audit_analysis.ipynb

{
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     "end_time": "2023-10-05T23:43:37.607509Z",
     "start_time": "2023-10-05T23:43:37.601963Z"
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   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "# Import data\n",
    "data_url = 'https://www.dol.gov/sites/dolgov/files/ETA/oflc/pdfs/PERM_Disclosure_Data_FY2023_Q3.xlsx'\n",
    "df = pd.read_excel(data_url)"
   ],
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    "ExecuteTime": {
     "end_time": "2023-10-05T23:44:36.322685Z",
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   "id": "7e7f021831cb3559"
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "# Add extra data\n",
    "df['DECISION_TIME'] = df['DECISION_DATE'] - df['RECEIVED_DATE']"
   ],
   "metadata": {
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     "end_time": "2023-10-05T23:44:36.330397Z",
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  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "         CASE_NUMBER CASE_STATUS RECEIVED_DATE DECISION_DATE REFILE  \\\n636    A-21260-45963      Denied    2021-11-01    2022-10-03      N   \n692    A-21025-45354      Denied    2021-02-26    2022-10-03      N   \n693    A-21025-45403      Denied    2021-02-26    2022-10-03      N   \n1343   A-21288-59078      Denied    2021-11-02    2022-10-04      N   \n1480   A-21106-75277      Denied    2021-05-04    2022-10-05      N   \n...              ...         ...           ...           ...    ...   \n86165  A-21330-78159      Denied    2021-11-26    2023-06-30      N   \n86189  A-22144-60310      Denied    2022-05-24    2023-06-30      N   \n86190  A-22144-60432      Denied    2022-05-24    2023-06-30      N   \n86191  A-22144-60441      Denied    2022-05-24    2023-06-30      N   \n86192  A-22144-60458      Denied    2022-05-24    2023-06-30      N   \n\n      ORIG_FILE_DATE PREVIOUS_SWA_CASE_NUMBER_STATE SCHD_A_SHEEPHERDER  \\\n636              NaT                            NaN                  N   \n692              NaT                            NaN                  N   \n693              NaT                            NaN                  N   \n1343             NaT                            NaN                  N   \n1480             NaT                            NaN                  N   \n...              ...                            ...                ...   \n86165            NaT                            NaN                  N   \n86189            NaT                            NaN                  N   \n86190            NaT                            NaN                  N   \n86191            NaT                            NaN                  N   \n86192            NaT                            NaN                  N   \n\n                          EMPLOYER_NAME         EMPLOYER_ADDRESS_1  ...  \\\n636             The Florida Company LLC          14804 NE 31st St.  ...   \n692             Stoughton Trailers, LLC         416 S. Academy St.  ...   \n693             Stoughton Trailers, LLC         416 S. Academy St.  ...   \n1343             Cleanstar National Inc           540 Collins Road  ...   \n1480                         GOOGLE LLC  1600 AMPHITHEATRE PARKWAY  ...   \n...                                 ...                        ...  ...   \n86165  RUNNERS DIVERSIFIED INCORPORATED        3428 GA HIGHWAY 297  ...   \n86189       Felher Transportation, Inc.          14703 Cassidy Dr.  ...   \n86190       Felher Transportation, Inc.          14703 Cassidy Dr.  ...   \n86191       Felher Transportation, Inc.          14703 Cassidy Dr.  ...   \n86192       Felher Transportation, Inc.          14703 Cassidy Dr.  ...   \n\n      FOREIGN_WORKER_EXP_WITH_EMPL FOREIGN_WORKER_EMPL_PAY_FOR_ED  \\\n636                            NaN                              N   \n692                            NaN                              N   \n693                            NaN                              N   \n1343                           NaN                              N   \n1480                             N                              N   \n...                            ...                            ...   \n86165                          NaN                              N   \n86189                            N                              N   \n86190                            N                              N   \n86191                            N                              N   \n86192                            N                              N   \n\n      FOREIGN_WORKER_CURR_EMPLOYED EMPLOYER_COMPLETED_APPLICATION  \\\n636                              N                              N   \n692                              N                              N   \n693                              N                              N   \n1343                             N                              N   \n1480                             Y                              N   \n...                            ...                            ...   \n86165                            N                              Y   \n86189                            N                              N   \n86190                            N                              N   \n86191                            N                              N   \n86192                            N                              N   \n\n                   PREPARER_NAME      PREPARER_TITLE  \\\n636           Lourdes S TANCINCO     Attorney at Law   \n692    JOAQUIN  ROSALES ARAMBURU            ATTORNEY   \n693    JOAQUIN  ROSALES ARAMBURU            ATTORNEY   \n1343               ROBERT  LUBIN            ATTORNEY   \n1480             Faraz  Qaisrani  Associate Attorney   \n...                          ...                 ...   \n86165                                            NaN   \n86189            Xavier A Mendez            Attorney   \n86190            Xavier A Mendez            Attorney   \n86191            Xavier A Mendez            Attorney   \n86192            Xavier A Mendez            Attorney   \n\n                     PREPARER_EMAIL  EMP_INFO_DECL_NAME  \\\n636                lou@tancinco.com       Jason V CORRE   \n692    CASEMANAGER@AMERIGOLEGAL.COM        TODD  ROLLER   \n693    CASEMANAGER@AMERIGOLEGAL.COM        TODD  ROLLER   \n1343              aiilaw@aiilaw.com       JACOB  AMEDEE   \n1480        GoogleDOL@balglobal.com     Victoria  Cheng   \n...                             ...                 ...   \n86165                           NaN        Moore  Kelly   \n86189           xamendez@xamlaw.com      Isaac  Herrera   \n86190           xamendez@xamlaw.com      Isaac  Herrera   \n86191           xamendez@xamlaw.com      Isaac  Herrera   \n86192           xamendez@xamlaw.com      Isaac  Herrera   \n\n                     EMP_DECL_TITLE DECISION_TIME  \n636              AdministratorOwner      336 days  \n692      TALENT ACQUISITION MANAGER      584 days  \n693      TALENT ACQUISITION MANAGER      584 days  \n1343                            CEO      336 days  \n1480   Global Mobility  Immigration      519 days  \n...                             ...           ...  \n86165       Human Resources Manager      581 days  \n86189                 VicePresident      402 days  \n86190                 VicePresident      402 days  \n86191                 VicePresident      402 days  \n86192                 VicePresident      402 days  \n\n[10959 rows x 155 columns]",
      "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>CASE_NUMBER</th>\n      <th>CASE_STATUS</th>\n      <th>RECEIVED_DATE</th>\n      <th>DECISION_DATE</th>\n      <th>REFILE</th>\n      <th>ORIG_FILE_DATE</th>\n      <th>PREVIOUS_SWA_CASE_NUMBER_STATE</th>\n      <th>SCHD_A_SHEEPHERDER</th>\n      <th>EMPLOYER_NAME</th>\n      <th>EMPLOYER_ADDRESS_1</th>\n      <th>...</th>\n      <th>FOREIGN_WORKER_EXP_WITH_EMPL</th>\n      <th>FOREIGN_WORKER_EMPL_PAY_FOR_ED</th>\n      <th>FOREIGN_WORKER_CURR_EMPLOYED</th>\n      <th>EMPLOYER_COMPLETED_APPLICATION</th>\n      <th>PREPARER_NAME</th>\n      <th>PREPARER_TITLE</th>\n      <th>PREPARER_EMAIL</th>\n      <th>EMP_INFO_DECL_NAME</th>\n      <th>EMP_DECL_TITLE</th>\n      <th>DECISION_TIME</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>636</th>\n      <td>A-21260-45963</td>\n      <td>Denied</td>\n      <td>2021-11-01</td>\n      <td>2022-10-03</td>\n      <td>N</td>\n      <td>NaT</td>\n      <td>NaN</td>\n      <td>N</td>\n      <td>The Florida Company LLC</td>\n      <td>14804 NE 31st St.</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>Lourdes S TANCINCO</td>\n      <td>Attorney at Law</td>\n      <td>lou@tancinco.com</td>\n      <td>Jason V CORRE</td>\n      <td>AdministratorOwner</td>\n      <td>336 days</td>\n    </tr>\n    <tr>\n      <th>692</th>\n      <td>A-21025-45354</td>\n      <td>Denied</td>\n      <td>2021-02-26</td>\n      <td>2022-10-03</td>\n      <td>N</td>\n      <td>NaT</td>\n      <td>NaN</td>\n      <td>N</td>\n      <td>Stoughton Trailers, LLC</td>\n      <td>416 S. Academy St.</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>JOAQUIN  ROSALES ARAMBURU</td>\n      <td>ATTORNEY</td>\n      <td>CASEMANAGER@AMERIGOLEGAL.COM</td>\n      <td>TODD  ROLLER</td>\n      <td>TALENT ACQUISITION MANAGER</td>\n      <td>584 days</td>\n    </tr>\n    <tr>\n      <th>693</th>\n      <td>A-21025-45403</td>\n      <td>Denied</td>\n      <td>2021-02-26</td>\n      <td>2022-10-03</td>\n      <td>N</td>\n      <td>NaT</td>\n      <td>NaN</td>\n      <td>N</td>\n      <td>Stoughton Trailers, LLC</td>\n      <td>416 S. Academy St.</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>JOAQUIN  ROSALES ARAMBURU</td>\n      <td>ATTORNEY</td>\n      <td>CASEMANAGER@AMERIGOLEGAL.COM</td>\n      <td>TODD  ROLLER</td>\n      <td>TALENT ACQUISITION MANAGER</td>\n      <td>584 days</td>\n    </tr>\n    <tr>\n      <th>1343</th>\n      <td>A-21288-59078</td>\n      <td>Denied</td>\n      <td>2021-11-02</td>\n      <td>2022-10-04</td>\n      <td>N</td>\n      <td>NaT</td>\n      <td>NaN</td>\n      <td>N</td>\n      <td>Cleanstar National Inc</td>\n      <td>540 Collins Road</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>ROBERT  LUBIN</td>\n      <td>ATTORNEY</td>\n      <td>aiilaw@aiilaw.com</td>\n      <td>JACOB  AMEDEE</td>\n      <td>CEO</td>\n      <td>336 days</td>\n    </tr>\n    <tr>\n      <th>1480</th>\n      <td>A-21106-75277</td>\n      <td>Denied</td>\n      <td>2021-05-04</td>\n      <td>2022-10-05</td>\n      <td>N</td>\n      <td>NaT</td>\n      <td>NaN</td>\n      <td>N</td>\n      <td>GOOGLE LLC</td>\n      <td>1600 AMPHITHEATRE PARKWAY</td>\n      <td>...</td>\n      <td>N</td>\n      <td>N</td>\n      <td>Y</td>\n      <td>N</td>\n      <td>Faraz  Qaisrani</td>\n      <td>Associate Attorney</td>\n      <td>GoogleDOL@balglobal.com</td>\n      <td>Victoria  Cheng</td>\n      <td>Global Mobility  Immigration</td>\n      <td>519 days</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    </tr>\n    <tr>\n      <th>86165</th>\n      <td>A-21330-78159</td>\n      <td>Denied</td>\n      <td>2021-11-26</td>\n      <td>2023-06-30</td>\n      <td>N</td>\n      <td>NaT</td>\n      <td>NaN</td>\n      <td>N</td>\n      <td>RUNNERS DIVERSIFIED INCORPORATED</td>\n      <td>3428 GA HIGHWAY 297</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>N</td>\n      <td>N</td>\n      <td>Y</td>\n      <td></td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Moore  Kelly</td>\n      <td>Human Resources Manager</td>\n      <td>581 days</td>\n    </tr>\n    <tr>\n      <th>86189</th>\n      <td>A-22144-60310</td>\n      <td>Denied</td>\n      <td>2022-05-24</td>\n      <td>2023-06-30</td>\n      <td>N</td>\n      <td>NaT</td>\n      <td>NaN</td>\n      <td>N</td>\n      <td>Felher Transportation, Inc.</td>\n      <td>14703 Cassidy Dr.</td>\n      <td>...</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>Xavier A Mendez</td>\n      <td>Attorney</td>\n      <td>xamendez@xamlaw.com</td>\n      <td>Isaac  Herrera</td>\n      <td>VicePresident</td>\n      <td>402 days</td>\n    </tr>\n    <tr>\n      <th>86190</th>\n      <td>A-22144-60432</td>\n      <td>Denied</td>\n      <td>2022-05-24</td>\n      <td>2023-06-30</td>\n      <td>N</td>\n      <td>NaT</td>\n      <td>NaN</td>\n      <td>N</td>\n      <td>Felher Transportation, Inc.</td>\n      <td>14703 Cassidy Dr.</td>\n      <td>...</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>Xavier A Mendez</td>\n      <td>Attorney</td>\n      <td>xamendez@xamlaw.com</td>\n      <td>Isaac  Herrera</td>\n      <td>VicePresident</td>\n      <td>402 days</td>\n    </tr>\n    <tr>\n      <th>86191</th>\n      <td>A-22144-60441</td>\n      <td>Denied</td>\n      <td>2022-05-24</td>\n      <td>2023-06-30</td>\n      <td>N</td>\n      <td>NaT</td>\n      <td>NaN</td>\n      <td>N</td>\n      <td>Felher Transportation, Inc.