01 - working with csv files/day1_working-with-csv.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Importing pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Opening a local csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>enrollee_id</th>\n",
       "      <th>city</th>\n",
       "      <th>city_development_index</th>\n",
       "      <th>gender</th>\n",
       "      <th>relevent_experience</th>\n",
       "      <th>enrolled_university</th>\n",
       "      <th>education_level</th>\n",
       "      <th>major_discipline</th>\n",
       "      <th>experience</th>\n",
       "      <th>company_size</th>\n",
       "      <th>company_type</th>\n",
       "      <th>last_new_job</th>\n",
       "      <th>training_hours</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8949</td>\n",
       "      <td>city_103</td>\n",
       "      <td>0.920</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>&gt;20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29725</td>\n",
       "      <td>city_40</td>\n",
       "      <td>0.776</td>\n",
       "      <td>Male</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>15</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>&gt;4</td>\n",
       "      <td>47</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11561</td>\n",
       "      <td>city_21</td>\n",
       "      <td>0.624</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>Full time course</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never</td>\n",
       "      <td>83</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33241</td>\n",
       "      <td>city_115</td>\n",
       "      <td>0.789</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Business Degree</td>\n",
       "      <td>&lt;1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>never</td>\n",
       "      <td>52</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666</td>\n",
       "      <td>city_162</td>\n",
       "      <td>0.767</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Masters</td>\n",
       "      <td>STEM</td>\n",
       "      <td>&gt;20</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Funded Startup</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19153</th>\n",
       "      <td>7386</td>\n",
       "      <td>city_173</td>\n",
       "      <td>0.878</td>\n",
       "      <td>Male</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Humanities</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19154</th>\n",
       "      <td>31398</td>\n",
       "      <td>city_103</td>\n",
       "      <td>0.920</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>52</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19155</th>\n",
       "      <td>24576</td>\n",
       "      <td>city_103</td>\n",
       "      <td>0.920</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>&gt;20</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19156</th>\n",
       "      <td>5756</td>\n",
       "      <td>city_65</td>\n",
       "      <td>0.802</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>High School</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;1</td>\n",
       "      <td>500-999</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>2</td>\n",
       "      <td>97</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19157</th>\n",
       "      <td>23834</td>\n",
       "      <td>city_67</td>\n",
       "      <td>0.855</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Primary School</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>127</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19158 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       enrollee_id      city  city_development_index gender  \\\n",
       "0             8949  city_103                   0.920   Male   \n",
       "1            29725   city_40                   0.776   Male   \n",
       "2            11561   city_21                   0.624    NaN   \n",
       "3            33241  city_115                   0.789    NaN   \n",
       "4              666  city_162                   0.767   Male   \n",
       "...            ...       ...                     ...    ...   \n",
       "19153         7386  city_173                   0.878   Male   \n",
       "19154        31398  city_103                   0.920   Male   \n",
       "19155        24576  city_103                   0.920   Male   \n",
       "19156         5756   city_65                   0.802   Male   \n",
       "19157        23834   city_67                   0.855    NaN   \n",
       "\n",
       "           relevent_experience enrolled_university education_level  \\\n",
       "0      Has relevent experience       no_enrollment        Graduate   \n",
       "1       No relevent experience       no_enrollment        Graduate   \n",
       "2       No relevent experience    Full time course        Graduate   \n",
       "3       No relevent experience                 NaN        Graduate   \n",
       "4      Has relevent experience       no_enrollment         Masters   \n",
       "...                        ...                 ...             ...   \n",
       "19153   No relevent experience       no_enrollment        Graduate   \n",
       "19154  Has relevent experience       no_enrollment        Graduate   \n",
       "19155  Has relevent experience       no_enrollment        Graduate   \n",
       "19156  Has relevent experience       no_enrollment     High School   \n",
       "19157   No relevent experience       no_enrollment  Primary School   \n",
       "\n",
       "      major_discipline experience company_size    company_type last_new_job  \\\n",
       "0                 STEM        >20          NaN             NaN            1   \n",
       "1                 STEM         15        50-99         Pvt Ltd           >4   \n",
       "2                 STEM          5          NaN             NaN        never   \n",
       "3      Business Degree         <1          NaN         Pvt Ltd        never   \n",
       "4                 STEM        >20        50-99  Funded Startup            4   \n",
       "...                ...        ...          ...             ...          ...   \n",
       "19153       Humanities         14          NaN             NaN            1   \n",
       "19154             STEM         14          NaN             NaN            4   \n",
       "19155             STEM        >20        50-99         Pvt Ltd            4   \n",
       "19156              NaN         <1      500-999         Pvt Ltd            2   \n",
       "19157              NaN          2          NaN             NaN            1   \n",
       "\n",
       "       training_hours  target  \n",
       "0                  36     1.0  \n",
       "1                  47     0.0  \n",
       "2                  83     0.0  \n",
       "3                  52     1.0  \n",
       "4                   8     0.0  \n",
       "...               ...     ...  \n",
       "19153              42     1.0  \n",
       "19154              52     1.0  \n",
       "19155              44     0.0  \n",
       "19156              97     0.0  \n",
       "19157             127     0.0  \n",
       "\n",
       "[19158 rows x 14 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('aug_train.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Opening a csv file from an URL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Country</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>AFRICA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AFRICA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Benin</td>\n",
       "      <td>AFRICA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>AFRICA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Burkina</td>\n",
       "      <td>AFRICA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Paraguay</td>\n",
       "      <td>SOUTH AMERICA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Peru</td>\n",
       "      <td>SOUTH AMERICA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Suriname</td>\n",
       "      <td>SOUTH AMERICA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>SOUTH AMERICA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>SOUTH AMERICA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>194 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country         Region\n",
       "0      Algeria         AFRICA\n",
       "1       Angola         AFRICA\n",
       "2        Benin         AFRICA\n",
       "3     Botswana         AFRICA\n",
       "4      Burkina         AFRICA\n",
       "..         ...            ...\n",
       "189   Paraguay  SOUTH AMERICA\n",
       "190       Peru  SOUTH AMERICA\n",
       "191   Suriname  SOUTH AMERICA\n",
       "192    Uruguay  SOUTH AMERICA\n",
       "193  Venezuela  SOUTH AMERICA\n",
       "\n",
       "[194 rows x 2 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "from io import StringIO\n",
    "\n",
    "url = \"https://raw.githubusercontent.com/cs109/2014_data/master/countries.csv\"\n",
    "headers = {\"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10.14; rv:66.0) Gecko/20100101 Firefox/66.0\"}\n",
    "req = requests.get(url, headers=headers)\n",
    "data = StringIO(req.text)\n",
    "\n",
    "pd.read_csv(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Sep Parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>sno</th>\n",
       "      <th>name</th>\n",
       "      <th>release_year</th>\n",
       "      <th>rating</th>\n",
       "      <th>votes</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>m0</td>\n",
       "      <td>10 things i hate about you</td>\n",
       "      <td>1999</td>\n",
       "      <td>6.9</td>\n",
       "      <td>62847.0</td>\n",
       "      <td>['comedy' 'romance']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>m1</td>\n",
       "      <td>1492: conquest of paradise</td>\n",
       "      <td>1992</td>\n",
       "      <td>6.2</td>\n",
       "      <td>10421.0</td>\n",
