lecture_11(null,apply).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "[문제132] emp.csv file에 있는 데이터 중에 100번 사원정보를 출력하세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import pandas as pd\n",
    "from pandas import Series,DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "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>emp_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>email</th>\n",
       "      <th>phone_num</th>\n",
       "      <th>hire_date</th>\n",
       "      <th>job_id</th>\n",
       "      <th>salary</th>\n",
       "      <th>commission</th>\n",
       "      <th>manager_id</th>\n",
       "      <th>department_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EMPLOYEE_ID</td>\n",
       "      <td>FIRST_NAME</td>\n",
       "      <td>LAST_NAME</td>\n",
       "      <td>EMAIL</td>\n",
       "      <td>PHONE_NUMBER</td>\n",
       "      <td>HIRE_DATE</td>\n",
       "      <td>JOB_ID</td>\n",
       "      <td>SALARY</td>\n",
       "      <td>COMMISSION_PCT</td>\n",
       "      <td>MANAGER_ID</td>\n",
       "      <td>DEPARTMENT_ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>Steven</td>\n",
       "      <td>King</td>\n",
       "      <td>SKING</td>\n",
       "      <td>515.123.4567</td>\n",
       "      <td>2003/06/17</td>\n",
       "      <td>AD_PRES</td>\n",
       "      <td>24000</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101</td>\n",
       "      <td>Neena</td>\n",
       "      <td>Kochhar</td>\n",
       "      <td>NKOCHHAR</td>\n",
       "      <td>515.123.4568</td>\n",
       "      <td>2005/09/21</td>\n",
       "      <td>AD_VP</td>\n",
       "      <td>17000</td>\n",
       "      <td></td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102</td>\n",
       "      <td>Lex</td>\n",
       "      <td>De Haan</td>\n",
       "      <td>LDEHAAN</td>\n",
       "      <td>515.123.4569</td>\n",
       "      <td>2001/01/13</td>\n",
       "      <td>AD_VP</td>\n",
       "      <td>17000</td>\n",
       "      <td></td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103</td>\n",
       "      <td>Alexander</td>\n",
       "      <td>Hunold</td>\n",
       "      <td>AHUNOLD</td>\n",
       "      <td>590.423.4567</td>\n",
       "      <td>2006/01/03</td>\n",
       "      <td>IT_PROG</td>\n",
       "      <td>9000</td>\n",
       "      <td></td>\n",
       "      <td>102</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>104</td>\n",
       "      <td>Bruce</td>\n",
       "      <td>Ernst</td>\n",
       "      <td>BERNST</td>\n",
       "      <td>590.423.4568</td>\n",
       "      <td>2007/05/21</td>\n",
       "      <td>IT_PROG</td>\n",
       "      <td>6000</td>\n",
       "      <td></td>\n",
       "      <td>103</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>105</td>\n",
       "      <td>David</td>\n",
       "      <td>Austin</td>\n",
       "      <td>DAUSTIN</td>\n",
       "      <td>590.423.4569</td>\n",
       "      <td>2005/06/25</td>\n",
       "      <td>IT_PROG</td>\n",
       "      <td>4800</td>\n",
       "      <td></td>\n",
       "      <td>103</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>106</td>\n",
       "      <td>Valli</td>\n",
       "      <td>Pataballa</td>\n",
       "      <td>VPATABAL</td>\n",
       "      <td>590.423.4560</td>\n",
       "      <td>2006/02/05</td>\n",
       "      <td>IT_PROG</td>\n",
       "      <td>4800</td>\n",
       "      <td></td>\n",
       "      <td>103</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>107</td>\n",
       "      <td>Diana</td>\n",
       "      <td>Lorentz</td>\n",
       "      <td>DLORENTZ</td>\n",
       "      <td>590.423.5567</td>\n",
       "      <td>2007/02/07</td>\n",
       "      <td>IT_PROG</td>\n",
       "      <td>4200</td>\n",
       "      <td></td>\n",
       "      <td>103</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>108</td>\n",
       "      <td>Nancy</td>\n",
       "      <td>Greenberg</td>\n",
       "      <td>NGREENBE</td>\n",
       "      <td>515.124.4569</td>\n",
       "      <td>2002/08/17</td>\n",
       "      <td>FI_MGR</td>\n",
       "      <td>12008</td>\n",
       "      <td></td>\n",
       "      <td>101</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>109</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>Faviet</td>\n",
       "      <td>DFAVIET</td>\n",
       "      <td>515.124.4169</td>\n",
       "      <td>2002/08/16</td>\n",
       "      <td>FI_ACCOUNT</td>\n",
       "      <td>9000</td>\n",
       "      <td></td>\n",
       "      <td>108</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>110</td>\n",
       "      <td>John</td>\n",
       "      <td>Chen</td>\n",
       "      <td>JCHEN</td>\n",
       "      <td>515.124.4269</td>\n",
       "      <td>2005/09/28</td>\n",
       "      <td>FI_ACCOUNT</td>\n",
       "      <td>8200</td>\n",
       "      <td></td>\n",
       "      <td>108</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>111</td>\n",
       "      <td>Ismael</td>\n",
       "      <td>Sciarra</td>\n",
       "      <td>ISCIARRA</td>\n",
       "      <td>515.124.4369</td>\n",
       "      <td>2005/09/30</td>\n",
       "      <td>FI_ACCOUNT</td>\n",
       "      <td>7700</td>\n",
       "      <td></td>\n",
       "      <td>108</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>112</td>\n",
       "      <td>Jose Manuel</td>\n",
       "      <td>Urman</td>\n",
       "      <td>JMURMAN</td>\n",
       "      <td>515.124.4469</td>\n",
       "      <td>2006/03/07</td>\n",
       "      <td>FI_ACCOUNT</td>\n",
       "      <td>7800</td>\n",
       "      <td></td>\n",
       "      <td>108</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>113</td>\n",
       "      <td>Luis</td>\n",
