Session - 3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "252bff59",
   "metadata": {},
   "source": [
    "- read the data\n",
    "- how to create a data frame\n",
    "- how to create a column\n",
    "- shape - size\n",
    "- head - tail\n",
    "- take - loc - iloc\n",
    "- info - len\n",
    "- is null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ecf6cb65",
   "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": "markdown",
   "id": "931ef56f",
   "metadata": {},
   "source": [
    "$Step:- 1$\n",
    "    \n",
    "- Import required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bde5cd14",
   "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": "markdown",
   "id": "d6d40c53",
   "metadata": {},
   "source": [
    "$ Step:-2$\n",
    "\n",
    "**Read- the- data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "39958129",
   "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>...</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>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": 3,
     "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f287efb5",
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03aca1a2",
   "metadata": {},
   "source": [
    "$Step:-3$\n",
    "    \n",
    "- Read the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "932038eb",
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e3890680",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df['continent']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "65e82ba7",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25475</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25476</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25477</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25478</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25479</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25480 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      continent\n",
       "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",
       "\n",
       "[25480 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "visa_df[['continent']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6e510fdc",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25475</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25476</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25477</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25478</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25479</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25480 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      continent\n",
       "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",
       "\n",
       "[25480 rows x 1 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols=['continent']\n",
    "visa_df[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5fcde9e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(visa_df['continent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2af357e9",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25475</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25476</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25477</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25478</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25479</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25480 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      continent\n",
       "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",
       "\n",
       "[25480 rows x 1 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols=['continent']\n",
    "visa_df[cols]\n",
    "\n",
    "# visa_df['continent']============= series\n",
    "# visa_df[['continent']] ================== dataframe(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "000cf815",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df.continent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fa19be2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=visa_df.education_of_employee\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aca0c1fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# visa_df['continent']\n",
    "# visa_df[['continent']]\n",
    "# visa_df.continent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1de4f211",
   "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>education_of_employee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Master's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25475</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25476</th>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25477</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25478</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25479</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25480 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      continent education_of_employee\n",
       "0          Asia           High School\n",
       "1          Asia              Master's\n",
       "2          Asia            Bachelor's\n",
       "3          Asia            Bachelor's\n",
       "4        Africa              Master's\n",
       "...         ...                   ...\n",
       "25475      Asia            Bachelor's\n",
       "25476      Asia           High School\n",
       "25477      Asia              Master's\n",
       "25478      Asia              Master's\n",
       "25479      Asia            Bachelor's\n",
       "\n",
       "[25480 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Two columns at a  time\n",
    "cols=['continent','education_of_employee']\n",
    "visa_df[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "afcc3581",
   "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>education_of_employee</th>\n",
       "      <th>case_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Certified</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>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25476</th>\n",
       "      <td>Asia</td>\n",
       "      <td>High School</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25477</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25478</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25479</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25480 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      continent education_of_employee case_status\n",
       "0          Asia           High School      Denied\n",
       "1          Asia              Master's   Certified\n",
       "2          Asia            Bachelor's      Denied\n",
       "3          Asia            Bachelor's      Denied\n",
       "4        Africa              Master's   Certified\n",
       "...         ...                   ...         ...\n",
       "25475      Asia            Bachelor's   Certified\n",
       "25476      Asia           High School   Certified\n",