       "      <td>['adventure' 'biography' 'drama' 'history']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>m2</td>\n",
       "      <td>15 minutes</td>\n",
       "      <td>2001</td>\n",
       "      <td>6.1</td>\n",
       "      <td>25854.0</td>\n",
       "      <td>['action' 'crime' 'drama' 'thriller']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>m3</td>\n",
       "      <td>2001: a space odyssey</td>\n",
       "      <td>1968</td>\n",
       "      <td>8.4</td>\n",
       "      <td>163227.0</td>\n",
       "      <td>['adventure' 'mystery' 'sci-fi']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>m4</td>\n",
       "      <td>48 hrs.</td>\n",
       "      <td>1982</td>\n",
       "      <td>6.9</td>\n",
       "      <td>22289.0</td>\n",
       "      <td>['action' 'comedy' 'crime' 'drama' 'thriller']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>m612</td>\n",
       "      <td>watchmen</td>\n",
       "      <td>2009</td>\n",
       "      <td>7.8</td>\n",
       "      <td>135229.0</td>\n",
       "      <td>['action' 'crime' 'fantasy' 'mystery' 'sci-fi'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>m613</td>\n",
       "      <td>xxx</td>\n",
       "      <td>2002</td>\n",
       "      <td>5.6</td>\n",
       "      <td>53505.0</td>\n",
       "      <td>['action' 'adventure' 'crime']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>m614</td>\n",
       "      <td>x-men</td>\n",
       "      <td>2000</td>\n",
       "      <td>7.4</td>\n",
       "      <td>122149.0</td>\n",
       "      <td>['action' 'sci-fi']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>m615</td>\n",
       "      <td>young frankenstein</td>\n",
       "      <td>1974</td>\n",
       "      <td>8.0</td>\n",
       "      <td>57618.0</td>\n",
       "      <td>['comedy' 'sci-fi']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>m616</td>\n",
       "      <td>zulu dawn</td>\n",
       "      <td>1979</td>\n",
       "      <td>6.4</td>\n",
       "      <td>1911.0</td>\n",
       "      <td>['action' 'adventure' 'drama' 'history' 'war']</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>617 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      sno                        name release_year  rating     votes  \\\n",
       "0      m0  10 things i hate about you         1999     6.9   62847.0   \n",
       "1      m1  1492: conquest of paradise         1992     6.2   10421.0   \n",
       "2      m2                  15 minutes         2001     6.1   25854.0   \n",
       "3      m3       2001: a space odyssey         1968     8.4  163227.0   \n",
       "4      m4                     48 hrs.         1982     6.9   22289.0   \n",
       "..    ...                         ...          ...     ...       ...   \n",
       "612  m612                    watchmen         2009     7.8  135229.0   \n",
       "613  m613                         xxx         2002     5.6   53505.0   \n",
       "614  m614                       x-men         2000     7.4  122149.0   \n",
       "615  m615          young frankenstein         1974     8.0   57618.0   \n",
       "616  m616                   zulu dawn         1979     6.4    1911.0   \n",
       "\n",
       "                                                genres  \n",
       "0                                 ['comedy' 'romance']  \n",
       "1          ['adventure' 'biography' 'drama' 'history']  \n",
       "2                ['action' 'crime' 'drama' 'thriller']  \n",
       "3                     ['adventure' 'mystery' 'sci-fi']  \n",
       "4       ['action' 'comedy' 'crime' 'drama' 'thriller']  \n",
       "..                                                 ...  \n",
       "612  ['action' 'crime' 'fantasy' 'mystery' 'sci-fi'...  \n",
       "613                     ['action' 'adventure' 'crime']  \n",
       "614                                ['action' 'sci-fi']  \n",
       "615                                ['comedy' 'sci-fi']  \n",
       "616     ['action' 'adventure' 'drama' 'history' 'war']  \n",
       "\n",
       "[617 rows x 6 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('movie_titles_metadata.tsv',sep='\\t',names=['sno','name','release_year','rating','votes','genres'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Index_col parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>city</th>\n",
       "      <th>city_development_index</th>\n",
       "      <th>gender</th>\n",
       "      <th>relevent_experience</th>\n",
       "      <th>enrolled_university</th>\n",
       "      <th>education_level</th>\n",
       "      <th>major_discipline</th>\n",
       "      <th>experience</th>\n",
       "      <th>company_size</th>\n",
       "      <th>company_type</th>\n",
       "      <th>last_new_job</th>\n",
       "      <th>training_hours</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>enrollee_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8949</th>\n",
       "      <td>city_103</td>\n",
       "      <td>0.920</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>&gt;20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29725</th>\n",
       "      <td>city_40</td>\n",
       "      <td>0.776</td>\n",
       "      <td>Male</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>15</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>&gt;4</td>\n",
       "      <td>47</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11561</th>\n",
       "      <td>city_21</td>\n",
       "      <td>0.624</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>Full time course</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never</td>\n",
       "      <td>83</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33241</th>\n",
       "      <td>city_115</td>\n",
       "      <td>0.789</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Business Degree</td>\n",
       "      <td>&lt;1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>never</td>\n",
       "      <td>52</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>city_162</td>\n",
       "      <td>0.767</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Masters</td>\n",
       "      <td>STEM</td>\n",
       "      <td>&gt;20</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Funded Startup</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7386</th>\n",
       "      <td>city_173</td>\n",
       "      <td>0.878</td>\n",
       "      <td>Male</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Humanities</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31398</th>\n",
       "      <td>city_103</td>\n",
       "      <td>0.920</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>52</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24576</th>\n",
       "      <td>city_103</td>\n",
       "      <td>0.920</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>&gt;20</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5756</th>\n",
       "      <td>city_65</td>\n",
       "      <td>0.802</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>High School</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;1</td>\n",
       "      <td>500-999</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>2</td>\n",
       "      <td>97</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23834</th>\n",
       "      <td>city_67</td>\n",
       "      <td>0.855</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Primary School</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>127</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19158 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city  city_development_index gender      relevent_experience  \\\n",
       "enrollee_id                                                                     \n",
       "8949         city_103                   0.920   Male  Has relevent experience   \n",
       "29725         city_40                   0.776   Male   No relevent experience   \n",
       "11561         city_21                   0.624    NaN   No relevent experience   \n",
       "33241        city_115                   0.789    NaN   No relevent experience   \n",
       "666          city_162                   0.767   Male  Has relevent experience   \n",
       "...               ...                     ...    ...                      ...   \n",
       "7386         city_173                   0.878   Male   No relevent experience   \n",
       "31398        city_103                   0.920   Male  Has relevent experience   \n",
       "24576        city_103                   0.920   Male  Has relevent experience   \n",
       "5756          city_65                   0.802   Male  Has relevent experience   \n",
       "23834         city_67                   0.855    NaN   No relevent experience   \n",
       "\n",
       "            enrolled_university education_level major_discipline experience  \\\n",
       "enrollee_id                                                                   \n",
       "8949              no_enrollment        Graduate             STEM        >20   \n",
       "29725             no_enrollment        Graduate             STEM         15   \n",
       "11561          Full time course        Graduate             STEM          5   \n",
       "33241                       NaN        Graduate  Business Degree         <1   \n",
       "666               no_enrollment         Masters             STEM        >20   \n",
       "...                         ...             ...              ...        ...   \n",
       "7386              no_enrollment        Graduate       Humanities         14   \n",
       "31398             no_enrollment        Graduate             STEM         14   \n",
       "24576             no_enrollment        Graduate             STEM        >20   \n",
       "5756              no_enrollment     High School              NaN         <1   \n",
       "23834             no_enrollment  Primary School              NaN          2   \n",
       "\n",