       "      <td>Popp</td>\n",
       "      <td>LPOPP</td>\n",
       "      <td>515.124.4567</td>\n",
       "      <td>2007/12/07</td>\n",
       "      <td>FI_ACCOUNT</td>\n",
       "      <td>6900</td>\n",
       "      <td></td>\n",
       "      <td>108</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>114</td>\n",
       "      <td>Den</td>\n",
       "      <td>Raphaely</td>\n",
       "      <td>DRAPHEAL</td>\n",
       "      <td>515.127.4561</td>\n",
       "      <td>2002/12/07</td>\n",
       "      <td>PU_MAN</td>\n",
       "      <td>11000</td>\n",
       "      <td></td>\n",
       "      <td>100</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>115</td>\n",
       "      <td>Alexander</td>\n",
       "      <td>Khoo</td>\n",
       "      <td>AKHOO</td>\n",
       "      <td>515.127.4562</td>\n",
       "      <td>2003/05/18</td>\n",
       "      <td>PU_CLERK</td>\n",
       "      <td>3100</td>\n",
       "      <td></td>\n",
       "      <td>114</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>116</td>\n",
       "      <td>Shelli</td>\n",
       "      <td>Baida</td>\n",
       "      <td>SBAIDA</td>\n",
       "      <td>515.127.4563</td>\n",
       "      <td>2005/12/24</td>\n",
       "      <td>PU_CLERK</td>\n",
       "      <td>2900</td>\n",
       "      <td></td>\n",
       "      <td>114</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>117</td>\n",
       "      <td>Sigal</td>\n",
       "      <td>Tobias</td>\n",
       "      <td>STOBIAS</td>\n",
       "      <td>515.127.4564</td>\n",
       "      <td>2005/07/24</td>\n",
       "      <td>PU_CLERK</td>\n",
       "      <td>2800</td>\n",
       "      <td></td>\n",
       "      <td>114</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>118</td>\n",
       "      <td>Guy</td>\n",
       "      <td>Himuro</td>\n",
       "      <td>GHIMURO</td>\n",
       "      <td>515.127.4565</td>\n",
       "      <td>2006/11/15</td>\n",
       "      <td>PU_CLERK</td>\n",
       "      <td>2600</td>\n",
       "      <td></td>\n",
       "      <td>114</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>119</td>\n",
       "      <td>Karen</td>\n",
       "      <td>Colmenares</td>\n",
       "      <td>KCOLMENA</td>\n",
       "      <td>515.127.4566</td>\n",
       "      <td>2007/08/10</td>\n",
       "      <td>PU_CLERK</td>\n",
       "      <td>2500</td>\n",
       "      <td></td>\n",
       "      <td>114</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>120</td>\n",
       "      <td>Matthew</td>\n",
       "      <td>Weiss</td>\n",
       "      <td>MWEISS</td>\n",
       "      <td>650.123.1234</td>\n",
       "      <td>2004/07/18</td>\n",
       "      <td>ST_MAN</td>\n",
       "      <td>8000</td>\n",
       "      <td></td>\n",
       "      <td>100</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>121</td>\n",
       "      <td>Adam</td>\n",
       "      <td>Fripp</td>\n",
       "      <td>AFRIPP</td>\n",
       "      <td>650.123.2234</td>\n",
       "      <td>2005/04/10</td>\n",
       "      <td>ST_MAN</td>\n",
       "      <td>8200</td>\n",
       "      <td></td>\n",
       "      <td>100</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>122</td>\n",
       "      <td>Payam</td>\n",
       "      <td>Kaufling</td>\n",
       "      <td>PKAUFLIN</td>\n",
       "      <td>650.123.3234</td>\n",
       "      <td>2003/05/01</td>\n",
       "      <td>ST_MAN</td>\n",
       "      <td>7900</td>\n",
       "      <td></td>\n",
       "      <td>100</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>123</td>\n",
       "      <td>Shanta</td>\n",
       "      <td>Vollman</td>\n",
       "      <td>SVOLLMAN</td>\n",
       "      <td>650.123.4234</td>\n",
       "      <td>2005/10/10</td>\n",
       "      <td>ST_MAN</td>\n",
       "      <td>6500</td>\n",
       "      <td></td>\n",
       "      <td>100</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>124</td>\n",
       "      <td>Kevin</td>\n",
       "      <td>Mourgos</td>\n",
       "      <td>KMOURGOS</td>\n",
       "      <td>650.123.5234</td>\n",
       "      <td>2007/11/16</td>\n",
       "      <td>ST_MAN</td>\n",
       "      <td>5800</td>\n",
       "      <td></td>\n",
       "      <td>100</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>125</td>\n",
       "      <td>Julia</td>\n",
       "      <td>Nayer</td>\n",
       "      <td>JNAYER</td>\n",
       "      <td>650.124.1214</td>\n",
       "      <td>2005/07/16</td>\n",
       "      <td>ST_CLERK</td>\n",
       "      <td>3200</td>\n",
       "      <td></td>\n",
       "      <td>120</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>126</td>\n",
       "      <td>Irene</td>\n",
       "      <td>Mikkilineni</td>\n",
       "      <td>IMIKKILI</td>\n",
       "      <td>650.124.1224</td>\n",
       "      <td>2006/09/28</td>\n",
       "      <td>ST_CLERK</td>\n",
       "      <td>2700</td>\n",
       "      <td></td>\n",
       "      <td>120</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>127</td>\n",
       "      <td>James</td>\n",
       "      <td>Landry</td>\n",
       "      <td>JLANDRY</td>\n",
       "      <td>650.124.1334</td>\n",
       "      <td>2007/01/14</td>\n",
       "      <td>ST_CLERK</td>\n",
       "      <td>2400</td>\n",
       "      <td></td>\n",
       "      <td>120</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>128</td>\n",
       "      <td>Steven</td>\n",
       "      <td>Markle</td>\n",
       "      <td>SMARKLE</td>\n",
       "      <td>650.124.1434</td>\n",
       "      <td>2008/03/08</td>\n",
       "      <td>ST_CLERK</td>\n",
       "      <td>2200</td>\n",
       "      <td></td>\n",
       "      <td>120</td>\n",
       "      <td>50</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>177</td>\n",
