Session - 2  Visa dataset .ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "246cf4f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "007427dc",
   "metadata": {},
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case_id</th>\n",
       "      <th>continent</th>\n",
       "      <th>education_of_employee</th>\n",
       "      <th>has_job_experience</th>\n",
       "      <th>requires_job_training</th>\n",
       "      <th>no_of_employees</th>\n",
       "      <th>yr_of_estab</th>\n",
       "      <th>region_of_employment</th>\n",
       "      <th>prevailing_wage</th>\n",
       "      <th>unit_of_wage</th>\n",
       "      <th>full_time_position</th>\n",
       "      <th>case_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EZYV01</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>14513</td>\n",
       "      <td>2007</td>\n",
       "      <td>West</td>\n",
       "      <td>592.2029</td>\n",
       "      <td>Hour</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EZYV02</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2412</td>\n",
       "      <td>2002</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>83425.6500</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EZYV03</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>44444</td>\n",
       "      <td>2008</td>\n",
       "      <td>West</td>\n",
       "      <td>122996.8600</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EZYV04</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>98</td>\n",
       "      <td>1897</td>\n",
       "      <td>West</td>\n",
       "      <td>83434.0300</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EZYV05</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1082</td>\n",
       "      <td>2005</td>\n",
       "      <td>South</td>\n",
       "      <td>149907.3900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</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",
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       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>25475</th>\n",
       "      <td>EZYV25476</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>2601</td>\n",
       "      <td>2008</td>\n",
       "      <td>South</td>\n",
       "      <td>77092.5700</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25476</th>\n",
       "      <td>EZYV25477</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3274</td>\n",
       "      <td>2006</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>279174.7900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25477</th>\n",
       "      <td>EZYV25478</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1121</td>\n",
       "      <td>1910</td>\n",
       "      <td>South</td>\n",
       "      <td>146298.8500</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25478</th>\n",
       "      <td>EZYV25479</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1918</td>\n",
       "      <td>1887</td>\n",
       "      <td>West</td>\n",
       "      <td>86154.7700</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25479</th>\n",
       "      <td>EZYV25480</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3195</td>\n",
       "      <td>1960</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>70876.9100</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25480 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         case_id continent education_of_employee has_job_experience  \\\n",
       "0         EZYV01      Asia           High School                  N   \n",
       "1         EZYV02      Asia              Master's                  Y   \n",
       "2         EZYV03      Asia            Bachelor's                  N   \n",
       "3         EZYV04      Asia            Bachelor's                  N   \n",
       "4         EZYV05    Africa              Master's                  Y   \n",
       "...          ...       ...                   ...                ...   \n",
       "25475  EZYV25476      Asia            Bachelor's                  Y   \n",
       "25476  EZYV25477      Asia           High School                  Y   \n",
       "25477  EZYV25478      Asia              Master's                  Y   \n",
       "25478  EZYV25479      Asia              Master's                  Y   \n",
       "25479  EZYV25480      Asia            Bachelor's                  Y   \n",
       "\n",
       "      requires_job_training  no_of_employees  yr_of_estab  \\\n",
       "0                         N            14513         2007   \n",
       "1                         N             2412         2002   \n",
       "2                         Y            44444         2008   \n",
       "3                         N               98         1897   \n",
       "4                         N             1082         2005   \n",
       "...                     ...              ...          ...   \n",
       "25475                     Y             2601         2008   \n",
       "25476                     N             3274         2006   \n",
       "25477                     N             1121         1910   \n",
       "25478                     Y             1918         1887   \n",
       "25479                     N             3195         1960   \n",
       "\n",
       "      region_of_employment  prevailing_wage unit_of_wage full_time_position  \\\n",
       "0                     West         592.2029         Hour                  Y   \n",
       "1                Northeast       83425.6500         Year                  Y   \n",
       "2                     West      122996.8600         Year                  Y   \n",
       "3                     West       83434.0300         Year                  Y   \n",
       "4                    South      149907.3900         Year                  Y   \n",
       "...                    ...              ...          ...                ...   \n",
       "25475                South       77092.5700         Year                  Y   \n",
       "25476            Northeast      279174.7900         Year                  Y   \n",
       "25477                South      146298.8500         Year                  N   \n",
       "25478                 West       86154.7700         Year                  Y   \n",
       "25479              Midwest       70876.9100         Year                  Y   \n",
       "\n",
       "      case_status  \n",
       "0          Denied  \n",
       "1       Certified  \n",
       "2          Denied  \n",
       "3          Denied  \n",
       "4       Certified  \n",
       "...           ...  \n",
       "25475   Certified  \n",
       "25476   Certified  \n",
       "25477   Certified  \n",
       "25478   Certified  \n",
       "25479   Certified  \n",
       "\n",
       "[25480 rows x 12 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path=\"C:\\\\Users\\\\hp\\\\Downloads\\\\Visadataset - Visadataset.csv\"\n",
    "visa_df=pd.read_csv(file_path)\n",
    "visa_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f7c12c7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(visa_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "98d8bade",
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       " 'mean',\n",
       " 'median',\n",
       " 'melt',\n",
       " 'memory_usage',\n",
       " 'merge',\n",
       " 'min',\n",
       " 'mod',\n",
       " 'mode',\n",
       " 'mul',\n",
       " 'multiply',\n",
       " 'ndim',\n",
       " 'ne',\n",
       " 'nlargest',\n",
       " 'no_of_employees',\n",
       " 'notna',\n",
       " 'notnull',\n",
       " 'nsmallest',\n",
       " 'nunique',\n",
       " 'pad',\n",
       " 'pct_change',\n",
       " 'pipe',\n",
       " 'pivot',\n",
       " 'pivot_table',\n",
       " 'plot',\n",
       " 'pop',\n",
       " 'pow',\n",
       " 'prevailing_wage',\n",
       " 'prod',\n",
       " 'product',\n",
       " 'quantile',\n",
       " 'query',\n",
       " 'radd',\n",
       " 'rank',\n",
       " 'rdiv',\n",
       " 'region_of_employment',\n",
       " 'reindex',\n",
       " 'reindex_like',\n",
       " 'rename',\n",
       " 'rename_axis',\n",
       " 'reorder_levels',\n",
       " 'replace',\n",
       " 'requires_job_training',\n",
       " 'resample',\n",
       " 'reset_index',\n",
       " 'rfloordiv',\n",
       " 'rmod',\n",
       " 'rmul',\n",
       " 'rolling',\n",
       " 'round',\n",
       " 'rpow',\n",
       " 'rsub',\n",
       " 'rtruediv',\n",
       " 'sample',\n",
       " 'select_dtypes',\n",
       " 'sem',\n",
       " 'set_axis',\n",
       " 'set_flags',\n",
       " 'set_index',\n",
       " 'shape',\n",
       " 'shift',\n",
       " 'size',\n",
       " 'skew',\n",
       " 'sort_index',\n",
       " 'sort_values',\n",
       " 'squeeze',\n",
       " 'stack',\n",
       " 'std',\n",
       " 'style',\n",
       " 'sub',\n",
       " 'subtract',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'swaplevel',\n",
       " 'tail',\n",
       " 'take',\n",
       " 'to_clipboard',\n",
       " 'to_csv',\n",
       " 'to_dict',\n",
       " 'to_excel',\n",
       " 'to_feather',\n",
       " 'to_gbq',\n",
       " 'to_hdf',\n",
       " 'to_html',\n",
       " 'to_json',\n",
       " 'to_latex',\n",
       " 'to_markdown',\n",
       " 'to_numpy',\n",
       " 'to_orc',\n",
       " 'to_parquet',\n",
       " 'to_period',\n",
       " 'to_pickle',\n",
       " 'to_records',\n",
       " 'to_sql',\n",
       " 'to_stata',\n",
       " 'to_string',\n",
       " 'to_timestamp',\n",
       " 'to_xarray',\n",
       " 'to_xml',\n",
       " 'transform',\n",
       " 'transpose',\n",
       " 'truediv',\n",
       " 'truncate',\n",
       " 'tz_convert',\n",
       " 'tz_localize',\n",
       " 'unit_of_wage',\n",
       " 'unstack',\n",
       " 'update',\n",
       " 'value_counts',\n",
       " 'values',\n",
       " 'var',\n",
       " 'where',\n",
       " 'xs',\n",
       " 'yr_of_estab']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(visa_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82dcbecb",
   "metadata": {},
   "source": [
    "#### SHAPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bbed1fe4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25480, 12)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shape=visa_df.shape\n",
    "shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3f688e76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aa5378a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the number of observation are: 25480\n",
      "the number of observation are: 12\n"
     ]
    }
   ],
   "source": [
    "print(\"the number of observation are:\",shape[0])\n",
    "print('the number of observation are:',shape[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "563501a1",
   "metadata": {},
   "source": [
    "##### SIZE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fae0aa10",
   "metadata": {},
   "source": [
    "- not callable means brackets are not required"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "24ce32c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "305760"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "799f26e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "305760"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shape[0]*shape[1]\n",
    "\n",
    "# number of rows * number of columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47643438",
   "metadata": {},
   "source": [
    "$dtypes$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bae2573b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "case_id                   object\n",
       "continent                 object\n",
       "education_of_employee     object\n",
       "has_job_experience        object\n",
       "requires_job_training     object\n",
       "no_of_employees            int64\n",
       "yr_of_estab                int64\n",
       "region_of_employment      object\n",
       "prevailing_wage          float64\n",
       "unit_of_wage              object\n",
       "full_time_position        object\n",
       "case_status               object\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_types=visa_df.dtypes\n",
    "data_types\n",
    "\n",
    "# objects means categorical column\n",
    "# int float means numerical column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1dbabe4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ac2f4f00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['case_id', 'continent', 'education_of_employee', 'has_job_experience',\n",
       "       'requires_job_training', 'no_of_employees', 'yr_of_estab',\n",
       "       'region_of_employment', 'prevailing_wage', 'unit_of_wage',\n",
       "       'full_time_position', 'case_status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_types.index\n",
    "\n",
    "# any series\n",
    "# if you apply index the output is in list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0d007c39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([dtype('O'), dtype('O'), dtype('O'), dtype('O'), dtype('O'),\n",
       "       dtype('int64'), dtype('int64'), dtype('O'), dtype('float64'),\n",
       "       dtype('O'), dtype('O'), dtype('O')], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_types.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3368a955",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([dtype('O'), dtype('O'), dtype('O'), dtype('O'), dtype('O'),\n",
       "       dtype('int64'), dtype('int64'), dtype('O'), dtype('float64'),\n",
       "       dtype('O'), dtype('O'), dtype('O')], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.dtypes.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e5c179e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(visa_df.dtypes.values)\n",
    "# numpy n dimensions array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6d4d2ce9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[dtype('O'),\n",
       " dtype('O'),\n",
       " dtype('O'),\n",
       " dtype('O'),\n",
       " dtype('O'),\n",
       " dtype('int64'),\n",
       " dtype('int64'),\n",
       " dtype('O'),\n",
       " dtype('float64'),\n",
       " dtype('O'),\n",
       " dtype('O'),\n",
       " dtype('O')]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(visa_df.dtypes.values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bbc2faf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1=[1,2,3,4]\n",
    "list1\n",
    "[1,2,3]+[4,5,6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "03127aa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f66a5fc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6,  8, 10, 12])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1,2,3,4])+np.array([5,6,7,8])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bed3f62b",
   "metadata": {},
   "source": [
    "- array[]===>numpy\n",
    "- index[]====> pandas.index\n",
    "- pandas.dataframes\n",
    "- pandas.series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9ec52cb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c8324d9c",
   "metadata": {},
   "source": [
    "- the series is a combination of index and values\n",
    "- if you want to seperate both we need to use index and values\n",
    "- values will coming in terms if array\n",
    "- array means numpy array"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ffc598f",
   "metadata": {},
   "source": [
    "$Task:-1$\n",
    "    \n",
