts', 'Credit Analysts', 'Financial Analysts',\n",
       "       'Personal Financial Advisors', 'Insurance Underwriters',\n",
       "       'Financial Examiners', 'Credit Counselors', 'Loan Counselors',\n",
       "       'Loan Officers',\n",
       "       'Tax Examiners and Collectors, and Revenue Agents',\n",
       "       'Tax Preparers', 'Risk Management Specialists',\n",
       "       'Fraud Examiners, Investigators and Analysts',\n",
       "       'Computer and Information Research Scientists',\n",
       "       'Computer Systems Analysts', 'Informatics Nurse Specialists',\n",
       "       'Information Security Analysts', 'Computer Programmers',\n",
       "       'Software Developers, Applications',\n",
       "       'Software Developers, Systems Software', 'Web Developers',\n",
       "       'Database Administrators',\n",
       "       'Network and Computer Systems Administrators',\n",
       "       'Computer Network Architects',\n",
       "       'Telecommunications Engineering Specialists',\n",
       "       'Computer User Support Specialists',\n",
       "       'Computer Network Support Specialists',\n",
       "       'Software Quality Assurance Engineers and Testers',\n",
       "       'Computer Systems Engineers/Architects', 'Web Administrators',\n",
       "       'Geospatial Information Scientists and Technologists',\n",
       "       'Geographic Information Systems Technicians',\n",
       "       'Database Architects', 'Business Intelligence Analysts',\n",
       "       'Information Technology Project Managers',\n",
       "       'Search Marketing Strategists', 'Video Game Designers',\n",
       "       'Document Management Specialists', 'Actuaries', 'Mathematicians',\n",
       "       'Operations Research Analysts', 'Statisticians',\n",
       "       'Biostatisticians', 'Clinical Data Managers',\n",
       "       'Mathematical Technicians',\n",
       "       'Architects, Except Landscape and Naval', 'Landscape Architects',\n",
       "       'Cartographers and Photogrammetrists', 'Surveyors',\n",
       "       'Geodetic Surveyors', 'Aerospace Engineers',\n",
       "       'Agricultural Engineers', 'Biomedical Engineers',\n",
       "       'Chemical Engineers', 'Civil Engineers',\n",
       "       'Transportation Engineers', 'Computer Hardware Engineers',\n",
       "       'Electrical Engineers', 'Electronics Engineers, Except Computer',\n",
       "       'Radio Frequency Identification Device Specialists',\n",
       "       'Environmental Engineers', 'Water/Wastewater Engineers',\n",
       "       'Industrial Safety and Health Engineers',\n",
       "       'Fire-Prevention and Protection Engineers',\n",
       "       'Product Safety Engineers', 'Industrial Engineers',\n",
       "       'Human Factors Engineers and Ergonomists', 'Marine Engineers',\n",
       "       'Marine Architects', 'Materials Engineers', 'Mechanical Engineers',\n",
       "       'Fuel Cell Engineers', 'Automotive Engineers',\n",
       "       'Mining and Geological Engineers, Including Mining Safety Engineers',\n",
       "       'Nuclear Engineers', 'Petroleum Engineers',\n",
       "       'Biochemical Engineers', 'Validation Engineers',\n",
       "       'Energy Engineers', 'Manufacturing Engineers',\n",
       "       'Mechatronics Engineers', 'Photonics Engineers',\n",
       "       'Robotics Engineers', 'Nanosystems Engineers',\n",
       "       'Wind Energy Engineers', 'Solar Energy Systems Engineers',\n",
       "       'Architectural Drafters', 'Civil Drafters', 'Electronic Drafters',\n",
       "       'Electrical Drafters', 'Mechanical Drafters',\n",
       "       'Aerospace Engineering and Operations Technicians',\n",
       "       'Civil Engineering Technicians',\n",
       "       'Electronics Engineering Technicians',\n",
       "       'Electrical Engineering Technicians',\n",
       "       'Electro-Mechanical Technicians', 'Robotics Technicians',\n",
       "       'Environmental Engineering Technicians',\n",
       "       'Industrial Engineering Technicians',\n",
       "       'Mechanical Engineering Technicians',\n",
       "       'Non-Destructive Testing Specialists',\n",
       "       'Electrical Engineering Technologists',\n",
       "       'Electromechanical Engineering Technologists',\n",
       "       'Electronics Engineering Technologists',\n",
       "       'Industrial Engineering Technologists',\n",
       "       'Manufacturing Engineering Technologists',\n",
       "       'Mechanical Engineering Technologists', 'Photonics Technicians',\n",
       "       'Manufacturing Production Technicians', 'Surveying Technicians',\n",
       "       'Mapping Technicians', 'Animal Scientists',\n",
       "       'Food Scientists and Technologists', 'Soil and Plant Scientists',\n",
       "       'Biologists', 'Biochemists and Biophysicists', 'Microbiologists',\n",
       "       'Zoologists and Wildlife Biologists', 'Bioinformatics Scientists',\n",
       "       'Molecular and Cellular Biologists', 'Geneticists',\n",
       "       'Soil and Water Conservationists', 'Range Managers',\n",
       "       'Park Naturalists', 'Foresters', 'Epidemiologists',\n",
       "       'Medical Scientists, Except Epidemiologists', 'Astronomers',\n",
       "       'Physicists', 'Atmospheric and Space Scientists', 'Chemists',\n",
       "       'Materials Scientists',\n",
       "       'Environmental Scientists and Specialists, Including Health',\n",
       "       'Climate Change Analysts', 'Environmental Restoration Planners',\n",
       "       'Industrial Ecologists',\n",
       "       'Geoscientists, Except Hydrologists and Geographers',\n",
       "       'Hydrologists', 'Remote Sensing Scientists and Technologists',\n",
       "       'Economists', 'Environmental Economists', 'Survey Researchers',\n",
       "       'School Psychologists', 'Clinical Psychologists',\n",
       "       'Counseling Psychologists',\n",
       "       'Industrial-Organizational Psychologists',\n",
       "       'Neuropsychologists and Clinical Neuropsychologists',\n",
       "       'Sociologists', 'Urban and Regional Planners', 'Anthropologists',\n",
       "       'Archeologists', 'Geographers', 'Historians',\n",
       "       'Political Scientists', 'Transportation Planners',\n",
       "       'Agricultural Technicians', 'Food Science Technicians',\n",
       "       'Biological Technicians', 'Chemical Technicians',\n",
       "       'Geophysical Data Technicians',\n",
       "       'Geological Sample Test Technicians',\n",
       "       'Nuclear Equipment Operation Technicians',\n",
       "       'Nuclear Monitoring Technicians',\n",
       "       'Social Science Research Assistants',\n",
       "       'City and Regional Planning Aides',\n",
       "       'Environmental Science and Protection Technicians, Including Health',\n",
       "       'Forensic Science Technicians',\n",
       "       'Forest and Conservation Technicians', 'Quality Control Analysts',\n",
       "       'Precision Agriculture Technicians', 'Remote Sensing Technicians',\n",
       "       'Substance Abuse and Behavioral Disorder Counselors',\n",
       "       'Educational, Guidance, School, and Vocational Counselors',\n",
       "       'Marriage and Family Therapists', 'Mental Health Counselors',\n",
       "       'Rehabilitation Counselors',\n",
       "       'Child, Family, and School Social Workers',\n",
       "       'Healthcare Social Workers',\n",
       "       'Mental Health and Substance Abuse Social Workers',\n",
       "       'Health Educators',\n",
       "       'Probation Officers and Correctional Treatment Specialists',\n",
       "       'Social and Human Service Assistants', 'Community Health Workers',\n",
       "       'Clergy', 'Directors, Religious Activities and Education',\n",
       "       'Lawyers', 'Judicial Law Clerks',\n",
       "       'Administrative Law Judges, Adjudicators, and Hearing Officers',\n",
       "       'Arbitrators, Mediators, and Conciliators',\n",
       "       'Judges, Magistrate Judges, and Magistrates',\n",
       "       'Paralegals and Legal Assistants', 'Court Reporters',\n",
       "       'Title Examiners, Abstractors, and Searchers',\n",
       "       'Business Teachers, Postsecondary',\n",
       "       'Computer Science Teachers, Postsecondary',\n",
       "       'Mathematical Science Teachers, Postsecondary',\n",
       "       'Architecture Teachers, Postsecondary',\n",
       "       'Engineering Teachers, Postsecondary',\n",
       "       'Agricultural Sciences Teachers, Postsecondary',\n",
       "       'Biological Science Teachers, Postsecondary',\n",
       "       'Forestry and Conservation Science Teachers, Postsecondary',\n",
       "       'Atmospheric, Earth, Marine, and Space Sciences Teachers, Postsecondary',\n",
       "       'Chemistry Teachers, Postsecondary',\n",
       "       'Environmental Science Teachers, Postsecondary',\n",
       "       'Physics Teachers, Postsecondary',\n",
       "       'Anthropology and Archeology Teachers, Postsecondary',\n",
       "       'Area, Ethnic, and Cultural Studies Teachers, Postsecondary',\n",
       "       'Economics Teachers, Postsecondary',\n",
       "       'Geography Teachers, Postsecondary',\n",
       "       'Political Science Teachers, Postsecondary',\n",
       "       'Psychology Teachers, Postsecondary',\n",
       "       'Sociology Teachers, Postsecondary',\n",
       "       'Health Specialties Teachers, Postsecondary',\n",
       "       'Nursing Instructors and Teachers, Postsecondary',\n",
       "       'Education Teachers, Postsecondary',\n",
       "       'Library Science Teachers, Postsecondary',\n",
       "       'Criminal Justice and Law Enforcement Teachers, Postsecondary',\n",
       "       'Law Teachers, Postsecondary',\n",
       "       'Social Work Teachers, Postsecondary',\n",
       "       'Art, Drama, and Music Teachers, Postsecondary',\n",
       "       'Communications Teachers, Postsecondary',\n",
       "       'English Language and Literature Teachers, Postsecondary',\n",
       "       'Foreign Language and Literature Teachers, Postsecondary',\n",
       "       'History Teachers, Postsecondary',\n",
       "       'Philosophy and Religion Teachers, Postsecondary',\n",
       "       'Graduate Teaching Assistants',\n",
       "       'Home Economics Teachers, Postsecondary',\n",
       "       'Recreation and Fitness Studies Teachers, Postsecondary',\n",
       "       'Vocational Education Teachers, Postsecondary',\n",
       "       'Preschool Teachers, Except Special Education',\n",
       "       'Kindergarten Teachers, Except Special Education',\n",
       "       'Elementary School Teachers, Except Special Education',\n",