       "      <td>Jack</td>\n",
       "      <td>Livingston</td>\n",
       "      <td>JLIVINGS</td>\n",
       "      <td>011.44.1644.429264</td>\n",
       "      <td>2006/04/23</td>\n",
       "      <td>SA_REP</td>\n",
       "      <td>8400</td>\n",
       "      <td>0.2</td>\n",
       "      <td>149</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>178</td>\n",
       "      <td>Kimberely</td>\n",
       "      <td>Grant</td>\n",
       "      <td>KGRANT</td>\n",
       "      <td>011.44.1644.429263</td>\n",
       "      <td>2007/05/24</td>\n",
       "      <td>SA_REP</td>\n",
       "      <td>7000</td>\n",
       "      <td>0.15</td>\n",
       "      <td>149</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>179</td>\n",
       "      <td>Charles</td>\n",
       "      <td>Johnson</td>\n",
       "      <td>CJOHNSON</td>\n",
       "      <td>011.44.1644.429262</td>\n",
       "      <td>2008/01/04</td>\n",
       "      <td>SA_REP</td>\n",
       "      <td>6200</td>\n",
       "      <td>0.1</td>\n",
       "      <td>149</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>180</td>\n",
       "      <td>Winston</td>\n",
       "      <td>Taylor</td>\n",
       "      <td>WTAYLOR</td>\n",
       "      <td>650.507.9876</td>\n",
       "      <td>2006/01/24</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>3200</td>\n",
       "      <td></td>\n",
       "      <td>120</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>181</td>\n",
       "      <td>Jean</td>\n",
       "      <td>Fleaur</td>\n",
       "      <td>JFLEAUR</td>\n",
       "      <td>650.507.9877</td>\n",
       "      <td>2006/02/23</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>3100</td>\n",
       "      <td></td>\n",
       "      <td>120</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>182</td>\n",
       "      <td>Martha</td>\n",
       "      <td>Sullivan</td>\n",
       "      <td>MSULLIVA</td>\n",
       "      <td>650.507.9878</td>\n",
       "      <td>2007/06/21</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>2500</td>\n",
       "      <td></td>\n",
       "      <td>120</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>183</td>\n",
       "      <td>Girard</td>\n",
       "      <td>Geoni</td>\n",
       "      <td>GGEONI</td>\n",
       "      <td>650.507.9879</td>\n",
       "      <td>2008/02/03</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>2800</td>\n",
       "      <td></td>\n",
       "      <td>120</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>184</td>\n",
       "      <td>Nandita</td>\n",
       "      <td>Sarchand</td>\n",
       "      <td>NSARCHAN</td>\n",
       "      <td>650.509.1876</td>\n",
       "      <td>2004/01/27</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>4200</td>\n",
       "      <td></td>\n",
       "      <td>121</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>185</td>\n",
       "      <td>Alexis</td>\n",
       "      <td>Bull</td>\n",
       "      <td>ABULL</td>\n",
       "      <td>650.509.2876</td>\n",
       "      <td>2005/02/20</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>4100</td>\n",
       "      <td></td>\n",
       "      <td>121</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>186</td>\n",
       "      <td>Julia</td>\n",
       "      <td>Dellinger</td>\n",
       "      <td>JDELLING</td>\n",
       "      <td>650.509.3876</td>\n",
       "      <td>2006/06/24</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>3400</td>\n",
       "      <td></td>\n",
       "      <td>121</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>187</td>\n",
       "      <td>Anthony</td>\n",
       "      <td>Cabrio</td>\n",
       "      <td>ACABRIO</td>\n",
       "      <td>650.509.4876</td>\n",
       "      <td>2007/02/07</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>3000</td>\n",
       "      <td></td>\n",
       "      <td>121</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>188</td>\n",
       "      <td>Kelly</td>\n",
       "      <td>Chung</td>\n",
       "      <td>KCHUNG</td>\n",
       "      <td>650.505.1876</td>\n",
       "      <td>2005/06/14</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>3800</td>\n",
       "      <td></td>\n",
       "      <td>122</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>189</td>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Dilly</td>\n",
       "      <td>JDILLY</td>\n",
       "      <td>650.505.2876</td>\n",
       "      <td>2005/08/13</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>3600</td>\n",
       "      <td></td>\n",
       "      <td>122</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>190</td>\n",
       "      <td>Timothy</td>\n",
       "      <td>Gates</td>\n",
       "      <td>TGATES</td>\n",
       "      <td>650.505.3876</td>\n",
       "      <td>2006/07/11</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>2900</td>\n",
       "      <td></td>\n",
       "      <td>122</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>191</td>\n",
       "      <td>Randall</td>\n",
       "      <td>Perkins</td>\n",
       "      <td>RPERKINS</td>\n",
       "      <td>650.505.4876</td>\n",
       "      <td>2007/12/19</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>2500</td>\n",
       "      <td></td>\n",
       "      <td>122</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>192</td>\n",
       "      <td>Sarah</td>\n",
       "      <td>Bell</td>\n",
       "      <td>SBELL</td>\n",
       "      <td>650.501.1876</td>\n",
       "      <td>2004/02/04</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>4000</td>\n",
       "      <td></td>\n",
       "      <td>123</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>193</td>\n",