       "            company_size    company_type last_new_job  training_hours  target  \n",
       "enrollee_id                                                                    \n",
       "8949                 NaN             NaN            1              36     1.0  \n",
       "29725              50-99         Pvt Ltd           >4              47     0.0  \n",
       "11561                NaN             NaN        never              83     0.0  \n",
       "33241                NaN         Pvt Ltd        never              52     1.0  \n",
       "666                50-99  Funded Startup            4               8     0.0  \n",
       "...                  ...             ...          ...             ...     ...  \n",
       "7386                 NaN             NaN            1              42     1.0  \n",
       "31398                NaN             NaN            4              52     1.0  \n",
       "24576              50-99         Pvt Ltd            4              44     0.0  \n",
       "5756             500-999         Pvt Ltd            2              97     0.0  \n",
       "23834                NaN             NaN            1             127     0.0  \n",
       "\n",
       "[19158 rows x 13 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('aug_train.csv',index_col='enrollee_id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Header parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>0</th>\n",
       "      <th>enrollee_id</th>\n",
       "      <th>city</th>\n",
       "      <th>city_development_index</th>\n",
       "      <th>gender</th>\n",
       "      <th>relevent_experience</th>\n",
       "      <th>enrolled_university</th>\n",
       "      <th>education_level</th>\n",
       "      <th>major_discipline</th>\n",
       "      <th>experience</th>\n",
       "      <th>company_size</th>\n",
       "      <th>company_type</th>\n",
       "      <th>last_new_job</th>\n",
       "      <th>training_hours</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>29725</td>\n",
       "      <td>city_40</td>\n",
       "      <td>0.776</td>\n",
       "      <td>Male</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>15</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>&gt;4</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>11561</td>\n",
       "      <td>city_21</td>\n",
       "      <td>0.624</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>Full time course</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never</td>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>33241</td>\n",
       "      <td>city_115</td>\n",
       "      <td>0.789</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Business Degree</td>\n",
       "      <td>&lt;1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>never</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>666</td>\n",
       "      <td>city_162</td>\n",
       "      <td>0.767</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Masters</td>\n",
       "      <td>STEM</td>\n",
       "      <td>&gt;20</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Funded Startup</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  enrollee_id      city  city_development_index gender  \\\n",
       "0  1        29725   city_40                   0.776   Male   \n",
       "1  2        11561   city_21                   0.624    NaN   \n",
       "2  3        33241  city_115                   0.789    NaN   \n",
       "3  4          666  city_162                   0.767   Male   \n",
       "\n",
       "       relevent_experience enrolled_university education_level  \\\n",
       "0   No relevent experience       no_enrollment        Graduate   \n",
       "1   No relevent experience    Full time course        Graduate   \n",
       "2   No relevent experience                 NaN        Graduate   \n",
       "3  Has relevent experience       no_enrollment         Masters   \n",
       "\n",
       "  major_discipline experience company_size    company_type last_new_job  \\\n",
       "0             STEM         15        50-99         Pvt Ltd           >4   \n",
       "1             STEM          5          NaN             NaN        never   \n",
       "2  Business Degree         <1          NaN         Pvt Ltd        never   \n",
       "3             STEM        >20        50-99  Funded Startup            4   \n",
       "\n",
       "   training_hours  target  \n",
       "0              47       0  \n",
       "1              83       0  \n",
       "2              52       1  \n",
       "3               8       0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('test.csv',header=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. use_cols parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>enrollee_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>education_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8949</td>\n",
       "      <td>Male</td>\n",
       "      <td>Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29725</td>\n",
       "      <td>Male</td>\n",
       "      <td>Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11561</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33241</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666</td>\n",
       "      <td>Male</td>\n",
       "      <td>Masters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19153</th>\n",
       "      <td>7386</td>\n",
       "      <td>Male</td>\n",
       "      <td>Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19154</th>\n",
       "      <td>31398</td>\n",
       "      <td>Male</td>\n",
       "      <td>Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19155</th>\n",
       "      <td>24576</td>\n",
       "      <td>Male</td>\n",
       "      <td>Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19156</th>\n",
       "      <td>5756</td>\n",
       "      <td>Male</td>\n",
       "      <td>High School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19157</th>\n",
       "      <td>23834</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Primary School</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19158 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       enrollee_id gender education_level\n",
       "0             8949   Male        Graduate\n",
       "1            29725   Male        Graduate\n",
       "2            11561    NaN        Graduate\n",
       "3            33241    NaN        Graduate\n",
       "4              666   Male         Masters\n",
       "...            ...    ...             ...\n",
       "19153         7386   Male        Graduate\n",
       "19154        31398   Male        Graduate\n",
       "19155        24576   Male        Graduate\n",
       "19156         5756   Male     High School\n",
       "19157        23834    NaN  Primary School\n",
       "\n",
       "[19158 rows x 3 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('aug_train.csv',usecols=['enrollee_id','gender','education_level'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Squeeze parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Male\n",
       "1        Male\n",
       "2         NaN\n",
       "3         NaN\n",
       "4        Male\n",
       "         ... \n",
       "19153    Male\n",
       "19154    Male\n",
       "19155    Male\n",
       "19156    Male\n",
       "19157     NaN\n",
       "Name: gender, Length: 19158, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('aug_train.csv',usecols=['gender'],squeeze=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Skiprows/nrows Parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>enrollee_id</th>\n",
       "      <th>city</th>\n",
       "      <th>city_development_index</th>\n",
       "      <th>gender</th>\n",
       "      <th>relevent_experience</th>\n",
       "      <th>enrolled_university</th>\n",
       "      <th>education_level</th>\n",
       "      <th>major_discipline</th>\n",
       "      <th>experience</th>\n",
       "      <th>company_size</th>\n",
       "      <th>company_type</th>\n",
       "      <th>last_new_job</th>\n",
       "      <th>training_hours</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8949</td>\n",
       "      <td>city_103</td>\n",
       "      <td>0.920</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>&gt;20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29725</td>\n",
       "      <td>city_40</td>\n",
       "      <td>0.776</td>\n",
       "      <td>Male</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>15</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>&gt;4</td>\n",
       "      <td>47</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11561</td>\n",
       "      <td>city_21</td>\n",
       "      <td>0.624</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>Full time course</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never</td>\n",
       "      <td>83</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33241</td>\n",
       "      <td>city_115</td>\n",
       "      <td>0.789</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Business Degree</td>\n",
       "      <td>&lt;1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>never</td>\n",
       "      <td>52</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666</td>\n",
       "      <td>city_162</td>\n",
       "      <td>0.767</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Masters</td>\n",
       "      <td>STEM</td>\n",
       "      <td>&gt;20</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Funded Startup</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>12081</td>\n",
       "      <td>city_65</td>\n",
       "      <td>0.802</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>Full time course</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>9</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>7364</td>\n",
       "      <td>city_160</td>\n",
       "      <td>0.920</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>Full time course</td>\n",