    "- seperate numerical columns and categorical columns seperately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8a0c058d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(data_types)['case_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7d092df6",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'types'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_20252\\1977021133.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mvisa_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtypes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mtype_list\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvisa_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mnum_list\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtype_list\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtypes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5985\u001b[0m             \u001b[1;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5986\u001b[0m             \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5987\u001b[0m         ):\n\u001b[0;32m   5988\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5989\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'types'"
     ]
    }
   ],
   "source": [
    "visa_df.types\n",
    "type_list=visa_df.dtype.values\n",
    "num_list=[i for i in type_list if types(i) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "62a0eab0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'case_id': dtype('O'),\n",
       " 'continent': dtype('O'),\n",
       " 'education_of_employee': dtype('O'),\n",
       " 'has_job_experience': dtype('O'),\n",
       " 'requires_job_training': dtype('O'),\n",
       " 'no_of_employees': dtype('int64'),\n",
       " 'yr_of_estab': dtype('int64'),\n",
       " 'region_of_employment': dtype('O'),\n",
       " 'prevailing_wage': dtype('float64'),\n",
       " 'unit_of_wage': dtype('O'),\n",
       " 'full_time_position': dtype('O'),\n",
       " 'case_status': dtype('O')}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(visa_df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d40ead60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'case_id': 0           EZYV01\n",
       " 1           EZYV02\n",
       " 2           EZYV03\n",
       " 3           EZYV04\n",
       " 4           EZYV05\n",
       "            ...    \n",
       " 25475    EZYV25476\n",
       " 25476    EZYV25477\n",
       " 25477    EZYV25478\n",
       " 25478    EZYV25479\n",
       " 25479    EZYV25480\n",
       " Name: case_id, Length: 25480, dtype: object,\n",
       " 'continent': 0          Asia\n",
       " 1          Asia\n",
       " 2          Asia\n",
       " 3          Asia\n",
       " 4        Africa\n",
       "           ...  \n",
       " 25475      Asia\n",
       " 25476      Asia\n",
       " 25477      Asia\n",
       " 25478      Asia\n",
       " 25479      Asia\n",
       " Name: continent, Length: 25480, dtype: object,\n",
       " 'education_of_employee': 0        High School\n",
       " 1           Master's\n",
       " 2         Bachelor's\n",
       " 3         Bachelor's\n",
       " 4           Master's\n",
       "             ...     \n",
       " 25475     Bachelor's\n",
       " 25476    High School\n",
       " 25477       Master's\n",
       " 25478       Master's\n",
       " 25479     Bachelor's\n",
       " Name: education_of_employee, Length: 25480, dtype: object,\n",
       " 'has_job_experience': 0        N\n",
       " 1        Y\n",
       " 2        N\n",
       " 3        N\n",
       " 4        Y\n",
       "         ..\n",
       " 25475    Y\n",
       " 25476    Y\n",
       " 25477    Y\n",
       " 25478    Y\n",
       " 25479    Y\n",
       " Name: has_job_experience, Length: 25480, dtype: object,\n",
       " 'requires_job_training': 0        N\n",
       " 1        N\n",
       " 2        Y\n",
       " 3        N\n",
       " 4        N\n",
       "         ..\n",
       " 25475    Y\n",
       " 25476    N\n",
       " 25477    N\n",
       " 25478    Y\n",
       " 25479    N\n",
       " Name: requires_job_training, Length: 25480, dtype: object,\n",
       " 'no_of_employees': 0        14513\n",
       " 1         2412\n",
       " 2        44444\n",
       " 3           98\n",
       " 4         1082\n",
       "          ...  \n",
       " 25475     2601\n",
       " 25476     3274\n",
       " 25477     1121\n",
       " 25478     1918\n",
       " 25479     3195\n",
       " Name: no_of_employees, Length: 25480, dtype: int64,\n",
       " 'yr_of_estab': 0        2007\n",
       " 1        2002\n",
       " 2        2008\n",
       " 3        1897\n",
       " 4        2005\n",
       "          ... \n",
       " 25475    2008\n",
       " 25476    2006\n",
       " 25477    1910\n",
       " 25478    1887\n",
       " 25479    1960\n",
       " Name: yr_of_estab, Length: 25480, dtype: int64,\n",
       " 'region_of_employment': 0             West\n",
       " 1        Northeast\n",
       " 2             West\n",
       " 3             West\n",
       " 4            South\n",
       "            ...    \n",
       " 25475        South\n",
       " 25476    Northeast\n",
       " 25477        South\n",
       " 25478         West\n",
       " 25479      Midwest\n",
       " Name: region_of_employment, Length: 25480, dtype: object,\n",
       " 'prevailing_wage': 0           592.2029\n",
       " 1         83425.6500\n",
       " 2        122996.8600\n",
       " 3         83434.0300\n",
       " 4        149907.3900\n",
       "             ...     \n",
       " 25475     77092.5700\n",
       " 25476    279174.7900\n",
       " 25477    146298.8500\n",
       " 25478     86154.7700\n",
       " 25479     70876.9100\n",
       " Name: prevailing_wage, Length: 25480, dtype: float64,\n",
       " 'unit_of_wage': 0        Hour\n",
       " 1        Year\n",
       " 2        Year\n",
       " 3        Year\n",
       " 4        Year\n",
       "          ... \n",
       " 25475    Year\n",
       " 25476    Year\n",
       " 25477    Year\n",
       " 25478    Year\n",
       " 25479    Year\n",
       " Name: unit_of_wage, Length: 25480, dtype: object,\n",
       " 'full_time_position': 0        Y\n",
       " 1        Y\n",
       " 2        Y\n",
       " 3        Y\n",
       " 4        Y\n",
       "         ..\n",
       " 25475    Y\n",
       " 25476    Y\n",
       " 25477    N\n",
       " 25478    Y\n",
       " 25479    Y\n",
       " Name: full_time_position, Length: 25480, dtype: object,\n",
       " 'case_status': 0           Denied\n",
       " 1        Certified\n",
       " 2           Denied\n",
       " 3           Denied\n",
       " 4        Certified\n",
       "            ...    \n",
       " 25475    Certified\n",
       " 25476    Certified\n",
       " 25477    Certified\n",
       " 25478    Certified\n",
       " 25479    Certified\n",
       " Name: case_status, Length: 25480, dtype: object}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_cols_dict=dict(visa_df)\n",
    "visa_cols_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c81c0710",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "case_id\n",
      "continent\n",
      "education_of_employee\n",
      "has_job_experience\n",
      "requires_job_training\n",
      "region_of_employment\n",
      "unit_of_wage\n",
      "full_time_position\n",
      "case_status\n"
     ]
    }
   ],
   "source": [
    "dict1=dict(visa_df.dtypes)\n",
    "for i,j in dict1.items():\n",
    "    if j=='object':\n",
    "        print(i)\n",
    "        \n",
    "    \n",
    "cat=[key for key, value in dict1.items() if value=='object']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c43b6271",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "948502dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "case_id\n",
      "continent\n",
      "education_of_employee\n",
      "has_job_experience\n",
      "requires_job_training\n",
      "region_of_employment\n",
      "unit_of_wage\n",
      "full_time_position\n",
      "case_status\n"
     ]
    }
   ],
   "source": [
    "dict1=dict(visa_df.dtypes)\n",
    "for i,j in dict1.items():\n",
    "    if j=='object':\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "131aa6be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['case_id',\n",
       " 'continent',\n",
       " 'education_of_employee',\n",
       " 'has_job_experience',\n",
       " 'requires_job_training',\n",
       " 'region_of_employment',\n",
       " 'unit_of_wage',\n",
       " 'full_time_position',\n",
       " 'case_status']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat=[key for key, value in dict1.items() if value=='object']\n",
    "cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10e2c2f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "559a5f19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['no_of_employees', 'yr_of_estab', 'prevailing_wage']\n",
      "['case_id', 'continent', 'education_of_employee', 'has_job_experience', 'requires_job_training', 'region_of_employment', 'unit_of_wage', 'full_time_position', 'case_status']\n"
     ]
    }
   ],
   "source": [
    "cat=[]\n",
    "num=[]\n",
    "data_types=visa_df.dtypes\n",
    "dict1=dict(data_types)\n",
    "for i in dict1:\n",
    "    if dict1[i]=='O':\n",
    "        cat.append(i)\n",
    "    else:\n",
    "        num.append(i)\n",
    "    \n",
    "print(num)\n",
    "print(cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "aa8e5d08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['case_id', 'continent', 'education_of_employee', 'has_job_experience', 'requires_job_training', 'region_of_employment', 'unit_of_wage', 'full_time_position', 'case_status']\n",
      "['no_of_employees', 'yr_of_estab', 'prevailing_wage']\n"
     ]
    }
   ],
   "source": [
    "cat=[i for i in dict(data_types) if dict(data_types)[i]=='object']\n",
    "num=[i for i in dict(data_types) if dict(data_types)[i]!='object']\n",
    "print(cat)\n",
    "print(num)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25f5f99d",
   "metadata": {},
   "source": [
    "$Head$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "93f505d6",
   "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>case_id</th>\n",
       "      <th>continent</th>\n",
       "      <th>education_of_employee</th>\n",
       "      <th>has_job_experience</th>\n",
       "      <th>requires_job_training</th>\n",
       "      <th>no_of_employees</th>\n",
       "      <th>yr_of_estab</th>\n",
       "      <th>region_of_employment</th>\n",
       "      <th>prevailing_wage</th>\n",
       "      <th>unit_of_wage</th>\n",
       "      <th>full_time_position</th>\n",
       "      <th>case_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EZYV01</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>14513</td>\n",
       "      <td>2007</td>\n",
       "      <td>West</td>\n",
       "      <td>592.2029</td>\n",
       "      <td>Hour</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EZYV02</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2412</td>\n",
       "      <td>2002</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>83425.6500</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EZYV03</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>44444</td>\n",
       "      <td>2008</td>\n",
       "      <td>West</td>\n",
       "      <td>122996.8600</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EZYV04</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>98</td>\n",
       "      <td>1897</td>\n",
       "      <td>West</td>\n",
       "      <td>83434.0300</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EZYV05</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1082</td>\n",
       "      <td>2005</td>\n",
       "      <td>South</td>\n",
       "      <td>149907.3900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  case_id continent education_of_employee has_job_experience  \\\n",
       "0  EZYV01      Asia           High School                  N   \n",
       "1  EZYV02      Asia              Master's                  Y   \n",
       "2  EZYV03      Asia            Bachelor's                  N   \n",
       "3  EZYV04      Asia            Bachelor's                  N   \n",
       "4  EZYV05    Africa              Master's                  Y   \n",
       "\n",
       "  requires_job_training  no_of_employees  yr_of_estab region_of_employment  \\\n",
       "0                     N            14513         2007                 West   \n",
       "1                     N             2412         2002            Northeast   \n",
       "2                     Y            44444         2008                 West   \n",
       "3                     N               98         1897                 West   \n",
       "4                     N             1082         2005                South   \n",
       "\n",
       "   prevailing_wage unit_of_wage full_time_position case_status  \n",
       "0         592.2029         Hour                  Y      Denied  \n",
       "1       83425.6500         Year                  Y   Certified  \n",
       "2      122996.8600         Year                  Y      Denied  \n",
       "3       83434.0300         Year                  Y      Denied  \n",
       "4      149907.3900         Year                  Y   Certified  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.head() # first 5 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "65a38bad",
   "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>case_id</th>\n",
       "      <th>continent</th>\n",
       "      <th>education_of_employee</th>\n",
       "      <th>has_job_experience</th>\n",
       "      <th>requires_job_training</th>\n",
       "      <th>no_of_employees</th>\n",
       "      <th>yr_of_estab</th>\n",
       "      <th>region_of_employment</th>\n",
       "      <th>prevailing_wage</th>\n",
       "      <th>unit_of_wage</th>\n",
       "      <th>full_time_position</th>\n",
       "      <th>case_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EZYV01</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>14513</td>\n",
       "      <td>2007</td>\n",
       "      <td>West</td>\n",
       "      <td>592.2029</td>\n",
       "      <td>Hour</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EZYV02</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2412</td>\n",
       "      <td>2002</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>83425.6500</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EZYV03</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>44444</td>\n",
       "      <td>2008</td>\n",
       "      <td>West</td>\n",
       "      <td>122996.8600</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EZYV04</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>98</td>\n",
       "      <td>1897</td>\n",
       "      <td>West</td>\n",
       "      <td>83434.0300</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EZYV05</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1082</td>\n",
       "      <td>2005</td>\n",
       "      <td>South</td>\n",
       "      <td>149907.3900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>EZYV06</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2339</td>\n",
       "      <td>2012</td>\n",
       "      <td>South</td>\n",
       "      <td>78252.1400</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>EZYV07</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>4985</td>\n",
       "      <td>1994</td>\n",
       "      <td>South</td>\n",
       "      <td>53635.3900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>EZYV08</td>\n",
       "      <td>North America</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3035</td>\n",
       "      <td>1924</td>\n",
       "      <td>West</td>\n",
       "      <td>418.2298</td>\n",
       "      <td>Hour</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>EZYV09</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>4810</td>\n",
       "      <td>2012</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>74362.1900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>EZYV10</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2251</td>\n",