       "       'Middle School Teachers, Except Special and Career/Technical Education',\n",
       "       'Career/Technical Education Teachers, Middle School',\n",
       "       'Secondary School Teachers, Except Special and Career/Technical Education',\n",
       "       'Career/Technical Education Teachers, Secondary School',\n",
       "       'Special Education Teachers, Kindergarten and Elementary School',\n",
       "       'Special Education Teachers, Middle School',\n",
       "       'Special Education Teachers, Secondary School',\n",
       "       'Adapted Physical Education Specialists',\n",
       "       'Adult Basic and Secondary Education and Literacy Teachers and Instructors',\n",
       "       'Self-Enrichment Education Teachers', 'Tutors', 'Archivists',\n",
       "       'Curators', 'Museum Technicians and Conservators', 'Librarians',\n",
       "       'Library Technicians',\n",
       "       'Audio-Visual and Multimedia Collections Specialists',\n",
       "       'Farm and Home Management Advisors', 'Instructional Coordinators',\n",
       "       'Instructional Designers and Technologists', 'Teacher Assistants',\n",
       "       'Art Directors', 'Craft Artists',\n",
       "       'Fine Artists, Including Painters, Sculptors, and Illustrators',\n",
       "       'Multimedia Artists and Animators',\n",
       "       'Commercial and Industrial Designers', 'Fashion Designers',\n",
       "       'Floral Designers', 'Graphic Designers', 'Interior Designers',\n",
       "       'Merchandise Displayers and Window Trimmers',\n",
       "       'Set and Exhibit Designers', 'Actors', 'Producers',\n",
       "       'Directors- Stage, Motion Pictures, Television, and Radio',\n",
       "       'Program Directors', 'Talent Directors',\n",
       "       'Technical Directors/Managers', 'Athletes and Sports Competitors',\n",
       "       'Coaches and Scouts',\n",
       "       'Umpires, Referees, and Other Sports Officials', 'Dancers',\n",
       "       'Choreographers', 'Music Directors',\n",
       "       'Music Composers and Arrangers', 'Singers',\n",
       "       'Musicians, Instrumental', 'Radio and Television Announcers',\n",
       "       'Public Address System and Other Announcers',\n",
       "       'Broadcast News Analysts', 'Reporters and Correspondents',\n",
       "       'Public Relations Specialists', 'Editors', 'Technical Writers',\n",
       "       'Copy Writers', 'Poets, Lyricists and Creative Writers',\n",
       "       'Interpreters and Translators',\n",
       "       'Audio and Video Equipment Technicians', 'Broadcast Technicians',\n",
       "       'Radio Operators', 'Sound Engineering Technicians',\n",
       "       'Photographers',\n",
       "       'Camera Operators, Television, Video, and Motion Picture',\n",
       "       'Film and Video Editors', 'Chiropractors', 'Dentists, General',\n",
       "       'Oral and Maxillofacial Surgeons', 'Orthodontists',\n",
       "       'Prosthodontists', 'Dietitians and Nutritionists', 'Optometrists',\n",
       "       'Pharmacists', 'Anesthesiologists',\n",
       "       'Family and General Practitioners', 'Internists, General',\n",
       "       'Obstetricians and Gynecologists', 'Pediatricians, General',\n",
       "       'Psychiatrists', 'Surgeons', 'Allergists and Immunologists',\n",
       "       'Dermatologists', 'Hospitalists', 'Neurologists',\n",
       "       'Nuclear Medicine Physicians', 'Ophthalmologists', 'Pathologists',\n",
       "       'Physical Medicine and Rehabilitation Physicians',\n",
       "       'Preventive Medicine Physicians', 'Radiologists',\n",
       "       'Sports Medicine Physicians', 'Urologists', 'Physician Assistants',\n",
       "       'Anesthesiologist Assistants', 'Podiatrists',\n",
       "       'Occupational Therapists',\n",
       "       'Low Vision Therapists, Orientation and Mobility Specialists, and Vision Rehabilitation Therapists',\n",
       "       'Physical Therapists', 'Radiation Therapists',\n",
       "       'Recreational Therapists', 'Art Therapists', 'Music Therapists',\n",
       "       'Respiratory Therapists', 'Speech-Language Pathologists',\n",
       "       'Exercise Physiologists', 'Veterinarians', 'Registered Nurses',\n",
       "       'Acute Care Nurses', 'Advanced Practice Psychiatric Nurses',\n",
       "       'Critical Care Nurses', 'Clinical Nurse Specialists',\n",
       "       'Nurse Anesthetists', 'Nurse Midwives', 'Nurse Practitioners',\n",
       "       'Audiologists', 'Acupuncturists', 'Naturopathic Physicians',\n",
       "       'Orthoptists', 'Medical and Clinical Laboratory Technologists',\n",
       "       'Cytogenetic Technologists', 'Cytotechnologists',\n",
       "       'Histotechnologists and Histologic Technicians',\n",
       "       'Medical and Clinical Laboratory Technicians', 'Dental Hygienists',\n",
       "       'Cardiovascular Technologists and Technicians',\n",
       "       'Diagnostic Medical Sonographers',\n",
       "       'Nuclear Medicine Technologists', 'Radiologic Technologists',\n",
       "       'Magnetic Resonance Imaging Technologists',\n",
       "       'Emergency Medical Technicians and Paramedics',\n",
       "       'Dietetic Technicians', 'Pharmacy Technicians',\n",
       "       'Psychiatric Technicians', 'Respiratory Therapy Technicians',\n",
       "       'Surgical Technologists',\n",
       "       'Veterinary Technologists and Technicians',\n",
       "       'Ophthalmic Medical Technicians',\n",
       "       'Licensed Practical and Licensed Vocational Nurses',\n",
       "       'Medical Records and Health Information Technicians',\n",
       "       'Opticians, Dispensing', 'Orthotists and Prosthetists',\n",
       "       'Hearing Aid Specialists', 'Neurodiagnostic Technologists',\n",
       "       'Ophthalmic Medical Technologists', 'Radiologic Technicians',\n",
       "       'Surgical Assistants',\n",
       "       'Occupational Health and Safety Specialists',\n",
       "       'Occupational Health and Safety Technicians', 'Athletic Trainers',\n",
       "       'Genetic Counselors', 'Midwives', 'Home Health Aides',\n",
       "       'Psychiatric Aides', 'Nursing Assistants',\n",
       "       'Occupational Therapy Assistants', 'Occupational Therapy Aides',\n",
       "       'Physical Therapist Assistants', 'Physical Therapist Aides',\n",
       "       'Massage Therapists', 'Dental Assistants', 'Medical Assistants',\n",
       "       'Medical Equipment Preparers', 'Medical Transcriptionists',\n",
       "       'Pharmacy Aides',\n",
       "       'Veterinary Assistants and Laboratory Animal Caretakers',\n",
       "       'Phlebotomists', 'Speech-Language Pathology Assistants',\n",
       "       'Endoscopy Technicians',\n",
       "       'First-Line Supervisors of Correctional Officers',\n",
       "       'First-Line Supervisors of Police and Detectives',\n",
       "       'Municipal Fire Fighting and Prevention Supervisors',\n",
       "       'Forest Fire Fighting and Prevention Supervisors',\n",
       "       'Municipal Firefighters', 'Forest Firefighters', 'Fire Inspectors',\n",
       "       'Fire Investigators',\n",
       "       'Forest Fire Inspectors and Prevention Specialists', 'Bailiffs',\n",
       "       'Correctional Officers and Jailers', 'Police Detectives',\n",
       "       'Police Identification and Records Officers',\n",
       "       'Criminal Investigators and Special Agents',\n",
       "       'Immigration and Customs Inspectors', 'Intelligence Analysts',\n",
       "       'Fish and Game Wardens', 'Parking Enforcement Workers',\n",
       "       'Police Patrol Officers', 'Sheriffs and Deputy Sheriffs',\n",
       "       'Transit and Railroad Police', 'Animal Control Workers',\n",
       "       'Private Detectives and Investigators',\n",
       "       'Gaming Surveillance Officers and Gaming Investigators',\n",
       "       'Security Guards', 'Crossing Guards',\n",
       "       'Lifeguards, Ski Patrol, and Other Recreational Protective Service Workers',\n",
       "       'Transportation Security Screeners',\n",
       "       'Retail Loss Prevention Specialists', 'Chefs and Head Cooks',\n",
       "       'First-Line Supervisors of Food Preparation and Serving Workers',\n",
       "       'Cooks, Fast Food', 'Cooks, Institution and Cafeteria',\n",
       "       'Cooks, Private Household', 'Cooks, Restaurant',\n",
       "       'Cooks, Short Order', 'Food Preparation Workers', 'Bartenders',\n",
       "       'Combined Food Preparation and Serving Workers, Including Fast Food',\n",
       "       'Counter Attendants, Cafeteria, Food Concession, and Coffee Shop',\n",
       "       'Baristas', 'Waiters and Waitresses',\n",
       "       'Food Servers, Nonrestaurant',\n",
       "       'Dining Room and Cafeteria Attendants and Bartender Helpers',\n",
       "       'Dishwashers',\n",
       "       'Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop',\n",
       "       'First-Line Supervisors of Housekeeping and Janitorial Workers',\n",
       "       'First-Line Supervisors of Landscaping, Lawn Service, and Groundskeeping Workers',\n",
       "       'Janitors and Cleaners, Except Maids and Housekeeping Cleaners',\n",
       "       'Maids and Housekeeping Cleaners', 'Pest Control Workers',\n",
       "       'Landscaping and Groundskeeping Workers',\n",
       "       'Pesticide Handlers, Sprayers, and Applicators, Vegetation',\n",
       "       'Tree Trimmers and Pruners', 'Gaming Supervisors',\n",
       "       'Slot Supervisors',\n",
       "       'First-Line Supervisors of Personal Service Workers',\n",
       "       'Spa Managers', 'Animal Trainers', 'Nonfarm Animal Caretakers',\n",
       "       'Gaming Dealers', 'Gaming and Sports Book Writers and Runners',\n",
       "       'Motion Picture Projectionists',\n",
       "       'Ushers, Lobby Attendants, and Ticket Takers',\n",
       "       'Amusement and Recreation Attendants', 'Costume Attendants',\n",
       "       'Locker Room, Coatroom, and Dressing Room Attendants', 'Embalmers',\n",
       "       'Funeral Attendants',\n",
       "       'Morticians, Undertakers, and Funeral Directors', 'Barbers',\n",
       "       'Hairdressers, Hairstylists, and Cosmetologists',\n",
       "       'Makeup Artists, Theatrical and Performance',\n",
       "       'Manicurists and Pedicurists', 'Shampooers',\n",
       "       'Skincare Specialists', 'Baggage Porters and Bellhops',\n",
       "       'Concierges', 'Tour Guides and Escorts', 'Travel Guides',\n",
       "       'Childcare Workers', 'Nannies', 'Personal Care Aides',\n",
       "       'Fitness Trainers and Aerobics Instructors', 'Recreation Workers',\n",
       "       'Residential Advisors',\n",
       "       'First-Line Supervisors of Retail Sales Workers',\n",