       "      <td>Britney</td>\n",
       "      <td>Everett</td>\n",
       "      <td>BEVERETT</td>\n",
       "      <td>650.501.2876</td>\n",
       "      <td>2005/03/03</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>3900</td>\n",
       "      <td></td>\n",
       "      <td>123</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>194</td>\n",
       "      <td>Samuel</td>\n",
       "      <td>McCain</td>\n",
       "      <td>SMCCAIN</td>\n",
       "      <td>650.501.3876</td>\n",
       "      <td>2006/07/01</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>3200</td>\n",
       "      <td></td>\n",
       "      <td>123</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>195</td>\n",
       "      <td>Vance</td>\n",
       "      <td>Jones</td>\n",
       "      <td>VJONES</td>\n",
       "      <td>650.501.4876</td>\n",
       "      <td>2007/03/17</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>2800</td>\n",
       "      <td></td>\n",
       "      <td>123</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>196</td>\n",
       "      <td>Alana</td>\n",
       "      <td>Walsh</td>\n",
       "      <td>AWALSH</td>\n",
       "      <td>650.507.9811</td>\n",
       "      <td>2006/04/24</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>3100</td>\n",
       "      <td></td>\n",
       "      <td>124</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>197</td>\n",
       "      <td>Kevin</td>\n",
       "      <td>Feeney</td>\n",
       "      <td>KFEENEY</td>\n",
       "      <td>650.507.9822</td>\n",
       "      <td>2006/05/23</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>3000</td>\n",
       "      <td></td>\n",
       "      <td>124</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>198</td>\n",
       "      <td>Donald</td>\n",
       "      <td>OConnell</td>\n",
       "      <td>DOCONNEL</td>\n",
       "      <td>650.507.9833</td>\n",
       "      <td>2007/06/21</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>2600</td>\n",
       "      <td></td>\n",
       "      <td>124</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>199</td>\n",
       "      <td>Douglas</td>\n",
       "      <td>Grant</td>\n",
       "      <td>DGRANT</td>\n",
       "      <td>650.507.9844</td>\n",
       "      <td>2008/01/13</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>2600</td>\n",
       "      <td></td>\n",
       "      <td>124</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>200</td>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Whalen</td>\n",
       "      <td>JWHALEN</td>\n",
       "      <td>515.123.4444</td>\n",
       "      <td>2003/09/17</td>\n",
       "      <td>AD_ASST</td>\n",
       "      <td>4400</td>\n",
       "      <td></td>\n",
       "      <td>101</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>201</td>\n",
       "      <td>Michael</td>\n",
       "      <td>Hartstein</td>\n",
       "      <td>MHARTSTE</td>\n",
       "      <td>515.123.5555</td>\n",
       "      <td>2004/02/17</td>\n",
       "      <td>MK_MAN</td>\n",
       "      <td>13000</td>\n",
       "      <td></td>\n",
       "      <td>100</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>202</td>\n",
       "      <td>Pat</td>\n",
       "      <td>Fay</td>\n",
       "      <td>PFAY</td>\n",
       "      <td>603.123.6666</td>\n",
       "      <td>2005/08/17</td>\n",
       "      <td>MK_REP</td>\n",
       "      <td>6000</td>\n",
       "      <td></td>\n",
       "      <td>201</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>203</td>\n",
       "      <td>Susan</td>\n",
       "      <td>Mavris</td>\n",
       "      <td>SMAVRIS</td>\n",
       "      <td>515.123.7777</td>\n",
       "      <td>2002/06/07</td>\n",
       "      <td>HR_REP</td>\n",
       "      <td>6500</td>\n",
       "      <td></td>\n",
       "      <td>101</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>204</td>\n",
       "      <td>Hermann</td>\n",
       "      <td>Baer</td>\n",
       "      <td>HBAER</td>\n",
       "      <td>515.123.8888</td>\n",
       "      <td>2002/06/07</td>\n",
       "      <td>PR_REP</td>\n",
       "      <td>10000</td>\n",
       "      <td></td>\n",
       "      <td>101</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>205</td>\n",
       "      <td>Shelley</td>\n",
       "      <td>Higgins</td>\n",
       "      <td>SHIGGINS</td>\n",
       "      <td>515.123.8080</td>\n",
       "      <td>2002/06/07</td>\n",
       "      <td>AC_MGR</td>\n",
       "      <td>12008</td>\n",
       "      <td></td>\n",
       "      <td>101</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>206</td>\n",
       "      <td>William</td>\n",
       "      <td>Gietz</td>\n",
       "      <td>WGIETZ</td>\n",
       "      <td>515.123.8181</td>\n",
       "      <td>2002/06/07</td>\n",
       "      <td>AC_ACCOUNT</td>\n",
       "      <td>8300</td>\n",
       "      <td></td>\n",
       "      <td>205</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>108 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          emp_id   first_name    last_name     email           phone_num  \\\n",
       "0    EMPLOYEE_ID   FIRST_NAME    LAST_NAME     EMAIL        PHONE_NUMBER   \n",
       "1            100       Steven         King     SKING        515.123.4567   \n",
       "2            101        Neena      Kochhar  NKOCHHAR        515.123.4568   \n",
       "3            102          Lex      De Haan   LDEHAAN        515.123.4569   \n",
       "4            103    Alexander       Hunold   AHUNOLD        590.423.4567   \n",
       "5            104        Bruce        Ernst    BERNST        590.423.4568   \n",
       "6            105        David       Austin   DAUSTIN        590.423.4569   \n",
