9_ML_US_visa.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a3e3ccd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "%matplotlib inline\n",
    "\n",
    "pd.set_option('display.max_columns', 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "20343016",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ****************************** \n",
    "# EDA_3 : US Visa Data Set \n",
    "# ****************************** \n",
    "\n",
    "df = pd.read_csv(\"us_perm_visas.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fffe3636",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>add_these_pw_job_title_9089</th>\n",
       "      <th>agent_city</th>\n",
       "      <th>agent_firm_name</th>\n",
       "      <th>agent_state</th>\n",
       "      <th>application_type</th>\n",
       "      <th>case_no</th>\n",
       "      <th>case_number</th>\n",
       "      <th>case_received_date</th>\n",
       "      <th>case_status</th>\n",
       "      <th>class_of_admission</th>\n",
       "      <th>country_of_citizenship</th>\n",
       "      <th>country_of_citzenship</th>\n",
       "      <th>decision_date</th>\n",
       "      <th>employer_address_1</th>\n",
       "      <th>employer_address_2</th>\n",
       "      <th>...</th>\n",
       "      <th>ri_layoff_in_past_six_months</th>\n",
       "      <th>ri_local_ethnic_paper_from</th>\n",
       "      <th>ri_local_ethnic_paper_to</th>\n",
       "      <th>ri_posted_notice_at_worksite</th>\n",
       "      <th>ri_pvt_employment_firm_from</th>\n",
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       "      <th>ri_us_workers_considered</th>\n",
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       "      <th>wage_offer_from_9089</th>\n",
       "      <th>wage_offer_to_9089</th>\n",
       "      <th>wage_offer_unit_of_pay_9089</th>\n",
       "      <th>wage_offered_from_9089</th>\n",
       "      <th>wage_offered_to_9089</th>\n",
       "      <th>wage_offered_unit_of_pay_9089</th>\n",
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       "  </thead>\n",
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       "      <td>4276</td>\n",
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       "      <td>10135</td>\n",
       "      <td>108</td>\n",
       "      <td>2</td>\n",
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       "      <td>95</td>\n",
       "      <td>28496</td>\n",
       "      <td>1689</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>75629.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>4276</td>\n",
       "      <td>1838</td>\n",
       "      <td>10135</td>\n",
       "      <td>108</td>\n",
       "      <td>2</td>\n",
       "      <td>51</td>\n",
       "      <td>238418</td>\n",
       "      <td>2180</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>202</td>\n",
       "      <td>103</td>\n",
       "      <td>60</td>\n",
       "      <td>22520</td>\n",
       "      <td>7931</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other Economic Sector</td>\n",
       "      <td>37024.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4276</td>\n",
       "      <td>1838</td>\n",
       "      <td>10135</td>\n",
       "      <td>108</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "      <td>238418</td>\n",
       "      <td>2180</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>202</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>3674</td>\n",
       "      <td>7931</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aerospace</td>\n",
       "      <td>47923.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4276</td>\n",
       "      <td>1838</td>\n",
       "      <td>10135</td>\n",
       "      <td>108</td>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>238418</td>\n",
       "      <td>2180</td>\n",
       "      <td>0</td>\n",
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       "      <td>202</td>\n",
       "      <td>116</td>\n",
       "      <td>45</td>\n",
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       "      <td>7931</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other Economic Sector</td>\n",
       "      <td>10.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Advanced Mfg</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>4276</td>\n",
       "      <td>187</td>\n",
       "      <td>6316</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>134990</td>\n",
       "      <td>238406</td>\n",
       "      <td>2178</td>\n",
       "      <td>3</td>\n",
       "      <td>57</td>\n",
       "      <td>164</td>\n",
       "      <td>148</td>\n",
       "      <td>1449</td>\n",
       "      <td>44880</td>\n",
       "      <td>7931</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28,330.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374358</th>\n",
       "      <td>4276</td>\n",
       "      <td>1460</td>\n",
       "      <td>6772</td>\n",
       "      <td>101</td>\n",
       "      <td>3</td>\n",
       "      <td>134990</td>\n",
       "      <td>235675</td>\n",
       "      <td>2179</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>197</td>\n",
       "      <td>148</td>\n",
       "      <td>1449</td>\n",
       "      <td>69103</td>\n",
       "      <td>7931</td>\n",
       "      <td>...</td>\n",
       "      <td>Y</td>\n",
       "      <td>2016-07-27</td>\n",
       "      <td>2016-07-27</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>122,000.00</td>\n",
       "      <td>142,000.00</td>\n",
       "      <td>Year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374359</th>\n",
       "      <td>4276</td>\n",
       "      <td>1454</td>\n",
       "      <td>3180</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>134990</td>\n",
       "      <td>238387</td>\n",
       "      <td>2179</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>79</td>\n",
       "      <td>148</td>\n",
       "      <td>1449</td>\n",
       "      <td>26090</td>\n",
       "      <td>7931</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>2016-07-21</td>\n",
       "      <td>2016-07-28</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79,082.00</td>\n",
       "      <td>79,082.00</td>\n",
       "      <td>Year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374360</th>\n",
       "      <td>4276</td>\n",
       "      <td>786</td>\n",
       "      <td>4212</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>134990</td>\n",
       "      <td>238396</td>\n",
       "      <td>2179</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>164</td>\n",
       "      <td>148</td>\n",
       "      <td>1449</td>\n",
       "      <td>41783</td>\n",
       "      <td>7931</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37,981.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374361</th>\n",
       "      <td>4276</td>\n",
       "      <td>1229</td>\n",
       "      <td>2171</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>134990</td>\n",
       "      <td>236714</td>\n",
       "      <td>2179</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>40</td>\n",
       "      <td>148</td>\n",
       "      <td>1449</td>\n",
       "      <td>26151</td>\n",
       "      <td>7931</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>2016-08-19</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84,926.00</td>\n",
       "      <td>121,500.00</td>\n",
       "      <td>Year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>374362 rows × 154 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        add_these_pw_job_title_9089  agent_city  agent_firm_name  agent_state  \\\n",
       "0                              4276        1838            10135          108   \n",
       "1                              4276        1838            10135          108   \n",
       "2                              4276        1838            10135          108   \n",
       "3                              4276        1838            10135          108   \n",
       "4                              4276        1838            10135          108   \n",
       "...                             ...         ...              ...          ...   \n",
       "374357                         4276         187             6316            8   \n",
       "374358                         4276        1460             6772          101   \n",
       "374359                         4276        1454             3180           29   \n",
       "374360                         4276         786             4212            8   \n",
       "374361                         4276        1229             2171            7   \n",
       "\n",
       "        application_type  case_no  case_number  case_received_date  \\\n",
       "0                      2       49       238418                2180   \n",
       "1                      2       51       238418                2180   \n",
       "2                      2       52       238418                2180   \n",
       "3                      2       53       238418                2180   \n",
       "4                      2       56       238418                2180   \n",
       "...                  ...      ...          ...                 ...   \n",
       "374357                 3   134990       238406                2178   \n",
       "374358                 3   134990       235675                2179   \n",
       "374359                 3   134990       238387                2179   \n",
       "374360                 3   134990       238396                2179   \n",
       "374361                 3   134990       236714                2179   \n",
       "\n",
       "        case_status  class_of_admission  country_of_citizenship  \\\n",
       "0                 0                  28                     202   \n",
       "1                 2                   5                     202   \n",
       "2                 0                  19                     202   \n",
       "3                 0                   5                     202   \n",
       "4                 0                  31                     202   \n",
       "...             ...                 ...                     ...   \n",
       "374357            3                  57                     164   \n",
       "374358            3                  31                     197   \n",
       "374359            3                  19                      79   \n",
       "374360            3                   5                     164   \n",
       "374361            3                  19                      40   \n",