       "       'First-Line Supervisors of Non-Retail Sales Workers', 'Cashiers',\n",
       "       'Gaming Change Persons and Booth Cashiers',\n",
       "       'Counter and Rental Clerks', 'Parts Salespersons',\n",
       "       'Retail Salespersons', 'Advertising Sales Agents',\n",
       "       'Insurance Sales Agents',\n",
       "       'Sales Agents, Securities and Commodities',\n",
       "       'Sales Agents, Financial Services',\n",
       "       'Securities and Commodities Traders', 'Travel Agents',\n",
       "       'Energy Brokers',\n",
       "       'Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products',\n",
       "       'Solar Sales Representatives and Assessors',\n",
       "       'Sales Representatives, Wholesale and Manufacturing, Except Technical and Scientific Products',\n",
       "       'Demonstrators and Product Promoters', 'Models',\n",
       "       'Real Estate Brokers', 'Real Estate Sales Agents',\n",
       "       'Sales Engineers', 'Telemarketers',\n",
       "       'Door-To-Door Sales Workers, News and Street Vendors, and Related Workers',\n",
       "       'First-Line Supervisors of Office and Administrative Support Workers',\n",
       "       'Switchboard Operators, Including Answering Service',\n",
       "       'Telephone Operators', 'Bill and Account Collectors',\n",
       "       'Statement Clerks', 'Billing, Cost, and Rate Clerks',\n",
       "       'Bookkeeping, Accounting, and Auditing Clerks',\n",
       "       'Gaming Cage Workers', 'Payroll and Timekeeping Clerks',\n",
       "       'Procurement Clerks', 'Tellers', 'Brokerage Clerks',\n",
       "       'Correspondence Clerks', 'Court Clerks', 'Municipal Clerks',\n",
       "       'License Clerks', 'Credit Authorizers', 'Credit Checkers',\n",
       "       'Customer Service Representatives', 'Patient Representatives',\n",
       "       'Eligibility Interviewers, Government Programs', 'File Clerks',\n",
       "       'Hotel, Motel, and Resort Desk Clerks',\n",
       "       'Interviewers, Except Eligibility and Loan',\n",
       "       'Library Assistants, Clerical', 'Loan Interviewers and Clerks',\n",
       "       'New Accounts Clerks', 'Order Clerks',\n",
       "       'Human Resources Assistants, Except Payroll and Timekeeping',\n",
       "       'Receptionists and Information Clerks',\n",
       "       'Reservation and Transportation Ticket Agents and Travel Clerks',\n",
       "       'Cargo and Freight Agents', 'Freight Forwarders',\n",
       "       'Couriers and Messengers',\n",
       "       'Police, Fire, and Ambulance Dispatchers',\n",
       "       'Dispatchers, Except Police, Fire, and Ambulance',\n",
       "       'Meter Readers, Utilities', 'Postal Service Clerks',\n",
       "       'Postal Service Mail Carriers',\n",
       "       'Postal Service Mail Sorters, Processors, and Processing Machine Operators',\n",
       "       'Production, Planning, and Expediting Clerks',\n",
       "       'Shipping, Receiving, and Traffic Clerks',\n",
       "       'Stock Clerks, Sales Floor', 'Marking Clerks',\n",
       "       'Stock Clerks- Stockroom, Warehouse, or Storage Yard',\n",
       "       'Order Fillers, Wholesale and Retail Sales',\n",
       "       'Weighers, Measurers, Checkers, and Samplers, Recordkeeping',\n",
       "       'Executive Secretaries and Executive Administrative Assistants',\n",
       "       'Legal Secretaries', 'Medical Secretaries',\n",
       "       'Secretaries and Administrative Assistants, Except Legal, Medical, and Executive',\n",
       "       'Computer Operators', 'Data Entry Keyers',\n",
       "       'Word Processors and Typists', 'Desktop Publishers',\n",
       "       'Insurance Claims Clerks', 'Insurance Policy Processing Clerks',\n",
       "       'Mail Clerks and Mail Machine Operators, Except Postal Service',\n",
       "       'Office Clerks, General',\n",
       "       'Office Machine Operators, Except Computer',\n",
       "       'Proofreaders and Copy Markers', 'Statistical Assistants',\n",
       "       'Bioinformatics Technicians',\n",
       "       'First-Line Supervisors of Logging Workers',\n",
       "       'First-Line Supervisors of Aquacultural Workers',\n",
       "       'First-Line Supervisors of Agricultural Crop and Horticultural Workers',\n",
       "       'First-Line Supervisors of Animal Husbandry and Animal Care Workers',\n",
       "       'Agricultural Inspectors', 'Animal Breeders',\n",
       "       'Graders and Sorters, Agricultural Products',\n",
       "       'Agricultural Equipment Operators', 'Nursery Workers',\n",
       "       'Farmworkers and Laborers, Crop',\n",
       "       'Farmworkers, Farm, Ranch, and Aquacultural Animals',\n",
       "       'Fishers and Related Fishing Workers', 'Hunters and Trappers',\n",
       "       'Forest and Conservation Workers', 'Fallers',\n",
       "       'Logging Equipment Operators', 'Log Graders and Scalers',\n",
       "       'First-Line Supervisors of Construction Trades and Extraction Workers',\n",
       "       'Solar Energy Installation Managers', 'Boilermakers',\n",
       "       'Brickmasons and Blockmasons', 'Stonemasons',\n",
       "       'Construction Carpenters', 'Rough Carpenters', 'Carpet Installers',\n",
       "       'Floor Layers, Except Carpet, Wood, and Hard Tiles',\n",
       "       'Floor Sanders and Finishers', 'Tile and Marble Setters',\n",
       "       'Cement Masons and Concrete Finishers',\n",
       "       'Terrazzo Workers and Finishers', 'Construction Laborers',\n",
       "       'Paving, Surfacing, and Tamping Equipment Operators',\n",
       "       'Pile-Driver Operators',\n",
       "       'Operating Engineers and Other Construction Equipment Operators',\n",
       "       'Drywall and Ceiling Tile Installers', 'Tapers', 'Electricians',\n",
       "       'Glaziers', 'Insulation Workers, Floor, Ceiling, and Wall',\n",
       "       'Insulation Workers, Mechanical',\n",
       "       'Painters, Construction and Maintenance', 'Paperhangers',\n",
       "       'Pipelayers', 'Pipe Fitters and Steamfitters', 'Plumbers',\n",
       "       'Plasterers and Stucco Masons',\n",
       "       'Reinforcing Iron and Rebar Workers', 'Roofers',\n",
       "       'Sheet Metal Workers', 'Structural Iron and Steel Workers',\n",
       "       'Solar Photovoltaic Installers',\n",
       "       'Helpers--Brickmasons, Blockmasons, Stonemasons, and Tile and Marble Setters',\n",
       "       'Helpers--Carpenters', 'Helpers--Electricians',\n",
       "       'Helpers--Painters, Paperhangers, Plasterers, and Stucco Masons',\n",
       "       'Helpers--Pipelayers, Plumbers, Pipefitters, and Steamfitters',\n",
       "       'Helpers--Roofers', 'Construction and Building Inspectors',\n",
       "       'Elevator Installers and Repairers', 'Fence Erectors',\n",
       "       'Hazardous Materials Removal Workers',\n",
       "       'Highway Maintenance Workers',\n",
       "       'Rail-Track Laying and Maintenance Equipment Operators',\n",
       "       'Septic Tank Servicers and Sewer Pipe Cleaners',\n",
       "       'Segmental Pavers', 'Solar Thermal Installers and Technicians',\n",
       "       'Weatherization Installers and Technicians',\n",
       "       'Derrick Operators, Oil and Gas',\n",
       "       'Rotary Drill Operators, Oil and Gas',\n",
       "       'Service Unit Operators, Oil, Gas, and Mining',\n",
       "       'Earth Drillers, Except Oil and Gas',\n",
       "       'Explosives Workers, Ordnance Handling Experts, and Blasters',\n",
       "       'Continuous Mining Machine Operators',\n",
       "       'Mine Cutting and Channeling Machine Operators',\n",
       "       'Rock Splitters, Quarry', 'Roof Bolters, Mining',\n",
       "       'Roustabouts, Oil and Gas', 'Helpers--Extraction Workers',\n",
       "       'First-Line Supervisors of Mechanics, Installers, and Repairers',\n",
       "       'Computer, Automated Teller, and Office Machine Repairers',\n",
       "       'Radio, Cellular, and Tower Equipment Installers and Repairers',\n",
       "       'Radio Mechanics',\n",
       "       'Telecommunications Equipment Installers and Repairers, Except Line Installers',\n",
       "       'Avionics Technicians',\n",
       "       'Electric Motor, Power Tool, and Related Repairers',\n",
       "       'Electrical and Electronics Installers and Repairers, Transportation Equipment',\n",
       "       'Electrical and Electronics Repairers, Commercial and Industrial Equipment',\n",
       "       'Electrical and Electronics Repairers, Powerhouse, Substation, and Relay',\n",
       "       'Electronic Equipment Installers and Repairers, Motor Vehicles',\n",
       "       'Electronic Home Entertainment Equipment Installers and Repairers',\n",
       "       'Security and Fire Alarm Systems Installers',\n",
       "       'Aircraft Mechanics and Service Technicians',\n",
       "       'Automotive Body and Related Repairers',\n",
       "       'Automotive Glass Installers and Repairers',\n",
       "       'Automotive Master Mechanics', 'Automotive Specialty Technicians',\n",
       "       'Bus and Truck Mechanics and Diesel Engine Specialists',\n",
       "       'Farm Equipment Mechanics and Service Technicians',\n",
       "       'Mobile Heavy Equipment Mechanics, Except Engines',\n",
       "       'Rail Car Repairers',\n",
       "       'Motorboat Mechanics and Service Technicians',\n",
       "       'Motorcycle Mechanics',\n",
       "       'Outdoor Power Equipment and Other Small Engine Mechanics',\n",
       "       'Bicycle Repairers', 'Recreational Vehicle Service Technicians',\n",
       "       'Tire Repairers and Changers', 'Mechanical Door Repairers',\n",
       "       'Control and Valve Installers and Repairers, Except Mechanical Door',\n",
       "       'Heating and Air Conditioning Mechanics and Installers',\n",
       "       'Refrigeration Mechanics and Installers',\n",
       "       'Home Appliance Repairers', 'Industrial Machinery Mechanics',\n",
       "       'Maintenance Workers, Machinery', 'Millwrights',\n",
       "       'Refractory Materials Repairers, Except Brickmasons',\n",
       "       'Electrical Power-Line Installers and Repairers',\n",
       "       'Telecommunications Line Installers and Repairers',\n",
       "       'Camera and Photographic Equipment Repairers',\n",
       "       'Medical Equipment Repairers',\n",
       "       'Musical Instrument Repairers and Tuners', 'Watch Repairers',\n",
       "       'Maintenance and Repair Workers, General',\n",
       "       'Wind Turbine Service Technicians',\n",
       "       'Coin, Vending, and Amusement Machine Servicers and Repairers',\n",
       "       'Commercial Divers', 'Fabric Menders, Except Garment',\n",