       "25477      Asia              Master's   Certified\n",
       "25478      Asia              Master's   Certified\n",
       "25479      Asia            Bachelor's   Certified\n",
       "\n",
       "[25480 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=['continent','education_of_employee','case_status']\n",
    "visa_df[x]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "473d57c2",
   "metadata": {},
   "source": [
    "$Unique$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "19937f78",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'unique'",
     "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_6672\\796555025.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'continent'\u001b[0m\u001b[1;33m]\u001b[0m           \u001b[1;31m####  problem\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mvisa_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[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 'unique'"
     ]
    }
   ],
   "source": [
    "x=['continent']           ####  problem\n",
    "visa_df[x].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c1d19729",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Asia', 'Africa', 'North America', 'Europe', 'South America',\n",
       "       'Oceania'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first read the column\n",
    "# then apply unique\n",
    "\n",
    "# dont apply unique operation for dataframe :[[]]\n",
    "# apply only for series:[]\n",
    "visa_df['continent'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8fcf9a07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(visa_df['continent'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a19e5f5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25480"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(visa_df['continent'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00080f6c",
   "metadata": {},
   "source": [
    "$Nunique$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "20260085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df['continent'].nunique()\n",
    "\n",
    "# number of unique labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "90786551",
   "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>0</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Denied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25475</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25476</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25477</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25478</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25479</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Certified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25480 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      continent case_status\n",
       "0          Asia      Denied\n",
       "1          Asia   Certified\n",
       "2          Asia      Denied\n",
       "3          Asia      Denied\n",
       "4        Africa   Certified\n",
       "...         ...         ...\n",
       "25475      Asia   Certified\n",
       "25476      Asia   Certified\n",
       "25477      Asia   Certified\n",
       "25478      Asia   Certified\n",
       "25479      Asia   Certified\n",
       "\n",
       "[25480 rows x 2 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df[['continent','case_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a956ceeb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "624009d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we read continent columns\n",
    "# we understand there are 6 unique labels are there\n",
    "# these 6 unique labels repeating that total 25480 observation\n",
    "# how many are asia are there\n",
    "# how many are africa are there"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "677a4e52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         True\n",
       "1         True\n",
       "2         True\n",
       "3         True\n",
       "4        False\n",
       "         ...  \n",
       "25475     True\n",
       "25476     True\n",
       "25477     True\n",
       "25478     True\n",
       "25479     True\n",
       "Name: continent, Length: 25480, dtype: bool"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df['continent']=='Asia'\n",
    "\n",
    "# do you want to know how many trues are there\n",
    "# how many rows are satisfying\n",
    "# how many observations are having continent as asia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a5b0af9b",
   "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>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>...</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>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>16861 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",
       "5         EZYV06      Asia              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",
       "5                         N             2339         2012   \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",
       "5                    South       78252.1400         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",
       "5       Certified  \n",
       "...           ...  \n",
       "25475   Certified  \n",
       "25476   Certified  \n",
       "25477   Certified  \n",
       "25478   Certified  \n",
       "25479   Certified  \n",
       "\n",
       "[16861 rows x 12 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df[visa_df['continent']=='Asia']\n",
    "\n",
    "# do you want to know how many trues are there\n",
    "# how many rows are satisfying\n",
    "# how many observations are having continent as asia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4daf9d48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16861"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(visa_df[visa_df['continent']=='Asia'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dbaf91af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16861\n"
     ]
    }
   ],
   "source": [
    "x=visa_df['continent']\n",
    "count=0\n",
    "for i in x:\n",
    "    if i=='Asia':\n",
    "        count=count+1\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6a7afdb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16861"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df                       # complete dataframe\n",
    "visa_df['continent']          # column\n",
    "visa_df['continent']=='Asia'\n",
    "visa_df[visa_df['continent']=='Asia']\n",
    "len(visa_df[visa_df['continent']=='Asia'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b67a9850",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16861\n",
      "551\n",
      "3292\n",
      "3732\n",
      "852\n",
      "192\n"
     ]
    }
   ],
   "source": [