       "\n",
       "        country_of_citzenship  decision_date  employer_address_1  \\\n",
       "0                           4             95               28496   \n",
       "1                         103             60               22520   \n",
       "2                          57             45                3674   \n",
       "3                         116             45               37235   \n",
       "4                          24             89               12101   \n",
       "...                       ...            ...                 ...   \n",
       "374357                    148           1449               44880   \n",
       "374358                    148           1449               69103   \n",
       "374359                    148           1449               26090   \n",
       "374360                    148           1449               41783   \n",
       "374361                    148           1449               26151   \n",
       "\n",
       "        employer_address_2  ...  ri_layoff_in_past_six_months  \\\n",
       "0                     1689  ...                           NaN   \n",
       "1                     7931  ...                           NaN   \n",
       "2                     7931  ...                           NaN   \n",
       "3                     7931  ...                           NaN   \n",
       "4                     7931  ...                           NaN   \n",
       "...                    ...  ...                           ...   \n",
       "374357                7931  ...                             N   \n",
       "374358                7931  ...                             Y   \n",
       "374359                7931  ...                             N   \n",
       "374360                7931  ...                             N   \n",
       "374361                7931  ...                             N   \n",
       "\n",
       "        ri_local_ethnic_paper_from  ri_local_ethnic_paper_to  \\\n",
       "0                              NaN                       NaN   \n",
       "1                              NaN                       NaN   \n",
       "2                              NaN                       NaN   \n",
       "3                              NaN                       NaN   \n",
       "4                              NaN                       NaN   \n",
       "...                            ...                       ...   \n",
       "374357                         NaN                       NaN   \n",
       "374358                  2016-07-27                2016-07-27   \n",
       "374359                  2016-07-21                2016-07-28   \n",
       "374360                         NaN                       NaN   \n",
       "374361                  2016-08-19                2016-09-01   \n",
       "\n",
       "        ri_posted_notice_at_worksite  ri_pvt_employment_firm_from  \\\n",
       "0                                NaN                          NaN   \n",
       "1                                NaN                          NaN   \n",
       "2                                NaN                          NaN   \n",
       "3                                NaN                          NaN   \n",
       "4                                NaN                          NaN   \n",
       "...                              ...                          ...   \n",
       "374357                             Y                          NaN   \n",
       "374358                             Y                          NaN   \n",
       "374359                             Y                          NaN   \n",
       "374360                             Y                          NaN   \n",
       "374361                             Y                          NaN   \n",
       "\n",
       "        ri_pvt_employment_firm_to ri_us_workers_considered schd_a_sheepherder  \\\n",
       "0                             NaN                      NaN                NaN   \n",
       "1                             NaN                      NaN                NaN   \n",
       "2                             NaN                      NaN                NaN   \n",
       "3                             NaN                      NaN                NaN   \n",
       "4                             NaN                      NaN                NaN   \n",
       "...                           ...                      ...                ...   \n",
       "374357                        NaN                      NaN                  N   \n",
       "374358                        NaN                        Y                  N   \n",
       "374359                        NaN                      NaN                  N   \n",
       "374360                        NaN                      NaN                  N   \n",
       "374361                        NaN                      NaN                  N   \n",
       "\n",
       "           us_economic_sector  wage_offer_from_9089 wage_offer_to_9089  \\\n",
       "0                          IT               75629.0                NaN   \n",
       "1       Other Economic Sector               37024.0                NaN   \n",
       "2                   Aerospace               47923.0                NaN   \n",
       "3       Other Economic Sector                 10.97                NaN   \n",
       "4                Advanced Mfg              100000.0                NaN   \n",
       "...                       ...                   ...                ...   \n",
       "374357                    NaN             28,330.00                NaN   \n",
       "374358                    NaN            122,000.00         142,000.00   \n",
       "374359                    NaN             79,082.00          79,082.00   \n",
       "374360                    NaN             37,981.00                NaN   \n",
       "374361                    NaN             84,926.00         121,500.00   \n",
       "\n",
       "       wage_offer_unit_of_pay_9089 wage_offered_from_9089  \\\n",
       "0                               yr                    NaN   \n",
       "1                               yr                    NaN   \n",
       "2                               yr                    NaN   \n",
       "3                               hr                    NaN   \n",
       "4                               yr                    NaN   \n",
       "...                            ...                    ...   \n",
       "374357                        Year                    NaN   \n",
       "374358                        Year                    NaN   \n",
       "374359                        Year                    NaN   \n",
       "374360                        Year                    NaN   \n",
       "374361                        Year                    NaN   \n",
       "\n",
       "       wage_offered_to_9089 wage_offered_unit_of_pay_9089  \n",
       "0                       NaN                           NaN  \n",
       "1                       NaN                           NaN  \n",
       "2                       NaN                           NaN  \n",
       "3                       NaN                           NaN  \n",
       "4                       NaN                           NaN  \n",
       "...                     ...                           ...  \n",
       "374357                  NaN                           NaN  \n",
       "374358                  NaN                           NaN  \n",
       "374359                  NaN                           NaN  \n",
       "374360                  NaN                           NaN  \n",
       "374361                  NaN                           NaN  \n",
       "\n",
       "[374362 rows x 154 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9a45b21a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['add_these_pw_job_title_9089', 'agent_city', 'agent_firm_name',\n",
       "       'agent_state', 'application_type', 'case_no', 'case_number',\n",
       "       'case_received_date', 'case_status', 'class_of_admission',\n",
       "       ...\n",
       "       'ri_pvt_employment_firm_to', 'ri_us_workers_considered',\n",
       "       'schd_a_sheepherder', 'us_economic_sector', 'wage_offer_from_9089',\n",
       "       'wage_offer_to_9089', 'wage_offer_unit_of_pay_9089',\n",
       "       'wage_offered_from_9089', 'wage_offered_to_9089',\n",
       "       'wage_offered_unit_of_pay_9089'],\n",
       "      dtype='object', length=154)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f7aeb5a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(374362, 154)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "99efbcd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "add_these_pw_job_title_9089           0\n",
       "agent_city                            0\n",
       "agent_firm_name                       0\n",
       "agent_state                           0\n",
       "application_type                      0\n",
       "                                  ...  \n",
       "wage_offer_to_9089               300728\n",
       "wage_offer_unit_of_pay_9089      115916\n",
       "wage_offered_from_9089           259715\n",
       "wage_offered_to_9089             343347\n",
       "wage_offered_unit_of_pay_9089    303663\n",
       "Length: 154, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7535a571",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "add_these_pw_job_title_9089        int64\n",
       "agent_city                         int64\n",
       "agent_firm_name                    int64\n",
       "agent_state                        int64\n",
       "application_type                   int64\n",
       "                                  ...   \n",
       "wage_offer_to_9089                object\n",
       "wage_offer_unit_of_pay_9089       object\n",
       "wage_offered_from_9089           float64\n",
       "wage_offered_to_9089             float64\n",
       "wage_offered_unit_of_pay_9089     object\n",
       "Length: 154, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9ec04d12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(374362, 154)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "45c088d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 374362 entries, 0 to 374361\n",
      "Columns: 154 entries, add_these_pw_job_title_9089 to wage_offered_unit_of_pay_9089\n",
      "dtypes: float64(9), int64(21), object(124)\n",
      "memory usage: 439.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ced25bbf",