       "       'Locksmiths and Safe Repairers',\n",
       "       'Manufactured Building and Mobile Home Installers', 'Riggers',\n",
       "       'Signal and Track Switch Repairers',\n",
       "       'Helpers--Installation, Maintenance, and Repair Workers',\n",
       "       'Geothermal Technicians',\n",
       "       'First-Line Supervisors of Production and Operating Workers',\n",
       "       'Aircraft Structure, Surfaces, Rigging, and Systems Assemblers',\n",
       "       'Coil Winders, Tapers, and Finishers',\n",
       "       'Electrical and Electronic Equipment Assemblers',\n",
       "       'Electromechanical Equipment Assemblers',\n",
       "       'Engine and Other Machine Assemblers',\n",
       "       'Structural Metal Fabricators and Fitters',\n",
       "       'Fiberglass Laminators and Fabricators', 'Team Assemblers',\n",
       "       'Timing Device Assemblers and Adjusters', 'Bakers',\n",
       "       'Butchers and Meat Cutters',\n",
       "       'Meat, Poultry, and Fish Cutters and Trimmers',\n",
       "       'Slaughterers and Meat Packers',\n",
       "       'Food and Tobacco Roasting, Baking, and Drying Machine Operators and Tenders',\n",
       "       'Food Batchmakers', 'Food Cooking Machine Operators and Tenders',\n",
       "       'Computer-Controlled Machine Tool Operators, Metal and Plastic',\n",
       "       'Computer Numerically Controlled Machine Tool Programmers, Metal and Plastic',\n",
       "       'Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic',\n",
       "       'Forging Machine Setters, Operators, and Tenders, Metal and Plastic',\n",
       "       'Rolling Machine Setters, Operators, and Tenders, Metal and Plastic',\n",
       "       'Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic',\n",
       "       'Drilling and Boring Machine Tool Setters, Operators, and Tenders, Metal and Plastic',\n",
       "       'Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic',\n",
       "       'Lathe and Turning Machine Tool Setters, Operators, and Tenders, Metal and Plastic',\n",
       "       'Milling and Planing Machine Setters, Operators, and Tenders, Metal and Plastic',\n",
       "       'Machinists', 'Metal-Refining Furnace Operators and Tenders',\n",
       "       'Pourers and Casters, Metal', 'Model Makers, Metal and Plastic',\n",
       "       'Patternmakers, Metal and Plastic', 'Foundry Mold and Coremakers',\n",
       "       'Molding, Coremaking, and Casting Machine Setters, Operators, and Tenders, Metal and Plastic',\n",
       "       'Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic',\n",
       "       'Tool and Die Makers', 'Welders, Cutters, and Welder Fitters',\n",
       "       'Solderers and Brazers',\n",
       "       'Welding, Soldering, and Brazing Machine Setters, Operators, and Tenders',\n",
       "       'Heat Treating Equipment Setters, Operators, and Tenders, Metal and Plastic',\n",
       "       'Layout Workers, Metal and Plastic',\n",
       "       'Plating and Coating Machine Setters, Operators, and Tenders, Metal and Plastic',\n",
       "       'Tool Grinders, Filers, and Sharpeners',\n",
       "       'Prepress Technicians and Workers', 'Printing Press Operators',\n",
       "       'Print Binding and Finishing Workers',\n",
       "       'Laundry and Dry-Cleaning Workers',\n",
       "       'Pressers, Textile, Garment, and Related Materials',\n",
       "       'Sewing Machine Operators',\n",
       "       'Shoe and Leather Workers and Repairers',\n",
       "       'Shoe Machine Operators and Tenders', 'Sewers, Hand',\n",
       "       'Tailors, Dressmakers, and Custom Sewers',\n",
       "       'Textile Bleaching and Dyeing Machine Operators and Tenders',\n",
       "       'Textile Cutting Machine Setters, Operators, and Tenders',\n",
       "       'Textile Knitting and Weaving Machine Setters, Operators, and Tenders',\n",
       "       'Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders',\n",
       "       'Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers',\n",
       "       'Fabric and Apparel Patternmakers', 'Upholsterers',\n",
       "       'Cabinetmakers and Bench Carpenters', 'Furniture Finishers',\n",
       "       'Model Makers, Wood', 'Patternmakers, Wood',\n",
       "       'Sawing Machine Setters, Operators, and Tenders, Wood',\n",
       "       'Woodworking Machine Setters, Operators, and Tenders, Except Sawing',\n",
       "       'Nuclear Power Reactor Operators',\n",
       "       'Power Distributors and Dispatchers', 'Power Plant Operators',\n",
       "       'Stationary Engineers and Boiler Operators',\n",
       "       'Water and Wastewater Treatment Plant and System Operators',\n",
       "       'Chemical Plant and System Operators', 'Gas Plant Operators',\n",
       "       'Petroleum Pump System Operators, Refinery Operators, and Gaugers',\n",
       "       'Biomass Plant Technicians',\n",
       "       'Chemical Equipment Operators and Tenders',\n",
       "       'Separating, Filtering, Clarifying, Precipitating, and Still Machine Setters, Operators, and Tenders',\n",
       "       'Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders',\n",
       "       'Grinding and Polishing Workers, Hand',\n",
       "       'Mixing and Blending Machine Setters, Operators, and Tenders',\n",
       "       'Cutters and Trimmers, Hand',\n",
       "       'Cutting and Slicing Machine Setters, Operators, and Tenders',\n",
       "       'Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders',\n",
       "       'Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders',\n",
       "       'Inspectors, Testers, Sorters, Samplers, and Weighers', 'Jewelers',\n",
       "       'Gem and Diamond Workers', 'Precious Metal Workers',\n",
       "       'Dental Laboratory Technicians', 'Medical Appliance Technicians',\n",
       "       'Ophthalmic Laboratory Technicians',\n",
       "       'Packaging and Filling Machine Operators and Tenders',\n",
       "       'Coating, Painting, and Spraying Machine Setters, Operators, and Tenders',\n",
       "       'Painters, Transportation Equipment',\n",
       "       'Painting, Coating, and Decorating Workers',\n",
       "       'Semiconductor Processors',\n",
       "       'Photographic Process Workers and Processing Machine Operators',\n",
       "       'Adhesive Bonding Machine Operators and Tenders',\n",
       "       'Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders',\n",
       "       'Cooling and Freezing Equipment Operators and Tenders',\n",
       "       'Etchers and Engravers',\n",
       "       'Stone Cutters and Carvers, Manufacturing',\n",
       "       'Glass Blowers, Molders, Benders, and Finishers',\n",
       "       'Potters, Manufacturing', 'Molding and Casting Workers',\n",
       "       'Paper Goods Machine Setters, Operators, and Tenders',\n",
       "       'Tire Builders', 'Helpers--Production Workers',\n",
       "       'Recycling and Reclamation Workers',\n",
       "       'Aircraft Cargo Handling Supervisors',\n",
       "       'First-Line Supervisors of Helpers, Laborers, and Material Movers, Hand',\n",
       "       'Recycling Coordinators',\n",
       "       'First-Line Supervisors of Transportation and Material-Moving Machine and Vehicle Operators',\n",
       "       'Airline Pilots, Copilots, and Flight Engineers',\n",
       "       'Commercial Pilots', 'Air Traffic Controllers',\n",
       "       'Airfield Operations Specialists', 'Flight Attendants',\n",
       "       'Ambulance Drivers and Attendants, Except Emergency Medical Technicians',\n",
       "       'Bus Drivers, Transit and Intercity',\n",
       "       'Bus Drivers, School or Special Client', 'Driver/Sales Workers',\n",
       "       'Heavy and Tractor-Trailer Truck Drivers',\n",
       "       'Light Truck or Delivery Services Drivers',\n",
       "       'Taxi Drivers and Chauffeurs', 'Locomotive Engineers',\n",
       "       'Locomotive Firers',\n",
       "       'Rail Yard Engineers, Dinkey Operators, and Hostlers',\n",
       "       'Railroad Brake, Signal, and Switch Operators',\n",
       "       'Railroad Conductors and Yardmasters',\n",
       "       'Subway and Streetcar Operators', 'Sailors and Marine Oilers',\n",
       "       'Ship and Boat Captains', 'Mates- Ship, Boat, and Barge',\n",
       "       'Pilots, Ship', 'Motorboat Operators', 'Ship Engineers',\n",
       "       'Bridge and Lock Tenders', 'Parking Lot Attendants',\n",
       "       'Automotive and Watercraft Service Attendants',\n",
       "       'Traffic Technicians', 'Aviation Inspectors',\n",
       "       'Transportation Vehicle, Equipment and Systems Inspectors, Except Aviation',\n",
       "       'Freight and Cargo Inspectors',\n",
       "       'Transportation Attendants, Except Flight Attendants',\n",
       "       'Conveyor Operators and Tenders', 'Crane and Tower Operators',\n",
       "       'Dredge Operators',\n",
       "       'Excavating and Loading Machine and Dragline Operators',\n",
       "       'Loading Machine Operators, Underground Mining',\n",
       "       'Hoist and Winch Operators',\n",
       "       'Industrial Truck and Tractor Operators',\n",
       "       'Cleaners of Vehicles and Equipment',\n",
       "       'Laborers and Freight, Stock, and Material Movers, Hand',\n",
       "       'Machine Feeders and Offbearers', 'Packers and Packagers, Hand',\n",
       "       'Gas Compressor and Gas Pumping Station Operators',\n",
       "       'Pump Operators, Except Wellhead Pumpers', 'Wellhead Pumpers',\n",
       "       'Refuse and Recyclable Material Collectors',\n",
       "       'Mine Shuttle Car Operators', 'Tank Car, Truck, and Ship Loaders'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Unititle = true.Title.unique()\n",
    "Unititle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chief Executives</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chief Sustainability Officers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>General and Operations Managers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Advertising and Promotions Managers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Marketing Managers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>Pump Operators, Except Wellhead Pumpers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>Wellhead Pumpers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951</th>\n",
       "      <td>Refuse and Recyclable Material Collectors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>952</th>\n",
       "      <td>Mine Shuttle Car Operators</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>Tank Car, Truck, and Ship Loaders</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>954 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         title\n",