       "      <td>1995</td>\n",
       "      <td>South</td>\n",
       "      <td>67514.7600</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>EZYV11</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>2465</td>\n",
       "      <td>2004</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>83588.5600</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>EZYV12</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>4069</td>\n",
       "      <td>2005</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>70813.0900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   case_id      continent education_of_employee has_job_experience  \\\n",
       "0   EZYV01           Asia           High School                  N   \n",
       "1   EZYV02           Asia              Master's                  Y   \n",
       "2   EZYV03           Asia            Bachelor's                  N   \n",
       "3   EZYV04           Asia            Bachelor's                  N   \n",
       "4   EZYV05         Africa              Master's                  Y   \n",
       "5   EZYV06           Asia              Master's                  Y   \n",
       "6   EZYV07           Asia            Bachelor's                  N   \n",
       "7   EZYV08  North America            Bachelor's                  Y   \n",
       "8   EZYV09           Asia            Bachelor's                  N   \n",
       "9   EZYV10         Europe             Doctorate                  Y   \n",
       "10  EZYV11           Asia              Master's                  N   \n",
       "11  EZYV12           Asia           High School                  Y   \n",
       "\n",
       "   requires_job_training  no_of_employees  yr_of_estab region_of_employment  \\\n",
       "0                      N            14513         2007                 West   \n",
       "1                      N             2412         2002            Northeast   \n",
       "2                      Y            44444         2008                 West   \n",
       "3                      N               98         1897                 West   \n",
       "4                      N             1082         2005                South   \n",
       "5                      N             2339         2012                South   \n",
       "6                      N             4985         1994                South   \n",
       "7                      N             3035         1924                 West   \n",
       "8                      N             4810         2012              Midwest   \n",
       "9                      N             2251         1995                South   \n",
       "10                     N             2465         2004              Midwest   \n",
       "11                     N             4069         2005            Northeast   \n",
       "\n",
       "    prevailing_wage unit_of_wage full_time_position case_status  \n",
       "0          592.2029         Hour                  Y      Denied  \n",
       "1        83425.6500         Year                  Y   Certified  \n",
       "2       122996.8600         Year                  Y      Denied  \n",
       "3        83434.0300         Year                  Y      Denied  \n",
       "4       149907.3900         Year                  Y   Certified  \n",
       "5        78252.1400         Year                  Y   Certified  \n",
       "6        53635.3900         Year                  Y   Certified  \n",
       "7          418.2298         Hour                  Y      Denied  \n",
       "8        74362.1900         Year                  Y   Certified  \n",
       "9        67514.7600         Year                  Y   Certified  \n",
       "10       83588.5600         Year                  Y   Certified  \n",
       "11       70813.0900         Year                  Y      Denied  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e91270d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case_id</th>\n",
       "      <th>continent</th>\n",
       "      <th>education_of_employee</th>\n",
       "      <th>has_job_experience</th>\n",
       "      <th>requires_job_training</th>\n",
       "      <th>no_of_employees</th>\n",
       "      <th>yr_of_estab</th>\n",
       "      <th>region_of_employment</th>\n",
       "      <th>prevailing_wage</th>\n",
       "      <th>unit_of_wage</th>\n",
       "      <th>full_time_position</th>\n",
       "      <th>case_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EZYV01</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>14513</td>\n",
       "      <td>2007</td>\n",
       "      <td>West</td>\n",
       "      <td>592.2029</td>\n",
       "      <td>Hour</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EZYV02</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2412</td>\n",
       "      <td>2002</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>83425.6500</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  case_id continent education_of_employee has_job_experience  \\\n",
       "0  EZYV01      Asia           High School                  N   \n",
       "1  EZYV02      Asia              Master's                  Y   \n",
       "\n",
       "  requires_job_training  no_of_employees  yr_of_estab region_of_employment  \\\n",
       "0                     N            14513         2007                 West   \n",
       "1                     N             2412         2002            Northeast   \n",
       "\n",
       "   prevailing_wage unit_of_wage full_time_position case_status  \n",
       "0         592.2029         Hour                  Y      Denied  \n",
       "1       83425.6500         Year                  Y   Certified  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38bba4ef",
   "metadata": {},
   "source": [
    "### 𝑇𝑎𝑖𝑙"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "aa359017",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case_id</th>\n",
       "      <th>continent</th>\n",
       "      <th>education_of_employee</th>\n",
       "      <th>has_job_experience</th>\n",
       "      <th>requires_job_training</th>\n",
       "      <th>no_of_employees</th>\n",
       "      <th>yr_of_estab</th>\n",
       "      <th>region_of_employment</th>\n",
       "      <th>prevailing_wage</th>\n",
       "      <th>unit_of_wage</th>\n",
       "      <th>full_time_position</th>\n",
       "      <th>case_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25475</th>\n",
       "      <td>EZYV25476</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>2601</td>\n",
       "      <td>2008</td>\n",
       "      <td>South</td>\n",
       "      <td>77092.57</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25476</th>\n",
       "      <td>EZYV25477</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3274</td>\n",
       "      <td>2006</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>279174.79</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25477</th>\n",
       "      <td>EZYV25478</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1121</td>\n",
       "      <td>1910</td>\n",
       "      <td>South</td>\n",
       "      <td>146298.85</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25478</th>\n",
       "      <td>EZYV25479</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1918</td>\n",
       "      <td>1887</td>\n",
       "      <td>West</td>\n",
       "      <td>86154.77</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25479</th>\n",
       "      <td>EZYV25480</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3195</td>\n",
       "      <td>1960</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>70876.91</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         case_id continent education_of_employee has_job_experience  \\\n",
       "25475  EZYV25476      Asia            Bachelor's                  Y   \n",
       "25476  EZYV25477      Asia           High School                  Y   \n",
       "25477  EZYV25478      Asia              Master's                  Y   \n",
       "25478  EZYV25479      Asia              Master's                  Y   \n",
       "25479  EZYV25480      Asia            Bachelor's                  Y   \n",
       "\n",
       "      requires_job_training  no_of_employees  yr_of_estab  \\\n",
       "25475                     Y             2601         2008   \n",
       "25476                     N             3274         2006   \n",
       "25477                     N             1121         1910   \n",
       "25478                     Y             1918         1887   \n",
       "25479                     N             3195         1960   \n",
       "\n",
       "      region_of_employment  prevailing_wage unit_of_wage full_time_position  \\\n",
       "25475                South         77092.57         Year                  Y   \n",
       "25476            Northeast        279174.79         Year                  Y   \n",
       "25477                South        146298.85         Year                  N   \n",
       "25478                 West         86154.77         Year                  Y   \n",
       "25479              Midwest         70876.91         Year                  Y   \n",
       "\n",
       "      case_status  \n",
       "25475   Certified  \n",
       "25476   Certified  \n",
       "25477   Certified  \n",
       "25478   Certified  \n",
       "25479   Certified  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.tail()  # last 5 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "fbae51ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case_id</th>\n",
       "      <th>continent</th>\n",
       "      <th>education_of_employee</th>\n",
       "      <th>has_job_experience</th>\n",
       "      <th>requires_job_training</th>\n",
       "      <th>no_of_employees</th>\n",
       "      <th>yr_of_estab</th>\n",
       "      <th>region_of_employment</th>\n",
       "      <th>prevailing_wage</th>\n",
       "      <th>unit_of_wage</th>\n",
       "      <th>full_time_position</th>\n",
       "      <th>case_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25464</th>\n",
       "      <td>EZYV25465</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>48785</td>\n",
       "      <td>1974</td>\n",
       "      <td>South</td>\n",
       "      <td>145102.3600</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25465</th>\n",
       "      <td>EZYV25466</td>\n",
       "      <td>North America</td>\n",
       "      <td>High School</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>2577</td>\n",
       "      <td>1995</td>\n",
       "      <td>South</td>\n",
       "      <td>481.2238</td>\n",
       "      <td>Hour</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25466</th>\n",
       "      <td>EZYV25467</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1938</td>\n",
       "      <td>2005</td>\n",
       "      <td>West</td>\n",
       "      <td>6973.4000</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25467</th>\n",
       "      <td>EZYV25468</td>\n",
       "      <td>North America</td>\n",
       "      <td>Master's</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>2504</td>\n",
       "      <td>1882</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>105900.6600</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25468</th>\n",
       "      <td>EZYV25469</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>373</td>\n",
       "      <td>2005</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>272715.7400</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25469</th>\n",
       "      <td>EZYV25470</td>\n",
       "      <td>North America</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2261</td>\n",
       "      <td>1997</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>273772.4700</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25470</th>\n",
       "      <td>EZYV25471</td>\n",
       "      <td>North America</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2272</td>\n",
       "      <td>1970</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>516.4101</td>\n",
       "      <td>Hour</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25471</th>\n",
       "      <td>EZYV25472</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>40224</td>\n",
       "      <td>1962</td>\n",
       "      <td>Island</td>\n",
       "      <td>75587.4200</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25472</th>\n",
       "      <td>EZYV25473</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>1346</td>\n",
       "      <td>2003</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>76155.6000</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25473</th>\n",
       "      <td>EZYV25474</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2421</td>\n",
       "      <td>2007</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>22845.5600</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25474</th>\n",
       "      <td>EZYV25475</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>2594</td>\n",
       "      <td>1979</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>51104.7800</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25475</th>\n",
       "      <td>EZYV25476</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>2601</td>\n",
       "      <td>2008</td>\n",
       "      <td>South</td>\n",
       "      <td>77092.5700</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25476</th>\n",
       "      <td>EZYV25477</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3274</td>\n",
       "      <td>2006</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>279174.7900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25477</th>\n",
       "      <td>EZYV25478</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1121</td>\n",
       "      <td>1910</td>\n",
       "      <td>South</td>\n",
       "      <td>146298.8500</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25478</th>\n",
       "      <td>EZYV25479</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1918</td>\n",
       "      <td>1887</td>\n",
       "      <td>West</td>\n",
       "      <td>86154.7700</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25479</th>\n",
       "      <td>EZYV25480</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3195</td>\n",
       "      <td>1960</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>70876.9100</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         case_id      continent education_of_employee has_job_experience  \\\n",
       "25464  EZYV25465           Asia              Master's                  N   \n",
       "25465  EZYV25466  North America           High School                  N   \n",
       "25466  EZYV25467         Europe            Bachelor's                  Y   \n",
       "25467  EZYV25468  North America              Master's                  N   \n",
       "25468  EZYV25469           Asia            Bachelor's                  N   \n",
       "25469  EZYV25470  North America              Master's                  Y   \n",
       "25470  EZYV25471  North America              Master's                  Y   \n",
       "25471  EZYV25472           Asia           High School                  N   \n",
       "25472  EZYV25473           Asia           High School                  N   \n",
       "25473  EZYV25474           Asia            Bachelor's                  Y   \n",
       "25474  EZYV25475         Africa             Doctorate                  N   \n",
       "25475  EZYV25476           Asia            Bachelor's                  Y   \n",
       "25476  EZYV25477           Asia           High School                  Y   \n",
       "25477  EZYV25478           Asia              Master's                  Y   \n",