       "      <td>High School</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>100-500</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>1</td>\n",
       "      <td>142</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>11184</td>\n",
       "      <td>city_74</td>\n",
       "      <td>0.579</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>Full time course</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>2</td>\n",
       "      <td>100-500</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>7016</td>\n",
       "      <td>city_65</td>\n",
       "      <td>0.802</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>6</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>8695</td>\n",
       "      <td>city_11</td>\n",
       "      <td>0.550</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>6</td>\n",
       "      <td>10/49</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    enrollee_id      city  city_development_index gender  \\\n",
       "0          8949  city_103                   0.920   Male   \n",
       "1         29725   city_40                   0.776   Male   \n",
       "2         11561   city_21                   0.624    NaN   \n",
       "3         33241  city_115                   0.789    NaN   \n",
       "4           666  city_162                   0.767   Male   \n",
       "..          ...       ...                     ...    ...   \n",
       "95        12081   city_65                   0.802   Male   \n",
       "96         7364  city_160                   0.920    NaN   \n",
       "97        11184   city_74                   0.579    NaN   \n",
       "98         7016   city_65                   0.802   Male   \n",
       "99         8695   city_11                   0.550   Male   \n",
       "\n",
       "        relevent_experience enrolled_university education_level  \\\n",
       "0   Has relevent experience       no_enrollment        Graduate   \n",
       "1    No relevent experience       no_enrollment        Graduate   \n",
       "2    No relevent experience    Full time course        Graduate   \n",
       "3    No relevent experience                 NaN        Graduate   \n",
       "4   Has relevent experience       no_enrollment         Masters   \n",
       "..                      ...                 ...             ...   \n",
       "95  Has relevent experience    Full time course        Graduate   \n",
       "96   No relevent experience    Full time course     High School   \n",
       "97   No relevent experience    Full time course        Graduate   \n",
       "98  Has relevent experience       no_enrollment        Graduate   \n",
       "99  Has relevent experience       no_enrollment        Graduate   \n",
       "\n",
       "   major_discipline experience company_size    company_type last_new_job  \\\n",
       "0              STEM        >20          NaN             NaN            1   \n",
       "1              STEM         15        50-99         Pvt Ltd           >4   \n",
       "2              STEM          5          NaN             NaN        never   \n",
       "3   Business Degree         <1          NaN         Pvt Ltd        never   \n",
       "4              STEM        >20        50-99  Funded Startup            4   \n",
       "..              ...        ...          ...             ...          ...   \n",
       "95             STEM          9        50-99         Pvt Ltd            1   \n",
       "96              NaN          2      100-500         Pvt Ltd            1   \n",
       "97             STEM          2      100-500         Pvt Ltd            1   \n",
       "98             STEM          6        50-99         Pvt Ltd            2   \n",
       "99             STEM          6        10/49         Pvt Ltd            2   \n",
       "\n",
       "    training_hours  target  \n",
       "0               36     1.0  \n",
       "1               47     0.0  \n",
       "2               83     0.0  \n",
       "3               52     1.0  \n",
       "4                8     0.0  \n",
       "..             ...     ...  \n",
       "95              33     0.0  \n",
       "96             142     0.0  \n",
       "97              34     0.0  \n",
       "98              14     1.0  \n",
       "99              27     1.0  \n",
       "\n",
       "[100 rows x 14 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('aug_train.csv',nrows=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10. Encoding parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>Restaurant ID</th>\n",
       "      <th>Restaurant Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>City</th>\n",
       "      <th>Address</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Locality Verbose</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>...</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Has Table booking</th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>Is delivering now</th>\n",
       "      <th>Switch to order menu</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6317637</td>\n",
       "      <td>Le Petit Souffle</td>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Third Floor, Century City Mall, Kalayaan Avenu...</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City, Mak...</td>\n",
       "      <td>121.027535</td>\n",
       "      <td>14.565443</td>\n",
       "      <td>French, Japanese, Desserts</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6304287</td>\n",
       "      <td>Izakaya Kikufuji</td>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Little Tokyo, 2277 Chino Roces Avenue, Legaspi...</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City, Ma...</td>\n",
       "      <td>121.014101</td>\n",
       "      <td>14.553708</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6300002</td>\n",
       "      <td>Heat - Edsa Shangri-La</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...</td>\n",
       "      <td>121.056831</td>\n",
       "      <td>14.581404</td>\n",
       "      <td>Seafood, Asian, Filipino, Indian</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6318506</td>\n",
       "      <td>Ooma</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Third Floor, Mega Fashion Hall, SM Megamall, O...</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City, Mandal...</td>\n",
       "      <td>121.056475</td>\n",
       "      <td>14.585318</td>\n",
       "      <td>Japanese, Sushi</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6314302</td>\n",
       "      <td>Sambo Kojin</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Third Floor, Mega Atrium, SM Megamall, Ortigas...</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City, Mandal...</td>\n",
       "      <td>121.057508</td>\n",
       "      <td>14.584450</td>\n",
       "      <td>Japanese, Korean</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9546</th>\n",
       "      <td>5915730</td>\n",
       "      <td>NamlÛ± Gurme</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>Kemankeô Karamustafa Paôa Mahallesi, RÛ±htÛ±...</td>\n",
       "      <td>Karakí_y</td>\n",
       "      <td>Karakí_y, ÛÁstanbul</td>\n",
       "      <td>28.977392</td>\n",
       "      <td>41.022793</td>\n",
       "      <td>Turkish</td>\n",
       "      <td>...</td>\n",
       "      <td>Turkish Lira(TL)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9547</th>\n",
       "      <td>5908749</td>\n",
       "      <td>Ceviz AÛôacÛ±</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>Koôuyolu Mahallesi, Muhittin íìstí_ndaÛô Cadd...</td>\n",
       "      <td>Koôuyolu</td>\n",
       "      <td>Koôuyolu, ÛÁstanbul</td>\n",
       "      <td>29.041297</td>\n",
       "      <td>41.009847</td>\n",
       "      <td>World Cuisine, Patisserie, Cafe</td>\n",
       "      <td>...</td>\n",
       "      <td>Turkish Lira(TL)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>1034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9548</th>\n",
       "      <td>5915807</td>\n",
       "      <td>Huqqa</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>Kuruí_eôme Mahallesi, Muallim Naci Caddesi, N...</td>\n",
       "      <td>Kuruí_eôme</td>\n",
       "      <td>Kuruí_eôme, ÛÁstanbul</td>\n",
       "      <td>29.034640</td>\n",
       "      <td>41.055817</td>\n",
       "      <td>Italian, World Cuisine</td>\n",
       "      <td>...</td>\n",
       "      <td>Turkish Lira(TL)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9549</th>\n",
       "      <td>5916112</td>\n",
       "      <td>Aôôk Kahve</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>Kuruí_eôme Mahallesi, Muallim Naci Caddesi, N...</td>\n",
       "      <td>Kuruí_eôme</td>\n",
       "      <td>Kuruí_eôme, ÛÁstanbul</td>\n",
       "      <td>29.036019</td>\n",
       "      <td>41.057979</td>\n",
       "      <td>Restaurant Cafe</td>\n",
       "      <td>...</td>\n",
       "      <td>Turkish Lira(TL)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9550</th>\n",
       "      <td>5927402</td>\n",
       "      <td>Walter's Coffee Roastery</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>CafeaÛôa Mahallesi, BademaltÛ± Sokak, No 21/B,...</td>\n",
       "      <td>Moda</td>\n",
       "      <td>Moda, ÛÁstanbul</td>\n",
       "      <td>29.026016</td>\n",
       "      <td>40.984776</td>\n",
       "      <td>Cafe</td>\n",
       "      <td>...</td>\n",
       "      <td>Turkish Lira(TL)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9551 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Restaurant ID           Restaurant Name  Country Code              City  \\\n",
       "0           6317637          Le Petit Souffle           162       Makati City   \n",
       "1           6304287          Izakaya Kikufuji           162       Makati City   \n",
       "2           6300002    Heat - Edsa Shangri-La           162  Mandaluyong City   \n",
       "3           6318506                      Ooma           162  Mandaluyong City   \n",