   "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>add_these_pw_job_title_9089</th>\n",
       "      <th>agent_city</th>\n",
       "      <th>agent_firm_name</th>\n",
       "      <th>agent_state</th>\n",
       "      <th>application_type</th>\n",
       "      <th>case_no</th>\n",
       "      <th>case_number</th>\n",
       "      <th>case_received_date</th>\n",
       "      <th>case_status</th>\n",
       "      <th>class_of_admission</th>\n",
       "      <th>country_of_citizenship</th>\n",
       "      <th>country_of_citzenship</th>\n",
       "      <th>decision_date</th>\n",
       "      <th>employer_address_1</th>\n",
       "      <th>employer_address_2</th>\n",
       "      <th>employer_city</th>\n",
       "      <th>employer_country</th>\n",
       "      <th>employer_decl_info_title</th>\n",
       "      <th>employer_name</th>\n",
       "      <th>employer_num_employees</th>\n",
       "      <th>employer_phone</th>\n",
       "      <th>employer_yr_estab</th>\n",
       "      <th>foreign_worker_yr_rel_edu_completed</th>\n",
       "      <th>fw_info_yr_rel_edu_completed</th>\n",
       "      <th>job_info_alt_cmb_ed_oth_yrs</th>\n",
       "      <th>job_info_alt_occ_num_months</th>\n",
       "      <th>job_info_experience_num_months</th>\n",
       "      <th>job_info_training_num_months</th>\n",
       "      <th>wage_offered_from_9089</th>\n",
       "      <th>wage_offered_to_9089</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>374362.000000</td>\n",
       "      <td>374362.000000</td>\n",
       "      <td>374362.000000</td>\n",
       "      <td>374362.000000</td>\n",
       "      <td>374362.000000</td>\n",
       "      <td>374362.000000</td>\n",
       "      <td>374362.000000</td>\n",
       "      <td>374362.000000</td>\n",
       "      <td>374362.000000</td>\n",
       "      <td>374362.000000</td>\n",
       "      <td>374362.000000</td>\n",
       "      <td>374362.000000</td>\n",
       "      <td>374362.000000</td>\n",
       "      <td>374362.000000</td>\n",
       "      <td>374362.000000</td>\n",
       "      <td>374362.000000</td>\n",
       "      <td>374362.000000</td>\n",
       "      <td>374362.000000</td>\n",
       "      <td>374362.000000</td>\n",
       "      <td>374362.000000</td>\n",
       "      <td>374362.000000</td>\n",
       "      <td>238958.000000</td>\n",
       "      <td>83364.000000</td>\n",
       "      <td>137198.000000</td>\n",
       "      <td>70220.000000</td>\n",
       "      <td>147200.000000</td>\n",
       "      <td>133188.000000</td>\n",
       "      <td>4779.000000</td>\n",
       "      <td>1.146470e+05</td>\n",
       "      <td>3.101500e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4057.646398</td>\n",
       "      <td>1313.656429</td>\n",
       "      <td>6911.476325</td>\n",
       "      <td>71.880060</td>\n",
       "      <td>2.326612</td>\n",
       "      <td>110595.541764</td>\n",
       "      <td>162294.444818</td>\n",
       "      <td>1815.875928</td>\n",
       "      <td>0.679732</td>\n",
       "      <td>22.467569</td>\n",
       "      <td>96.043450</td>\n",
       "      <td>143.476408</td>\n",
       "      <td>870.224171</td>\n",
       "      <td>35019.659354</td>\n",
       "      <td>6700.202013</td>\n",
       "      <td>4106.871929</td>\n",
       "      <td>5.361394</td>\n",
       "      <td>11233.471813</td>\n",
       "      <td>34049.704991</td>\n",
       "      <td>2806.610097</td>\n",
       "      <td>32810.238681</td>\n",
       "      <td>1984.208267</td>\n",
       "      <td>2001.345761</td>\n",
       "      <td>2002.633603</td>\n",
       "      <td>4.593876</td>\n",
       "      <td>36.483118</td>\n",
       "      <td>33.103560</td>\n",
       "      <td>32.685081</td>\n",
       "      <td>9.588846e+04</td>\n",
       "      <td>1.232273e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>744.083164</td>\n",
       "      <td>602.153206</td>\n",
       "      <td>3636.507555</td>\n",
       "      <td>40.133478</td>\n",
       "      <td>0.924892</td>\n",
       "      <td>40000.630094</td>\n",
       "      <td>79390.529944</td>\n",
       "      <td>377.065984</td>\n",
       "      <td>0.803944</td>\n",
       "      <td>10.719500</td>\n",
       "      <td>47.884175</td>\n",
       "      <td>20.077696</td>\n",
       "      <td>396.160101</td>\n",
       "      <td>20633.554044</td>\n",
       "      <td>2060.533989</td>\n",
       "      <td>2233.618678</td>\n",
       "      <td>0.481105</td>\n",
       "      <td>5115.345836</td>\n",
       "      <td>20372.252431</td>\n",
       "      <td>1919.572838</td>\n",
       "      <td>16681.421842</td>\n",
       "      <td>38.470555</td>\n",
       "      <td>91.444715</td>\n",
       "      <td>89.674423</td>\n",
       "      <td>4.838582</td>\n",
       "      <td>23.391668</td>\n",
       "      <td>22.733393</td>\n",
       "      <td>20.593288</td>\n",
       "      <td>1.463822e+05</td>\n",
       "      <td>1.080289e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.700000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4276.000000</td>\n",
       "      <td>914.000000</td>\n",
       "      <td>2541.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>93339.250000</td>\n",
       "      <td>93368.250000</td>\n",
       "      <td>1525.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>587.000000</td>\n",
       "      <td>18167.000000</td>\n",
       "      <td>5661.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7419.250000</td>\n",
       "      <td>16265.000000</td>\n",
       "      <td>353.000000</td>\n",
       "      <td>18782.000000</td>\n",
       "      <td>1980.000000</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.453400e+04</td>\n",
       "      <td>9.700100e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4276.000000</td>\n",
       "      <td>1454.000000</td>\n",
       "      <td>8755.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>134990.000000</td>\n",
       "      <td>186636.500000</td>\n",
       "      <td>1907.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>936.000000</td>\n",
       "      <td>34296.000000</td>\n",
       "      <td>7931.000000</td>\n",
       "      <td>4485.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10876.000000</td>\n",
       "      <td>31201.000000</td>\n",
       "      <td>3946.000000</td>\n",
       "      <td>36823.000000</td>\n",
       "      <td>1996.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>9.064400e+04</td>\n",
       "      <td>1.206000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4276.000000</td>\n",
       "      <td>1838.000000</td>\n",
       "      <td>10135.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>134990.000000</td>\n",
       "      <td>238418.000000</td>\n",
       "      <td>2180.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>1210.000000</td>\n",
       "      <td>52660.000000</td>\n",
       "      <td>7931.000000</td>\n",
       "      <td>6229.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>16509.000000</td>\n",
       "      <td>51197.000000</td>\n",
       "      <td>4559.000000</td>\n",
       "      <td>49220.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1.100000e+05</td>\n",
       "      <td>1.448419e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4276.000000</td>\n",
       "      <td>1838.000000</td>\n",
       "      <td>10135.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>134990.000000</td>\n",
       "      <td>238418.000000</td>\n",
       "      <td>2180.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>1449.000000</td>\n",
       "      <td>70243.000000</td>\n",
       "      <td>7931.000000</td>\n",
       "      <td>8384.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>16509.000000</td>\n",
       "      <td>71709.000000</td>\n",
       "      <td>4559.000000</td>\n",
       "      <td>49220.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2103.000000</td>\n",
       "      <td>2207.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>244.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>1.629060e+07</td>\n",
       "      <td>1.328500e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       add_these_pw_job_title_9089     agent_city  agent_firm_name  \\\n",
       "count                374362.000000  374362.000000    374362.000000   \n",
       "mean                   4057.646398    1313.656429      6911.476325   \n",
       "std                     744.083164     602.153206      3636.507555   \n",
       "min                       0.000000       0.000000         0.000000   \n",
       "25%                    4276.000000     914.000000      2541.000000   \n",
       "50%                    4276.000000    1454.000000      8755.000000   \n",
       "75%                    4276.000000    1838.000000     10135.000000   \n",
       "max                    4276.000000    1838.000000     10135.000000   \n",
       "\n",
       "         agent_state  application_type        case_no    case_number  \\\n",
       "count  374362.000000     374362.000000  374362.000000  374362.000000   \n",
       "mean       71.880060          2.326612  110595.541764  162294.444818   \n",
       "std        40.133478          0.924892   40000.630094   79390.529944   \n",
       "min         0.000000          0.000000       0.000000       0.000000   \n",
       "25%        29.000000          1.000000   93339.250000   93368.250000   \n",
       "50%        93.000000          3.000000  134990.000000  186636.500000   \n",
       "75%       108.000000          3.000000  134990.000000  238418.000000   \n",
       "max       108.000000          3.000000  134990.000000  238418.000000   \n",
       "\n",
       "       case_received_date    case_status  class_of_admission  \\\n",
       "count       374362.000000  374362.000000       374362.000000   \n",
       "mean          1815.875928       0.679732           22.467569   \n",
       "std            377.065984       0.803944           10.719500   \n",
       "min              0.000000       0.000000            0.000000   \n",
       "25%           1525.000000       0.000000           19.000000   \n",
       "50%           1907.000000       1.000000           19.000000   \n",
       "75%           2180.000000       1.000000           19.000000   \n",
       "max           2180.000000       3.000000           57.000000   \n",
       "\n",
       "       country_of_citizenship  country_of_citzenship  decision_date  \\\n",
       "count           374362.000000          374362.000000  374362.000000   \n",