    "print(len(visa_df[visa_df['continent']=='Asia']))\n",
    "print(len(visa_df[visa_df['continent']=='Africa']))\n",
    "print(len(visa_df[visa_df['continent']=='North America']))\n",
    "print(len(visa_df[visa_df['continent']=='Europe']))\n",
    "print(len(visa_df[visa_df['continent']=='South America']))\n",
    "print(len(visa_df[visa_df['continent']=='Oceania']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "739d8602",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "192\n"
     ]
    }
   ],
   "source": [
    "print(len(visa_df[visa_df['continent']=='Oceania']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6be185c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Asia', 'Africa', 'North America', 'Europe', 'South America', 'Oceania']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uni=list(visa_df['continent'].unique())\n",
    "uni"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "09052c27",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'array' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[26], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m array([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAsia\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAfrica\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNorth America\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEurope\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSouth America\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m      2\u001b[0m        \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOceania\u001b[39m\u001b[38;5;124m'\u001b[39m], dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mobject\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'array' is not defined"
     ]
    }
   ],
   "source": [
    "array(['Asia', 'Africa', 'North America', 'Europe', 'South America',\n",
    "       'Oceania'], dtype=object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "eb892b4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Asia\n",
      "16861\n",
      "Africa\n",
      "551\n",
      "North America\n",
      "3292\n",
      "Europe\n",
      "3732\n",
      "South America\n",
      "852\n",
      "Oceania\n",
      "192\n"
     ]
    }
   ],
   "source": [
    "for i in uni:\n",
    "    print(i)\n",
    "    print(len(visa_df[visa_df['continent']==i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9c58081f",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'continents' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[28], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m count\u001b[38;5;241m=\u001b[39m[]\n\u001b[0;32m      2\u001b[0m continent\u001b[38;5;241m=\u001b[39mvisa_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcontinent\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39munique()\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m continents:\n\u001b[0;32m      4\u001b[0m     c\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(visa_df[visa_df])\n",
      "\u001b[1;31mNameError\u001b[0m: name 'continents' is not defined"
     ]
    }
   ],
   "source": [
    "count=[]\n",
    "continent=visa_df['continent'].unique()\n",
    "for i in continents:\n",
    "    c=len(visa_df[visa_df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a573213",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5257f727",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Asia': 16861,\n",
       " 'Africa': 551,\n",
       " 'North America': 3292,\n",
       " 'Europe': 3732,\n",
       " 'South America': 852,\n",
       " 'Oceania': 192}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1={}\n",
    "for i in uni:\n",
    "    nums=len(visa_df[visa_df['continent']==i])\n",
    "    d1[i]=nums\n",
    "d1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "61071ee1",
   "metadata": {},
   "outputs": [],
   "source": [
    "continents=visa_df['continent'].unique()\n",
    "count=[len(visa_df[visa_df['continent']==i]) for i in continents]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c8201e2a",
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ajinkya</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Deepak</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sairaj</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rohan</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mani</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age\n",
       "0  Ajinkya   23\n",
       "1   Deepak   24\n",
       "2   Sairaj   25\n",
       "3    Rohan   20\n",
       "4     Mani   22"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1=[\"Ajinkya\",\"Deepak\",\"Sairaj\",\"Rohan\",\"Mani\"]\n",
    "list2=[23,24,25,20,22]\n",
    "cols=['Name','Age']\n",
    "name=pd.DataFrame(zip(list1,list2),columns=cols)\n",
    "name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5fb7aab6",
   "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>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asia</td>\n",
       "      <td>16861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Africa</td>\n",
       "      <td>551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>North America</td>\n",
       "      <td>3292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Europe</td>\n",
       "      <td>3732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South America</td>\n",
       "      <td>852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       continent  count\n",
       "0           Asia  16861\n",
       "1         Africa    551\n",
       "2  North America   3292\n",
       "3         Europe   3732\n",
       "4  South America    852\n",
       "5        Oceania    192"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continents=visa_df['continent'].unique()\n",
    "count=[len(visa_df[visa_df['continent']==i]) for i in continents]\n",
    "cols=['continent','count']\n",
    "continent_df=pd.DataFrame(zip(continents,count),columns=cols)\n",
    "continent_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "98301bee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "',continent,count\\r\\n0,Asia,16861\\r\\n1,Africa,551\\r\\n2,North America,3292\\r\\n3,Europe,3732\\r\\n4,South America,852\\r\\n5,Oceania,192\\r\\n'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continent_df.to_csv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ef7acb40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\hp\\\\OneDrive\\\\Documents\\\\EDA session'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5cfc35c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "continent_df.to_csv('continent1_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f6a74fb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "name.to_csv(\"C:\\\\Users\\\\hp\\\\OneDrive\\\\Pictures\\\\Screenshots\\\\continent1_df.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "00987811",