       "0                             Chief Executives\n",
       "1                Chief Sustainability Officers\n",
       "2              General and Operations Managers\n",
       "3          Advertising and Promotions Managers\n",
       "4                           Marketing Managers\n",
       "..                                         ...\n",
       "949    Pump Operators, Except Wellhead Pumpers\n",
       "950                           Wellhead Pumpers\n",
       "951  Refuse and Recyclable Material Collectors\n",
       "952                 Mine Shuttle Car Operators\n",
       "953          Tank Car, Truck, and Ship Loaders\n",
       "\n",
       "[954 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(Unititle, columns = ['title'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Title</th>\n",
       "      <th>Element_Name</th>\n",
       "      <th>Category</th>\n",
       "      <th>Data Value</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chief Executives</td>\n",
       "      <td>Public Speaking</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.39</td>\n",
       "      <td>07/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chief Executives</td>\n",
       "      <td>Public Speaking</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.02</td>\n",
       "      <td>07/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chief Executives</td>\n",
       "      <td>Public Speaking</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.40</td>\n",
       "      <td>07/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chief Executives</td>\n",
       "      <td>Public Speaking</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.36</td>\n",
       "      <td>07/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chief Executives</td>\n",
       "      <td>Public Speaking</td>\n",
       "      <td>4.0</td>\n",
       "      <td>52.55</td>\n",
       "      <td>07/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321585</th>\n",
       "      <td>Tank Car, Truck, and Ship Loaders</td>\n",
       "      <td>Work Schedules</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.28</td>\n",
       "      <td>12/2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321586</th>\n",
       "      <td>Tank Car, Truck, and Ship Loaders</td>\n",
       "      <td>Duration of Typical Work Week</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.76</td>\n",
       "      <td>12/2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321587</th>\n",
       "      <td>Tank Car, Truck, and Ship Loaders</td>\n",
       "      <td>Duration of Typical Work Week</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12/2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321588</th>\n",
       "      <td>Tank Car, Truck, and Ship Loaders</td>\n",
       "      <td>Duration of Typical Work Week</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.54</td>\n",
       "      <td>12/2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321589</th>\n",
       "      <td>Tank Car, Truck, and Ship Loaders</td>\n",
       "      <td>Duration of Typical Work Week</td>\n",
       "      <td>3.0</td>\n",
       "      <td>76.46</td>\n",
       "      <td>12/2006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>321590 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Title                   Element_Name  \\\n",
       "0                        Chief Executives                Public Speaking   \n",
       "1                        Chief Executives                Public Speaking   \n",
       "2                        Chief Executives                Public Speaking   \n",
       "3                        Chief Executives                Public Speaking   \n",
       "4                        Chief Executives                Public Speaking   \n",
       "...                                   ...                            ...   \n",
       "321585  Tank Car, Truck, and Ship Loaders                 Work Schedules   \n",
       "321586  Tank Car, Truck, and Ship Loaders  Duration of Typical Work Week   \n",
       "321587  Tank Car, Truck, and Ship Loaders  Duration of Typical Work Week   \n",
       "321588  Tank Car, Truck, and Ship Loaders  Duration of Typical Work Week   \n",
       "321589  Tank Car, Truck, and Ship Loaders  Duration of Typical Work Week   \n",
       "\n",
       "        Category  Data Value     Date  \n",
       "0            NaN        3.39  07/2014  \n",
       "1            1.0        6.02  07/2014  \n",
       "2            2.0       12.40  07/2014  \n",
       "3            3.0       23.36  07/2014  \n",
       "4            4.0       52.55  07/2014  \n",
       "...          ...         ...      ...  \n",
       "321585       3.0        1.28  12/2006  \n",
       "321586       NaN        2.76  12/2006  \n",
       "321587       1.0        0.00  12/2006  \n",
       "321588       2.0       23.54  12/2006  \n",
       "321589       3.0       76.46  12/2006  \n",
       "\n",
       "[321590 rows x 5 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true.rename(columns={'Element Name':'Element_Name'}, inplace=True)\n",
    "\n",
    "true\n",
    "#changes column name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Public Speaking', 'Telephone', 'Electronic Mail',\n",
       "       'Letters and Memos', 'Face-to-Face Discussions',\n",
       "       'Contact With Others', 'Work With Work Group or Team',\n",
       "       'Deal With External Customers', 'Coordinate or Lead Others',\n",
       "       \"Responsible for Others' Health and Safety\",\n",
       "       'Responsibility for Outcomes and Results',\n",
       "       'Frequency of Conflict Situations',\n",
       "       'Deal With Unpleasant or Angry People',\n",
       "       'Deal With Physically Aggressive People',\n",
       "       'Indoors, Environmentally Controlled',\n",
       "       'Indoors, Not Environmentally Controlled',\n",
       "       'Outdoors, Exposed to Weather', 'Outdoors, Under Cover',\n",
       "       'In an Open Vehicle or Equipment',\n",
       "       'In an Enclosed Vehicle or Equipment', 'Physical Proximity',\n",
       "       'Sounds, Noise Levels Are Distracting or Uncomfortable',\n",
       "       'Very Hot or Cold Temperatures',\n",
       "       'Extremely Bright or Inadequate Lighting',\n",
       "       'Exposed to Contaminants', 'Cramped Work Space, Awkward Positions',\n",
       "       'Exposed to Whole Body Vibration', 'Exposed to Radiation',\n",
       "       'Exposed to Disease or Infections', 'Exposed to High Places',\n",
       "       'Exposed to Hazardous Conditions',\n",
       "       'Exposed to Hazardous Equipment',\n",
       "       'Exposed to Minor Burns, Cuts, Bites, or Stings',\n",
       "       'Spend Time Sitting', 'Spend Time Standing',\n",
       "       'Spend Time Climbing Ladders, Scaffolds, or Poles',\n",
       "       'Spend Time Walking and Running',\n",
       "       'Spend Time Kneeling, Crouching, Stooping, or Crawling',\n",
       "       'Spend Time Keeping or Regaining Balance',\n",
       "       'Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls',\n",
       "       'Spend Time Bending or Twisting the Body',\n",
       "       'Spend Time Making Repetitive Motions',\n",
       "       'Wear Common Protective or Safety Equipment such as Safety Shoes, Glasses, Gloves, Hearing Protection, Hard Hats, or Life Jackets',\n",
       "       'Wear Specialized Protective or Safety Equipment such as Breathing Apparatus, Safety Harness, Full Protection Suits, or Radiation Protection',\n",
       "       'Consequence of Error',\n",
       "       'Impact of Decisions on Co-workers or Company Results',\n",
       "       'Frequency of Decision Making', 'Freedom to Make Decisions',\n",
       "       'Degree of Automation', 'Importance of Being Exact or Accurate',\n",
       "       'Importance of Repeating Same Tasks',\n",
       "       'Structured versus Unstructured Work', 'Level of Competition',\n",
       "       'Time Pressure', 'Pace Determined by Speed of Equipment',\n",
       "       'Work Schedules', 'Duration of Typical Work Week'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "element = true.Element_Name.unique()\n",
    "element\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "element_list = pd.DataFrame(element, columns = ['Element'])\n",
    "type(element_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Column_A'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\Documents\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2645\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2646\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2647\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Column_A'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-60764009587c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m#     print(df['Column_A'][Q],)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mQ\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m      \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Column_A'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mQ\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Documents\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2798\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2799\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2800\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2801\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2802\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Documents\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2646\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2647\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2648\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2650\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Column_A'"
     ]
    }
   ],
   "source": [
    "#for Q in df.index: \n",
    "#     print(df['Column_A'][Q],)\n",
    "#for Q in df.index: \n",
    "    # x= (df['Column_A'][Q],)\n",
    "#print(type(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for job in df[\"title\"]:\n",
    "    for element in element_list[\"Element\"]:\n",
    "    \n",
    "    #print(str(job)+\", \"+(str(element)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create output data frame \n",
    "score_by_title_element = pd.DataFrame(columns = [\"title\",\"element\",\"score\"])\n",
    "\n",
    "temp = []\n",
    "\n",
    "for i, job in enumerate( df[\"title\"]):\n",
    "    for element in element_list[\"Element\"]:                             # selecting rows based on condit\n",
    "        result_df = true[(true['Element_Name'] == element ) & \n",
    "                (true['Title'] == job)]\n",
    "        #importance = rslt_df['Data Value']\n",