       "mean                96.043450             143.476408     870.224171   \n",
       "std                 47.884175              20.077696     396.160101   \n",
       "min                  0.000000               0.000000       0.000000   \n",
       "25%                 79.000000             148.000000     587.000000   \n",
       "50%                 79.000000             148.000000     936.000000   \n",
       "75%                116.000000             148.000000    1210.000000   \n",
       "max                202.000000             148.000000    1449.000000   \n",
       "\n",
       "       employer_address_1  employer_address_2  employer_city  \\\n",
       "count       374362.000000       374362.000000  374362.000000   \n",
       "mean         35019.659354         6700.202013    4106.871929   \n",
       "std          20633.554044         2060.533989    2233.618678   \n",
       "min              0.000000            0.000000       0.000000   \n",
       "25%          18167.000000         5661.000000    2012.000000   \n",
       "50%          34296.000000         7931.000000    4485.000000   \n",
       "75%          52660.000000         7931.000000    6229.000000   \n",
       "max          70243.000000         7931.000000    8384.000000   \n",
       "\n",
       "       employer_country  employer_decl_info_title  employer_name  \\\n",
       "count     374362.000000             374362.000000  374362.000000   \n",
       "mean           5.361394              11233.471813   34049.704991   \n",
       "std            0.481105               5115.345836   20372.252431   \n",
       "min            0.000000                  0.000000       0.000000   \n",
       "25%            5.000000               7419.250000   16265.000000   \n",
       "50%            5.000000              10876.000000   31201.000000   \n",
       "75%            6.000000              16509.000000   51197.000000   \n",
       "max            6.000000              16509.000000   71709.000000   \n",
       "\n",
       "       employer_num_employees  employer_phone  employer_yr_estab  \\\n",
       "count           374362.000000   374362.000000      238958.000000   \n",
       "mean              2806.610097    32810.238681        1984.208267   \n",
       "std               1919.572838    16681.421842          38.470555   \n",
       "min                  0.000000        0.000000           0.000000   \n",
       "25%                353.000000    18782.000000        1980.000000   \n",
       "50%               3946.000000    36823.000000        1996.000000   \n",
       "75%               4559.000000    49220.000000        2004.000000   \n",
       "max               4559.000000    49220.000000        2016.000000   \n",
       "\n",
       "       foreign_worker_yr_rel_edu_completed  fw_info_yr_rel_edu_completed  \\\n",
       "count                         83364.000000                 137198.000000   \n",
       "mean                           2001.345761                   2002.633603   \n",
       "std                              91.444715                     89.674423   \n",
       "min                               0.000000                      0.000000   \n",
       "25%                            2003.000000                   2004.000000   \n",
       "50%                            2006.000000                   2007.000000   \n",
       "75%                            2010.000000                   2011.000000   \n",
       "max                            2103.000000                   2207.000000   \n",
       "\n",
       "       job_info_alt_cmb_ed_oth_yrs  job_info_alt_occ_num_months  \\\n",
       "count                 70220.000000                147200.000000   \n",
       "mean                      4.593876                    36.483118   \n",
       "std                       4.838582                    23.391668   \n",
       "min                       0.000000                     0.000000   \n",
       "25%                       3.000000                    24.000000   \n",
       "50%                       5.000000                    24.000000   \n",
       "75%                       5.000000                    60.000000   \n",
       "max                      96.000000                   240.000000   \n",
       "\n",
       "       job_info_experience_num_months  job_info_training_num_months  \\\n",
       "count                   133188.000000                   4779.000000   \n",
       "mean                        33.103560                     32.685081   \n",
       "std                         22.733393                     20.593288   \n",
       "min                          0.000000                      0.000000   \n",
       "25%                         12.000000                     12.000000   \n",
       "50%                         24.000000                     36.000000   \n",
       "75%                         60.000000                     36.000000   \n",
       "max                        244.000000                    240.000000   \n",
       "\n",
       "       wage_offered_from_9089  wage_offered_to_9089  \n",
       "count            1.146470e+05          3.101500e+04  \n",
       "mean             9.588846e+04          1.232273e+05  \n",
       "std              1.463822e+05          1.080289e+05  \n",
       "min              6.700000e+00          0.000000e+00  \n",
       "25%              7.453400e+04          9.700100e+04  \n",
       "50%              9.064400e+04          1.206000e+05  \n",
       "75%              1.100000e+05          1.448419e+05  \n",
       "max              1.629060e+07          1.328500e+07  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c7b44ae8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>add_these_pw_job_title_9089</th>\n",
       "      <th>agent_city</th>\n",
       "      <th>agent_firm_name</th>\n",
       "      <th>agent_state</th>\n",
       "      <th>application_type</th>\n",
       "      <th>case_no</th>\n",
       "      <th>case_number</th>\n",
       "      <th>case_received_date</th>\n",
       "      <th>case_status</th>\n",
       "      <th>class_of_admission</th>\n",
       "      <th>country_of_citizenship</th>\n",
       "      <th>country_of_citzenship</th>\n",
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       "      <th>employer_address_1</th>\n",
       "      <th>employer_address_2</th>\n",
       "      <th>...</th>\n",
       "      <th>ri_layoff_in_past_six_months</th>\n",
       "      <th>ri_local_ethnic_paper_from</th>\n",
       "      <th>ri_local_ethnic_paper_to</th>\n",
       "      <th>ri_posted_notice_at_worksite</th>\n",
       "      <th>ri_pvt_employment_firm_from</th>\n",
       "      <th>ri_pvt_employment_firm_to</th>\n",
       "      <th>ri_us_workers_considered</th>\n",
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       "      <th>wage_offer_to_9089</th>\n",
       "      <th>wage_offer_unit_of_pay_9089</th>\n",
       "      <th>wage_offered_from_9089</th>\n",
       "      <th>wage_offered_to_9089</th>\n",
       "      <th>wage_offered_unit_of_pay_9089</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4276</td>\n",
       "      <td>1838</td>\n",
       "      <td>10135</td>\n",
       "      <td>108</td>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>238418</td>\n",
       "      <td>2180</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>202</td>\n",
       "      <td>4</td>\n",
       "      <td>95</td>\n",
       "      <td>28496</td>\n",
       "      <td>1689</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>75629.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4276</td>\n",
       "      <td>1838</td>\n",
       "      <td>10135</td>\n",
       "      <td>108</td>\n",
       "      <td>2</td>\n",
       "      <td>51</td>\n",
       "      <td>238418</td>\n",
       "      <td>2180</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>202</td>\n",
       "      <td>103</td>\n",
       "      <td>60</td>\n",
       "      <td>22520</td>\n",
       "      <td>7931</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other Economic Sector</td>\n",
       "      <td>37024.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4276</td>\n",
       "      <td>1838</td>\n",
       "      <td>10135</td>\n",
       "      <td>108</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "      <td>238418</td>\n",
       "      <td>2180</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>202</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>3674</td>\n",
       "      <td>7931</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aerospace</td>\n",
       "      <td>47923.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4276</td>\n",
       "      <td>1838</td>\n",
       "      <td>10135</td>\n",
       "      <td>108</td>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>238418</td>\n",
       "      <td>2180</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>202</td>\n",
       "      <td>116</td>\n",
       "      <td>45</td>\n",
       "      <td>37235</td>\n",
       "      <td>7931</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other Economic Sector</td>\n",
       "      <td>10.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4276</td>\n",
       "      <td>1838</td>\n",
       "      <td>10135</td>\n",
       "      <td>108</td>\n",
       "      <td>2</td>\n",
       "      <td>56</td>\n",
       "      <td>238418</td>\n",
       "      <td>2180</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>202</td>\n",
       "      <td>24</td>\n",
       "      <td>89</td>\n",
       "      <td>12101</td>\n",
       "      <td>7931</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Advanced Mfg</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yr</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374357</th>\n",
       "      <td>4276</td>\n",
       "      <td>187</td>\n",
       "      <td>6316</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>134990</td>\n",
       "      <td>238406</td>\n",
       "      <td>2178</td>\n",
       "      <td>3</td>\n",
       "      <td>57</td>\n",
       "      <td>164</td>\n",
       "      <td>148</td>\n",
       "      <td>1449</td>\n",
       "      <td>44880</td>\n",
       "      <td>7931</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28,330.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374358</th>\n",
       "      <td>4276</td>\n",
       "      <td>1460</td>\n",
       "      <td>6772</td>\n",
       "      <td>101</td>\n",
       "      <td>3</td>\n",