   "metadata": {},
   "outputs": [],
   "source": [
    "name.to_csv('friends_name.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2120ab40",
   "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>continents</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asia</td>\n",
       "      <td>16861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Europe</td>\n",
       "      <td>3732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>North America</td>\n",
       "      <td>3292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>South America</td>\n",
       "      <td>852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Africa</td>\n",
       "      <td>551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      continents  count\n",
       "0           Asia  16861\n",
       "1         Europe   3732\n",
       "2  North America   3292\n",
       "3  South America    852\n",
       "4         Africa    551\n",
       "5        Oceania    192"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continent=visa_df['continent'].value_counts().keys()\n",
    "count=visa_df['continent'].value_counts().values\n",
    "pd.DataFrame(zip(continent,count),columns=['continents','count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6f4dc3ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Asia': 16861,\n",
       " 'Europe': 3732,\n",
       " 'North America': 3292,\n",
       " 'South America': 852,\n",
       " 'Africa': 551,\n",
       " 'Oceania': 192}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(visa_df['continent'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a65c35d1",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'numpy.ndarray' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[40], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m dict1\u001b[38;5;241m=\u001b[39m{}\n\u001b[0;32m      2\u001b[0m keys\u001b[38;5;241m=\u001b[39mvisa_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcontinent\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalue_counts()\u001b[38;5;241m.\u001b[39mkeys()\n\u001b[1;32m----> 3\u001b[0m values\u001b[38;5;241m=\u001b[39mvisa_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcontinent\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalue_counts()\u001b[38;5;241m.\u001b[39mvalues()\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(keys)\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28mprint\u001b[39m(values)\n",
      "\u001b[1;31mTypeError\u001b[0m: 'numpy.ndarray' object is not callable"
     ]
    }
   ],
   "source": [
    "dict1={}\n",
    "keys=visa_df['continent'].value_counts().keys()\n",
    "values=visa_df['continent'].value_counts().values()\n",
    "print(keys)\n",
    "print(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "35177df2",
   "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>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asia</td>\n",
       "      <td>16861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Africa</td>\n",
       "      <td>551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>North America</td>\n",
       "      <td>3292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Europe</td>\n",
       "      <td>3732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South America</td>\n",
       "      <td>852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       continent  count\n",
       "0           Asia  16861\n",
       "1         Africa    551\n",
       "2  North America   3292\n",
       "3         Europe   3732\n",
       "4  South America    852\n",
       "5        Oceania    192"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continent_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "823f304e",
   "metadata": {},
   "source": [
    "- barplot\n",
    "- piechart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0b39579",
   "metadata": {},
   "source": [
    "bar-plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dec030ff",
   "metadata": {},
   "source": [
    "- barplot\n",
    "- x-axis :categorical column\n",
    "- y - axis: numerical column\n",
    "- where you are taking the data:continent_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5832df33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 6 artists>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar('continent','count',data=continent_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b8aa554b",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Series' object has no attribute 'values_counts'",
     "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_6672\\1636120473.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mvisa_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'continent'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_counts\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----> 2\u001b[1;33m \u001b[0mcontinents\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvisa_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'continent'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mcount\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvisa_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'continent'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\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      4\u001b[0m \u001b[0mcontinent_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcontinent\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'continents'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'count'\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: 'Series' object has no attribute 'values_counts'"
     ]
    }
   ],
   "source": [
    "visa_df['continent'].value_counts()\n",
    "continents=visa_df['continent'].values_counts().keys()\n",
    "count=visa_df['continent'].values_counts().values\n",
    "continent_data=pd.DataFrame(zip(continent,count),columns=['continents','count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "492d29c0",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'continents_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[44], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m5\u001b[39m))\n\u001b[1;32m----> 2\u001b[0m plt\u001b[38;5;241m.\u001b[39mbar(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcontinents\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcount\u001b[39m\u001b[38;5;124m'\u001b[39m,data\u001b[38;5;241m=\u001b[39mcontinents_data)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'continents_data' is not defined"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.bar('continents','count',data=continents_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1872f5b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7631ba62",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cf0910d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}