       "7            106        Valli    Pataballa  VPATABAL        590.423.4560   \n",
       "8            107        Diana      Lorentz  DLORENTZ        590.423.5567   \n",
       "9            108        Nancy    Greenberg  NGREENBE        515.124.4569   \n",
       "10           109       Daniel       Faviet   DFAVIET        515.124.4169   \n",
       "11           110         John         Chen     JCHEN        515.124.4269   \n",
       "12           111       Ismael      Sciarra  ISCIARRA        515.124.4369   \n",
       "13           112  Jose Manuel        Urman   JMURMAN        515.124.4469   \n",
       "14           113         Luis         Popp     LPOPP        515.124.4567   \n",
       "15           114          Den     Raphaely  DRAPHEAL        515.127.4561   \n",
       "16           115    Alexander         Khoo     AKHOO        515.127.4562   \n",
       "17           116       Shelli        Baida    SBAIDA        515.127.4563   \n",
       "18           117        Sigal       Tobias   STOBIAS        515.127.4564   \n",
       "19           118          Guy       Himuro   GHIMURO        515.127.4565   \n",
       "20           119        Karen   Colmenares  KCOLMENA        515.127.4566   \n",
       "21           120      Matthew        Weiss    MWEISS        650.123.1234   \n",
       "22           121         Adam        Fripp    AFRIPP        650.123.2234   \n",
       "23           122        Payam     Kaufling  PKAUFLIN        650.123.3234   \n",
       "24           123       Shanta      Vollman  SVOLLMAN        650.123.4234   \n",
       "25           124        Kevin      Mourgos  KMOURGOS        650.123.5234   \n",
       "26           125        Julia        Nayer    JNAYER        650.124.1214   \n",
       "27           126        Irene  Mikkilineni  IMIKKILI        650.124.1224   \n",
       "28           127        James       Landry   JLANDRY        650.124.1334   \n",
       "29           128       Steven       Markle   SMARKLE        650.124.1434   \n",
       "..           ...          ...          ...       ...                 ...   \n",
       "78           177         Jack   Livingston  JLIVINGS  011.44.1644.429264   \n",
       "79           178    Kimberely        Grant    KGRANT  011.44.1644.429263   \n",
       "80           179      Charles      Johnson  CJOHNSON  011.44.1644.429262   \n",
       "81           180      Winston       Taylor   WTAYLOR        650.507.9876   \n",
       "82           181         Jean       Fleaur   JFLEAUR        650.507.9877   \n",
       "83           182       Martha     Sullivan  MSULLIVA        650.507.9878   \n",
       "84           183       Girard        Geoni    GGEONI        650.507.9879   \n",
       "85           184      Nandita     Sarchand  NSARCHAN        650.509.1876   \n",
       "86           185       Alexis         Bull     ABULL        650.509.2876   \n",
       "87           186        Julia    Dellinger  JDELLING        650.509.3876   \n",
       "88           187      Anthony       Cabrio   ACABRIO        650.509.4876   \n",
       "89           188        Kelly        Chung    KCHUNG        650.505.1876   \n",
       "90           189     Jennifer        Dilly    JDILLY        650.505.2876   \n",
       "91           190      Timothy        Gates    TGATES        650.505.3876   \n",
       "92           191      Randall      Perkins  RPERKINS        650.505.4876   \n",
       "93           192        Sarah         Bell     SBELL        650.501.1876   \n",
       "94           193      Britney      Everett  BEVERETT        650.501.2876   \n",
       "95           194       Samuel       McCain   SMCCAIN        650.501.3876   \n",
       "96           195        Vance        Jones    VJONES        650.501.4876   \n",
       "97           196        Alana        Walsh    AWALSH        650.507.9811   \n",
       "98           197        Kevin       Feeney   KFEENEY        650.507.9822   \n",
       "99           198       Donald     OConnell  DOCONNEL        650.507.9833   \n",
       "100          199      Douglas        Grant    DGRANT        650.507.9844   \n",
       "101          200     Jennifer       Whalen   JWHALEN        515.123.4444   \n",
       "102          201      Michael    Hartstein  MHARTSTE        515.123.5555   \n",
       "103          202          Pat          Fay      PFAY        603.123.6666   \n",
       "104          203        Susan       Mavris   SMAVRIS        515.123.7777   \n",
       "105          204      Hermann         Baer     HBAER        515.123.8888   \n",
       "106          205      Shelley      Higgins  SHIGGINS        515.123.8080   \n",
       "107          206      William        Gietz    WGIETZ        515.123.8181   \n",
       "\n",
       "      hire_date      job_id  salary      commission  manager_id  department_id  \n",
       "0     HIRE_DATE      JOB_ID  SALARY  COMMISSION_PCT  MANAGER_ID  DEPARTMENT_ID  \n",
       "1    2003/06/17     AD_PRES   24000                                         90  \n",
       "2    2005/09/21       AD_VP   17000                         100             90  \n",
       "3    2001/01/13       AD_VP   17000                         100             90  \n",
       "4    2006/01/03     IT_PROG    9000                         102             60  \n",
       "5    2007/05/21     IT_PROG    6000                         103             60  \n",
       "6    2005/06/25     IT_PROG    4800                         103             60  \n",
       "7    2006/02/05     IT_PROG    4800                         103             60  \n",
       "8    2007/02/07     IT_PROG    4200                         103             60  \n",
       "9    2002/08/17      FI_MGR   12008                         101            100  \n",