    "        #print(result_df)\n",
    "        #print(result_df.iloc[1].loc[\"Data Value\"])\n",
    "        try:\n",
    "            #percentage of days doing the action days out of the year so exponetial system  -- multiple by days... number of week day... by percent\n",
    "            #other 3 thing\n",
    "            score = 0 * result_df.iloc[1].loc[\"Data Value\"] + .043809*result_df.iloc[2].loc[\"Data Value\"]+ .12380952*result_df.iloc[3].loc[\"Data Value\"]+ .6*result_df.iloc[4].loc[\"Data Value\"]+ result_df.iloc[5].loc[\"Data Value\"]\n",
    "            new_row = {\"title\":job, \"element\": element, \"score\": score}\n",
    "            score_by_title_element.append(new_row, ignore_index=True)\n",
    "        except IndexError: # catch the error\n",
    "             pass\n",
    "            #if IndexError:\n",
    "          #  score = -1 * result_df.iloc[1].loc[\"Data Value\"] + result_df.iloc[3].loc[\"Data Value\"]\n",
    "        \n",
    "        #append to the output a row with of form [job,element,score]\n",
    "        temp.append([job, element, score])\n",
    "    if i==2:\n",
    "        break\n",
    "        \n",
    "        \n",
    "        #LabeledScore = pd.DataFrame(job, element, score, columns = ['title', 'Element Name', 'Score'])\n",
    "        \n",
    "       # new_row = {\"title\":job, \"element\": element, \"score\": score}\n",
    "       # score_by_title_element.append(new_row, ignore_index=True)\n",
    "\n",
    "   \n",
    "    \n",
    "score_by_title_element2014 = pd.DataFrame(temp,columns=['Title','Description','Score'])\n",
    "type(score_by_title_element2014)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               Title                            Description  \\\n",
      "0                   Chief Executives                        Public Speaking   \n",
      "1                   Chief Executives                              Telephone   \n",
      "2                   Chief Executives                        Electronic Mail   \n",
      "3                   Chief Executives                      Letters and Memos   \n",
      "4                   Chief Executives               Face-to-Face Discussions   \n",
      "..                               ...                                    ...   \n",
      "166  General and Operations Managers                   Level of Competition   \n",
      "167  General and Operations Managers                          Time Pressure   \n",
      "168  General and Operations Managers  Pace Determined by Speed of Equipment   \n",
      "169  General and Operations Managers                         Work Schedules   \n",
      "170  General and Operations Managers          Duration of Typical Work Week   \n",
      "\n",
      "          Score  \n",
      "0     40.635422  \n",
      "1    100.000000  \n",
      "2    100.000000  \n",
      "3     71.807619  \n",
      "4     99.208000  \n",
      "..          ...  \n",
      "166   47.009578  \n",
      "167   66.771524  \n",
      "168   11.237366  \n",
      "169   11.237366  \n",
      "170   11.237366  \n",
      "\n",
      "[171 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "print(score_by_title_element2014)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# selecting rows based on condition \n",
    "#rslt_df = dataframe[(dataframe['Age'] == 21) & \n",
    "#          dataframe['Stream'].isin(options)] \n",
    "rslt_df = dataframe[(dataframe['Age'] == 21) & \n",
    "          dataframe['Stream'].isin(options)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(score_by_title_element)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_by_title_element.to_excel(r'LaborScores2014.xlsx', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['11-1011.00', '11-1011.03', '11-1021.00', '11-2011.00',\n",
       "       '11-2021.00', '11-2022.00', '11-2031.00', '11-3011.00',\n",
       "       '11-3021.00', '11-3031.01', '11-3031.02', '11-3051.00',\n",
       "       '11-3051.01', '11-3051.02', '11-3051.03', '11-3051.04',\n",
       "       '11-3051.06', '11-3061.00', '11-3071.01', '11-3071.02',\n",
       "       '11-3071.03', '11-3111.00', '11-3121.00', '11-3131.00',\n",
       "       '11-9013.01', '11-9013.02', '11-9013.03', '11-9021.00',\n",
       "       '11-9031.00', '11-9032.00', '11-9033.00', '11-9039.01',\n",
       "       '11-9039.02', '11-9041.00', '11-9041.01', '11-9051.00',\n",
       "       '11-9061.00', '11-9071.00', '11-9081.00', '11-9111.00',\n",
       "       '11-9121.00', '11-9121.01', '11-9121.02', '11-9131.00',\n",
       "       '11-9141.00', '11-9151.00', '11-9161.00', '11-9199.01',\n",
       "       '11-9199.02', '11-9199.03', '11-9199.04', '11-9199.07',\n",
       "       '11-9199.08', '11-9199.09', '11-9199.10', '11-9199.11',\n",
       "       '13-1011.00', '13-1021.00', '13-1022.00', '13-1023.00',\n",
       "       '13-1031.01', '13-1031.02', '13-1032.00', '13-1041.01',\n",
       "       '13-1041.02', '13-1041.03', '13-1041.04', '13-1041.06',\n",
       "       '13-1041.07', '13-1051.00', '13-1071.00', '13-1074.00',\n",
       "       '13-1075.00', '13-1081.00', '13-1081.01', '13-1081.02',\n",
       "       '13-1111.00', '13-1121.00', '13-1131.00', '13-1141.00',\n",
       "       '13-1151.00', '13-1161.00', '13-1199.01', '13-1199.02',\n",
       "       '13-1199.03', '13-1199.04', '13-1199.05', '13-1199.06',\n",
       "       '13-2011.01', '13-2011.02', '13-2021.01', '13-2021.02',\n",
       "       '13-2031.00', '13-2041.00', '13-2051.00', '13-2052.00',\n",
       "       '13-2053.00', '13-2061.00', '13-2071.00', '13-2071.01',\n",
       "       '13-2072.00', '13-2081.00', '13-2082.00', '13-2099.01',\n",
       "       '13-2099.02', '13-2099.04', '15-1111.00', '15-1121.00',\n",
       "       '15-1121.01', '15-1122.00', '15-1131.00', '15-1132.00',\n",
       "       '15-1133.00', '15-1134.00', '15-1141.00', '15-1142.00',\n",
       "       '15-1143.00', '15-1143.01', '15-1151.00', '15-1152.00',\n",
       "       '15-1199.01', '15-1199.02', '15-1199.03', '15-1199.04',\n",
       "       '15-1199.05', '15-1199.06', '15-1199.07', '15-1199.08',\n",
       "       '15-1199.09', '15-1199.10', '15-1199.11', '15-1199.12',\n",
       "       '15-2011.00', '15-2021.00', '15-2031.00', '15-2041.00',\n",
       "       '15-2041.01', '15-2041.02', '15-2091.00', '17-1011.00',\n",
       "       '17-1012.00', '17-1021.00', '17-1022.00', '17-1022.01',\n",
       "       '17-2011.00', '17-2021.00', '17-2031.00', '17-2041.00',\n",
       "       '17-2051.00', '17-2051.01', '17-2061.00', '17-2071.00',\n",
       "       '17-2072.00', '17-2072.01', '17-2081.00', '17-2081.01',\n",
       "       '17-2111.01', '17-2111.02', '17-2111.03', '17-2112.00',\n",
       "       '17-2112.01', '17-2121.01', '17-2121.02', '17-2131.00',\n",
       "       '17-2141.00', '17-2141.01', '17-2141.02', '17-2151.00',\n",
       "       '17-2161.00', '17-2171.00', '17-2199.01', '17-2199.02',\n",
       "       '17-2199.03', '17-2199.04', '17-2199.05', '17-2199.06',\n",
       "       '17-2199.07', '17-2199.08', '17-2199.09', '17-2199.10',\n",
       "       '17-2199.11', '17-3011.01', '17-3011.02', '17-3012.01',\n",
       "       '17-3012.02', '17-3013.00', '17-3021.00', '17-3022.00',\n",
       "       '17-3023.01', '17-3023.03', '17-3024.00', '17-3024.01',\n",
       "       '17-3025.00', '17-3026.00', '17-3027.00', '17-3027.01',\n",
       "       '17-3029.01', '17-3029.02', '17-3029.03', '17-3029.04',\n",
       "       '17-3029.05', '17-3029.06', '17-3029.07', '17-3029.08',\n",
       "       '17-3029.09', '17-3029.11', '17-3029.12', '17-3031.01',\n",
       "       '17-3031.02', '19-1011.00', '19-1012.00', '19-1013.00',\n",
       "       '19-1020.01', '19-1021.00', '19-1022.00', '19-1023.00',\n",
       "       '19-1029.01', '19-1029.02', '19-1029.03', '19-1031.01',\n",
       "       '19-1031.02', '19-1031.03', '19-1032.00', '19-1041.00',\n",
       "       '19-1042.00', '19-2011.00', '19-2012.00', '19-2021.00',\n",
       "       '19-2031.00', '19-2032.00', '19-2041.00', '19-2041.01',\n",
       "       '19-2041.02', '19-2041.03', '19-2042.00', '19-2043.00',\n",
       "       '19-2099.01', '19-3011.00', '19-3011.01', '19-3022.00',\n",
       "       '19-3031.01', '19-3031.02', '19-3031.03', '19-3032.00',\n",
       "       '19-3039.01', '19-3041.00', '19-3051.00', '19-3091.01',\n",
       "       '19-3091.02', '19-3092.00', '19-3093.00', '19-3094.00',\n",
       "       '19-3099.01', '19-4011.01', '19-4011.02', '19-4021.00',\n",
       "       '19-4031.00', '19-4041.01', '19-4041.02', '19-4051.01',\n",
       "       '19-4051.02', '19-4061.00', '19-4061.01', '19-4091.00',\n",
       "       '19-4092.00', '19-4093.00', '19-4099.01', '19-4099.02',\n",
       "       '19-4099.03', '21-1011.00', '21-1012.00', '21-1013.00',\n",
       "       '21-1014.00', '21-1015.00', '21-1021.00', '21-1022.00',\n",
       "       '21-1023.00', '21-1091.00', '21-1092.00', '21-1093.00',\n",
       "       '21-1094.00', '21-2011.00', '21-2021.00', '23-1011.00',\n",
       "       '23-1012.00', '23-1021.00', '23-1022.00', '23-1023.00',\n",
       "       '23-2011.00', '23-2091.00', '23-2093.00', '25-1011.00',\n",
       "       '25-1021.00', '25-1022.00', '25-1031.00', '25-1032.00',\n",
       "       '25-1041.00', '25-1042.00', '25-1043.00', '25-1051.00',\n",
       "       '25-1052.00', '25-1053.00', '25-1054.00', '25-1061.00',\n",
       "       '25-1062.00', '25-1063.00', '25-1064.00', '25-1065.00',\n",
       "       '25-1066.00', '25-1067.00', '25-1071.00', '25-1072.00',\n",
       "       '25-1081.00', '25-1082.00', '25-1111.00', '25-1112.00',\n",
       "       '25-1113.00', '25-1121.00', '25-1122.00', '25-1123.00',\n",
       "       '25-1124.00', '25-1125.00', '25-1126.00', '25-1191.00',\n",
       "       '25-1192.00', '25-1193.00', '25-1194.00', '25-2011.00',\n",
       "       '25-2012.00', '25-2021.00', '25-2022.00', '25-2023.00',\n",
       "       '25-2031.00', '25-2032.00', '25-2051.00', '25-2052.00',\n",
       "       '25-2053.00', '25-2054.00', '25-2059.01', '25-3011.00',\n",
       "       '25-3021.00', '25-3099.02', '25-4011.00', '25-4012.00',\n",
       "       '25-4013.00', '25-4021.00', '25-4031.00', '25-9011.00',\n",
       "       '25-9021.00', '25-9031.00', '25-9031.01', '25-9041.00',\n",
       "       '27-1011.00', '27-1012.00', '27-1013.00', '27-1014.00',\n",
       "       '27-1021.00', '27-1022.00', '27-1023.00', '27-1024.00',\n",
       "       '27-1025.00', '27-1026.00', '27-1027.00', '27-2011.00',\n",
       "       '27-2012.01', '27-2012.02', '27-2012.03', '27-2012.04',\n",
       "       '27-2012.05', '27-2021.00', '27-2022.00', '27-2023.00',\n",
       "       '27-2031.00', '27-2032.00', '27-2041.01', '27-2041.04',\n",
       "       '27-2042.01', '27-2042.02', '27-3011.00', '27-3012.00',\n",