       "      <td>134990</td>\n",
       "      <td>235675</td>\n",
       "      <td>2179</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>197</td>\n",
       "      <td>148</td>\n",
       "      <td>1449</td>\n",
       "      <td>69103</td>\n",
       "      <td>7931</td>\n",
       "      <td>...</td>\n",
       "      <td>Y</td>\n",
       "      <td>2016-07-27</td>\n",
       "      <td>2016-07-27</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>122,000.00</td>\n",
       "      <td>142,000.00</td>\n",
       "      <td>Year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374359</th>\n",
       "      <td>4276</td>\n",
       "      <td>1454</td>\n",
       "      <td>3180</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>134990</td>\n",
       "      <td>238387</td>\n",
       "      <td>2179</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>79</td>\n",
       "      <td>148</td>\n",
       "      <td>1449</td>\n",
       "      <td>26090</td>\n",
       "      <td>7931</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>2016-07-21</td>\n",
       "      <td>2016-07-28</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79,082.00</td>\n",
       "      <td>79,082.00</td>\n",
       "      <td>Year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374360</th>\n",
       "      <td>4276</td>\n",
       "      <td>786</td>\n",
       "      <td>4212</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>134990</td>\n",
       "      <td>238396</td>\n",
       "      <td>2179</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>164</td>\n",
       "      <td>148</td>\n",
       "      <td>1449</td>\n",
       "      <td>41783</td>\n",
       "      <td>7931</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37,981.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374361</th>\n",
       "      <td>4276</td>\n",
       "      <td>1229</td>\n",
       "      <td>2171</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>134990</td>\n",
       "      <td>236714</td>\n",
       "      <td>2179</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>40</td>\n",
       "      <td>148</td>\n",
       "      <td>1449</td>\n",
       "      <td>26151</td>\n",
       "      <td>7931</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>2016-08-19</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84,926.00</td>\n",
       "      <td>121,500.00</td>\n",
       "      <td>Year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>374362 rows × 154 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        add_these_pw_job_title_9089  agent_city  agent_firm_name  agent_state  \\\n",
       "0                              4276        1838            10135          108   \n",
       "1                              4276        1838            10135          108   \n",
       "2                              4276        1838            10135          108   \n",
       "3                              4276        1838            10135          108   \n",
       "4                              4276        1838            10135          108   \n",
       "...                             ...         ...              ...          ...   \n",
       "374357                         4276         187             6316            8   \n",
       "374358                         4276        1460             6772          101   \n",
       "374359                         4276        1454             3180           29   \n",
       "374360                         4276         786             4212            8   \n",
       "374361                         4276        1229             2171            7   \n",
       "\n",
       "        application_type  case_no  case_number  case_received_date  \\\n",
       "0                      2       49       238418                2180   \n",
       "1                      2       51       238418                2180   \n",
       "2                      2       52       238418                2180   \n",
       "3                      2       53       238418                2180   \n",
       "4                      2       56       238418                2180   \n",
       "...                  ...      ...          ...                 ...   \n",
       "374357                 3   134990       238406                2178   \n",
       "374358                 3   134990       235675                2179   \n",
       "374359                 3   134990       238387                2179   \n",
       "374360                 3   134990       238396                2179   \n",
       "374361                 3   134990       236714                2179   \n",
       "\n",
       "        case_status  class_of_admission  country_of_citizenship  \\\n",
       "0                 0                  28                     202   \n",
       "1                 2                   5                     202   \n",
       "2                 0                  19                     202   \n",
       "3                 0                   5                     202   \n",
       "4                 0                  31                     202   \n",
       "...             ...                 ...                     ...   \n",
       "374357            3                  57                     164   \n",
       "374358            3                  31                     197   \n",
       "374359            3                  19                      79   \n",
       "374360            3                   5                     164   \n",
       "374361            3                  19                      40   \n",
       "\n",
       "        country_of_citzenship  decision_date  employer_address_1  \\\n",
       "0                           4             95               28496   \n",
       "1                         103             60               22520   \n",
       "2                          57             45                3674   \n",
       "3                         116             45               37235   \n",
       "4                          24             89               12101   \n",
       "...                       ...            ...                 ...   \n",
       "374357                    148           1449               44880   \n",
       "374358                    148           1449               69103   \n",
       "374359                    148           1449               26090   \n",
       "374360                    148           1449               41783   \n",
       "374361                    148           1449               26151   \n",
       "\n",
       "        employer_address_2  ...  ri_layoff_in_past_six_months  \\\n",
       "0                     1689  ...                           NaN   \n",
       "1                     7931  ...                           NaN   \n",
       "2                     7931  ...                           NaN   \n",
       "3                     7931  ...                           NaN   \n",
       "4                     7931  ...                           NaN   \n",
       "...                    ...  ...                           ...   \n",
       "374357                7931  ...                             N   \n",
       "374358                7931  ...                             Y   \n",
       "374359                7931  ...                             N   \n",
       "374360                7931  ...                             N   \n",
       "374361                7931  ...                             N   \n",
       "\n",
       "        ri_local_ethnic_paper_from  ri_local_ethnic_paper_to  \\\n",
       "0                              NaN                       NaN   \n",
       "1                              NaN                       NaN   \n",
       "2                              NaN                       NaN   \n",
       "3                              NaN                       NaN   \n",
       "4                              NaN                       NaN   \n",
       "...                            ...                       ...   \n",
       "374357                         NaN                       NaN   \n",
       "374358                  2016-07-27                2016-07-27   \n",
       "374359                  2016-07-21                2016-07-28   \n",
       "374360                         NaN                       NaN   \n",
       "374361                  2016-08-19                2016-09-01   \n",
       "\n",
       "        ri_posted_notice_at_worksite  ri_pvt_employment_firm_from  \\\n",
       "0                                NaN                          NaN   \n",
       "1                                NaN                          NaN   \n",
       "2                                NaN                          NaN   \n",
       "3                                NaN                          NaN   \n",
       "4                                NaN                          NaN   \n",
       "...                              ...                          ...   \n",
       "374357                             Y                          NaN   \n",
       "374358                             Y                          NaN   \n",
       "374359                             Y                          NaN   \n",
       "374360                             Y                          NaN   \n",
       "374361                             Y                          NaN   \n",
       "\n",
       "        ri_pvt_employment_firm_to ri_us_workers_considered schd_a_sheepherder  \\\n",
       "0                             NaN                      NaN                NaN   \n",
       "1                             NaN                      NaN                NaN   \n",
       "2                             NaN                      NaN                NaN   \n",
       "3                             NaN                      NaN                NaN   \n",
       "4                             NaN                      NaN                NaN   \n",
       "...                           ...                      ...                ...   \n",
       "374357                        NaN                      NaN                  N   \n",
       "374358                        NaN                        Y                  N   \n",
       "374359                        NaN                      NaN                  N   \n",
       "374360                        NaN                      NaN                  N   \n",
       "374361                        NaN                      NaN                  N   \n",
       "\n",