       "10   2002/08/16  FI_ACCOUNT    9000                         108            100  \n",
       "11   2005/09/28  FI_ACCOUNT    8200                         108            100  \n",
       "12   2005/09/30  FI_ACCOUNT    7700                         108            100  \n",
       "13   2006/03/07  FI_ACCOUNT    7800                         108            100  \n",
       "14   2007/12/07  FI_ACCOUNT    6900                         108            100  \n",
       "15   2002/12/07      PU_MAN   11000                         100             30  \n",
       "16   2003/05/18    PU_CLERK    3100                         114             30  \n",
       "17   2005/12/24    PU_CLERK    2900                         114             30  \n",
       "18   2005/07/24    PU_CLERK    2800                         114             30  \n",
       "19   2006/11/15    PU_CLERK    2600                         114             30  \n",
       "20   2007/08/10    PU_CLERK    2500                         114             30  \n",
       "21   2004/07/18      ST_MAN    8000                         100             50  \n",
       "22   2005/04/10      ST_MAN    8200                         100             50  \n",
       "23   2003/05/01      ST_MAN    7900                         100             50  \n",
       "24   2005/10/10      ST_MAN    6500                         100             50  \n",
       "25   2007/11/16      ST_MAN    5800                         100             50  \n",
       "26   2005/07/16    ST_CLERK    3200                         120             50  \n",
       "27   2006/09/28    ST_CLERK    2700                         120             50  \n",
       "28   2007/01/14    ST_CLERK    2400                         120             50  \n",
       "29   2008/03/08    ST_CLERK    2200                         120             50  \n",
       "..          ...         ...     ...             ...         ...            ...  \n",
       "78   2006/04/23      SA_REP    8400             0.2         149             80  \n",
       "79   2007/05/24      SA_REP    7000            0.15         149                 \n",
       "80   2008/01/04      SA_REP    6200             0.1         149             80  \n",
       "81   2006/01/24    SH_CLERK    3200                         120             50  \n",
       "82   2006/02/23    SH_CLERK    3100                         120             50  \n",
       "83   2007/06/21    SH_CLERK    2500                         120             50  \n",
       "84   2008/02/03    SH_CLERK    2800                         120             50  \n",
       "85   2004/01/27    SH_CLERK    4200                         121             50  \n",
       "86   2005/02/20    SH_CLERK    4100                         121             50  \n",
       "87   2006/06/24    SH_CLERK    3400                         121             50  \n",
       "88   2007/02/07    SH_CLERK    3000                         121             50  \n",
       "89   2005/06/14    SH_CLERK    3800                         122             50  \n",
       "90   2005/08/13    SH_CLERK    3600                         122             50  \n",
       "91   2006/07/11    SH_CLERK    2900                         122             50  \n",
       "92   2007/12/19    SH_CLERK    2500                         122             50  \n",
       "93   2004/02/04    SH_CLERK    4000                         123             50  \n",
       "94   2005/03/03    SH_CLERK    3900                         123             50  \n",
       "95   2006/07/01    SH_CLERK    3200                         123             50  \n",
       "96   2007/03/17    SH_CLERK    2800                         123             50  \n",
       "97   2006/04/24    SH_CLERK    3100                         124             50  \n",
       "98   2006/05/23    SH_CLERK    3000                         124             50  \n",
       "99   2007/06/21    SH_CLERK    2600                         124             50  \n",
       "100  2008/01/13    SH_CLERK    2600                         124             50  \n",
       "101  2003/09/17     AD_ASST    4400                         101             10  \n",
       "102  2004/02/17      MK_MAN   13000                         100             20  \n",
       "103  2005/08/17      MK_REP    6000                         201             20  \n",
       "104  2002/06/07      HR_REP    6500                         101             40  \n",
       "105  2002/06/07      PR_REP   10000                         101             70  \n",
       "106  2002/06/07      AC_MGR   12008                         101            110  \n",
       "107  2002/06/07  AC_ACCOUNT    8300                         205            110  \n",
       "\n",
       "[108 rows x 11 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = open(\"c:\\R\\emp.csv\",\"r\")\n",
    "emp_csv = csv.reader(file)\n",
    "\n",
    "emp = DataFrame(emp_csv,\n",
    "                columns=['emp_id','first_name','last_name','email','phone_num','hire_date','job_id','salary','commission','manager_id','department_id'])\n",
    "emp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>emp_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>email</th>\n",
       "      <th>phone_num</th>\n",
       "      <th>hire_date</th>\n",
       "      <th>job_id</th>\n",
       "      <th>salary</th>\n",
       "      <th>commission</th>\n",
       "      <th>manager_id</th>\n",
       "      <th>department_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>Steven</td>\n",
       "      <td>King</td>\n",
       "      <td>SKING</td>\n",
       "      <td>515.123.4567</td>\n",
       "      <td>2003/06/17</td>\n",
       "      <td>AD_PRES</td>\n",
       "      <td>24000</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  emp_id first_name last_name  email     phone_num   hire_date   job_id  \\\n",