       "       '27-3021.00', '27-3022.00', '27-3031.00', '27-3041.00',\n",
       "       '27-3042.00', '27-3043.04', '27-3043.05', '27-3091.00',\n",
       "       '27-4011.00', '27-4012.00', '27-4013.00', '27-4014.00',\n",
       "       '27-4021.00', '27-4031.00', '27-4032.00', '29-1011.00',\n",
       "       '29-1021.00', '29-1022.00', '29-1023.00', '29-1024.00',\n",
       "       '29-1031.00', '29-1041.00', '29-1051.00', '29-1061.00',\n",
       "       '29-1062.00', '29-1063.00', '29-1064.00', '29-1065.00',\n",
       "       '29-1066.00', '29-1067.00', '29-1069.01', '29-1069.02',\n",
       "       '29-1069.03', '29-1069.04', '29-1069.05', '29-1069.06',\n",
       "       '29-1069.07', '29-1069.08', '29-1069.09', '29-1069.10',\n",
       "       '29-1069.11', '29-1069.12', '29-1071.00', '29-1071.01',\n",
       "       '29-1081.00', '29-1122.00', '29-1122.01', '29-1123.00',\n",
       "       '29-1124.00', '29-1125.00', '29-1125.01', '29-1125.02',\n",
       "       '29-1126.00', '29-1127.00', '29-1128.00', '29-1131.00',\n",
       "       '29-1141.00', '29-1141.01', '29-1141.02', '29-1141.03',\n",
       "       '29-1141.04', '29-1151.00', '29-1161.00', '29-1171.00',\n",
       "       '29-1181.00', '29-1199.01', '29-1199.04', '29-1199.05',\n",
       "       '29-2011.00', '29-2011.01', '29-2011.02', '29-2011.03',\n",
       "       '29-2012.00', '29-2021.00', '29-2031.00', '29-2032.00',\n",
       "       '29-2033.00', '29-2034.00', '29-2035.00', '29-2041.00',\n",
       "       '29-2051.00', '29-2052.00', '29-2053.00', '29-2054.00',\n",
       "       '29-2055.00', '29-2056.00', '29-2057.00', '29-2061.00',\n",
       "       '29-2071.00', '29-2081.00', '29-2091.00', '29-2092.00',\n",
       "       '29-2099.01', '29-2099.05', '29-2099.06', '29-2099.07',\n",
       "       '29-9011.00', '29-9012.00', '29-9091.00', '29-9092.00',\n",
       "       '29-9099.01', '31-1011.00', '31-1013.00', '31-1014.00',\n",
       "       '31-1015.00', '31-2011.00', '31-2012.00', '31-2021.00',\n",
       "       '31-2022.00', '31-9011.00', '31-9091.00', '31-9092.00',\n",
       "       '31-9093.00', '31-9094.00', '31-9095.00', '31-9096.00',\n",
       "       '31-9097.00', '31-9099.01', '31-9099.02', '33-1011.00',\n",
       "       '33-1012.00', '33-1021.01', '33-1021.02', '33-2011.01',\n",
       "       '33-2011.02', '33-2021.01', '33-2021.02', '33-2022.00',\n",
       "       '33-3011.00', '33-3012.00', '33-3021.01', '33-3021.02',\n",
       "       '33-3021.03', '33-3021.05', '33-3021.06', '33-3031.00',\n",
       "       '33-3041.00', '33-3051.01', '33-3051.03', '33-3052.00',\n",
       "       '33-9011.00', '33-9021.00', '33-9031.00', '33-9032.00',\n",
       "       '33-9091.00', '33-9092.00', '33-9093.00', '33-9099.02',\n",
       "       '35-1011.00', '35-1012.00', '35-2011.00', '35-2012.00',\n",
       "       '35-2013.00', '35-2014.00', '35-2015.00', '35-2021.00',\n",
       "       '35-3011.00', '35-3021.00', '35-3022.00', '35-3022.01',\n",
       "       '35-3031.00', '35-3041.00', '35-9011.00', '35-9021.00',\n",
       "       '35-9031.00', '37-1011.00', '37-1012.00', '37-2011.00',\n",
       "       '37-2012.00', '37-2021.00', '37-3011.00', '37-3012.00',\n",
       "       '37-3013.00', '39-1011.00', '39-1012.00', '39-1021.00',\n",
       "       '39-1021.01', '39-2011.00', '39-2021.00', '39-3011.00',\n",
       "       '39-3012.00', '39-3021.00', '39-3031.00', '39-3091.00',\n",
       "       '39-3092.00', '39-3093.00', '39-4011.00', '39-4021.00',\n",
       "       '39-4031.00', '39-5011.00', '39-5012.00', '39-5091.00',\n",
       "       '39-5092.00', '39-5093.00', '39-5094.00', '39-6011.00',\n",
       "       '39-6012.00', '39-7011.00', '39-7012.00', '39-9011.00',\n",
       "       '39-9011.01', '39-9021.00', '39-9031.00', '39-9032.00',\n",
       "       '39-9041.00', '41-1011.00', '41-1012.00', '41-2011.00',\n",
       "       '41-2012.00', '41-2021.00', '41-2022.00', '41-2031.00',\n",
       "       '41-3011.00', '41-3021.00', '41-3031.01', '41-3031.02',\n",
       "       '41-3031.03', '41-3041.00', '41-3099.01', '41-4011.00',\n",
       "       '41-4011.07', '41-4012.00', '41-9011.00', '41-9012.00',\n",
       "       '41-9021.00', '41-9022.00', '41-9031.00', '41-9041.00',\n",
       "       '41-9091.00', '43-1011.00', '43-2011.00', '43-2021.00',\n",
       "       '43-3011.00', '43-3021.01', '43-3021.02', '43-3031.00',\n",
       "       '43-3041.00', '43-3051.00', '43-3061.00', '43-3071.00',\n",
       "       '43-4011.00', '43-4021.00', '43-4031.01', '43-4031.02',\n",
       "       '43-4031.03', '43-4041.01', '43-4041.02', '43-4051.00',\n",
       "       '43-4051.03', '43-4061.00', '43-4071.00', '43-4081.00',\n",
       "       '43-4111.00', '43-4121.00', '43-4131.00', '43-4141.00',\n",
       "       '43-4151.00', '43-4161.00', '43-4171.00', '43-4181.00',\n",
       "       '43-5011.00', '43-5011.01', '43-5021.00', '43-5031.00',\n",
       "       '43-5032.00', '43-5041.00', '43-5051.00', '43-5052.00',\n",
       "       '43-5053.00', '43-5061.00', '43-5071.00', '43-5081.01',\n",
       "       '43-5081.02', '43-5081.03', '43-5081.04', '43-5111.00',\n",
       "       '43-6011.00', '43-6012.00', '43-6013.00', '43-6014.00',\n",
       "       '43-9011.00', '43-9021.00', '43-9022.00', '43-9031.00',\n",
       "       '43-9041.01', '43-9041.02', '43-9051.00', '43-9061.00',\n",
       "       '43-9071.00', '43-9081.00', '43-9111.00', '43-9111.01',\n",
       "       '45-1011.05', '45-1011.06', '45-1011.07', '45-1011.08',\n",
       "       '45-2011.00', '45-2021.00', '45-2041.00', '45-2091.00',\n",
       "       '45-2092.01', '45-2092.02', '45-2093.00', '45-3011.00',\n",
       "       '45-3021.00', '45-4011.00', '45-4021.00', '45-4022.00',\n",
       "       '45-4023.00', '47-1011.00', '47-1011.03', '47-2011.00',\n",
       "       '47-2021.00', '47-2022.00', '47-2031.01', '47-2031.02',\n",
       "       '47-2041.00', '47-2042.00', '47-2043.00', '47-2044.00',\n",
       "       '47-2051.00', '47-2053.00', '47-2061.00', '47-2071.00',\n",
       "       '47-2072.00', '47-2073.00', '47-2081.00', '47-2082.00',\n",
       "       '47-2111.00', '47-2121.00', '47-2131.00', '47-2132.00',\n",
       "       '47-2141.00', '47-2142.00', '47-2151.00', '47-2152.01',\n",
       "       '47-2152.02', '47-2161.00', '47-2171.00', '47-2181.00',\n",
       "       '47-2211.00', '47-2221.00', '47-2231.00', '47-3011.00',\n",
       "       '47-3012.00', '47-3013.00', '47-3014.00', '47-3015.00',\n",
       "       '47-3016.00', '47-4011.00', '47-4021.00', '47-4031.00',\n",
       "       '47-4041.00', '47-4051.00', '47-4061.00', '47-4071.00',\n",
       "       '47-4091.00', '47-4099.02', '47-4099.03', '47-5011.00',\n",
       "       '47-5012.00', '47-5013.00', '47-5021.00', '47-5031.00',\n",
       "       '47-5041.00', '47-5042.00', '47-5051.00', '47-5061.00',\n",
       "       '47-5071.00', '47-5081.00', '49-1011.00', '49-2011.00',\n",
       "       '49-2021.00', '49-2021.01', '49-2022.00', '49-2091.00',\n",
       "       '49-2092.00', '49-2093.00', '49-2094.00', '49-2095.00',\n",
       "       '49-2096.00', '49-2097.00', '49-2098.00', '49-3011.00',\n",
       "       '49-3021.00', '49-3022.00', '49-3023.01', '49-3023.02',\n",
       "       '49-3031.00', '49-3041.00', '49-3042.00', '49-3043.00',\n",
       "       '49-3051.00', '49-3052.00', '49-3053.00', '49-3091.00',\n",
       "       '49-3092.00', '49-3093.00', '49-9011.00', '49-9012.00',\n",
       "       '49-9021.01', '49-9021.02', '49-9031.00', '49-9041.00',\n",
       "       '49-9043.00', '49-9044.00', '49-9045.00', '49-9051.00',\n",
       "       '49-9052.00', '49-9061.00', '49-9062.00', '49-9063.00',\n",
       "       '49-9064.00', '49-9071.00', '49-9081.00', '49-9091.00',\n",
       "       '49-9092.00', '49-9093.00', '49-9094.00', '49-9095.00',\n",
       "       '49-9096.00', '49-9097.00', '49-9098.00', '49-9099.01',\n",
       "       '51-1011.00', '51-2011.00', '51-2021.00', '51-2022.00',\n",
       "       '51-2023.00', '51-2031.00', '51-2041.00', '51-2091.00',\n",
       "       '51-2092.00', '51-2093.00', '51-3011.00', '51-3021.00',\n",
       "       '51-3022.00', '51-3023.00', '51-3091.00', '51-3092.00',\n",
       "       '51-3093.00', '51-4011.00', '51-4012.00', '51-4021.00',\n",
       "       '51-4022.00', '51-4023.00', '51-4031.00', '51-4032.00',\n",
       "       '51-4033.00', '51-4034.00', '51-4035.00', '51-4041.00',\n",
       "       '51-4051.00', '51-4052.00', '51-4061.00', '51-4062.00',\n",
       "       '51-4071.00', '51-4072.00', '51-4081.00', '51-4111.00',\n",
       "       '51-4121.06', '51-4121.07', '51-4122.00', '51-4191.00',\n",
       "       '51-4192.00', '51-4193.00', '51-4194.00', '51-5111.00',\n",
       "       '51-5112.00', '51-5113.00', '51-6011.00', '51-6021.00',\n",
       "       '51-6031.00', '51-6041.00', '51-6042.00', '51-6051.00',\n",
       "       '51-6052.00', '51-6061.00', '51-6062.00', '51-6063.00',\n",
       "       '51-6064.00', '51-6091.00', '51-6092.00', '51-6093.00',\n",
       "       '51-7011.00', '51-7021.00', '51-7031.00', '51-7032.00',\n",
       "       '51-7041.00', '51-7042.00', '51-8011.00', '51-8012.00',\n",
       "       '51-8013.00', '51-8021.00', '51-8031.00', '51-8091.00',\n",
       "       '51-8092.00', '51-8093.00', '51-8099.01', '51-8099.03',\n",
       "       '51-8099.04', '51-9011.00', '51-9012.00', '51-9021.00',\n",
       "       '51-9022.00', '51-9023.00', '51-9031.00', '51-9032.00',\n",
       "       '51-9041.00', '51-9051.00', '51-9061.00', '51-9071.01',\n",
       "       '51-9071.06', '51-9071.07', '51-9081.00', '51-9082.00',\n",
       "       '51-9083.00', '51-9111.00', '51-9121.00', '51-9122.00',\n",
       "       '51-9123.00', '51-9141.00', '51-9151.00', '51-9191.00',\n",
       "       '51-9192.00', '51-9193.00', '51-9194.00', '51-9195.03',\n",
       "       '51-9195.04', '51-9195.05', '51-9195.07', '51-9196.00',\n",
       "       '51-9197.00', '51-9198.00', '51-9199.01', '53-1011.00',\n",
       "       '53-1021.00', '53-1021.01', '53-1031.00', '53-2011.00',\n",
       "       '53-2012.00', '53-2021.00', '53-2022.00', '53-2031.00',\n",
       "       '53-3011.00', '53-3021.00', '53-3022.00', '53-3031.00',\n",
       "       '53-3032.00', '53-3033.00', '53-3041.00', '53-4011.00',\n",
       "       '53-4012.00', '53-4013.00', '53-4021.00', '53-4031.00',\n",
       "       '53-4041.00', '53-5011.00', '53-5021.01', '53-5021.02',\n",
       "       '53-5021.03', '53-5022.00', '53-5031.00', '53-6011.00',\n",
       "       '53-6021.00', '53-6031.00', '53-6041.00', '53-6051.01',\n",
       "       '53-6051.07', '53-6051.08', '53-6061.00', '53-7011.00',\n",
       "       '53-7021.00', '53-7031.00', '53-7032.00', '53-7033.00',\n",
       "       '53-7041.00', '53-7051.00', '53-7061.00', '53-7062.00',\n",