       "25478  EZYV25479           Asia              Master's                  Y   \n",
       "25479  EZYV25480           Asia            Bachelor's                  Y   \n",
       "\n",
       "      requires_job_training  no_of_employees  yr_of_estab  \\\n",
       "25464                     N            48785         1974   \n",
       "25465                     N             2577         1995   \n",
       "25466                     N             1938         2005   \n",
       "25467                     Y             2504         1882   \n",
       "25468                     N              373         2005   \n",
       "25469                     N             2261         1997   \n",
       "25470                     N             2272         1970   \n",
       "25471                     N            40224         1962   \n",
       "25472                     N             1346         2003   \n",
       "25473                     N             2421         2007   \n",
       "25474                     N             2594         1979   \n",
       "25475                     Y             2601         2008   \n",
       "25476                     N             3274         2006   \n",
       "25477                     N             1121         1910   \n",
       "25478                     Y             1918         1887   \n",
       "25479                     N             3195         1960   \n",
       "\n",
       "      region_of_employment  prevailing_wage unit_of_wage full_time_position  \\\n",
       "25464                South      145102.3600         Year                  N   \n",
       "25465                South         481.2238         Hour                  Y   \n",
       "25466                 West        6973.4000         Year                  Y   \n",
       "25467            Northeast      105900.6600         Year                  Y   \n",
       "25468              Midwest      272715.7400         Year                  N   \n",
       "25469            Northeast      273772.4700         Year                  N   \n",
       "25470            Northeast         516.4101         Hour                  Y   \n",
       "25471               Island       75587.4200         Year                  Y   \n",
       "25472              Midwest       76155.6000         Year                  N   \n",
       "25473            Northeast       22845.5600         Year                  Y   \n",
       "25474            Northeast       51104.7800         Year                  Y   \n",
       "25475                South       77092.5700         Year                  Y   \n",
       "25476            Northeast      279174.7900         Year                  Y   \n",
       "25477                South      146298.8500         Year                  N   \n",
       "25478                 West       86154.7700         Year                  Y   \n",
       "25479              Midwest       70876.9100         Year                  Y   \n",
       "\n",
       "      case_status  \n",
       "25464   Certified  \n",
       "25465   Certified  \n",
       "25466      Denied  \n",
       "25467   Certified  \n",
       "25468   Certified  \n",
       "25469   Certified  \n",
       "25470   Certified  \n",
       "25471   Certified  \n",
       "25472   Certified  \n",
       "25473   Certified  \n",
       "25474   Certified  \n",
       "25475   Certified  \n",
       "25476   Certified  \n",
       "25477   Certified  \n",
       "25478   Certified  \n",
       "25479   Certified  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.tail(16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "374bbb6f",
   "metadata": {},
   "source": [
    "#### Take\n",
    "\n",
    "- take only specific index values\n",
    "- inside take ,one more argument is axis\n",
    "- the default value of axis :0\n",
    "- axis =0 means rows\n",
    "- which means 100,200,300, rows will come\n",
    "- axis = 1 means column\n",
    "- which means 9, 10 , 12 columns will come "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d44cb9af",
   "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>case_id</th>\n",
       "      <th>continent</th>\n",
       "      <th>education_of_employee</th>\n",
       "      <th>has_job_experience</th>\n",
       "      <th>requires_job_training</th>\n",
       "      <th>no_of_employees</th>\n",
       "      <th>yr_of_estab</th>\n",
       "      <th>region_of_employment</th>\n",
       "      <th>prevailing_wage</th>\n",
       "      <th>unit_of_wage</th>\n",
       "      <th>full_time_position</th>\n",
       "      <th>case_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>EZYV101</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2227</td>\n",
       "      <td>2007</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>28243.79</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>EZYV201</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3282</td>\n",
       "      <td>1979</td>\n",
       "      <td>West</td>\n",
       "      <td>74441.11</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>EZYV301</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3268</td>\n",
       "      <td>2012</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>101371.21</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     case_id continent education_of_employee has_job_experience  \\\n",
       "100  EZYV101      Asia              Master's                  Y   \n",
       "200  EZYV201      Asia             Doctorate                  Y   \n",
       "300  EZYV301      Asia              Master's                  Y   \n",
       "\n",
       "    requires_job_training  no_of_employees  yr_of_estab region_of_employment  \\\n",
       "100                     N             2227         2007            Northeast   \n",
       "200                     N             3282         1979                 West   \n",
       "300                     N             3268         2012              Midwest   \n",
       "\n",
       "     prevailing_wage unit_of_wage full_time_position case_status  \n",
       "100         28243.79         Year                  Y   Certified  \n",
       "200         74441.11         Year                  Y   Certified  \n",
       "300        101371.21         Year                  Y   Certified  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.take([100,200,300])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1ddf5f59",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "indices are out-of-bounds",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[43], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m visa_df\u001b[38;5;241m.\u001b[39mtake([\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m200\u001b[39m,\u001b[38;5;241m300\u001b[39m],axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:3909\u001b[0m, in \u001b[0;36mNDFrame.take\u001b[1;34m(self, indices, axis, **kwargs)\u001b[0m\n\u001b[0;32m   3833\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   3834\u001b[0m \u001b[38;5;124;03mReturn the elements in the given *positional* indices along an axis.\u001b[39;00m\n\u001b[0;32m   3835\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   3904\u001b[0m \u001b[38;5;124;03m3    lion  mammal       80.5\u001b[39;00m\n\u001b[0;32m   3905\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   3907\u001b[0m nv\u001b[38;5;241m.\u001b[39mvalidate_take((), kwargs)\n\u001b[1;32m-> 3909\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_take(indices, axis)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:3932\u001b[0m, in \u001b[0;36mNDFrame._take\u001b[1;34m(self, indices, axis, convert_indices)\u001b[0m\n\u001b[0;32m   3924\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m   3925\u001b[0m         axis \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m   3926\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m indices\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m   3927\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m using_copy_on_write()\n\u001b[0;32m   3928\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m is_range_indexer(indices, \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m))\n\u001b[0;32m   3929\u001b[0m     ):\n\u001b[0;32m   3930\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m-> 3932\u001b[0m new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mgr\u001b[38;5;241m.\u001b[39mtake(\n\u001b[0;32m   3933\u001b[0m     indices,\n\u001b[0;32m   3934\u001b[0m     axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_block_manager_axis(axis),\n\u001b[0;32m   3935\u001b[0m     verify\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m   3936\u001b[0m     convert_indices\u001b[38;5;241m=\u001b[39mconvert_indices,\n\u001b[0;32m   3937\u001b[0m )\n\u001b[0;32m   3938\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor(new_data)\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtake\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:960\u001b[0m, in \u001b[0;36mBaseBlockManager.take\u001b[1;34m(self, indexer, axis, verify, convert_indices)\u001b[0m\n\u001b[0;32m    958\u001b[0m n \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshape[axis]\n\u001b[0;32m    959\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m convert_indices:\n\u001b[1;32m--> 960\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m maybe_convert_indices(indexer, n, verify\u001b[38;5;241m=\u001b[39mverify)\n\u001b[0;32m    962\u001b[0m new_labels \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes[axis]\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[0;32m    963\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreindex_indexer(\n\u001b[0;32m    964\u001b[0m     new_axis\u001b[38;5;241m=\u001b[39mnew_labels,\n\u001b[0;32m    965\u001b[0m     indexer\u001b[38;5;241m=\u001b[39mindexer,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    968\u001b[0m     copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m    969\u001b[0m )\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexers\\utils.py:284\u001b[0m, in \u001b[0;36mmaybe_convert_indices\u001b[1;34m(indices, n, verify)\u001b[0m\n\u001b[0;32m    282\u001b[0m     mask \u001b[38;5;241m=\u001b[39m (indices \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m n) \u001b[38;5;241m|\u001b[39m (indices \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m    283\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[1;32m--> 284\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mindices are out-of-bounds\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    285\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m indices\n",
      "\u001b[1;31mIndexError\u001b[0m: indices are out-of-bounds"
     ]
    }
   ],
   "source": [
    "visa_df.take([100,200,300],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "36fa7b43",
   "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>has_job_experience</th>\n",
       "      <th>no_of_employees</th>\n",
       "      <th>region_of_employment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N</td>\n",
       "      <td>14513</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Y</td>\n",
       "      <td>2412</td>\n",
       "      <td>Northeast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>N</td>\n",
       "      <td>44444</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>N</td>\n",
       "      <td>98</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Y</td>\n",
       "      <td>1082</td>\n",
       "      <td>South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25475</th>\n",
       "      <td>Y</td>\n",
       "      <td>2601</td>\n",
       "      <td>South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25476</th>\n",
       "      <td>Y</td>\n",
       "      <td>3274</td>\n",
       "      <td>Northeast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25477</th>\n",
       "      <td>Y</td>\n",
       "      <td>1121</td>\n",
       "      <td>South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25478</th>\n",
       "      <td>Y</td>\n",
       "      <td>1918</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25479</th>\n",
       "      <td>Y</td>\n",
       "      <td>3195</td>\n",
       "      <td>Midwest</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25480 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      has_job_experience  no_of_employees region_of_employment\n",
       "0                      N            14513                 West\n",
       "1                      Y             2412            Northeast\n",
       "2                      N            44444                 West\n",
       "3                      N               98                 West\n",
       "4                      Y             1082                South\n",
       "...                  ...              ...                  ...\n",
       "25475                  Y             2601                South\n",
       "25476                  Y             3274            Northeast\n",
       "25477                  Y             1121                South\n",
       "25478                  Y             1918                 West\n",
       "25479                  Y             3195              Midwest\n",
       "\n",
       "[25480 rows x 3 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.take([3,5,7],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f77b0ca4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>education_of_employee</th>\n",
       "      <th>has_job_experience</th>\n",
       "      <th>region_of_employment</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>25478</th>\n",
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       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25479</th>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>Midwest</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25480 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      education_of_employee has_job_experience region_of_employment\n",
       "0               High School                  N                 West\n",
       "1                  Master's                  Y            Northeast\n",
       "2                Bachelor's                  N                 West\n",
       "3                Bachelor's                  N                 West\n",
       "4                  Master's                  Y                South\n",
       "...                     ...                ...                  ...\n",
       "25475            Bachelor's                  Y                South\n",
       "25476           High School                  Y            Northeast\n",
       "25477              Master's                  Y                South\n",
       "25478              Master's                  Y                 West\n",
       "25479            Bachelor's                  Y              Midwest\n",
       "\n",
       "[25480 rows x 3 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1=[2,3,7]\n",
    "visa_df.take(list1,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "95aa394f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>education_of_employee</th>\n",
       "      <th>no_of_employees</th>\n",
       "      <th>prevailing_wage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>Master's</td>\n",
       "      <td>2412</td>\n",
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       "      <th>2</th>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>44444</td>\n",
       "      <td>122996.8600</td>\n",
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       "      <th>3</th>\n",
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       "      <td>98</td>\n",
       "      <td>83434.0300</td>\n",
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       "      <th>4</th>\n",