       "           us_economic_sector  wage_offer_from_9089 wage_offer_to_9089  \\\n",
       "0                          IT               75629.0                NaN   \n",
       "1       Other Economic Sector               37024.0                NaN   \n",
       "2                   Aerospace               47923.0                NaN   \n",
       "3       Other Economic Sector                 10.97                NaN   \n",
       "4                Advanced Mfg              100000.0                NaN   \n",
       "...                       ...                   ...                ...   \n",
       "374357                    NaN             28,330.00                NaN   \n",
       "374358                    NaN            122,000.00         142,000.00   \n",
       "374359                    NaN             79,082.00          79,082.00   \n",
       "374360                    NaN             37,981.00                NaN   \n",
       "374361                    NaN             84,926.00         121,500.00   \n",
       "\n",
       "       wage_offer_unit_of_pay_9089 wage_offered_from_9089  \\\n",
       "0                               yr                    NaN   \n",
       "1                               yr                    NaN   \n",
       "2                               yr                    NaN   \n",
       "3                               hr                    NaN   \n",
       "4                               yr                    NaN   \n",
       "...                            ...                    ...   \n",
       "374357                        Year                    NaN   \n",
       "374358                        Year                    NaN   \n",
       "374359                        Year                    NaN   \n",
       "374360                        Year                    NaN   \n",
       "374361                        Year                    NaN   \n",
       "\n",
       "       wage_offered_to_9089 wage_offered_unit_of_pay_9089  \n",
       "0                       NaN                           NaN  \n",
       "1                       NaN                           NaN  \n",
       "2                       NaN                           NaN  \n",
       "3                       NaN                           NaN  \n",
       "4                       NaN                           NaN  \n",
       "...                     ...                           ...  \n",
       "374357                  NaN                           NaN  \n",
       "374358                  NaN                           NaN  \n",
       "374359                  NaN                           NaN  \n",
       "374360                  NaN                           NaN  \n",
       "374361                  NaN                           NaN  \n",
       "\n",
       "[374362 rows x 154 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3280a24b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['add_these_pw_job_title_9089', 'agent_city', 'agent_firm_name',\n",
       "       'agent_state', 'application_type', 'case_no', 'case_number',\n",
       "       'case_received_date', 'case_status', 'class_of_admission',\n",
       "       'country_of_citizenship', 'country_of_citzenship', 'decision_date',\n",
       "       'employer_address_1', 'employer_address_2', 'employer_city',\n",
       "       'employer_country', 'employer_decl_info_title', 'employer_name',\n",
       "       'employer_num_employees', 'employer_phone', 'employer_phone_ext',\n",
       "       'employer_postal_code', 'employer_state', 'employer_yr_estab',\n",
       "       'foreign_worker_info_alt_edu_experience',\n",
       "       'foreign_worker_info_birth_country', 'foreign_worker_info_city',\n",
       "       'foreign_worker_info_education',\n",
       "       'foreign_worker_info_education_other', 'foreign_worker_info_inst',\n",
       "       'foreign_worker_info_major', 'foreign_worker_info_postal_code',\n",
       "       'foreign_worker_info_rel_occup_exp',\n",
       "       'foreign_worker_info_req_experience', 'foreign_worker_info_state',\n",
       "       'foreign_worker_info_training_comp',\n",
       "       'foreign_worker_ownership_interest',\n",
       "       'foreign_worker_yr_rel_edu_completed',\n",
       "       'fw_info_alt_edu_experience', 'fw_info_birth_country',\n",
       "       'fw_info_education_other', 'fw_info_postal_code',\n",
       "       'fw_info_rel_occup_exp', 'fw_info_req_experience',\n",
       "       'fw_info_training_comp', 'fw_info_yr_rel_edu_completed',\n",
       "       'fw_ownership_interest', 'ji_foreign_worker_live_on_premises',\n",
       "       'ji_fw_live_on_premises', 'ji_live_in_dom_svc_contract',\n",
       "       'ji_live_in_domestic_service',\n",
       "       'ji_offered_to_sec_j_foreign_worker', 'ji_offered_to_sec_j_fw',\n",
       "       'job_info_alt_cmb_ed_oth_yrs', 'job_info_alt_combo_ed',\n",
       "       'job_info_alt_combo_ed_exp', 'job_info_alt_combo_ed_other',\n",
       "       'job_info_alt_field', 'job_info_alt_field_name',\n",
       "       'job_info_alt_occ', 'job_info_alt_occ_job_title',\n",
       "       'job_info_alt_occ_num_months', 'job_info_combo_occupation',\n",
       "       'job_info_education', 'job_info_education_other',\n",
       "       'job_info_experience', 'job_info_experience_num_months',\n",
       "       'job_info_foreign_ed', 'job_info_foreign_lang_req',\n",
       "       'job_info_job_req_normal', 'job_info_job_title', 'job_info_major',\n",
       "       'job_info_training', 'job_info_training_field',\n",
       "       'job_info_training_num_months', 'job_info_work_city',\n",
       "       'job_info_work_postal_code', 'job_info_work_state',\n",
       "       'naics_2007_us_code', 'naics_2007_us_title', 'naics_code',\n",
       "       'naics_title', 'naics_us_code', 'naics_us_code_2007',\n",
       "       'naics_us_title', 'naics_us_title_2007', 'orig_case_no',\n",
       "       'orig_file_date', 'preparer_info_emp_completed',\n",
       "       'preparer_info_title', 'pw_amount_9089', 'pw_determ_date',\n",
       "       'pw_expire_date', 'pw_job_title_908', 'pw_job_title_9089',\n",
       "       'pw_level_9089', 'pw_soc_code', 'pw_soc_title',\n",
       "       'pw_source_name_9089', 'pw_source_name_other_9089', 'pw_track_num',\n",
       "       'pw_unit_of_pay_9089', 'rec_info_barg_rep_notified',\n",
       "       'recr_info_barg_rep_notified', 'recr_info_coll_teach_comp_proc',\n",
       "       'recr_info_coll_univ_teacher', 'recr_info_employer_rec_payment',\n",
       "       'recr_info_first_ad_start', 'recr_info_job_fair_from',\n",
       "       'recr_info_job_fair_to', 'recr_info_on_campus_recr_from',\n",
       "       'recr_info_on_campus_recr_to', 'recr_info_pro_org_advert_from',\n",
       "       'recr_info_pro_org_advert_to', 'recr_info_prof_org_advert_from',\n",
       "       'recr_info_prof_org_advert_to', 'recr_info_professional_occ',\n",
       "       'recr_info_radio_tv_ad_from', 'recr_info_radio_tv_ad_to',\n",
       "       'recr_info_second_ad_start', 'recr_info_sunday_newspaper',\n",
       "       'recr_info_swa_job_order_end', 'recr_info_swa_job_order_start',\n",
       "       'refile', 'ri_1st_ad_newspaper_name', 'ri_2nd_ad_newspaper_name',\n",
       "       'ri_2nd_ad_newspaper_or_journal', 'ri_campus_placement_from',\n",
       "       'ri_campus_placement_to', 'ri_coll_tch_basic_process',\n",
       "       'ri_coll_teach_pro_jnl', 'ri_coll_teach_select_date',\n",
       "       'ri_employee_referral_prog_from', 'ri_employee_referral_prog_to',\n",
       "       'ri_employer_web_post_from', 'ri_employer_web_post_to',\n",
       "       'ri_job_search_website_from', 'ri_job_search_website_to',\n",
       "       'ri_layoff_in_past_six_months', 'ri_local_ethnic_paper_from',\n",
       "       'ri_local_ethnic_paper_to', 'ri_posted_notice_at_worksite',\n",
       "       'ri_pvt_employment_firm_from', 'ri_pvt_employment_firm_to',\n",
       "       'ri_us_workers_considered', 'schd_a_sheepherder',\n",
       "       'us_economic_sector', 'wage_offer_from_9089', 'wage_offer_to_9089',\n",
       "       'wage_offer_unit_of_pay_9089', 'wage_offered_from_9089',\n",
       "       'wage_offered_to_9089', 'wage_offered_unit_of_pay_9089'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ae3b8929",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         238418\n",
       "1         238418\n",
       "2         238418\n",
       "3         238418\n",
       "4         238418\n",
       "           ...  \n",
       "374357    238406\n",
       "374358    235675\n",
       "374359    238387\n",
       "374360    238396\n",
       "374361    236714\n",
       "Name: case_number, Length: 374362, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['case_number']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "141f7355",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             49\n",
       "1             51\n",
       "2             52\n",
       "3             53\n",
       "4             56\n",
       "           ...  \n",
       "374357    134990\n",
       "374358    134990\n",
       "374359    134990\n",
       "374360    134990\n",
       "374361    134990\n",
       "Name: case_no, Length: 374362, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['case_no']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1e477ba1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"case_no\", axis =1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bc7f77b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['add_these_pw_job_title_9089', 'agent_city', 'agent_firm_name',\n",
       "       'agent_state', 'application_type', 'case_number',\n",
       "       'case_received_date', 'case_status', 'class_of_admission',\n",
       "       'country_of_citizenship', 'country_of_citzenship', 'decision_date',\n",
       "       'employer_address_1', 'employer_address_2', 'employer_city',\n",
       "       'employer_country', 'employer_decl_info_title', 'employer_name',\n",
       "       'employer_num_employees', 'employer_phone', 'employer_phone_ext',\n",
       "       'employer_postal_code', 'employer_state', 'employer_yr_estab',\n",
       "       'foreign_worker_info_alt_edu_experience',\n",
       "       'foreign_worker_info_birth_country', 'foreign_worker_info_city',\n",
       "       'foreign_worker_info_education',\n",
       "       'foreign_worker_info_education_other', 'foreign_worker_info_inst',\n",
       "       'foreign_worker_info_major', 'foreign_worker_info_postal_code',\n",
       "       'foreign_worker_info_rel_occup_exp',\n",