       "       '53-7063.00', '53-7064.00', '53-7071.00', '53-7072.00',\n",
       "       '53-7073.00', '53-7081.00', '53-7111.00', '53-7121.00'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#all of textfile except main loop\n",
    "#5\n",
    "dftxt.rename(columns={'O*NET-SOC Code':'ONETSOC_Code'}, inplace=True)\n",
    "\n",
    "dftxt\n",
    "Unititle1 = dftxt.ONETSOC_Code.unique()\n",
    "Unititle1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>title1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11-1011.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11-1011.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11-1021.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11-2011.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11-2021.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>963</th>\n",
       "      <td>53-7072.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>964</th>\n",
       "      <td>53-7073.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>965</th>\n",
       "      <td>53-7081.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966</th>\n",
       "      <td>53-7111.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>967</th>\n",
       "      <td>53-7121.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>968 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         title1\n",
       "0    11-1011.00\n",
       "1    11-1011.03\n",
       "2    11-1021.00\n",
       "3    11-2011.00\n",
       "4    11-2021.00\n",
       "..          ...\n",
       "963  53-7072.00\n",
       "964  53-7073.00\n",
       "965  53-7081.00\n",
       "966  53-7111.00\n",
       "967  53-7121.00\n",
       "\n",
       "[968 rows x 1 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#6\n",
    "df1 = pd.DataFrame(Unititle1, columns = ['title1'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>ONETSOC_Code</th>\n",
       "      <th>Element_Name</th>\n",
       "      <th>Category</th>\n",
       "      <th>Data Value</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11-1011.00</td>\n",
       "      <td>Public Speaking</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.39</td>\n",
       "      <td>07/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11-1011.00</td>\n",
       "      <td>Public Speaking</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.02</td>\n",
       "      <td>07/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11-1011.00</td>\n",
       "      <td>Public Speaking</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.40</td>\n",
       "      <td>07/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11-1011.00</td>\n",
       "      <td>Public Speaking</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.36</td>\n",
       "      <td>07/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11-1011.00</td>\n",
       "      <td>Public Speaking</td>\n",
       "      <td>4.0</td>\n",
       "      <td>52.55</td>\n",
       "      <td>07/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326879</th>\n",
       "      <td>53-7121.00</td>\n",
       "      <td>Work Schedules</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.89</td>\n",
       "      <td>08/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326880</th>\n",
       "      <td>53-7121.00</td>\n",
       "      <td>Duration of Typical Work Week</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.82</td>\n",
       "      <td>08/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326881</th>\n",
       "      <td>53-7121.00</td>\n",
       "      <td>Duration of Typical Work Week</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>08/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326882</th>\n",
       "      <td>53-7121.00</td>\n",
       "      <td>Duration of Typical Work Week</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.56</td>\n",
       "      <td>08/2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326883</th>\n",
       "      <td>53-7121.00</td>\n",
       "      <td>Duration of Typical Work Week</td>\n",
       "      <td>3.0</td>\n",
       "      <td>82.44</td>\n",
       "      <td>08/2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>326884 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ONETSOC_Code                   Element_Name  Category  Data Value  \\\n",
       "0        11-1011.00                Public Speaking       NaN        3.39   \n",
       "1        11-1011.00                Public Speaking       1.0        6.02   \n",
       "2        11-1011.00                Public Speaking       2.0       12.40   \n",
       "3        11-1011.00                Public Speaking       3.0       23.36   \n",
       "4        11-1011.00                Public Speaking       4.0       52.55   \n",
       "...             ...                            ...       ...         ...   \n",
       "326879   53-7121.00                 Work Schedules       3.0        0.89   \n",
       "326880   53-7121.00  Duration of Typical Work Week       NaN        2.82   \n",
       "326881   53-7121.00  Duration of Typical Work Week       1.0        0.00   \n",
       "326882   53-7121.00  Duration of Typical Work Week       2.0       17.56   \n",
       "326883   53-7121.00  Duration of Typical Work Week       3.0       82.44   \n",
       "\n",
       "           Date  \n",
       "0       07/2014  \n",
       "1       07/2014  \n",
       "2       07/2014  \n",
       "3       07/2014  \n",
       "4       07/2014  \n",
       "...         ...  \n",
       "326879  08/2019  \n",
       "326880  08/2019  \n",
       "326881  08/2019  \n",
       "326882  08/2019  \n",
       "326883  08/2019  \n",
       "\n",
       "[326884 rows x 5 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#7\n",
    "dftxt.rename(columns={'Element Name':'Element_Name1'}, inplace=True)\n",
    "\n",
    "dftxt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'Element_Name1'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-107-c665d38d5645>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0melement1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mElement_Name1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0melement1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Documents\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5272\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5273\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5274\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5275\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5276\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'Element_Name1'"
     ]
    }
   ],
   "source": [
    "element1 = true.Element_Name1.unique()\n",
    "element1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "element_list1 = pd.DataFrame(element1, columns = ['Element1'])\n",
    "type(element_list1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create output data frame \n",
    "#score_by_title_element = pd.DataFrame(columns = [\"title\",\"element\",\"score\"])\n",
    "\n",
    "temp = []\n",
    "\n",
    "for i, job in enumerate( df[\"title1\"]):\n",
    "    for element1 in element_list1[\"Element1\"]:                             # selecting rows based on condit\n",
    "        result_df = dftxt[(dftxt['Element_Name1'] == element ) & \n",
    "                (dftxt['ONETSOC_Code'] == job)]\n",
    "        #importance = rslt_df['Data Value']\n",
    "        #print(result_df)\n",
    "        #print(result_df.iloc[1].loc[\"Data Value\"])\n",
    "        try:\n",
    "            #percentage of days doing the action days out of the year so exponetial system  -- multiple by days... number of week day... by percent\n",
    "            #other 3 thing\n",
    "            score = 0 * result_df.iloc[1].loc[\"Data Value\"] + .043809*result_df.iloc[2].loc[\"Data Value\"]+ .12380952*result_df.iloc[3].loc[\"Data Value\"]+ .6*result_df.iloc[4].loc[\"Data Value\"]+ result_df.iloc[5].loc[\"Data Value\"]\n",
    "            new_row = {\"title\":job, \"element\": element, \"score\": score}\n",
    "            score_by_title_element.append(new_row, ignore_index=True)\n",
    "        except IndexError: # catch the error\n",
    "             pass\n",
    "            #if IndexError:\n",
    "          #  score = -1 * result_df.iloc[1].loc[\"Data Value\"] + result_df.iloc[3].loc[\"Data Value\"]\n",
    "        \n",
    "        #append to the output a row with of form [job,element,score]\n",
    "        temp.append([job, element, score])\n",
    "    if i==2:\n",
    "        break\n",
    "        \n",
    "        \n",
    "        #LabeledScore = pd.DataFrame(job, element, score, columns = ['title', 'Element Name', 'Score'])\n",
    "        \n",
    "       # new_row = {\"title\":job, \"element\": element, \"score\": score}\n",
    "       # score_by_title_element.append(new_row, ignore_index=True)\n",
    "\n",
    "   \n",
    "    \n",
    "score_by_title_element2006 = pd.DataFrame(temp,columns=['Title','Description','Score'])\n",
    "type(score_by_title_element2006)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               Title                            Description  \\\n",
      "0                   Chief Executives                        Public Speaking   \n",
      "1                   Chief Executives                              Telephone   \n",
      "2                   Chief Executives                        Electronic Mail   \n",
      "3                   Chief Executives                      Letters and Memos   \n",
      "4                   Chief Executives               Face-to-Face Discussions   \n",
      "..                               ...                                    ...   \n",
      "166  General and Operations Managers                   Level of Competition   \n",
      "167  General and Operations Managers                          Time Pressure   \n",
      "168  General and Operations Managers  Pace Determined by Speed of Equipment   \n",
      "169  General and Operations Managers                         Work Schedules   \n",
      "170  General and Operations Managers          Duration of Typical Work Week   \n",
      "\n",
      "         Score  \n",
      "0    62.684551  \n",
      "1    62.684551  \n",
      "2    62.684551  \n",
      "3    62.684551  \n",
      "4    62.684551  \n",
      "..         ...  \n",
      "166  62.684551  \n",
      "167  62.684551  \n",
      "168  62.684551  \n",
      "169  62.684551  \n",
      "170  62.684551  \n",
      "\n",
      "[171 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(score_by_title_element2006)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_by_title_element2006.to_excel(r'LaborScores2006.xlsx', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}