       "      <td>Master's</td>\n",
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       "      <td>High School</td>\n",
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       "    <tr>\n",
       "      <th>25477</th>\n",
       "      <td>Master's</td>\n",
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       "      <th>25478</th>\n",
       "      <td>Master's</td>\n",
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       "    <tr>\n",
       "      <th>25479</th>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>3195</td>\n",
       "      <td>70876.9100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25480 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      education_of_employee  no_of_employees  prevailing_wage\n",
       "0               High School            14513         592.2029\n",
       "1                  Master's             2412       83425.6500\n",
       "2                Bachelor's            44444      122996.8600\n",
       "3                Bachelor's               98       83434.0300\n",
       "4                  Master's             1082      149907.3900\n",
       "...                     ...              ...              ...\n",
       "25475            Bachelor's             2601       77092.5700\n",
       "25476           High School             3274      279174.7900\n",
       "25477              Master's             1121      146298.8500\n",
       "25478              Master's             1918       86154.7700\n",
       "25479            Bachelor's             3195       70876.9100\n",
       "\n",
       "[25480 rows x 3 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.take([2,5,8],axis=1)\n",
    "\n",
    "# in python index start with =0\n",
    "# 2===== 3rd column\n",
    "# 5====== 6th column\n",
    "# 8======= 9th column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "60e9e9d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>no_of_employees</th>\n",
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       "      <th>2</th>\n",
       "      <td>EZYV03</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>44444</td>\n",
       "      <td>2008</td>\n",
       "      <td>West</td>\n",
       "      <td>122996.86</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>EZYV06</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2339</td>\n",
       "      <td>2012</td>\n",
       "      <td>South</td>\n",
       "      <td>78252.14</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>EZYV09</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>4810</td>\n",
       "      <td>2012</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>74362.19</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  case_id continent education_of_employee has_job_experience  \\\n",
       "2  EZYV03      Asia            Bachelor's                  N   \n",
       "5  EZYV06      Asia              Master's                  Y   \n",
       "8  EZYV09      Asia            Bachelor's                  N   \n",
       "\n",
       "  requires_job_training  no_of_employees  yr_of_estab region_of_employment  \\\n",
       "2                     Y            44444         2008                 West   \n",
       "5                     N             2339         2012                South   \n",
       "8                     N             4810         2012              Midwest   \n",
       "\n",
       "   prevailing_wage unit_of_wage full_time_position case_status  \n",
       "2        122996.86         Year                  Y      Denied  \n",
       "5         78252.14         Year                  Y   Certified  \n",
       "8         74362.19         Year                  Y   Certified  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.take([2,5,8],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5b8d4283",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>25475</th>\n",
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       "      <td>South</td>\n",
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       "    <tr>\n",
       "      <th>25476</th>\n",
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       "    <tr>\n",
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       "      <td>Master's</td>\n",
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       "    <tr>\n",
       "      <th>25478</th>\n",
       "      <td>Master's</td>\n",
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       "      <td>West</td>\n",
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       "    <tr>\n",
       "      <th>25479</th>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>3195</td>\n",
       "      <td>Midwest</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25480 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      education_of_employee  no_of_employees region_of_employment\n",
       "0               High School            14513                 West\n",
       "1                  Master's             2412            Northeast\n",
       "2                Bachelor's            44444                 West\n",
       "3                Bachelor's               98                 West\n",
       "4                  Master's             1082                South\n",
       "...                     ...              ...                  ...\n",
       "25475            Bachelor's             2601                South\n",
       "25476           High School             3274            Northeast\n",
       "25477              Master's             1121                South\n",
       "25478              Master's             1918                 West\n",
       "25479            Bachelor's             3195              Midwest\n",
       "\n",
       "[25480 rows x 3 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.take((2,5,7),axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97d663a1",
   "metadata": {},
   "source": [
    "$TASK:-2$\n",
    "    \n",
    "- i want to 150,300,450 rows from 5,8,12 by usimg take"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef5fabc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# python index start with 0\n",
    "# we have total 12 column\n",
    "# but in python 12th column index means 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "334226db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>requires_job_training</th>\n",
       "      <th>region_of_employment</th>\n",
       "      <th>case_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>N</td>\n",
       "      <td>West</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>N</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>Y</td>\n",
       "      <td>West</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    requires_job_training region_of_employment case_status\n",
       "150                     N                 West      Denied\n",
       "300                     N              Midwest   Certified\n",
       "450                     Y                 West      Denied"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.take([4,7,11],axis=1).take([150,300,450])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "17e48b35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>150</th>\n",
       "      <td>N</td>\n",
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       "      <td>Denied</td>\n",
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      ],
      "text/plain": [
       "    requires_job_training region_of_employment case_status\n",
       "150                     N                 West      Denied\n",
       "300                     N              Midwest   Certified\n",
       "450                     Y                 West      Denied"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.take((4,7,11),axis=1).take((150,300,450))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "22a27f3c",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "indices are out-of-bounds",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[51], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m visa_df\u001b[38;5;241m.\u001b[39mtake((\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m7\u001b[39m,\u001b[38;5;241m11\u001b[39m),axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mtake((\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m5\u001b[39m))\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:3909\u001b[0m, in \u001b[0;36mNDFrame.take\u001b[1;34m(self, indices, axis, **kwargs)\u001b[0m\n\u001b[0;32m   3833\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   3834\u001b[0m \u001b[38;5;124;03mReturn the elements in the given *positional* indices along an axis.\u001b[39;00m\n\u001b[0;32m   3835\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   3904\u001b[0m \u001b[38;5;124;03m3    lion  mammal       80.5\u001b[39;00m\n\u001b[0;32m   3905\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   3907\u001b[0m nv\u001b[38;5;241m.\u001b[39mvalidate_take((), kwargs)\n\u001b[1;32m-> 3909\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_take(indices, axis)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:3932\u001b[0m, in \u001b[0;36mNDFrame._take\u001b[1;34m(self, indices, axis, convert_indices)\u001b[0m\n\u001b[0;32m   3924\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m   3925\u001b[0m         axis \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m   3926\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m indices\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m   3927\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m using_copy_on_write()\n\u001b[0;32m   3928\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m is_range_indexer(indices, \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m))\n\u001b[0;32m   3929\u001b[0m     ):\n\u001b[0;32m   3930\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m-> 3932\u001b[0m new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mgr\u001b[38;5;241m.\u001b[39mtake(\n\u001b[0;32m   3933\u001b[0m     indices,\n\u001b[0;32m   3934\u001b[0m     axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_block_manager_axis(axis),\n\u001b[0;32m   3935\u001b[0m     verify\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m   3936\u001b[0m     convert_indices\u001b[38;5;241m=\u001b[39mconvert_indices,\n\u001b[0;32m   3937\u001b[0m )\n\u001b[0;32m   3938\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor(new_data)\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtake\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:960\u001b[0m, in \u001b[0;36mBaseBlockManager.take\u001b[1;34m(self, indexer, axis, verify, convert_indices)\u001b[0m\n\u001b[0;32m    958\u001b[0m n \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshape[axis]\n\u001b[0;32m    959\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m convert_indices:\n\u001b[1;32m--> 960\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m maybe_convert_indices(indexer, n, verify\u001b[38;5;241m=\u001b[39mverify)\n\u001b[0;32m    962\u001b[0m new_labels \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes[axis]\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[0;32m    963\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreindex_indexer(\n\u001b[0;32m    964\u001b[0m     new_axis\u001b[38;5;241m=\u001b[39mnew_labels,\n\u001b[0;32m    965\u001b[0m     indexer\u001b[38;5;241m=\u001b[39mindexer,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    968\u001b[0m     copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m    969\u001b[0m )\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexers\\utils.py:284\u001b[0m, in \u001b[0;36mmaybe_convert_indices\u001b[1;34m(indices, n, verify)\u001b[0m\n\u001b[0;32m    282\u001b[0m     mask \u001b[38;5;241m=\u001b[39m (indices \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m n) \u001b[38;5;241m|\u001b[39m (indices \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m    283\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[1;32m--> 284\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mindices are out-of-bounds\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    285\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m indices\n",
      "\u001b[1;31mIndexError\u001b[0m: indices are out-of-bounds"
     ]
    }
   ],
   "source": [
    "visa_df.take((4,7,11),axis=0).take((2,3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "44a7b969",
   "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>no_of_employees</th>\n",
       "      <th>prevailing_wage</th>\n",
       "      <th>case_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>50351</td>\n",
       "      <td>529.1105</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>3268</td>\n",
       "      <td>101371.2100</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>1894</td>\n",
       "      <td>106937.8400</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     no_of_employees  prevailing_wage case_status\n",
       "150            50351         529.1105      Denied\n",
       "300             3268      101371.2100   Certified\n",
       "350             1894      106937.8400      Denied"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.take([5,8,11],axis=1).take([150,300,350])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fcfabb4",
   "metadata": {},
   "source": [
    "$loc-iloc$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "760f84e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data.iloc(<rows>,<columns>)\n",
    "# data.iloc(starts:ends,starts:ends)\n",
    "\n",
    "# assume that i want 20:25 rows\n",
    "#                     3:6 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d1d7e106",
   "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>has_job_experience</th>\n",
       "      <th>requires_job_training</th>\n",
       "      <th>no_of_employees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>1517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   has_job_experience requires_job_training  no_of_employees\n",
       "20                  N                     N              880\n",
       "21                  Y                     N             1706\n",
       "22                  Y                     N             2878\n",
       "23                  N                     N             1517\n",
       "24                  Y                     N              241"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.iloc[20:25,3:6]\n",
    "\n",
    "# last=end-1   25-1=24   6-1=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "69a0132b",
   "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>case_id</th>\n",
       "      <th>continent</th>\n",
       "      <th>education_of_employee</th>\n",
       "      <th>has_job_experience</th>\n",
       "      <th>requires_job_training</th>\n",
       "      <th>no_of_employees</th>\n",
       "      <th>yr_of_estab</th>\n",
       "      <th>region_of_employment</th>\n",
       "      <th>prevailing_wage</th>\n",
       "      <th>unit_of_wage</th>\n",
       "      <th>full_time_position</th>\n",
       "      <th>case_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>EZYV21</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>880</td>\n",
       "      <td>2008</td>\n",
       "      <td>West</td>\n",
       "      <td>71128.98</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>EZYV22</td>\n",
       "      <td>North America</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1706</td>\n",
       "      <td>2013</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>137694.98</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>EZYV23</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2878</td>\n",
       "      <td>1968</td>\n",
       "      <td>West</td>\n",
       "      <td>45642.39</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>EZYV24</td>\n",