       "4           6314302               Sambo Kojin           162  Mandaluyong City   \n",
       "...             ...                       ...           ...               ...   \n",
       "9546        5915730              NamlÛ± Gurme           208         ÛÁstanbul   \n",
       "9547        5908749             Ceviz AÛôacÛ±           208         ÛÁstanbul   \n",
       "9548        5915807                     Huqqa           208         ÛÁstanbul   \n",
       "9549        5916112              Aôôk Kahve           208         ÛÁstanbul   \n",
       "9550        5927402  Walter's Coffee Roastery           208         ÛÁstanbul   \n",
       "\n",
       "                                                Address  \\\n",
       "0     Third Floor, Century City Mall, Kalayaan Avenu...   \n",
       "1     Little Tokyo, 2277 Chino Roces Avenue, Legaspi...   \n",
       "2     Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...   \n",
       "3     Third Floor, Mega Fashion Hall, SM Megamall, O...   \n",
       "4     Third Floor, Mega Atrium, SM Megamall, Ortigas...   \n",
       "...                                                 ...   \n",
       "9546  Kemankeô Karamustafa Paôa Mahallesi, RÛ±htÛ±...   \n",
       "9547  Koôuyolu Mahallesi, Muhittin íìstí_ndaÛô Cadd...   \n",
       "9548  Kuruí_eôme Mahallesi, Muallim Naci Caddesi, N...   \n",
       "9549  Kuruí_eôme Mahallesi, Muallim Naci Caddesi, N...   \n",
       "9550  CafeaÛôa Mahallesi, BademaltÛ± Sokak, No 21/B,...   \n",
       "\n",
       "                                        Locality  \\\n",
       "0      Century City Mall, Poblacion, Makati City   \n",
       "1     Little Tokyo, Legaspi Village, Makati City   \n",
       "2     Edsa Shangri-La, Ortigas, Mandaluyong City   \n",
       "3         SM Megamall, Ortigas, Mandaluyong City   \n",
       "4         SM Megamall, Ortigas, Mandaluyong City   \n",
       "...                                          ...   \n",
       "9546                                    Karakí_y   \n",
       "9547                                   Koôuyolu   \n",
       "9548                                 Kuruí_eôme   \n",
       "9549                                 Kuruí_eôme   \n",
       "9550                                        Moda   \n",
       "\n",
       "                                       Locality Verbose   Longitude  \\\n",
       "0     Century City Mall, Poblacion, Makati City, Mak...  121.027535   \n",
       "1     Little Tokyo, Legaspi Village, Makati City, Ma...  121.014101   \n",
       "2     Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...  121.056831   \n",
       "3     SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.056475   \n",
       "4     SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.057508   \n",
       "...                                                 ...         ...   \n",
       "9546                                Karakí_y, ÛÁstanbul   28.977392   \n",
       "9547                               Koôuyolu, ÛÁstanbul   29.041297   \n",
       "9548                             Kuruí_eôme, ÛÁstanbul   29.034640   \n",
       "9549                             Kuruí_eôme, ÛÁstanbul   29.036019   \n",
       "9550                                    Moda, ÛÁstanbul   29.026016   \n",
       "\n",
       "       Latitude                          Cuisines  ...          Currency  \\\n",
       "0     14.565443        French, Japanese, Desserts  ...  Botswana Pula(P)   \n",
       "1     14.553708                          Japanese  ...  Botswana Pula(P)   \n",
       "2     14.581404  Seafood, Asian, Filipino, Indian  ...  Botswana Pula(P)   \n",
       "3     14.585318                   Japanese, Sushi  ...  Botswana Pula(P)   \n",
       "4     14.584450                  Japanese, Korean  ...  Botswana Pula(P)   \n",
       "...         ...                               ...  ...               ...   \n",
       "9546  41.022793                           Turkish  ...  Turkish Lira(TL)   \n",
       "9547  41.009847   World Cuisine, Patisserie, Cafe  ...  Turkish Lira(TL)   \n",
       "9548  41.055817            Italian, World Cuisine  ...  Turkish Lira(TL)   \n",
       "9549  41.057979                   Restaurant Cafe  ...  Turkish Lira(TL)   \n",
       "9550  40.984776                              Cafe  ...  Turkish Lira(TL)   \n",
       "\n",
       "     Has Table booking Has Online delivery Is delivering now  \\\n",
       "0                  Yes                  No                No   \n",
       "1                  Yes                  No                No   \n",
       "2                  Yes                  No                No   \n",
       "3                   No                  No                No   \n",
       "4                  Yes                  No                No   \n",
       "...                ...                 ...               ...   \n",
       "9546                No                  No                No   \n",
       "9547                No                  No                No   \n",
       "9548                No                  No                No   \n",
       "9549                No                  No                No   \n",
       "9550                No                  No                No   \n",
       "\n",
       "     Switch to order menu Price range  Aggregate rating  Rating color  \\\n",
       "0                      No           3               4.8    Dark Green   \n",
       "1                      No           3               4.5    Dark Green   \n",
       "2                      No           4               4.4         Green   \n",
       "3                      No           4               4.9    Dark Green   \n",
       "4                      No           4               4.8    Dark Green   \n",
       "...                   ...         ...               ...           ...   \n",
       "9546                   No           3               4.1         Green   \n",
       "9547                   No           3               4.2         Green   \n",
       "9548                   No           4               3.7        Yellow   \n",
       "9549                   No           4               4.0         Green   \n",
       "9550                   No           2               4.0         Green   \n",
       "\n",
       "     Rating text Votes  \n",
       "0      Excellent   314  \n",
       "1      Excellent   591  \n",
       "2      Very Good   270  \n",
       "3      Excellent   365  \n",
       "4      Excellent   229  \n",
       "...          ...   ...  \n",
       "9546   Very Good   788  \n",
       "9547   Very Good  1034  \n",
       "9548        Good   661  \n",
       "9549   Very Good   901  \n",
       "9550   Very Good   591  \n",
       "\n",
       "[9551 rows x 21 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('zomato.csv',encoding='latin-1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 11. Skip bad lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "b'Skipping line 6452: expected 8 fields, saw 9\\nSkipping line 43667: expected 8 fields, saw 10\\nSkipping line 51751: expected 8 fields, saw 9\\n'\n",
      "b'Skipping line 92038: expected 8 fields, saw 9\\nSkipping line 104319: expected 8 fields, saw 9\\nSkipping line 121768: expected 8 fields, saw 9\\n'\n",
      "b'Skipping line 144058: expected 8 fields, saw 9\\nSkipping line 150789: expected 8 fields, saw 9\\nSkipping line 157128: expected 8 fields, saw 9\\nSkipping line 180189: expected 8 fields, saw 9\\nSkipping line 185738: expected 8 fields, saw 9\\n'\n",
      "b'Skipping line 209388: expected 8 fields, saw 9\\nSkipping line 220626: expected 8 fields, saw 9\\nSkipping line 227933: expected 8 fields, saw 11\\nSkipping line 228957: expected 8 fields, saw 10\\nSkipping line 245933: expected 8 fields, saw 9\\nSkipping line 251296: expected 8 fields, saw 9\\nSkipping line 259941: expected 8 fields, saw 9\\nSkipping line 261529: expected 8 fields, saw 9\\n'\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ISBN</th>\n",
       "      <th>Book-Title</th>\n",
       "      <th>Book-Author</th>\n",
       "      <th>Year-Of-Publication</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>Image-URL-S</th>\n",
       "      <th>Image-URL-M</th>\n",
       "      <th>Image-URL-L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0195153448</td>\n",
       "      <td>Classical Mythology</td>\n",
       "      <td>Mark P. O. Morford</td>\n",
       "      <td>2002</td>\n",
       "      <td>Oxford University Press</td>\n",
       "      <td>http://images.amazon.com/images/P/0195153448.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0195153448.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0195153448.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0002005018</td>\n",
       "      <td>Clara Callan</td>\n",
       "      <td>Richard Bruce Wright</td>\n",
       "      <td>2001</td>\n",
       "      <td>HarperFlamingo Canada</td>\n",
       "      <td>http://images.amazon.com/images/P/0002005018.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0002005018.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0002005018.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0060973129</td>\n",
       "      <td>Decision in Normandy</td>\n",
       "      <td>Carlo D'Este</td>\n",
       "      <td>1991</td>\n",
       "      <td>HarperPerennial</td>\n",
       "      <td>http://images.amazon.com/images/P/0060973129.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0060973129.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0060973129.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0374157065</td>\n",
       "      <td>Flu: The Story of the Great Influenza Pandemic...</td>\n",
       "      <td>Gina Bari Kolata</td>\n",
       "      <td>1999</td>\n",
       "      <td>Farrar Straus Giroux</td>\n",