       "1    100     Steven      King  SKING  515.123.4567  2003/06/17  AD_PRES   \n",
       "\n",
       "  salary commission manager_id department_id  \n",
       "1  24000                                  90  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp.loc[emp['emp_id']=='100']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[문제133] emp.csv file에 있는 데이터 중에 직업이 ST_CLERK 인 사원들의 이름과 급여, 직업을 출력하세요"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import pandas\n",
    "from pandas import Series,DataFrame"
   ]
  },
  {
   "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>last_name</th>\n",
       "      <th>salary</th>\n",
       "      <th>job_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>yer</td>\n",
       "      <td>3200</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Mikkilineni</td>\n",
       "      <td>2700</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Landry</td>\n",
       "      <td>2400</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Markle</td>\n",
       "      <td>2200</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Bissot</td>\n",
       "      <td>3300</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Atkinson</td>\n",
       "      <td>2800</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Marlow</td>\n",
       "      <td>2500</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Olson</td>\n",
       "      <td>2100</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Mallin</td>\n",
       "      <td>3300</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Rogers</td>\n",
       "      <td>2900</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Gee</td>\n",
       "      <td>2400</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Philtanker</td>\n",
       "      <td>2200</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Ladwig</td>\n",
       "      <td>3600</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Stiles</td>\n",
       "      <td>3200</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Seo</td>\n",
       "      <td>2700</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Patel</td>\n",
       "      <td>2500</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Rajs</td>\n",
       "      <td>3500</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Davies</td>\n",
       "      <td>3100</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Matos</td>\n",
       "      <td>2600</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Vargas</td>\n",
       "      <td>2500</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      last_name salary    job_id\n",
       "25          yer   3200  ST_CLERK\n",
       "26  Mikkilineni   2700  ST_CLERK\n",
       "27       Landry   2400  ST_CLERK\n",
       "28       Markle   2200  ST_CLERK\n",
       "29       Bissot   3300  ST_CLERK\n",
       "30     Atkinson   2800  ST_CLERK\n",
       "31       Marlow   2500  ST_CLERK\n",
       "32        Olson   2100  ST_CLERK\n",
       "33       Mallin   3300  ST_CLERK\n",
       "34       Rogers   2900  ST_CLERK\n",
       "35          Gee   2400  ST_CLERK\n",
       "36   Philtanker   2200  ST_CLERK\n",
       "37       Ladwig   3600  ST_CLERK\n",
       "38       Stiles   3200  ST_CLERK\n",
       "39          Seo   2700  ST_CLERK\n",
       "40        Patel   2500  ST_CLERK\n",
       "41         Rajs   3500  ST_CLERK\n",
       "42       Davies   3100  ST_CLERK\n",
       "43        Matos   2600  ST_CLERK\n",
       "44       Vargas   2500  ST_CLERK"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = open(\"c:\\python\\emp2.csv\")\n",
    "emp_csv = csv.reader(file)\n",
    "\n",
    "emp = DataFrame(emp_csv,\n",
    "               columns = ['emp_id','last_name','job_id','dep_id','hire_date','salary','commission','manager_id'])\n",
    "emp.loc[emp['job_id']=='ST_CLERK',['last_name','salary','job_id']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[문제134] 급여 10000 이상인 사원들의 이름과 급여, 입사일를 출력하세요"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "from pandas import Series,DataFrame\n",
    "\n",
    "emp = pd.read_csv(\"c:\\python\\emp1.csv\",\n",
    "                  names=[\"empid\",\"name\",\"job\",\"mgr\",\"hire_date\",\"sal\",\"comm\",\"deptno\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sal</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>King</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>2003-06-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kochhar</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>2005-09-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>De Haan</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>2001-01-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Greenberg</td>\n",
       "      <td>13208.8</td>\n",
       "      <td>2002-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Raphaely</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>2002-12-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Russell</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>2004-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Partners</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>2005-01-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Errazuriz</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>2005-03-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Cambrault</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>2007-10-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Zlotkey</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>2008-01-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Tucker</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>2005-01-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>King</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>2004-01-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Vishney</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>2005-11-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Ozer</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>2005-03-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Bloom</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>2006-03-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Abel</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>2004-05-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Hartstein</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>2004-02-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Baer</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>2002-06-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Higgins</td>\n",