       "       'foreign_worker_info_req_experience', 'foreign_worker_info_state',\n",
       "       'foreign_worker_info_training_comp',\n",
       "       'foreign_worker_ownership_interest',\n",
       "       'foreign_worker_yr_rel_edu_completed',\n",
       "       'fw_info_alt_edu_experience', 'fw_info_birth_country',\n",
       "       'fw_info_education_other', 'fw_info_postal_code',\n",
       "       'fw_info_rel_occup_exp', 'fw_info_req_experience',\n",
       "       'fw_info_training_comp', 'fw_info_yr_rel_edu_completed',\n",
       "       'fw_ownership_interest', 'ji_foreign_worker_live_on_premises',\n",
       "       'ji_fw_live_on_premises', 'ji_live_in_dom_svc_contract',\n",
       "       'ji_live_in_domestic_service',\n",
       "       'ji_offered_to_sec_j_foreign_worker', 'ji_offered_to_sec_j_fw',\n",
       "       'job_info_alt_cmb_ed_oth_yrs', 'job_info_alt_combo_ed',\n",
       "       'job_info_alt_combo_ed_exp', 'job_info_alt_combo_ed_other',\n",
       "       'job_info_alt_field', 'job_info_alt_field_name',\n",
       "       'job_info_alt_occ', 'job_info_alt_occ_job_title',\n",
       "       'job_info_alt_occ_num_months', 'job_info_combo_occupation',\n",
       "       'job_info_education', 'job_info_education_other',\n",
       "       'job_info_experience', 'job_info_experience_num_months',\n",
       "       'job_info_foreign_ed', 'job_info_foreign_lang_req',\n",
       "       'job_info_job_req_normal', 'job_info_job_title', 'job_info_major',\n",
       "       'job_info_training', 'job_info_training_field',\n",
       "       'job_info_training_num_months', 'job_info_work_city',\n",
       "       'job_info_work_postal_code', 'job_info_work_state',\n",
       "       'naics_2007_us_code', 'naics_2007_us_title', 'naics_code',\n",
       "       'naics_title', 'naics_us_code', 'naics_us_code_2007',\n",
       "       'naics_us_title', 'naics_us_title_2007', 'orig_case_no',\n",
       "       'orig_file_date', 'preparer_info_emp_completed',\n",
       "       'preparer_info_title', 'pw_amount_9089', 'pw_determ_date',\n",
       "       'pw_expire_date', 'pw_job_title_908', 'pw_job_title_9089',\n",
       "       'pw_level_9089', 'pw_soc_code', 'pw_soc_title',\n",
       "       'pw_source_name_9089', 'pw_source_name_other_9089', 'pw_track_num',\n",
       "       'pw_unit_of_pay_9089', 'rec_info_barg_rep_notified',\n",
       "       'recr_info_barg_rep_notified', 'recr_info_coll_teach_comp_proc',\n",
       "       'recr_info_coll_univ_teacher', 'recr_info_employer_rec_payment',\n",
       "       'recr_info_first_ad_start', 'recr_info_job_fair_from',\n",
       "       'recr_info_job_fair_to', 'recr_info_on_campus_recr_from',\n",
       "       'recr_info_on_campus_recr_to', 'recr_info_pro_org_advert_from',\n",
       "       'recr_info_pro_org_advert_to', 'recr_info_prof_org_advert_from',\n",
       "       'recr_info_prof_org_advert_to', 'recr_info_professional_occ',\n",
       "       'recr_info_radio_tv_ad_from', 'recr_info_radio_tv_ad_to',\n",
       "       'recr_info_second_ad_start', 'recr_info_sunday_newspaper',\n",
       "       'recr_info_swa_job_order_end', 'recr_info_swa_job_order_start',\n",
       "       'refile', 'ri_1st_ad_newspaper_name', 'ri_2nd_ad_newspaper_name',\n",
       "       'ri_2nd_ad_newspaper_or_journal', 'ri_campus_placement_from',\n",
       "       'ri_campus_placement_to', 'ri_coll_tch_basic_process',\n",
       "       'ri_coll_teach_pro_jnl', 'ri_coll_teach_select_date',\n",
       "       'ri_employee_referral_prog_from', 'ri_employee_referral_prog_to',\n",
       "       'ri_employer_web_post_from', 'ri_employer_web_post_to',\n",
       "       'ri_job_search_website_from', 'ri_job_search_website_to',\n",
       "       'ri_layoff_in_past_six_months', 'ri_local_ethnic_paper_from',\n",
       "       'ri_local_ethnic_paper_to', 'ri_posted_notice_at_worksite',\n",
       "       'ri_pvt_employment_firm_from', 'ri_pvt_employment_firm_to',\n",
       "       'ri_us_workers_considered', 'schd_a_sheepherder',\n",
       "       'us_economic_sector', 'wage_offer_from_9089', 'wage_offer_to_9089',\n",
       "       'wage_offer_unit_of_pay_9089', 'wage_offered_from_9089',\n",
       "       'wage_offered_to_9089', 'wage_offered_unit_of_pay_9089'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "561e176f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0\n",
       "1         2\n",
       "2         0\n",
       "3         0\n",
       "4         0\n",
       "         ..\n",
       "374357    3\n",
       "374358    3\n",
       "374359    3\n",
       "374360    3\n",
       "374361    3\n",
       "Name: case_status, Length: 374362, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.case_status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "090a3b47",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df[\"case_status\"] != \"Withdrawn\"] #withdarawn application should not be analysed\n",
    "# withdrawn application should not be analysed because we are not interest knowing who has us vvisa status as withdrawn \n",
    "# we interested knowing either approved or denied "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6c55a4a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1, 3], dtype=int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.case_status.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "71474d4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df.case_status == 'Certified-Expired', 'case_status'] = 'Certified'\n",
    "# for replacing the word with someother word by using map or str.replace or using below line \n",
    "# df.loc[df.case_status == 'Certified-Expired', 'case_status'] = 'Certified' \n",
    "# the above line is syntax \n",
    "# loc: location based finding the data \n",
    "# loc: where ever the value 'Certified-Expired' is there give that column and replace  \n",
    "# the 'Certified-Expired' columnas 'Certified' \n",
    "# Here considering only one column as 'Certified' instead of two columns 'Certified-Expired', 'Certified' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "88028e87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1, 3], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.case_status.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7110d6ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "case_status\n",
       "0    0.485981\n",
       "1    0.396905\n",
       "2    0.068514\n",
       "3    0.048600\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.case_status.value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1cde3f9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#insights >>  only 7% people were denied\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "291c6d75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop NULL values in all rows and all columns \n",
    "df.dropna(axis = 1, how='all', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "60599cf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(axis = 0, how='all', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cf432247",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(374362, 153)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9aefb229",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['add_these_pw_job_title_9089', 'agent_city', 'agent_firm_name',\n",
       "       'agent_state', 'application_type', 'case_number', 'case_received_date',\n",
       "       'case_status', 'class_of_admission', 'country_of_citizenship',\n",
       "       ...\n",
       "       'ri_pvt_employment_firm_to', 'ri_us_workers_considered',\n",
       "       'schd_a_sheepherder', 'us_economic_sector', 'wage_offer_from_9089',\n",
       "       'wage_offer_to_9089', 'wage_offer_unit_of_pay_9089',\n",
       "       'wage_offered_from_9089', 'wage_offered_to_9089',\n",
       "       'wage_offered_unit_of_pay_9089'],\n",
       "      dtype='object', length=153)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5639ea25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         2\n",
       "1         2\n",
       "2         2\n",
       "3         2\n",
       "4         2\n",
       "         ..\n",
       "374357    3\n",
       "374358    3\n",
       "374359    3\n",
       "374360    3\n",
       "374361    3\n",
       "Name: application_type, Length: 374362, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.application_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "68c111c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='application_type'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['application_type'].value_counts().plot(kind = 'bar', color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4106273d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#most people online"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "72b2b0d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='application_type', ylabel='count'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x = 'application_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "724c0002",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['add_these_pw_job_title_9089', 'agent_city', 'agent_firm_name',\n",
       "       'agent_state', 'application_type', 'case_number', 'case_received_date',\n",
       "       'case_status', 'class_of_admission', 'country_of_citizenship',\n",
       "       ...\n",
       "       'ri_pvt_employment_firm_to', 'ri_us_workers_considered',\n",
       "       'schd_a_sheepherder', 'us_economic_sector', 'wage_offer_from_9089',\n",
       "       'wage_offer_to_9089', 'wage_offer_unit_of_pay_9089',\n",
       "       'wage_offered_from_9089', 'wage_offered_to_9089',\n",
       "       'wage_offered_unit_of_pay_9089'],\n",
       "      dtype='object', length=153)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f0e82bec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                            IT\n",
       "1         Other Economic Sector\n",
       "2                     Aerospace\n",
       "3         Other Economic Sector\n",
       "4                  Advanced Mfg\n",
       "                  ...          \n",
       "374357                      NaN\n",
       "374358                      NaN\n",
       "374359                      NaN\n",
       "374360                      NaN\n",
       "374361                      NaN\n",
       "Name: us_economic_sector, Length: 374362, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.us_economic_sector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b75736c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "economic_sector = df['us_economic_sector'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "18364f24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "us_economic_sector\n",