       "      <td>http://images.amazon.com/images/P/0374157065.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0374157065.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0374157065.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0393045218</td>\n",
       "      <td>The Mummies of Urumchi</td>\n",
       "      <td>E. J. W. Barber</td>\n",
       "      <td>1999</td>\n",
       "      <td>W. W. Norton &amp;amp; Company</td>\n",
       "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271355</th>\n",
       "      <td>0440400988</td>\n",
       "      <td>There's a Bat in Bunk Five</td>\n",
       "      <td>Paula Danziger</td>\n",
       "      <td>1988</td>\n",
       "      <td>Random House Childrens Pub (Mm)</td>\n",
       "      <td>http://images.amazon.com/images/P/0440400988.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0440400988.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0440400988.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271356</th>\n",
       "      <td>0525447644</td>\n",
       "      <td>From One to One Hundred</td>\n",
       "      <td>Teri Sloat</td>\n",
       "      <td>1991</td>\n",
       "      <td>Dutton Books</td>\n",
       "      <td>http://images.amazon.com/images/P/0525447644.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0525447644.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0525447644.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271357</th>\n",
       "      <td>006008667X</td>\n",
       "      <td>Lily Dale : The True Story of the Town that Ta...</td>\n",
       "      <td>Christine Wicker</td>\n",
       "      <td>2004</td>\n",
       "      <td>HarperSanFrancisco</td>\n",
       "      <td>http://images.amazon.com/images/P/006008667X.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/006008667X.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/006008667X.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271358</th>\n",
       "      <td>0192126040</td>\n",
       "      <td>Republic (World's Classics)</td>\n",
       "      <td>Plato</td>\n",
       "      <td>1996</td>\n",
       "      <td>Oxford University Press</td>\n",
       "      <td>http://images.amazon.com/images/P/0192126040.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0192126040.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0192126040.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271359</th>\n",
       "      <td>0767409752</td>\n",
       "      <td>A Guided Tour of Rene Descartes' Meditations o...</td>\n",
       "      <td>Christopher  Biffle</td>\n",
       "      <td>2000</td>\n",
       "      <td>McGraw-Hill Humanities/Social Sciences/Languages</td>\n",
       "      <td>http://images.amazon.com/images/P/0767409752.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0767409752.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0767409752.0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>271360 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              ISBN                                         Book-Title  \\\n",
       "0       0195153448                                Classical Mythology   \n",
       "1       0002005018                                       Clara Callan   \n",
       "2       0060973129                               Decision in Normandy   \n",
       "3       0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
       "4       0393045218                             The Mummies of Urumchi   \n",
       "...            ...                                                ...   \n",
       "271355  0440400988                         There's a Bat in Bunk Five   \n",
       "271356  0525447644                            From One to One Hundred   \n",
       "271357  006008667X  Lily Dale : The True Story of the Town that Ta...   \n",
       "271358  0192126040                        Republic (World's Classics)   \n",
       "271359  0767409752  A Guided Tour of Rene Descartes' Meditations o...   \n",
       "\n",
       "                 Book-Author Year-Of-Publication  \\\n",
       "0         Mark P. O. Morford                2002   \n",
       "1       Richard Bruce Wright                2001   \n",
       "2               Carlo D'Este                1991   \n",
       "3           Gina Bari Kolata                1999   \n",
       "4            E. J. W. Barber                1999   \n",
       "...                      ...                 ...   \n",
       "271355        Paula Danziger                1988   \n",
       "271356            Teri Sloat                1991   \n",
       "271357      Christine Wicker                2004   \n",
       "271358                 Plato                1996   \n",
       "271359   Christopher  Biffle                2000   \n",
       "\n",
       "                                               Publisher  \\\n",
       "0                                Oxford University Press   \n",
       "1                                  HarperFlamingo Canada   \n",
       "2                                        HarperPerennial   \n",
       "3                                   Farrar Straus Giroux   \n",
       "4                             W. W. Norton &amp; Company   \n",
       "...                                                  ...   \n",
       "271355                   Random House Childrens Pub (Mm)   \n",
       "271356                                      Dutton Books   \n",
       "271357                                HarperSanFrancisco   \n",
       "271358                           Oxford University Press   \n",
       "271359  McGraw-Hill Humanities/Social Sciences/Languages   \n",
       "\n",
       "                                              Image-URL-S  \\\n",
       "0       http://images.amazon.com/images/P/0195153448.0...   \n",
       "1       http://images.amazon.com/images/P/0002005018.0...   \n",
       "2       http://images.amazon.com/images/P/0060973129.0...   \n",
       "3       http://images.amazon.com/images/P/0374157065.0...   \n",
       "4       http://images.amazon.com/images/P/0393045218.0...   \n",
       "...                                                   ...   \n",
       "271355  http://images.amazon.com/images/P/0440400988.0...   \n",
       "271356  http://images.amazon.com/images/P/0525447644.0...   \n",
       "271357  http://images.amazon.com/images/P/006008667X.0...   \n",
       "271358  http://images.amazon.com/images/P/0192126040.0...   \n",
       "271359  http://images.amazon.com/images/P/0767409752.0...   \n",
       "\n",
       "                                              Image-URL-M  \\\n",
       "0       http://images.amazon.com/images/P/0195153448.0...   \n",
       "1       http://images.amazon.com/images/P/0002005018.0...   \n",
       "2       http://images.amazon.com/images/P/0060973129.0...   \n",
       "3       http://images.amazon.com/images/P/0374157065.0...   \n",
       "4       http://images.amazon.com/images/P/0393045218.0...   \n",
       "...                                                   ...   \n",
       "271355  http://images.amazon.com/images/P/0440400988.0...   \n",
       "271356  http://images.amazon.com/images/P/0525447644.0...   \n",
       "271357  http://images.amazon.com/images/P/006008667X.0...   \n",
       "271358  http://images.amazon.com/images/P/0192126040.0...   \n",
       "271359  http://images.amazon.com/images/P/0767409752.0...   \n",
       "\n",
       "                                              Image-URL-L  \n",
       "0       http://images.amazon.com/images/P/0195153448.0...  \n",
       "1       http://images.amazon.com/images/P/0002005018.0...  \n",
       "2       http://images.amazon.com/images/P/0060973129.0...  \n",
       "3       http://images.amazon.com/images/P/0374157065.0...  \n",
       "4       http://images.amazon.com/images/P/0393045218.0...  \n",
       "...                                                   ...  \n",
       "271355  http://images.amazon.com/images/P/0440400988.0...  \n",
       "271356  http://images.amazon.com/images/P/0525447644.0...  \n",
       "271357  http://images.amazon.com/images/P/006008667X.0...  \n",
       "271358  http://images.amazon.com/images/P/0192126040.0...  \n",
       "271359  http://images.amazon.com/images/P/0767409752.0...  \n",
       "\n",
       "[271360 rows x 8 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('BX-Books.csv', sep=';', encoding=\"latin-1\",error_bad_lines=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 12. dtypes parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19158 entries, 0 to 19157\n",
      "Data columns (total 14 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   enrollee_id             19158 non-null  int64  \n",
      " 1   city                    19158 non-null  object \n",
      " 2   city_development_index  19158 non-null  float64\n",
      " 3   gender                  14650 non-null  object \n",
      " 4   relevent_experience     19158 non-null  object \n",
      " 5   enrolled_university     18772 non-null  object \n",
      " 6   education_level         18698 non-null  object \n",
      " 7   major_discipline        16345 non-null  object \n",
      " 8   experience              19093 non-null  object \n",
      " 9   company_size            13220 non-null  object \n",
      " 10  company_type            13018 non-null  object \n",
      " 11  last_new_job            18735 non-null  object \n",
      " 12  training_hours          19158 non-null  int64  \n",
      " 13  target                  19158 non-null  int32  \n",
      "dtypes: float64(1), int32(1), int64(2), object(10)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "pd.read_csv('aug_train.csv',dtype={'target':int}).info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 13. Handling Dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 816 entries, 0 to 815\n",
      "Data columns (total 17 columns):\n",
      " #   Column           Non-Null Count  Dtype         \n",
      "---  ------           --------------  -----         \n",
      " 0   id               816 non-null    int64         \n",