</td>\n      <td>14703 Cassidy Dr.</td>\n      <td>...</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>Xavier A Mendez</td>\n      <td>Attorney</td>\n      <td>xamendez@xamlaw.com</td>\n      <td>Isaac  Herrera</td>\n      <td>VicePresident</td>\n      <td>402 days</td>\n    </tr>\n    <tr>\n      <th>86192</th>\n      <td>A-22144-60458</td>\n      <td>Denied</td>\n      <td>2022-05-24</td>\n      <td>2023-06-30</td>\n      <td>N</td>\n      <td>NaT</td>\n      <td>NaN</td>\n      <td>N</td>\n      <td>Felher Transportation, Inc.</td>\n      <td>14703 Cassidy Dr.</td>\n      <td>...</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>N</td>\n      <td>Xavier A Mendez</td>\n      <td>Attorney</td>\n      <td>xamendez@xamlaw.com</td>\n      <td>Isaac  Herrera</td>\n      <td>VicePresident</td>\n      <td>402 days</td>\n    </tr>\n  </tbody>\n</table>\n<p>10959 rows × 155 columns</p>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter the audit case, assuming that the audit case is the case that has a decision time of more than 365 days\n",
    "days_audit = 320\n",
    "df_audit = df[df['DECISION_TIME'] > pd.Timedelta(days=days_audit)]\n",
    "df_audit = df_audit[df_audit['DECISION_TIME'] < pd.Timedelta(days=600)]\n",
    "df_audit = df_audit[~df_audit['CASE_STATUS'].isin(['Certified-Expired', 'Withdrawn'])]\n",
    "df_audit"
   ],
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    "ExecuteTime": {
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  {
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    {
     "data": {
      "text/plain": "<Figure size 1170x827 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualise the histogram distribution audt decision time\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
    "sns.set(font_scale=1.5)\n",
    "ax = sns.histplot(x=df_audit['DECISION_TIME'].dt.days)\n",
    "ax.set(xlabel='Decision Time (days)', title='Distribution of Audit Decision Time')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-02T16:18:32.667925Z",
     "start_time": "2023-10-02T16:18:32.462088Z"
    }
   },
   "id": "e5c93c1698b2fa8e"
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.lines.Line2D at 0x29631b950>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1170x827 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot histogram with accumulated percentage\n",
    "import numpy as np\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
    "sns.set(font_scale=1.5)\n",
    "ax = sns.histplot(x=df_audit['DECISION_TIME'].dt.days, cumulative=True, stat='density', discrete=True)\n",
    "# add the y axis label\n",
    "ax.set(ylabel='Accumulated Percentage', title='Distribution of Audit Decision Time')\n",
    "#add y axis reference line in red\n",
    "ax.axhline(y=0.95, color='r', linestyle='--')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-02T16:21:14.340654Z",
     "start_time": "2023-10-02T16:21:14.067430Z"
    }
   },
   "id": "704e85c918f78722"
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "                                                   DECISION_TIME  \\\n                                                           count   \nFOREIGN_WORKER_INFO_MAJOR                                          \n3RD GRADE MIDDLE SCHOOL                                        1   \n3YEAR BACHELOR OF COMMERCE DEGREE W CONCENTRATI...             1   \nACADEMIC                                                      13   \nACADEMICS                                                      1   \nACCOUNTANCY                                                   10   \n...                                                          ...   \nWEB DESIGN                                                     1   \nWELDING ENGINEERING TECHNOLOGY METALLURGY                      1   \nWELDING INTERMEDIATE TIG                                       1   \nWORLD LANGUAGES                                                1   \nWORSHIP LEADERSHIP                                             1   \n\n                                                                                 \\\n                                                                           mean   \nFOREIGN_WORKER_INFO_MAJOR                                                         \n3RD GRADE MIDDLE SCHOOL                                       357 days 00:00:00   \n3YEAR BACHELOR OF COMMERCE DEGREE W CONCENTRATI...            351 days 00:00:00   \nACADEMIC                                            391 days 18:27:41.538461540   \nACADEMICS                                                     365 days 00:00:00   \nACCOUNTANCY                                                   385 days 07:12:00   \n...                                                                         ...   \nWEB DESIGN                                                    359 days 00:00:00   \nWELDING ENGINEERING TECHNOLOGY METALLURGY                     372 days 00:00:00   \nWELDING INTERMEDIATE TIG                                      355 days 00:00:00   \nWORLD LANGUAGES                                               349 days 00:00:00   \nWORSHIP LEADERSHIP                                            368 days 00:00:00   \n\n                                                                                \\\n                                                                           std   \nFOREIGN_WORKER_INFO_MAJOR                                                        \n3RD GRADE MIDDLE SCHOOL                                                    NaT   \n3YEAR BACHELOR OF COMMERCE DEGREE W CONCENTRATI...                         NaT   \nACADEMIC                                            58 days 18:40:23.382234960   \nACADEMICS                                                                  NaT   \nACCOUNTANCY                                         43 days 21:27:16.228072925   \n...                                                                        ...   \nWEB DESIGN                                                                 NaT   \nWELDING ENGINEERING TECHNOLOGY METALLURGY                                  NaT   \nWELDING INTERMEDIATE TIG                                                   NaT   \nWORLD LANGUAGES                                                            NaT   \nWORSHIP LEADERSHIP                                                         NaT   \n\n                                                                       \\\n                                                                  min   \nFOREIGN_WORKER_INFO_MAJOR                                               \n3RD GRADE MIDDLE SCHOOL                             357 days 00:00:00   \n3YEAR BACHELOR OF COMMERCE DEGREE W CONCENTRATI...  351 days 00:00:00   \nACADEMIC                                            348 days 00:00:00   \nACADEMICS                                           365 days 00:00:00   \nACCOUNTANCY                                         339 days 00:00:00   \n...                                                               ...   \nWEB DESIGN                                          359 days 00:00:00   \nWELDING ENGINEERING TECHNOLOGY METALLURGY           372 days 00:00:00   \nWELDING INTERMEDIATE TIG                            355 days 00:00:00   \nWORLD LANGUAGES                                     349 days 00:00:00   \nWORSHIP LEADERSHIP                                  368 days 00:00:00   \n\n                                                                       \\\n                                                                  25%   \nFOREIGN_WORKER_INFO_MAJOR                                               \n3RD GRADE MIDDLE SCHOOL                             357 days 00:00:00   \n3YEAR BACHELOR OF COMMERCE DEGREE W CONCENTRATI...  351 days 00:00:00   \nACADEMIC                                            365 days 00:00:00   \nACADEMICS                                           365 days 00:00:00   \nACCOUNTANCY                                         363 days 12:00:00   \n...                                                               ...   \nWEB DESIGN                                          359 days 00:00:00   \nWELDING ENGINEERING TECHNOLOGY METALLURGY           372 days 00:00:00   \nWELDING INTERMEDIATE TIG                            355 days 00:00:00   \nWORLD LANGUAGES                                     349 days 00:00:00   \nWORSHIP LEADERSHIP                                  368 days 00:00:00   \n\n                                                                       \\\n                                                                  50%   \nFOREIGN_WORKER_INFO_MAJOR                                               \n3RD GRADE MIDDLE SCHOOL                             357 days 00:00:00   \n3YEAR BACHELOR OF COMMERCE DEGREE W CONCENTRATI...  351 days 00:00:00   \nACADEMIC                                            378 days 00:00:00   \nACADEMICS                                           365 days 00:00:00   \nACCOUNTANCY                                         378 days 00:00:00   \n...                                                               ...   \nWEB DESIGN                                          359 days 00:00:00   \nWELDING ENGINEERING TECHNOLOGY METALLURGY           372 days 00:00:00   \nWELDING INTERMEDIATE TIG                            355 days 00:00:00   \nWORLD LANGUAGES                                     349 days 00:00:00   \nWORSHIP LEADERSHIP                                  368 days 00:00:00   \n\n                                                                       \\\n                                                                  75%   \nFOREIGN_WORKER_INFO_MAJOR                                               \n3RD GRADE MIDDLE SCHOOL                             357 days 00:00:00   \n3YEAR BACHELOR OF COMMERCE DEGREE W CONCENTRATI...  351 days 00:00:00   \nACADEMIC                                            392 days 00:00:00   \nACADEMICS                                           365 days 00:00:00   \nACCOUNTANCY                                         391 days 06:00:00   \n...                                                               ...   \nWEB DESIGN                                          359 days 00:00:00   \nWELDING ENGINEERING TECHNOLOGY METALLURGY           372 days 00:00:00   \nWELDING INTERMEDIATE TIG                            355 days 00:00:00   \nWORLD LANGUAGES                                     349 days 00:00:00   \nWORSHIP LEADERSHIP                                  368 days 00:00:00   \n\n                                                                       \n                                                                  max  \nFOREIGN_WORKER_INFO_MAJOR                                              \n3RD GRADE MIDDLE SCHOOL                             357 days 00:00:00  \n3YEAR BACHELOR OF COMMERCE DEGREE W CONCENTRATI...  351 days 00:00:00  \nACADEMIC                                            581 days 00:00:00  \nACADEMICS                                           365 days 00:00:00  \nACCOUNTANCY                                         498 days 00:00:00  \n...                                                               ...  \nWEB DESIGN                                          359 days 00:00:00  \nWELDING ENGINEERING TECHNOLOGY METALLURGY           372 days 00:00:00  \nWELDING INTERMEDIATE TIG                            355 days 00:00:00  \nWORLD LANGUAGES                                     349 days 00:00:00  \nWORSHIP LEADERSHIP                                  368 days 00:00:00  \n\n[1459 rows x 8 columns]",