       "      <td>North America</td>\n",
       "      <td>High School</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>1517</td>\n",
       "      <td>1884</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>90108.38</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>EZYV25</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>241</td>\n",
       "      <td>1981</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>71286.04</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   case_id      continent education_of_employee has_job_experience  \\\n",
       "20  EZYV21           Asia              Master's                  N   \n",
       "21  EZYV22  North America              Master's                  Y   \n",
       "22  EZYV23           Asia              Master's                  Y   \n",
       "23  EZYV24  North America           High School                  N   \n",
       "24  EZYV25         Europe             Doctorate                  Y   \n",
       "\n",
       "   requires_job_training  no_of_employees  yr_of_estab region_of_employment  \\\n",
       "20                     N              880         2008                 West   \n",
       "21                     N             1706         2013              Midwest   \n",
       "22                     N             2878         1968                 West   \n",
       "23                     N             1517         1884              Midwest   \n",
       "24                     N              241         1981              Midwest   \n",
       "\n",
       "    prevailing_wage unit_of_wage full_time_position case_status  \n",
       "20         71128.98         Year                  Y      Denied  \n",
       "21        137694.98         Year                  N   Certified  \n",
       "22         45642.39         Year                  Y   Certified  \n",
       "23         90108.38         Year                  N   Certified  \n",
       "24         71286.04         Year                  Y   Certified  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.iloc[20:25]   # all the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "20881d79",
   "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>has_job_experience</th>\n",
       "      <th>requires_job_training</th>\n",
       "      <th>no_of_employees</th>\n",
       "      <th>yr_of_estab</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>50351</td>\n",
       "      <td>1975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2261</td>\n",
       "      <td>1966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>255</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>373</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>22</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>894</td>\n",
       "      <td>1851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>636</td>\n",
       "      <td>1977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>255</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2311</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>781</td>\n",
       "      <td>1983</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    has_job_experience requires_job_training  no_of_employees  yr_of_estab\n",
       "150                  N                     N            50351         1975\n",
       "151                  Y                     N             2261         1966\n",
       "152                  Y                     N              255         2003\n",
       "153                  Y                     N              373         2009\n",
       "154                  Y                     Y               22         1999\n",
       "..                 ...                   ...              ...          ...\n",
       "445                  N                     N              894         1851\n",
       "446                  Y                     N              636         1977\n",
       "447                  Y                     N              255         2004\n",
       "448                  Y                     N             2311         2005\n",
       "449                  N                     N              781         1983\n",
       "\n",
       "[300 rows x 4 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.iloc[150:450,3:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3f787dcd",
   "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>case_id</th>\n",
       "      <th>continent</th>\n",
       "      <th>education_of_employee</th>\n",
       "      <th>has_job_experience</th>\n",
       "      <th>requires_job_training</th>\n",
       "      <th>no_of_employees</th>\n",
       "      <th>yr_of_estab</th>\n",
       "      <th>region_of_employment</th>\n",
       "      <th>prevailing_wage</th>\n",
       "      <th>unit_of_wage</th>\n",
       "      <th>full_time_position</th>\n",
       "      <th>case_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>EZYV101</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2227</td>\n",
       "      <td>2007</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>28243.7900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>EZYV102</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>334</td>\n",
       "      <td>2007</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>43421.8800</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>EZYV103</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>224</td>\n",
       "      <td>2008</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>80253.7800</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>EZYV104</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>367</td>\n",
       "      <td>1982</td>\n",
       "      <td>West</td>\n",
       "      <td>115235.9500</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>EZYV105</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>306</td>\n",
       "      <td>2003</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>95200.8600</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>EZYV106</td>\n",
       "      <td>North America</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2317</td>\n",
       "      <td>1995</td>\n",
       "      <td>Island</td>\n",
       "      <td>41613.5200</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>EZYV107</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>890</td>\n",
       "      <td>1981</td>\n",
       "      <td>West</td>\n",
       "      <td>64137.4100</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>EZYV108</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2002</td>\n",
       "      <td>2006</td>\n",
       "      <td>South</td>\n",
       "      <td>66295.4300</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>EZYV109</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>2903</td>\n",
       "      <td>1943</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>97520.0200</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>EZYV110</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2504</td>\n",
       "      <td>2001</td>\n",
       "      <td>West</td>\n",
       "      <td>65164.5700</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>EZYV111</td>\n",
       "      <td>North America</td>\n",
       "      <td>High School</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>951</td>\n",
       "      <td>1956</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>73957.1700</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>EZYV112</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1327</td>\n",
       "      <td>1864</td>\n",
       "      <td>West</td>\n",
       "      <td>71415.4300</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>EZYV113</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2421</td>\n",
       "      <td>1974</td>\n",
       "      <td>South</td>\n",
       "      <td>87336.5300</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>EZYV114</td>\n",
       "      <td>North America</td>\n",
       "      <td>High School</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1019</td>\n",
       "      <td>2005</td>\n",
       "      <td>South</td>\n",
       "      <td>98966.7200</td>\n",
       "      <td>Week</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>EZYV115</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>1048</td>\n",
       "      <td>1985</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>83503.6000</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>EZYV116</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>3323</td>\n",
       "      <td>2005</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>57590.6600</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>EZYV117</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>509</td>\n",
       "      <td>1997</td>\n",
       "      <td>South</td>\n",
       "      <td>32400.2000</td>\n",
       "      <td>Week</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>EZYV118</td>\n",
       "      <td>North America</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>2225</td>\n",
       "      <td>1976</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>65993.2100</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>EZYV119</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1641</td>\n",
       "      <td>1984</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>32161.6000</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>EZYV120</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>1144</td>\n",
       "      <td>2001</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>16421.3700</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>EZYV121</td>\n",
       "      <td>North America</td>\n",
       "      <td>Master's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>2334</td>\n",
       "      <td>1971</td>\n",
       "      <td>South</td>\n",
       "      <td>160676.3900</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>EZYV122</td>\n",
       "      <td>South America</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>1415</td>\n",
       "      <td>1913</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>53114.0200</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>EZYV123</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>2754</td>\n",
       "      <td>1997</td>\n",
       "      <td>South</td>\n",
       "      <td>19176.2100</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>EZYV124</td>\n",
       "      <td>North America</td>\n",
       "      <td>Master's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>1099</td>\n",
       "      <td>2005</td>\n",
       "      <td>West</td>\n",
       "      <td>204832.5200</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>EZYV125</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>960</td>\n",
       "      <td>2012</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>40256.0300</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>EZYV126</td>\n",
       "      <td>North America</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2750</td>\n",
       "      <td>1997</td>\n",
       "      <td>West</td>\n",
       "      <td>38841.4400</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>EZYV127</td>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2753</td>\n",
       "      <td>2010</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>133052.8400</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>EZYV128</td>\n",
       "      <td>North America</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>23287</td>\n",
       "      <td>2007</td>\n",
       "      <td>South</td>\n",
       "      <td>75234.6400</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>EZYV129</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>2140</td>\n",
       "      <td>1997</td>\n",
       "      <td>South</td>\n",
       "      <td>37032.8300</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>EZYV130</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>4499</td>\n",
       "      <td>2008</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>61617.7300</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>EZYV131</td>\n",
       "      <td>South America</td>\n",
       "      <td>High School</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>2554</td>\n",
       "      <td>2005</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>247393.0100</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>EZYV132</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1333</td>\n",
       "      <td>1969</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>341.0229</td>\n",
       "      <td>Hour</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>EZYV133</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>928</td>\n",
       "      <td>1818</td>\n",
       "      <td>South</td>\n",
       "      <td>30570.9500</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>EZYV134</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>4289</td>\n",
       "      <td>2005</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>19477.1000</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>EZYV135</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>113282</td>\n",
       "      <td>2011</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>164893.0000</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>EZYV136</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1948</td>\n",
       "      <td>2002</td>\n",
       "      <td>South</td>\n",
       "      <td>131584.5000</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>EZYV137</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1933</td>\n",
       "      <td>1839</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>63896.3500</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>EZYV138</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>2847</td>\n",
       "      <td>2013</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>77883.2200</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>EZYV139</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1195</td>\n",
       "      <td>1914</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>108519.1400</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>EZYV140</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>54</td>\n",
       "      <td>1988</td>\n",
       "      <td>South</td>\n",
       "      <td>47002.5300</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>EZYV141</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>4390</td>\n",
       "      <td>1944</td>\n",
       "      <td>South</td>\n",
       "      <td>88720.2900</td>\n",
       "      <td>Year</td>\n",
       "      <td>N</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>EZYV142</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>73909</td>\n",
       "      <td>2004</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>60821.7500</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>EZYV143</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>545</td>\n",
       "      <td>1855</td>\n",
       "      <td>South</td>\n",
       "      <td>66324.6900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>EZYV144</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2831</td>\n",
       "      <td>1977</td>\n",
       "      <td>South</td>\n",
       "      <td>100398.5800</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>EZYV145</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>1634</td>\n",
       "      <td>1999</td>\n",
       "      <td>South</td>\n",
       "      <td>51282.7500</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>EZYV146</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1810</td>\n",
       "      <td>1943</td>\n",
       "      <td>South</td>\n",