      " 1   city             803 non-null    object        \n",
      " 2   date             816 non-null    datetime64[ns]\n",
      " 3   player_of_match  812 non-null    object        \n",
      " 4   venue            816 non-null    object        \n",
      " 5   neutral_venue    816 non-null    int64         \n",
      " 6   team1            816 non-null    object        \n",
      " 7   team2            816 non-null    object        \n",
      " 8   toss_winner      816 non-null    object        \n",
      " 9   toss_decision    816 non-null    object        \n",
      " 10  winner           812 non-null    object        \n",
      " 11  result           812 non-null    object        \n",
      " 12  result_margin    799 non-null    float64       \n",
      " 13  eliminator       812 non-null    object        \n",
      " 14  method           19 non-null     object        \n",
      " 15  umpire1          816 non-null    object        \n",
      " 16  umpire2          816 non-null    object        \n",
      "dtypes: datetime64[ns](1), float64(1), int64(2), object(13)\n",
      "memory usage: 108.5+ KB\n"
     ]
    }
   ],
   "source": [
    "pd.read_csv('IPL Matches 2008-2020.csv',parse_dates=['date']).info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rename(name):\n",
    "    if name == \"Royal Challengers Bangalore\":\n",
    "        return \"RCB\"\n",
    "    else:\n",
    "        return name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'RCB'"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rename(\"Royal Challengers Bangalore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 14. Convertors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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>id</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>neutral_venue</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>winner</th>\n",
       "      <th>result</th>\n",
       "      <th>result_margin</th>\n",
       "      <th>eliminator</th>\n",
       "      <th>method</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>335982</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2008-04-18</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>0</td>\n",
       "      <td>RCB</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>runs</td>\n",
       "      <td>140.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Asad Rauf</td>\n",
       "      <td>RE Koertzen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>335983</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>2008-04-19</td>\n",
       "      <td>MEK Hussey</td>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>bat</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>runs</td>\n",
       "      <td>33.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MR Benson</td>\n",
       "      <td>SL Shastri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>335984</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2008-04-19</td>\n",
       "      <td>MF Maharoof</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>bat</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>wickets</td>\n",
       "      <td>9.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aleem Dar</td>\n",
       "      <td>GA Pratapkumar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>335985</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2008-04-20</td>\n",
       "      <td>MV Boucher</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>0</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>bat</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SJ Davis</td>\n",
       "      <td>DJ Harper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>335986</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>2008-04-20</td>\n",
       "      <td>DJ Hussey</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>0</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Deccan Chargers</td>\n",
       "      <td>Deccan Chargers</td>\n",
       "      <td>bat</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BF Bowden</td>\n",
       "      <td>K Hariharan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>1216547</td>\n",
       "      <td>Dubai</td>\n",
       "      <td>2020-09-28</td>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>Dubai International Cricket Stadium</td>\n",
       "      <td>0</td>\n",
       "      <td>RCB</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>field</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>tie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>PR Reiffel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>812</th>\n",
       "      <td>1237177</td>\n",
       "      <td>Dubai</td>\n",
       "      <td>2020-11-05</td>\n",
       "      <td>JJ Bumrah</td>\n",
       "      <td>Dubai International Cricket Stadium</td>\n",
       "      <td>0</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>field</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>runs</td>\n",
       "      <td>57.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CB Gaffaney</td>\n",
       "      <td>Nitin Menon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>1237178</td>\n",
       "      <td>Abu Dhabi</td>\n",
       "      <td>2020-11-06</td>\n",
       "      <td>KS Williamson</td>\n",
       "      <td>Sheikh Zayed Stadium</td>\n",
       "      <td>0</td>\n",
       "      <td>RCB</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>field</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>wickets</td>\n",
       "      <td>6.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PR Reiffel</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>1237180</td>\n",
       "      <td>Abu Dhabi</td>\n",
       "      <td>2020-11-08</td>\n",
       "      <td>MP Stoinis</td>\n",
       "      <td>Sheikh Zayed Stadium</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>bat</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>runs</td>\n",
       "      <td>17.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PR Reiffel</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>1237181</td>\n",
       "      <td>Dubai</td>\n",
       "      <td>2020-11-10</td>\n",
       "      <td>TA Boult</td>\n",
       "      <td>Dubai International Cricket Stadium</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>bat</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CB Gaffaney</td>\n",
       "      <td>Nitin Menon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>816 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id        city        date player_of_match  \\\n",
       "0     335982   Bangalore  2008-04-18     BB McCullum   \n",
       "1     335983  Chandigarh  2008-04-19      MEK Hussey   \n",
       "2     335984       Delhi  2008-04-19     MF Maharoof   \n",
       "3     335985      Mumbai  2008-04-20      MV Boucher   \n",
       "4     335986     Kolkata  2008-04-20       DJ Hussey   \n",
       "..       ...         ...         ...             ...   \n",
       "811  1216547       Dubai  2020-09-28  AB de Villiers   \n",
       "812  1237177       Dubai  2020-11-05       JJ Bumrah   \n",
       "813  1237178   Abu Dhabi  2020-11-06   KS Williamson   \n",
       "814  1237180   Abu Dhabi  2020-11-08      MP Stoinis   \n",
       "815  1237181       Dubai  2020-11-10        TA Boult   \n",
       "\n",
       "                                          venue  neutral_venue  \\\n",
       "0                         M Chinnaswamy Stadium              0   \n",
       "1    Punjab Cricket Association Stadium, Mohali              0   \n",
       "2                              Feroz Shah Kotla              0   \n",
       "3                              Wankhede Stadium              0   \n",
       "4                                  Eden Gardens              0   \n",
       "..                                          ...            ...   \n",
       "811         Dubai International Cricket Stadium              0   \n",
       "812         Dubai International Cricket Stadium              0   \n",
       "813                        Sheikh Zayed Stadium              0   \n",
       "814                        Sheikh Zayed Stadium              0   \n",
       "815         Dubai International Cricket Stadium              0   \n",
       "\n",
       "                     team1                        team2  \\\n",
       "0                      RCB        Kolkata Knight Riders   \n",
       "1          Kings XI Punjab          Chennai Super Kings   \n",
       "2         Delhi Daredevils             Rajasthan Royals   \n",
       "3           Mumbai Indians  Royal Challengers Bangalore   \n",
       "4    Kolkata Knight Riders              Deccan Chargers   \n",
       "..                     ...                          ...   \n",
       "811                    RCB               Mumbai Indians   \n",
       "812         Mumbai Indians               Delhi Capitals   \n",
       "813                    RCB          Sunrisers Hyderabad   \n",
       "814         Delhi Capitals          Sunrisers Hyderabad   \n",
       "815         Delhi Capitals               Mumbai Indians   \n",
       "\n",
       "                     toss_winner toss_decision                       winner  \\\n",
       "0    Royal Challengers Bangalore         field        Kolkata Knight Riders   \n",
       "1            Chennai Super Kings           bat          Chennai Super Kings   \n",
       "2               Rajasthan Royals           bat             Delhi Daredevils   \n",
       "3                 Mumbai Indians           bat  Royal Challengers Bangalore   \n",
       "4                Deccan Chargers           bat        Kolkata Knight Riders   \n",
       "..                           ...           ...                          ...   \n",
       "811               Mumbai Indians         field  Royal Challengers Bangalore   \n",
       "812               Delhi Capitals         field               Mumbai Indians   \n",