      "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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"8\" halign=\"left\">DECISION_TIME</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n    <tr>\n      <th>FOREIGN_WORKER_INFO_MAJOR</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>3RD GRADE MIDDLE SCHOOL</th>\n      <td>1</td>\n      <td>357 days 00:00:00</td>\n      <td>NaT</td>\n      <td>357 days 00:00:00</td>\n      <td>357 days 00:00:00</td>\n      <td>357 days 00:00:00</td>\n      <td>357 days 00:00:00</td>\n      <td>357 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>3YEAR BACHELOR OF COMMERCE DEGREE W CONCENTRATION IN ACCOUNTING, MANAGERIAL ACCOUNTING  FINANCE</th>\n      <td>1</td>\n      <td>351 days 00:00:00</td>\n      <td>NaT</td>\n      <td>351 days 00:00:00</td>\n      <td>351 days 00:00:00</td>\n      <td>351 days 00:00:00</td>\n      <td>351 days 00:00:00</td>\n      <td>351 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>ACADEMIC</th>\n      <td>13</td>\n      <td>391 days 18:27:41.538461540</td>\n      <td>58 days 18:40:23.382234960</td>\n      <td>348 days 00:00:00</td>\n      <td>365 days 00:00:00</td>\n      <td>378 days 00:00:00</td>\n      <td>392 days 00:00:00</td>\n      <td>581 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>ACADEMICS</th>\n      <td>1</td>\n      <td>365 days 00:00:00</td>\n      <td>NaT</td>\n      <td>365 days 00:00:00</td>\n      <td>365 days 00:00:00</td>\n      <td>365 days 00:00:00</td>\n      <td>365 days 00:00:00</td>\n      <td>365 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>ACCOUNTANCY</th>\n      <td>10</td>\n      <td>385 days 07:12:00</td>\n      <td>43 days 21:27:16.228072925</td>\n      <td>339 days 00:00:00</td>\n      <td>363 days 12:00:00</td>\n      <td>378 days 00:00:00</td>\n      <td>391 days 06:00:00</td>\n      <td>498 days 00:00:00</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    </tr>\n    <tr>\n      <th>WEB DESIGN</th>\n      <td>1</td>\n      <td>359 days 00:00:00</td>\n      <td>NaT</td>\n      <td>359 days 00:00:00</td>\n      <td>359 days 00:00:00</td>\n      <td>359 days 00:00:00</td>\n      <td>359 days 00:00:00</td>\n      <td>359 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>WELDING ENGINEERING TECHNOLOGY METALLURGY</th>\n      <td>1</td>\n      <td>372 days 00:00:00</td>\n      <td>NaT</td>\n      <td>372 days 00:00:00</td>\n      <td>372 days 00:00:00</td>\n      <td>372 days 00:00:00</td>\n      <td>372 days 00:00:00</td>\n      <td>372 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>WELDING INTERMEDIATE TIG</th>\n      <td>1</td>\n      <td>355 days 00:00:00</td>\n      <td>NaT</td>\n      <td>355 days 00:00:00</td>\n      <td>355 days 00:00:00</td>\n      <td>355 days 00:00:00</td>\n      <td>355 days 00:00:00</td>\n      <td>355 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>WORLD LANGUAGES</th>\n      <td>1</td>\n      <td>349 days 00:00:00</td>\n      <td>NaT</td>\n      <td>349 days 00:00:00</td>\n      <td>349 days 00:00:00</td>\n      <td>349 days 00:00:00</td>\n      <td>349 days 00:00:00</td>\n      <td>349 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>WORSHIP LEADERSHIP</th>\n      <td>1</td>\n      <td>368 days 00:00:00</td>\n      <td>NaT</td>\n      <td>368 days 00:00:00</td>\n      <td>368 days 00:00:00</td>\n      <td>368 days 00:00:00</td>\n      <td>368 days 00:00:00</td>\n      <td>368 days 00:00:00</td>\n    </tr>\n  </tbody>\n</table>\n<p>1459 rows × 8 columns</p>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get some stats about the average days waiting per foreing major and add count of cases\n",
    "df_audit[['FOREIGN_WORKER_INFO_MAJOR', 'DECISION_TIME']].groupby('FOREIGN_WORKER_INFO_MAJOR').describe()\n",
    "# df_audit[['FOREIGN_WORKER_INFO_MAJOR', 'DECISION_TIME']].groupby('FOREIGN_WORKER_INFO_MAJOR').mean()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-05T23:54:05.595185Z",
     "start_time": "2023-10-05T23:54:04.567673Z"
    }
   },
   "id": "a1e2404d6404a763"
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "                             DECISION_TIME                               \\\n                                     count                         mean   \nEMPLOYER_STATE_PROVINCE                                                   \n UTA                                     1            336 days 00:00:00   \nALABAMA                                 78            359 days 16:00:00   \nALABAMA ALABAMA                          2            370 days 00:00:00   \nALASKA                                   5            390 days 19:12:00   \nARIZONA                                 97  372 days 04:27:12.989690720   \n...                                    ...                          ...   \nWEST VIRGINIA                            1            346 days 00:00:00   \nWISCONSIN                              308  407 days 05:03:53.766233768   \nWISCONSIN CENTEC                         1            385 days 00:00:00   \nWISCONSIN Chula Vista Resort            10            384 days 09:36:00   \nWYOMING                                  4            396 days 00:00:00   \n\n                                                                             \\\n                                                     std                min   \nEMPLOYER_STATE_PROVINCE                                                       \n UTA                                                 NaT  336 days 00:00:00   \nALABAMA                       19 days 07:22:27.877215848  321 days 00:00:00   \nALABAMA ALABAMA               22 days 15:03:28.828624566  354 days 00:00:00   \nALASKA                        64 days 17:17:02.417781165  345 days 00:00:00   \nARIZONA                       38 days 13:32:46.690344759  321 days 00:00:00   \n...                                                  ...                ...   \nWEST VIRGINIA                                        NaT  346 days 00:00:00   \nWISCONSIN                     77 days 18:32:58.057592670  321 days 00:00:00   \nWISCONSIN CENTEC                                     NaT  385 days 00:00:00   \nWISCONSIN Chula Vista Resort  11 days 02:20:28.084695061  353 days 00:00:00   \nWYOMING                        4 days 16:34:11.921648744  391 days 00:00:00   \n\n                                                                    \\\n                                            25%                50%   \nEMPLOYER_STATE_PROVINCE                                              \n UTA                          336 days 00:00:00  336 days 00:00:00   \nALABAMA                       347 days 00:00:00  358 days 00:00:00   \nALABAMA ALABAMA               362 days 00:00:00  370 days 00:00:00   \nALASKA                        356 days 00:00:00  358 days 00:00:00   \nARIZONA                       351 days 00:00:00  365 days 00:00:00   \n...                                         ...                ...   \nWEST VIRGINIA                 346 days 00:00:00  346 days 00:00:00   \nWISCONSIN                     353 days 18:00:00  371 days 00:00:00   \nWISCONSIN CENTEC              385 days 00:00:00  385 days 00:00:00   \nWISCONSIN Chula Vista Resort  388 days 00:00:00  388 days 00:00:00   \nWYOMING                       392 days 12:00:00  396 days 12:00:00   \n\n                                                                    \n                                            75%                max  \nEMPLOYER_STATE_PROVINCE                                             \n UTA                          336 days 00:00:00  336 days 00:00:00  \nALABAMA                       367 days 00:00:00  458 days 00:00:00  \nALABAMA ALABAMA               378 days 00:00:00  386 days 00:00:00  \nALASKA                        393 days 00:00:00  502 days 00:00:00  \nARIZONA                       374 days 00:00:00  576 days 00:00:00  \n...                                         ...                ...  \nWEST VIRGINIA                 346 days 00:00:00  346 days 00:00:00  \nWISCONSIN                     491 days 06:00:00  584 days 00:00:00  \nWISCONSIN CENTEC              385 days 00:00:00  385 days 00:00:00  \nWISCONSIN Chula Vista Resort  388 days 00:00:00  390 days 00:00:00  \nWYOMING                       400 days 00:00:00  400 days 00:00:00  \n\n[183 rows x 8 columns]",
      "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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"8\" halign=\"left\">DECISION_TIME</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n    <tr>\n      <th>EMPLOYER_STATE_PROVINCE</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>UTA</th>\n      <td>1</td>\n      <td>336 days 00:00:00</td>\n      <td>NaT</td>\n      <td>336 days 00:00:00</td>\n      <td>336 days 00:00:00</td>\n      <td>336 days 00:00:00</td>\n      <td>336 days 00:00:00</td>\n      <td>336 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>ALABAMA</th>\n      <td>78</td>\n      <td>359 days 16:00:00</td>\n      <td>19 days 07:22:27.877215848</td>\n      <td>321 days 00:00:00</td>\n      <td>347 days 00:00:00</td>\n      <td>358 days 00:00:00</td>\n      <td>367 days 00:00:00</td>\n      <td>458 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>ALABAMA ALABAMA</th>\n      <td>2</td>\n      <td>370 days 00:00:00</td>\n      <td>22 days 15:03:28.828624566</td>\n      <td>354 days 00:00:00</td>\n      <td>362 days 00:00:00</td>\n      <td>370 days 00:00:00</td>\n      <td>378 days 00:00:00</td>\n      <td>386 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>ALASKA</th>\n      <td>5</td>\n      <td>390 days 19:12:00</td>\n      <td>64 days 17:17:02.417781165</td>\n      <td>345 days 00:00:00</td>\n      <td>356 days 00:00:00</td>\n      <td>358 days 00:00:00</td>\n      <td>393 days 00:00:00</td>\n      <td>502 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>ARIZONA</th>\n      <td>97</td>\n      <td>372 days 04:27:12.989690720</td>\n      <td>38 days 13:32:46.690344759</td>\n      <td>321 days 00:00:00</td>\n      <td>351 days 00:00:00</td>\n      <td>365 days 00:00:00</td>\n      <td>374 days 00:00:00</td>\n      <td>576 days 00:00:00</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    </tr>\n    <tr>\n      <th>WEST VIRGINIA</th>\n      <td>1</td>\n      <td>346 days 00:00:00</td>\n      <td>NaT</td>\n      <td>346 days 00:00:00</td>\n      <td>346 days 00:00:00</td>\n      <td>346 days 00:00:00</td>\n      <td>346 days 00:00:00</td>\n      <td>346 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>WISCONSIN</th>\n      <td>308</td>\n      <td>407 days 05:03:53.766233768</td>\n      <td>77 days 18:32:58.057592670</td>\n      <td>321 days 00:00:00</td>\n      <td>353 days 18:00:00</td>\n      <td>371 days 00:00:00</td>\n      <td>491 days 06:00:00</td>\n      <td>584 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>WISCONSIN CENTEC</th>\n      <td>1</td>\n      <td>385 days 00:00:00</td>\n      <td>NaT</td>\n      <td>385 days 00:00:00</td>\n      <td>385 days 00:00:00</td>\n      <td>385 days 00:00:00</td>\n      <td>385 days 00:00:00</td>\n      <td>385 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>WISCONSIN Chula Vista Resort</th>\n      <td>10</td>\n      <td>384 days 09:36:00</td>\n      <td>11 days 02:20:28.084695061</td>\n      <td>353 days 00:00:00</td>\n      <td>388 days 00:00:00</td>\n      <td>388 days 00:00:00</td>\n      <td>388 days 00:00:00</td>\n      <td>390 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>WYOMING</th>\n      <td>4</td>\n      <td>396 days 00:00:00</td>\n      <td>4 days 16:34:11.921648744</td>\n      <td>391 days 00:00:00</td>\n      <td>392 days 12:00:00</td>\n      <td>396 days 12:00:00</td>\n      <td>400 days 00:00:00</td>\n      <td>400 days 00:00:00</td>\n    </tr>\n  </tbody>\n</table>\n<p>183 rows × 8 columns</p>\n</div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_audit[['EMPLOYER_STATE_PROVINCE', 'DECISION_TIME']].groupby('EMPLOYER_STATE_PROVINCE').describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-05T23:54:15.587565Z",
     "start_time": "2023-10-05T23:54:15.435404Z"
    }
   },
   "id": "19d72d3bd4d164d0"
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "            DECISION_TIME                               \\\n                    count                         mean   \nCASE_STATUS                                              \nCertified            8502  370 days 08:32:41.326746648   \nDenied               2457  395 days 04:55:23.076923076   \n\n                                                                               \\\n                                    std                min                25%   \nCASE_STATUS                                                                     \nCertified    36 days 15:13:09.059314263  321 days 00:00:00  349 days 00:00:00   \nDenied       66 days 13:05:49.450714724  321 days 00:00:00  351 days 00:00:00   \n\n                                                                      \n                           50%                75%                max  \nCASE_STATUS                                                           \nCertified    365 days 00:00:00  384 days 00:00:00  599 days 00:00:00  \nDenied       370 days 00:00:00  401 days 00:00:00  599 days 00:00:00  ",