       "      <td>13322.7200</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>EZYV147</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3290</td>\n",
       "      <td>2005</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>357.8194</td>\n",
       "      <td>Hour</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>EZYV148</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>472</td>\n",
       "      <td>2004</td>\n",
       "      <td>West</td>\n",
       "      <td>4787.4300</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>EZYV149</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>4273</td>\n",
       "      <td>1884</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>149023.9200</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>EZYV150</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>2326</td>\n",
       "      <td>1933</td>\n",
       "      <td>West</td>\n",
       "      <td>123739.4100</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     case_id      continent education_of_employee has_job_experience  \\\n",
       "100  EZYV101           Asia              Master's                  Y   \n",
       "101  EZYV102           Asia              Master's                  Y   \n",
       "102  EZYV103           Asia            Bachelor's                  Y   \n",
       "103  EZYV104           Asia             Doctorate                  Y   \n",
       "104  EZYV105           Asia              Master's                  Y   \n",
       "105  EZYV106  North America             Doctorate                  Y   \n",
       "106  EZYV107           Asia            Bachelor's                  N   \n",
       "107  EZYV108           Asia            Bachelor's                  Y   \n",
       "108  EZYV109           Asia            Bachelor's                  N   \n",
       "109  EZYV110           Asia            Bachelor's                  Y   \n",
       "110  EZYV111  North America           High School                  Y   \n",
       "111  EZYV112           Asia              Master's                  Y   \n",
       "112  EZYV113           Asia            Bachelor's                  Y   \n",
       "113  EZYV114  North America           High School                  Y   \n",
       "114  EZYV115           Asia             Doctorate                  N   \n",
       "115  EZYV116           Asia              Master's                  N   \n",
       "116  EZYV117           Asia            Bachelor's                  Y   \n",
       "117  EZYV118  North America              Master's                  Y   \n",
       "118  EZYV119           Asia            Bachelor's                  Y   \n",
       "119  EZYV120           Asia              Master's                  N   \n",
       "120  EZYV121  North America              Master's                  N   \n",
       "121  EZYV122  South America            Bachelor's                  N   \n",
       "122  EZYV123           Asia             Doctorate                  Y   \n",
       "123  EZYV124  North America              Master's                  N   \n",
       "124  EZYV125           Asia              Master's                  Y   \n",
       "125  EZYV126  North America              Master's                  Y   \n",
       "126  EZYV127           Asia           High School                  Y   \n",
       "127  EZYV128  North America            Bachelor's                  Y   \n",
       "128  EZYV129           Asia            Bachelor's                  N   \n",
       "129  EZYV130           Asia            Bachelor's                  N   \n",
       "130  EZYV131  South America           High School                  N   \n",
       "131  EZYV132           Asia            Bachelor's                  Y   \n",
       "132  EZYV133           Asia             Doctorate                  N   \n",
       "133  EZYV134           Asia             Doctorate                  Y   \n",
       "134  EZYV135           Asia             Doctorate                  Y   \n",
       "135  EZYV136           Asia              Master's                  Y   \n",
       "136  EZYV137           Asia              Master's                  Y   \n",
       "137  EZYV138           Asia              Master's                  N   \n",
       "138  EZYV139           Asia            Bachelor's                  Y   \n",
       "139  EZYV140           Asia              Master's                  Y   \n",
       "140  EZYV141           Asia              Master's                  N   \n",
       "141  EZYV142           Asia              Master's                  Y   \n",
       "142  EZYV143           Asia              Master's                  N   \n",
       "143  EZYV144           Asia             Doctorate                  Y   \n",
       "144  EZYV145           Asia            Bachelor's                  N   \n",
       "145  EZYV146         Europe            Bachelor's                  Y   \n",
       "146  EZYV147         Europe            Bachelor's                  Y   \n",
       "147  EZYV148           Asia              Master's                  Y   \n",
       "148  EZYV149           Asia            Bachelor's                  Y   \n",
       "149  EZYV150           Asia              Master's                  N   \n",
       "\n",
       "    requires_job_training  no_of_employees  yr_of_estab region_of_employment  \\\n",
       "100                     N             2227         2007            Northeast   \n",
       "101                     N              334         2007              Midwest   \n",
       "102                     N              224         2008              Midwest   \n",
       "103                     N              367         1982                 West   \n",
       "104                     N              306         2003            Northeast   \n",
       "105                     N             2317         1995               Island   \n",
       "106                     N              890         1981                 West   \n",
       "107                     N             2002         2006                South   \n",
       "108                     N             2903         1943              Midwest   \n",
       "109                     N             2504         2001                 West   \n",
       "110                     N              951         1956              Midwest   \n",
       "111                     N             1327         1864                 West   \n",
       "112                     N             2421         1974                South   \n",
       "113                     N             1019         2005                South   \n",
       "114                     N             1048         1985              Midwest   \n",
       "115                     N             3323         2005              Midwest   \n",
       "116                     N              509         1997                South   \n",
       "117                     Y             2225         1976            Northeast   \n",
       "118                     N             1641         1984              Midwest   \n",
       "119                     N             1144         2001            Northeast   \n",
       "120                     N             2334         1971                South   \n",
       "121                     N             1415         1913            Northeast   \n",
       "122                     Y             2754         1997                South   \n",
       "123                     N             1099         2005                 West   \n",
       "124                     N              960         2012            Northeast   \n",
       "125                     N             2750         1997                 West   \n",
       "126                     N             2753         2010            Northeast   \n",
       "127                     N            23287         2007                South   \n",
       "128                     N             2140         1997                South   \n",
       "129                     N             4499         2008            Northeast   \n",
       "130                     N             2554         2005              Midwest   \n",
       "131                     N             1333         1969            Northeast   \n",
       "132                     N              928         1818                South   \n",
       "133                     N             4289         2005              Midwest   \n",
       "134                     Y           113282         2011              Midwest   \n",
       "135                     N             1948         2002                South   \n",
       "136                     N             1933         1839            Northeast   \n",
       "137                     N             2847         2013              Midwest   \n",
       "138                     N             1195         1914              Midwest   \n",
       "139                     N               54         1988                South   \n",
       "140                     N             4390         1944                South   \n",
       "141                     N            73909         2004              Midwest   \n",
       "142                     Y              545         1855                South   \n",
       "143                     N             2831         1977                South   \n",
       "144                     Y             1634         1999                South   \n",
       "145                     N             1810         1943                South   \n",
       "146                     N             3290         2005            Northeast   \n",
       "147                     N              472         2004                 West   \n",
       "148                     N             4273         1884              Midwest   \n",
       "149                     N             2326         1933                 West   \n",
       "\n",
       "     prevailing_wage unit_of_wage full_time_position case_status  \n",
       "100       28243.7900         Year                  Y   Certified  \n",
       "101       43421.8800         Year                  Y   Certified  \n",
       "102       80253.7800         Year                  Y   Certified  \n",
       "103      115235.9500         Year                  Y   Certified  \n",
       "104       95200.8600         Year                  N   Certified  \n",
       "105       41613.5200         Year                  Y   Certified  \n",
       "106       64137.4100         Year                  Y      Denied  \n",
       "107       66295.4300         Year                  Y   Certified  \n",
       "108       97520.0200         Year                  Y   Certified  \n",
       "109       65164.5700         Year                  Y   Certified  \n",
       "110       73957.1700         Year                  N   Certified  \n",
       "111       71415.4300         Year                  Y   Certified  \n",
       "112       87336.5300         Year                  Y   Certified  \n",
       "113       98966.7200         Week                  Y   Certified  \n",
       "114       83503.6000         Year                  Y   Certified  \n",
       "115       57590.6600         Year                  Y   Certified  \n",
       "116       32400.2000         Week                  Y   Certified  \n",
       "117       65993.2100         Year                  Y   Certified  \n",
       "118       32161.6000         Year                  Y   Certified  \n",
       "119       16421.3700         Year                  Y      Denied  \n",
       "120      160676.3900         Year                  N   Certified  \n",
       "121       53114.0200         Year                  Y      Denied  \n",
       "122       19176.2100         Year                  Y   Certified  \n",
       "123      204832.5200         Year                  N      Denied  \n",
       "124       40256.0300         Year                  Y   Certified  \n",
       "125       38841.4400         Year                  Y   Certified  \n",
       "126      133052.8400         Year                  Y      Denied  \n",
       "127       75234.6400         Year                  N   Certified  \n",
       "128       37032.8300         Year                  Y   Certified  \n",
       "129       61617.7300         Year                  Y      Denied  \n",
       "130      247393.0100         Year                  Y   Certified  \n",
       "131         341.0229         Hour                  Y   Certified  \n",
       "132       30570.9500         Year                  Y   Certified  \n",
       "133       19477.1000         Year                  Y   Certified  \n",
       "134      164893.0000         Year                  Y   Certified  \n",
       "135      131584.5000         Year                  Y   Certified  \n",
       "136       63896.3500         Year                  Y   Certified  \n",
       "137       77883.2200         Year                  Y   Certified  \n",
       "138      108519.1400         Year                  Y   Certified  \n",
       "139       47002.5300         Year                  N   Certified  \n",
       "140       88720.2900         Year                  N   Certified  \n",
       "141       60821.7500         Year                  Y   Certified  \n",
       "142       66324.6900         Year                  Y   Certified  \n",
       "143      100398.5800         Year                  Y   Certified  \n",
       "144       51282.7500         Year                  Y   Certified  \n",
       "145       13322.7200         Year                  Y   Certified  \n",
       "146         357.8194         Hour                  Y   Certified  \n",
       "147        4787.4300         Year                  Y   Certified  \n",
       "148      149023.9200         Year                  Y   Certified  \n",
       "149      123739.4100         Year                  Y      Denied  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.iloc[100:150]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ca5ed67c",
   "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>case_id</th>\n",
       "      <th>continent</th>\n",
       "      <th>education_of_employee</th>\n",
       "      <th>has_job_experience</th>\n",
       "      <th>requires_job_training</th>\n",
       "      <th>no_of_employees</th>\n",
       "      <th>yr_of_estab</th>\n",
       "      <th>region_of_employment</th>\n",
       "      <th>prevailing_wage</th>\n",
       "      <th>unit_of_wage</th>\n",
       "      <th>full_time_position</th>\n",
       "      <th>case_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>EZYV101</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2227</td>\n",
       "      <td>2007</td>\n",
       "      <td>Northeast</td>\n",
       "      <td>28243.7900</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>EZYV151</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>50351</td>\n",
       "      <td>1975</td>\n",
       "      <td>West</td>\n",
       "      <td>529.1105</td>\n",
       "      <td>Hour</td>\n",
       "      <td>Y</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>EZYV201</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>3282</td>\n",
       "      <td>1979</td>\n",
       "      <td>West</td>\n",
       "      <td>74441.1100</td>\n",
       "      <td>Year</td>\n",
       "      <td>Y</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     case_id continent education_of_employee has_job_experience  \\\n",
       "100  EZYV101      Asia              Master's                  Y   \n",
       "150  EZYV151      Asia            Bachelor's                  N   \n",
       "200  EZYV201      Asia             Doctorate                  Y   \n",
       "\n",
       "    requires_job_training  no_of_employees  yr_of_estab region_of_employment  \\\n",