       "813          Sunrisers Hyderabad         field          Sunrisers Hyderabad   \n",
       "814               Delhi Capitals           bat               Delhi Capitals   \n",
       "815               Delhi Capitals           bat               Mumbai Indians   \n",
       "\n",
       "      result  result_margin eliminator method      umpire1         umpire2  \n",
       "0       runs          140.0          N    NaN    Asad Rauf     RE Koertzen  \n",
       "1       runs           33.0          N    NaN    MR Benson      SL Shastri  \n",
       "2    wickets            9.0          N    NaN    Aleem Dar  GA Pratapkumar  \n",
       "3    wickets            5.0          N    NaN     SJ Davis       DJ Harper  \n",
       "4    wickets            5.0          N    NaN    BF Bowden     K Hariharan  \n",
       "..       ...            ...        ...    ...          ...             ...  \n",
       "811      tie            NaN          Y    NaN  Nitin Menon      PR Reiffel  \n",
       "812     runs           57.0          N    NaN  CB Gaffaney     Nitin Menon  \n",
       "813  wickets            6.0          N    NaN   PR Reiffel          S Ravi  \n",
       "814     runs           17.0          N    NaN   PR Reiffel          S Ravi  \n",
       "815  wickets            5.0          N    NaN  CB Gaffaney     Nitin Menon  \n",
       "\n",
       "[816 rows x 17 columns]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('IPL Matches 2008-2020.csv',converters={'team1':rename})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 15. na_values parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>enrollee_id</th>\n",
       "      <th>city</th>\n",
       "      <th>city_development_index</th>\n",
       "      <th>gender</th>\n",
       "      <th>relevent_experience</th>\n",
       "      <th>enrolled_university</th>\n",
       "      <th>education_level</th>\n",
       "      <th>major_discipline</th>\n",
       "      <th>experience</th>\n",
       "      <th>company_size</th>\n",
       "      <th>company_type</th>\n",
       "      <th>last_new_job</th>\n",
       "      <th>training_hours</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8949</td>\n",
       "      <td>city_103</td>\n",
       "      <td>0.920</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>&gt;20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29725</td>\n",
       "      <td>city_40</td>\n",
       "      <td>0.776</td>\n",
       "      <td>Male</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>15</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>&gt;4</td>\n",
       "      <td>47</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11561</td>\n",
       "      <td>city_21</td>\n",
       "      <td>0.624</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>Full time course</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never</td>\n",
       "      <td>83</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33241</td>\n",
       "      <td>city_115</td>\n",
       "      <td>0.789</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Business Degree</td>\n",
       "      <td>&lt;1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>never</td>\n",
       "      <td>52</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666</td>\n",
       "      <td>city_162</td>\n",
       "      <td>0.767</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Masters</td>\n",
       "      <td>STEM</td>\n",
       "      <td>&gt;20</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Funded Startup</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19153</th>\n",
       "      <td>7386</td>\n",
       "      <td>city_173</td>\n",
       "      <td>0.878</td>\n",
       "      <td>Male</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Humanities</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19154</th>\n",
       "      <td>31398</td>\n",
       "      <td>city_103</td>\n",
       "      <td>0.920</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>52</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19155</th>\n",
       "      <td>24576</td>\n",
       "      <td>city_103</td>\n",
       "      <td>0.920</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>STEM</td>\n",
       "      <td>&gt;20</td>\n",
       "      <td>50-99</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19156</th>\n",
       "      <td>5756</td>\n",
       "      <td>city_65</td>\n",
       "      <td>0.802</td>\n",
       "      <td>Male</td>\n",
       "      <td>Has relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>High School</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;1</td>\n",
       "      <td>500-999</td>\n",
       "      <td>Pvt Ltd</td>\n",
       "      <td>2</td>\n",
       "      <td>97</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19157</th>\n",
       "      <td>23834</td>\n",
       "      <td>city_67</td>\n",
       "      <td>0.855</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No relevent experience</td>\n",
       "      <td>no_enrollment</td>\n",
       "      <td>Primary School</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>127</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19158 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       enrollee_id      city  city_development_index gender  \\\n",
       "0             8949  city_103                   0.920   Male   \n",
       "1            29725   city_40                   0.776   Male   \n",
       "2            11561   city_21                   0.624    NaN   \n",
       "3            33241  city_115                   0.789    NaN   \n",
       "4              666  city_162                   0.767   Male   \n",
       "...            ...       ...                     ...    ...   \n",
       "19153         7386  city_173                   0.878   Male   \n",
       "19154        31398  city_103                   0.920   Male   \n",
       "19155        24576  city_103                   0.920   Male   \n",
       "19156         5756   city_65                   0.802   Male   \n",
       "19157        23834   city_67                   0.855    NaN   \n",
       "\n",
       "           relevent_experience enrolled_university education_level  \\\n",
       "0      Has relevent experience       no_enrollment        Graduate   \n",
       "1       No relevent experience       no_enrollment        Graduate   \n",
       "2       No relevent experience    Full time course        Graduate   \n",
       "3       No relevent experience                 NaN        Graduate   \n",
       "4      Has relevent experience       no_enrollment         Masters   \n",
       "...                        ...                 ...             ...   \n",
       "19153   No relevent experience       no_enrollment        Graduate   \n",
       "19154  Has relevent experience       no_enrollment        Graduate   \n",
       "19155  Has relevent experience       no_enrollment        Graduate   \n",
       "19156  Has relevent experience       no_enrollment     High School   \n",
       "19157   No relevent experience       no_enrollment  Primary School   \n",
       "\n",
       "      major_discipline experience company_size    company_type last_new_job  \\\n",
       "0                 STEM        >20          NaN             NaN            1   \n",
       "1                 STEM         15        50-99         Pvt Ltd           >4   \n",
       "2                 STEM          5          NaN             NaN        never   \n",
       "3      Business Degree         <1          NaN         Pvt Ltd        never   \n",
       "4                 STEM        >20        50-99  Funded Startup            4   \n",
       "...                ...        ...          ...             ...          ...   \n",
       "19153       Humanities         14          NaN             NaN            1   \n",
       "19154             STEM         14          NaN             NaN            4   \n",
       "19155             STEM        >20        50-99         Pvt Ltd            4   \n",
       "19156              NaN         <1      500-999         Pvt Ltd            2   \n",
       "19157              NaN          2          NaN             NaN            1   \n",
       "\n",
       "       training_hours  target  \n",
       "0                  36     1.0  \n",
       "1                  47     0.0  \n",
       "2                  83     0.0  \n",
       "3                  52     1.0  \n",
       "4                   8     0.0  \n",
       "...               ...     ...  \n",
       "19153              42     1.0  \n",
       "19154              52     1.0  \n",
       "19155              44     0.0  \n",
       "19156              97     0.0  \n",
       "19157             127     0.0  \n",
       "\n",
       "[19158 rows x 14 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('aug_train.csv',na_values=['Male',])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 16. Loading a huge dataset in chunks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = pd.read_csv('aug_train.csv',chunksize=5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4158, 14)\n",
      "(4158, 14)\n",
      "(4158, 14)\n",
      "(4158, 14)\n"
     ]
    }
   ],
   "source": [
    "for chunks in dfs:\n",
    "    print(chunk.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check This all -->\n",
    "\n",
    "    GitHub: https://github.com/Rasel1435\n",
    "    Linkedin: https://www.linkedin.com/in/shekhnirob1\n",
    "    YouTube: https://www.youtube.com/@codewithsheikhrasel\n",
    "    Medium: https://medium.com/@shekhrasel59\n",
    "    Kaggle: https://www.kaggle.com/sheikhraselahmed\n",
    "    Upwork: https://www.upwork.com/freelancers/~01b76af87fa6e9611e\n",
    "    leetcode: https://leetcode.com/shekh_rasel/\n",
    "    Twitter: https://twitter.com/shekh_nirob\n",
    "    Facebook: https://www.facebook.com/rasel1435"
   ]
  }
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