      "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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"8\" halign=\"left\">DECISION_TIME</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n    <tr>\n      <th>CASE_STATUS</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Certified</th>\n      <td>8502</td>\n      <td>370 days 08:32:41.326746648</td>\n      <td>36 days 15:13:09.059314263</td>\n      <td>321 days 00:00:00</td>\n      <td>349 days 00:00:00</td>\n      <td>365 days 00:00:00</td>\n      <td>384 days 00:00:00</td>\n      <td>599 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>Denied</th>\n      <td>2457</td>\n      <td>395 days 04:55:23.076923076</td>\n      <td>66 days 13:05:49.450714724</td>\n      <td>321 days 00:00:00</td>\n      <td>351 days 00:00:00</td>\n      <td>370 days 00:00:00</td>\n      <td>401 days 00:00:00</td>\n      <td>599 days 00:00:00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_audit[['CASE_STATUS', 'DECISION_TIME']].groupby('CASE_STATUS').describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-05T23:55:23.464301Z",
     "start_time": "2023-10-05T23:55:23.450363Z"
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   },
   "id": "eedac48cd63f7a7d"
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "                             DECISION_TIME                     \\\n                                     count               mean   \nAGENT_ATTORNEY_FIRM_NAME                                        \n12619521 Canada Inc.                     1  520 days 00:00:00   \n250 Howe Street                          1  359 days 00:00:00   \n80 SW 8 Street, Ste 2810                 1  351 days 00:00:00   \nA Biggar Law LLC                         1  355 days 00:00:00   \nA. Farkas LLC                            1  544 days 00:00:00   \n...                                    ...                ...   \ncinotti galgano                          1  554 days 00:00:00   \nconnie chan  associates P C              1  392 days 00:00:00   \nkim song and associates pllc             1  395 days 00:00:00   \nlaw offices of mario bozza               3  381 days 16:00:00   \nnizamian  nizamian llp                   1  353 days 00:00:00   \n\n                                                                             \\\n                                                     std                min   \nAGENT_ATTORNEY_FIRM_NAME                                                      \n12619521 Canada Inc.                                 NaT  520 days 00:00:00   \n250 Howe Street                                      NaT  359 days 00:00:00   \n80 SW 8 Street, Ste 2810                             NaT  351 days 00:00:00   \nA Biggar Law LLC                                     NaT  355 days 00:00:00   \nA. Farkas LLC                                        NaT  544 days 00:00:00   \n...                                                  ...                ...   \ncinotti galgano                                      NaT  554 days 00:00:00   \nconnie chan  associates P C                          NaT  392 days 00:00:00   \nkim song and associates pllc                         NaT  395 days 00:00:00   \nlaw offices of mario bozza    28 days 10:20:55.983729405  350 days 00:00:00   \nnizamian  nizamian llp                               NaT  353 days 00:00:00   \n\n                                                                    \\\n                                            25%                50%   \nAGENT_ATTORNEY_FIRM_NAME                                             \n12619521 Canada Inc.          520 days 00:00:00  520 days 00:00:00   \n250 Howe Street               359 days 00:00:00  359 days 00:00:00   \n80 SW 8 Street, Ste 2810      351 days 00:00:00  351 days 00:00:00   \nA Biggar Law LLC              355 days 00:00:00  355 days 00:00:00   \nA. Farkas LLC                 544 days 00:00:00  544 days 00:00:00   \n...                                         ...                ...   \ncinotti galgano               554 days 00:00:00  554 days 00:00:00   \nconnie chan  associates P C   392 days 00:00:00  392 days 00:00:00   \nkim song and associates pllc  395 days 00:00:00  395 days 00:00:00   \nlaw offices of mario bozza    370 days 00:00:00  390 days 00:00:00   \nnizamian  nizamian llp        353 days 00:00:00  353 days 00:00:00   \n\n                                                                    \n                                            75%                max  \nAGENT_ATTORNEY_FIRM_NAME                                            \n12619521 Canada Inc.          520 days 00:00:00  520 days 00:00:00  \n250 Howe Street               359 days 00:00:00  359 days 00:00:00  \n80 SW 8 Street, Ste 2810      351 days 00:00:00  351 days 00:00:00  \nA Biggar Law LLC              355 days 00:00:00  355 days 00:00:00  \nA. Farkas LLC                 544 days 00:00:00  544 days 00:00:00  \n...                                         ...                ...  \ncinotti galgano               554 days 00:00:00  554 days 00:00:00  \nconnie chan  associates P C   392 days 00:00:00  392 days 00:00:00  \nkim song and associates pllc  395 days 00:00:00  395 days 00:00:00  \nlaw offices of mario bozza    397 days 12:00:00  405 days 00:00:00  \nnizamian  nizamian llp        353 days 00:00:00  353 days 00:00:00  \n\n[1625 rows x 8 columns]",
      "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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"8\" halign=\"left\">DECISION_TIME</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n    <tr>\n      <th>AGENT_ATTORNEY_FIRM_NAME</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>12619521 Canada Inc.</th>\n      <td>1</td>\n      <td>520 days 00:00:00</td>\n      <td>NaT</td>\n      <td>520 days 00:00:00</td>\n      <td>520 days 00:00:00</td>\n      <td>520 days 00:00:00</td>\n      <td>520 days 00:00:00</td>\n      <td>520 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>250 Howe Street</th>\n      <td>1</td>\n      <td>359 days 00:00:00</td>\n      <td>NaT</td>\n      <td>359 days 00:00:00</td>\n      <td>359 days 00:00:00</td>\n      <td>359 days 00:00:00</td>\n      <td>359 days 00:00:00</td>\n      <td>359 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>80 SW 8 Street, Ste 2810</th>\n      <td>1</td>\n      <td>351 days 00:00:00</td>\n      <td>NaT</td>\n      <td>351 days 00:00:00</td>\n      <td>351 days 00:00:00</td>\n      <td>351 days 00:00:00</td>\n      <td>351 days 00:00:00</td>\n      <td>351 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>A Biggar Law LLC</th>\n      <td>1</td>\n      <td>355 days 00:00:00</td>\n      <td>NaT</td>\n      <td>355 days 00:00:00</td>\n      <td>355 days 