       "100                     N             2227         2007            Northeast   \n",
       "150                     N            50351         1975                 West   \n",
       "200                     N             3282         1979                 West   \n",
       "\n",
       "     prevailing_wage unit_of_wage full_time_position case_status  \n",
       "100       28243.7900         Year                  Y   Certified  \n",
       "150         529.1105         Hour                  Y      Denied  \n",
       "200       74441.1100         Year                  Y   Certified  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.iloc[[100,150,200]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ee265f0b",
   "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>no_of_employees</th>\n",
       "      <th>prevailing_wage</th>\n",
       "      <th>case_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2227</td>\n",
       "      <td>28243.7900</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>50351</td>\n",
       "      <td>529.1105</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>3282</td>\n",
       "      <td>74441.1100</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     no_of_employees  prevailing_wage case_status\n",
       "100             2227       28243.7900   Certified\n",
       "150            50351         529.1105      Denied\n",
       "200             3282       74441.1100   Certified"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.iloc[[100,150,200],[5,8,11]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "16f419f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['case_id', 'continent', 'education_of_employee', 'has_job_experience',\n",
       "       'requires_job_training', 'no_of_employees', 'yr_of_estab',\n",
       "       'region_of_employment', 'prevailing_wage', 'unit_of_wage',\n",
       "       'full_time_position', 'case_status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0e9e33e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100    Certified\n",
       "200    Certified\n",
       "300    Certified\n",
       "Name: case_status, dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 100,200,300 from only case status column\n",
    "visa_df.iloc[[100,200,300],11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5d257755",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Location based indexing can only have [integer, integer slice (START point is INCLUDED, END point is EXCLUDED), listlike of integers, boolean array] types",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexing.py:904\u001b[0m, in \u001b[0;36m_LocationIndexer._validate_tuple_indexer\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    903\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 904\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(k, i)\n\u001b[0;32m    905\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexing.py:1518\u001b[0m, in \u001b[0;36m_iLocIndexer._validate_key\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan only index by location with a [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_valid_types\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mValueError\u001b[0m: Can only index by location with a [integer, integer slice (START point is INCLUDED, END point is EXCLUDED), listlike of integers, boolean array]",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[61], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m visa_df\u001b[38;5;241m.\u001b[39miloc[[\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m200\u001b[39m,\u001b[38;5;241m300\u001b[39m],\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcase_status\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexing.py:1097\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1095\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_scalar_access(key):\n\u001b[0;32m   1096\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_value(\u001b[38;5;241m*\u001b[39mkey, takeable\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_takeable)\n\u001b[1;32m-> 1097\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_tuple(key)\n\u001b[0;32m   1098\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1099\u001b[0m     \u001b[38;5;66;03m# we by definition only have the 0th axis\u001b[39;00m\n\u001b[0;32m   1100\u001b[0m     axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexing.py:1594\u001b[0m, in \u001b[0;36m_iLocIndexer._getitem_tuple\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m   1593\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_getitem_tuple\u001b[39m(\u001b[38;5;28mself\u001b[39m, tup: \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m-> 1594\u001b[0m     tup \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_tuple_indexer(tup)\n\u001b[0;32m   1595\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m suppress(IndexingError):\n\u001b[0;32m   1596\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_lowerdim(tup)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexing.py:906\u001b[0m, in \u001b[0;36m_LocationIndexer._validate_tuple_indexer\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    904\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(k, i)\n\u001b[0;32m    905\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m--> 906\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    907\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLocation based indexing can only have \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    908\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_valid_types\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] types\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    909\u001b[0m         ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m    910\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m key\n",
      "\u001b[1;31mValueError\u001b[0m: Location based indexing can only have [integer, integer slice (START point is INCLUDED, END point is EXCLUDED), listlike of integers, boolean array] types"
     ]
    }
   ],
   "source": [
    "visa_df.iloc[[100,200,300],'case_status']\n",
    "# iloc will take only number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9d3b5cfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100    Certified\n",
       "200    Certified\n",
       "300    Certified\n",
       "Name: case_status, dtype: object"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list of rows\n",
    "# list of columns\n",
    "#rows=[100,200,300]\n",
    "#cols=['continent','case_status']\n",
    "visa_df.loc[[100,200,300],'case_status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "460c4366",
   "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>continent</th>\n",
       "      <th>case_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    continent case_status\n",
       "100      Asia   Certified\n",
       "200      Asia   Certified\n",
       "300      Asia   Certified"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows=[100,200,300]\n",
    "cols=['continent','case_status']\n",
    "visa_df.loc[rows,cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "081c0139",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25480"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(visa_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7251aec",
   "metadata": {},
   "source": [
    "##### Isnull"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32f1bf74",
   "metadata": {},
   "source": [
    "- any missing values are there in the data\n",
    "- is null you are asking qn to computer: True/False\n",
    "- if any missing values are there is it True\n",
    "- if data presents/no missing value False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "70b300dc",
   "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>case_id</th>\n",
       "      <th>continent</th>\n",
       "      <th>education_of_employee</th>\n",
       "      <th>has_job_experience</th>\n",
       "      <th>requires_job_training</th>\n",
       "      <th>no_of_employees</th>\n",
       "      <th>yr_of_estab</th>\n",
       "      <th>region_of_employment</th>\n",
       "      <th>prevailing_wage</th>\n",
       "      <th>unit_of_wage</th>\n",
       "      <th>full_time_position</th>\n",
       "      <th>case_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25475</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25476</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25477</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25478</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25479</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25480 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       case_id  continent  education_of_employee  has_job_experience  \\\n",
       "0        False      False                  False               False   \n",
       "1        False      False                  False               False   \n",
       "2        False      False                  False               False   \n",
       "3        False      False                  False               False   \n",
       "4        False      False                  False               False   \n",
       "...        ...        ...                    ...                 ...   \n",
       "25475    False      False                  False               False   \n",
       "25476    False      False                  False               False   \n",
       "25477    False      False                  False               False   \n",
       "25478    False      False                  False               False   \n",
       "25479    False      False                  False               False   \n",
       "\n",
       "       requires_job_training  no_of_employees  yr_of_estab  \\\n",
       "0                      False            False        False   \n",
       "1                      False            False        False   \n",
       "2                      False            False        False   \n",
       "3                      False            False        False   \n",
       "4                      False            False        False   \n",
       "...                      ...              ...          ...   \n",
       "25475                  False            False        False   \n",
       "25476                  False            False        False   \n",
       "25477                  False            False        False   \n",
       "25478                  False            False        False   \n",
       "25479                  False            False        False   \n",
       "\n",
       "       region_of_employment  prevailing_wage  unit_of_wage  \\\n",
       "0                     False            False         False   \n",
       "1                     False            False         False   \n",
       "2                     False            False         False   \n",
       "3                     False            False         False   \n",
       "4                     False            False         False   \n",
       "...                     ...              ...           ...   \n",
       "25475                 False            False         False   \n",
       "25476                 False            False         False   \n",
       "25477                 False            False         False   \n",
       "25478                 False            False         False   \n",
       "25479                 False            False         False   \n",
       "\n",
       "       full_time_position  case_status  \n",
       "0                   False        False  \n",
       "1                   False        False  \n",
       "2                   False        False  \n",
       "3                   False        False  \n",
       "4                   False        False  \n",
       "...                   ...          ...  \n",
       "25475               False        False  \n",
       "25476               False        False  \n",
       "25477               False        False  \n",
       "25478               False        False  \n",
       "25479               False        False  \n",
       "\n",
       "[25480 rows x 12 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5c33f413",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "case_id                  0\n",
       "continent                0\n",
       "education_of_employee    0\n",
       "has_job_experience       0\n",
       "requires_job_training    0\n",
       "no_of_employees          0\n",
       "yr_of_estab              0\n",
       "region_of_employment     0\n",
       "prevailing_wage          0\n",
       "unit_of_wage             0\n",
       "full_time_position       0\n",
       "case_status              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ed3d29b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25480 entries, 0 to 25479\n",
      "Data columns (total 12 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   case_id                25480 non-null  object \n",
      " 1   continent              25480 non-null  object \n",
      " 2   education_of_employee  25480 non-null  object \n",
      " 3   has_job_experience     25480 non-null  object \n",
      " 4   requires_job_training  25480 non-null  object \n",
      " 5   no_of_employees        25480 non-null  int64  \n",
      " 6   yr_of_estab            25480 non-null  int64  \n",
      " 7   region_of_employment   25480 non-null  object \n",
      " 8   prevailing_wage        25480 non-null  float64\n",
      " 9   unit_of_wage           25480 non-null  object \n",
      " 10  full_time_position     25480 non-null  object \n",
      " 11  case_status            25480 non-null  object \n",
      "dtypes: float64(1), int64(2), object(9)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "visa_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0eafeb7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25480 entries, 0 to 25479\n",
      "Data columns (total 12 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   case_id                25480 non-null  object \n",
      " 1   continent              25480 non-null  object \n",
      " 2   education_of_employee  25480 non-null  object \n",
      " 3   has_job_experience     25480 non-null  object \n",
      " 4   requires_job_training  25480 non-null  object \n",
      " 5   no_of_employees        25480 non-null  int64  \n",
      " 6   yr_of_estab            25480 non-null  int64  \n",
      " 7   region_of_employment   25480 non-null  object \n",
      " 8   prevailing_wage        25480 non-null  float64\n",
      " 9   unit_of_wage           25480 non-null  object \n",
      " 10  full_time_position     25480 non-null  object \n",
      " 11  case_status            25480 non-null  object \n",
      "dtypes: float64(1), int64(2), object(9)\n",
      "memory usage: 2.3+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "NoneType"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(visa_df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "221ca73c",
   "metadata": {},
   "source": [
    "$COMPARE$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4ebe21b",
   "metadata": {},
   "source": [
    "- compare to take , iloc is better\n",
    "- compare to iloc , loc is better\n",
    "- in take we need provide axis and column\n",
    "- in iloc and loc we need provide axis\n",
    "- in i loc we need provide column axis number\n",
    "- if there are huge column are there , it is not good to count the specific column number\n",
    "- instead of that loc function we will take directly the column name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85323cf8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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