       "IT                       52617\n",
       "Advanced Mfg             18749\n",
       "Other Economic Sector    15798\n",
       "Finance                   9224\n",
       "Educational Services      9130\n",
       "Health Care               6825\n",
       "Retail                    4408\n",
       "Aerospace                 4166\n",
       "Hospitality               1926\n",
       "Construction              1409\n",
       "Automotive                1163\n",
       "Energy                    1086\n",
       "Transportation             949\n",
       "Agribusiness               939\n",
       "Biotechnology              456\n",
       "Geospatial                 141\n",
       "Homeland Security           82\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "economic_sector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "262c7f5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 8))\n",
    "plt.pie(economic_sector, labels= economic_sector.index, autopct = '%1.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f289922d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#insights for above chart you will get US visa IT\n",
    "\n",
    "df['decision_date'] = pd.to_datetime(df['decision_date'])\n",
    "df['year'] = df['decision_date'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fdbe6e7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         1970\n",
       "1         1970\n",
       "2         1970\n",
       "3         1970\n",
       "4         1970\n",
       "          ... \n",
       "374357    1970\n",
       "374358    1970\n",
       "374359    1970\n",
       "374360    1970\n",
       "374361    1970\n",
       "Name: year, Length: 374362, dtype: int32"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8ee10bbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (10, 8))\n",
    "\n",
    "sns.countplot(x = 'year', hue='case_status', data=df)\n",
    "ax.set(xlabel = \"Visa application year\", ylabel = \"No of applications\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "62d5ff91",
   "metadata": {},
   "outputs": [],
   "source": [
    "#insights as application increase, denied and accepted both increasing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "33b8c8a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (10, 8))\n",
    "\n",
    "sns.countplot(x = 'employer_city', hue='year', data=df, order = df.employer_city.value_counts().iloc[:10].index)\n",
    "ax.set(xlabel = \"Visa application year\", ylabel = \"No of applications\")\n",
    "plt.xticks(rotation =90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d375abd",
   "metadata": {},
   "source": [
    "Encoding Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1599287c",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Encoders require their input argument must be uniformly strings or numbers. Got ['float', 'str']",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "File \u001b[1;32mc:\\Python311\\Lib\\site-packages\\sklearn\\utils\\_encode.py:174\u001b[0m, in \u001b[0;36m_unique_python\u001b[1;34m(values, return_inverse, return_counts)\u001b[0m\n\u001b[0;32m    172\u001b[0m uniques_set, missing_values \u001b[38;5;241m=\u001b[39m _extract_missing(uniques_set)\n\u001b[1;32m--> 174\u001b[0m uniques \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msorted\u001b[39m(uniques_set)\n\u001b[0;32m    175\u001b[0m uniques\u001b[38;5;241m.\u001b[39mextend(missing_values\u001b[38;5;241m.\u001b[39mto_list())\n",
      "\u001b[1;31mTypeError\u001b[0m: '<' not supported between instances of 'str' and 'float'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[47], line 9\u001b[0m\n\u001b[0;32m      7\u001b[0m cat_var_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcat_\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m col\n\u001b[0;32m      8\u001b[0m cat_var_name \u001b[38;5;241m=\u001b[39m LabelEncoder()\n\u001b[1;32m----> 9\u001b[0m \u001b[43mcat_var_name\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcol\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     10\u001b[0m df[col] \u001b[38;5;241m=\u001b[39m cat_var_name\u001b[38;5;241m.\u001b[39mtransform(df[col])\n\u001b[0;32m     11\u001b[0m categorical_variables[col] \u001b[38;5;241m=\u001b[39m cat_var_name\n",
      "File \u001b[1;32mc:\\Python311\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:98\u001b[0m, in \u001b[0;36mLabelEncoder.fit\u001b[1;34m(self, y)\u001b[0m\n\u001b[0;32m     85\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Fit label encoder.\u001b[39;00m\n\u001b[0;32m     86\u001b[0m \n\u001b[0;32m     87\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     95\u001b[0m \u001b[38;5;124;03m    Fitted label encoder.\u001b[39;00m\n\u001b[0;32m     96\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m     97\u001b[0m y \u001b[38;5;241m=\u001b[39m column_or_1d(y, warn\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m---> 98\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclasses_ \u001b[38;5;241m=\u001b[39m \u001b[43m_unique\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     99\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "File \u001b[1;32mc:\\Python311\\Lib\\site-packages\\sklearn\\utils\\_encode.py:42\u001b[0m, in \u001b[0;36m_unique\u001b[1;34m(values, return_inverse, return_counts)\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Helper function to find unique values with support for python objects.\u001b[39;00m\n\u001b[0;32m     12\u001b[0m \n\u001b[0;32m     13\u001b[0m \u001b[38;5;124;03mUses pure python method for object dtype, and numpy method for\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     39\u001b[0m \u001b[38;5;124;03m    array. Only provided if `return_counts` is True.\u001b[39;00m\n\u001b[0;32m     40\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m     41\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m values\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m:\n\u001b[1;32m---> 42\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_unique_python\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m     43\u001b[0m \u001b[43m        \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_inverse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_inverse\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_counts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_counts\u001b[49m\n\u001b[0;32m     44\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     45\u001b[0m \u001b[38;5;66;03m# numerical\u001b[39;00m\n\u001b[0;32m     46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _unique_np(\n\u001b[0;32m     47\u001b[0m     values, return_inverse\u001b[38;5;241m=\u001b[39mreturn_inverse, return_counts\u001b[38;5;241m=\u001b[39mreturn_counts\n\u001b[0;32m     48\u001b[0m )\n",
      "File \u001b[1;32mc:\\Python311\\Lib\\site-packages\\sklearn\\utils\\_encode.py:179\u001b[0m, in \u001b[0;36m_unique_python\u001b[1;34m(values, return_inverse, return_counts)\u001b[0m\n\u001b[0;32m    177\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m    178\u001b[0m     types \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msorted\u001b[39m(t\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mset\u001b[39m(\u001b[38;5;28mtype\u001b[39m(v) \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m values))\n\u001b[1;32m--> 179\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m    180\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEncoders require their input argument must be uniformly \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    181\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstrings or numbers. Got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtypes\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    182\u001b[0m     )\n\u001b[0;32m    183\u001b[0m ret \u001b[38;5;241m=\u001b[39m (uniques,)\n\u001b[0;32m    185\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_inverse:\n",
      "\u001b[1;31mTypeError\u001b[0m: Encoders require their input argument must be uniformly strings or numbers. Got ['float', 'str']"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "categorical_variables = {}\n",
    "\n",
    "# Creating categories denoted by integres from column values\n",
    "for col in df.columns:\n",
    "    # print(col)\n",
    "    cat_var_name = \"cat_\" + col\n",
    "    cat_var_name = LabelEncoder()\n",
    "    cat_var_name.fit(df[col])\n",
    "    df[col] = cat_var_name.transform(df[col])\n",
    "    categorical_variables[col] = cat_var_name\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99261883",
   "metadata": {},
   "outputs": [],
   "source": [
    "# finally now we can do train test split, do scaling and start training model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07782cc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# observations: \n",
    "# 1. data ingestion \n",
    "# 2. Don't consider redudant data or duplicate data, drop either one of them \n",
    "#    Example: \n",
    "#       case no, case number \n",
    "#            or  \n",
    "#       you can merge it \n",
    "\n",
    "\n",
    "\n",
    "# Doubts: \n",
    "# 1. When Data set is given and Big problem statement also given \n",
    "# S. from data set generate insights. \n",
    "\n",
    "# Insights: \n",
    "# a. Which type of people will default, may be like salary column, location column, geographical area column. \n",
    "# When ever big problem statement is given dive deeper into it. \n",
    "# If need more clarifications or discussion the connect to business team  \n",
    "\n",
    "# Example: \n",
    "# Problem Statement: \n",
    "\n",
    "# Q. Many people are taking loans but not paying money, is there any person defaulter or not? \n",
    "# S. Data set contains information about people that they have taken loan, did they repaid it or not. \n",
    " \n",
    "# 2. a. check basic data checks \n",
    "#    b. see the functions like describe(), info() \n",
    "#    c. covert string column to integer column \n",
    "#    d. integer column convert into string representation \n",
    "#    e. then start with univariate, bivariate, multivariate \n",
    "\n",
    "# 3. If you have 1000 columns then we can select relevant columns there are other techniques, machine learning: dimensionality reduction technique is available "
   ]
  }
 ],
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