00:00:00</td>\n      <td>355 days 00:00:00</td>\n      <td>355 days 00:00:00</td>\n      <td>355 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>A. Farkas LLC</th>\n      <td>1</td>\n      <td>544 days 00:00:00</td>\n      <td>NaT</td>\n      <td>544 days 00:00:00</td>\n      <td>544 days 00:00:00</td>\n      <td>544 days 00:00:00</td>\n      <td>544 days 00:00:00</td>\n      <td>544 days 00:00:00</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    </tr>\n    <tr>\n      <th>cinotti galgano</th>\n      <td>1</td>\n      <td>554 days 00:00:00</td>\n      <td>NaT</td>\n      <td>554 days 00:00:00</td>\n      <td>554 days 00:00:00</td>\n      <td>554 days 00:00:00</td>\n      <td>554 days 00:00:00</td>\n      <td>554 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>connie chan  associates P C</th>\n      <td>1</td>\n      <td>392 days 00:00:00</td>\n      <td>NaT</td>\n      <td>392 days 00:00:00</td>\n      <td>392 days 00:00:00</td>\n      <td>392 days 00:00:00</td>\n      <td>392 days 00:00:00</td>\n      <td>392 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>kim song and associates pllc</th>\n      <td>1</td>\n      <td>395 days 00:00:00</td>\n      <td>NaT</td>\n      <td>395 days 00:00:00</td>\n      <td>395 days 00:00:00</td>\n      <td>395 days 00:00:00</td>\n      <td>395 days 00:00:00</td>\n      <td>395 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>law offices of mario bozza</th>\n      <td>3</td>\n      <td>381 days 16:00:00</td>\n      <td>28 days 10:20:55.983729405</td>\n      <td>350 days 00:00:00</td>\n      <td>370 days 00:00:00</td>\n      <td>390 days 00:00:00</td>\n      <td>397 days 12:00:00</td>\n      <td>405 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>nizamian  nizamian llp</th>\n      <td>1</td>\n      <td>353 days 00:00:00</td>\n      <td>NaT</td>\n      <td>353 days 00:00:00</td>\n      <td>353 days 00:00:00</td>\n      <td>353 days 00:00:00</td>\n      <td>353 days 00:00:00</td>\n      <td>353 days 00:00:00</td>\n    </tr>\n  </tbody>\n</table>\n<p>1625 rows × 8 columns</p>\n</div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_audit[['AGENT_ATTORNEY_FIRM_NAME', 'DECISION_TIME']].groupby('AGENT_ATTORNEY_FIRM_NAME').describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-05T23:56:36.522771Z",
     "start_time": "2023-10-05T23:56:35.296728Z"
    }
   },
   "id": "85bf15fd8e1509f6"
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "               DECISION_TIME                               \\\n                       count                         mean   \nPW_SKILL_LEVEL                                              \nLevel I                 4744  376 days 21:19:25.598650928   \nLevel II                2177  379 days 15:31:59.889756544   \nLevel III               1615  368 days 23:37:42.538699692   \nLevel IV                1970  373 days 09:13:20.406091372   \n\n                                                               \\\n                                       std                min   \nPW_SKILL_LEVEL                                                  \nLevel I         45 days 09:02:16.851288068  321 days 00:00:00   \nLevel II        54 days 06:10:15.882193338  321 days 00:00:00   \nLevel III       41 days 12:09:50.161816027  321 days 00:00:00   \nLevel IV        39 days 12:35:21.063608250  321 days 00:00:00   \n\n                                                                         \\\n                              25%                50%                75%   \nPW_SKILL_LEVEL                                                            \nLevel I         350 days 00:00:00  367 days 00:00:00  389 days 00:00:00   \nLevel II        350 days 00:00:00  366 days 00:00:00  389 days 00:00:00   \nLevel III       348 days 00:00:00  365 days 00:00:00  382 days 00:00:00   \nLevel IV        351 days 00:00:00  365 days 12:00:00  385 days 00:00:00   \n\n                                   \n                              max  \nPW_SKILL_LEVEL                     \nLevel I         599 days 00:00:00  \nLevel II        599 days 00:00:00  \nLevel III       598 days 00:00:00  \nLevel IV        597 days 00:00:00  ",
      "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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"8\" halign=\"left\">DECISION_TIME</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n    <tr>\n      <th>PW_SKILL_LEVEL</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Level I</th>\n      <td>4744</td>\n      <td>376 days 21:19:25.598650928</td>\n      <td>45 days 09:02:16.851288068</td>\n      <td>321 days 00:00:00</td>\n      <td>350 days 00:00:00</td>\n      <td>367 days 00:00:00</td>\n      <td>389 days 00:00:00</td>\n      <td>599 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>Level II</th>\n      <td>2177</td>\n      <td>379 days 15:31:59.889756544</td>\n      <td>54 days 06:10:15.882193338</td>\n      <td>321 days 00:00:00</td>\n      <td>350 days 00:00:00</td>\n      <td>366 days 00:00:00</td>\n      <td>389 days 00:00:00</td>\n      <td>599 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>Level III</th>\n      <td>1615</td>\n      <td>368 days 23:37:42.538699692</td>\n      <td>41 days 12:09:50.161816027</td>\n      <td>321 days 00:00:00</td>\n      <td>348 days 00:00:00</td>\n      <td>365 days 00:00:00</td>\n      <td>382 days 00:00:00</td>\n      <td>598 days 00:00:00</td>\n    </tr>\n    <tr>\n      <th>Level IV</th>\n      <td>1970</td>\n      <td>373 days 09:13:20.406091372</td>\n      <td>39 days 12:35:21.063608250</td>\n      <td>321 days 00:00:00</td>\n      <td>351 days 00:00:00</td>\n      <td>365 days 12:00:00</td>\n      <td>385 days 00:00:00</td>\n      <td>597 days 00:00:00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_audit[['PW_SKILL_LEVEL', 'DECISION_TIME']].groupby('PW_SKILL_LEVEL').describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-05T23:58:47.965769Z",
     "start_time": "2023-10-05T23:58:47.952132Z"
    }
   },
   "id": "f243013633098b9c"
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   "cell_type": "code",
   "execution_count": null,
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   "source": [],
   "metadata": {
    "collapsed": false
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   "id": "7780f982e570314c"
  }
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