       "      <td>12008.0</td>\n",
       "      <td>2002-06-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          name      sal   hire_date\n",
       "0         King  24000.0  2003-06-17\n",
       "1      Kochhar  17000.0  2005-09-21\n",
       "2      De Haan  17000.0  2001-01-13\n",
       "8    Greenberg  13208.8  2002-08-17\n",
       "14    Raphaely  11000.0  2002-12-07\n",
       "45     Russell  14000.0  2004-10-01\n",
       "46    Partners  13500.0  2005-01-05\n",
       "47   Errazuriz  12000.0  2005-03-10\n",
       "48   Cambrault  11000.0  2007-10-15\n",
       "49     Zlotkey  10500.0  2008-01-29\n",
       "50      Tucker  10000.0  2005-01-30\n",
       "56        King  10000.0  2004-01-30\n",
       "62     Vishney  10500.0  2005-11-11\n",
       "68        Ozer  11500.0  2005-03-11\n",
       "69       Bloom  10000.0  2006-03-23\n",
       "74        Abel  11000.0  2004-05-11\n",
       "101  Hartstein  13000.0  2004-02-17\n",
       "104       Baer  10000.0  2002-06-07\n",
       "105    Higgins  12008.0  2002-06-07"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp.loc[emp['sal']>=10000,['name','sal','hire_date']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[문제135] 급여 10000 이상인 사원들의 이름과 급여, 입사일를 출력하세요. 급여를 기준으로 내림차순하세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "from pandas import Series,DataFrame\n",
    "\n",
    "\n",
    "emp = pd.read_csv(\"c:\\python\\emp1.csv\",\n",
    "                   names=[\"empid\",\"name\",\"job\",\"mgr\",\"hire_date\",\"sal\",\"comm\",\"deptno\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sal</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>King</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>2003-06-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kochhar</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>2005-09-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>De Haan</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>2001-01-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Russell</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>2004-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Partners</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>2005-01-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Greenberg</td>\n",
       "      <td>13208.8</td>\n",
       "      <td>2002-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Hartstein</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>2004-02-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Higgins</td>\n",
       "      <td>12008.0</td>\n",
       "      <td>2002-06-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Errazuriz</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>2005-03-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Ozer</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>2005-03-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Cambrault</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>2007-10-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Abel</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>2004-05-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Raphaely</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>2002-12-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Vishney</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>2005-11-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Zlotkey</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>2008-01-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Tucker</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>2005-01-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>King</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>2004-01-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Bloom</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>2006-03-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Baer</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>2002-06-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          name      sal   hire_date\n",
       "0         King  24000.0  2003-06-17\n",
       "1      Kochhar  17000.0  2005-09-21\n",
       "2      De Haan  17000.0  2001-01-13\n",
       "45     Russell  14000.0  2004-10-01\n",
       "46    Partners  13500.0  2005-01-05\n",
       "8    Greenberg  13208.8  2002-08-17\n",
       "101  Hartstein  13000.0  2004-02-17\n",
       "105    Higgins  12008.0  2002-06-07\n",
       "47   Errazuriz  12000.0  2005-03-10\n",
       "68        Ozer  11500.0  2005-03-11\n",
       "48   Cambrault  11000.0  2007-10-15\n",
       "74        Abel  11000.0  2004-05-11\n",
       "14    Raphaely  11000.0  2002-12-07\n",
       "62     Vishney  10500.0  2005-11-11\n",
       "49     Zlotkey  10500.0  2008-01-29\n",
       "50      Tucker  10000.0  2005-01-30\n",
       "56        King  10000.0  2004-01-30\n",
       "69       Bloom  10000.0  2006-03-23\n",
       "104       Baer  10000.0  2002-06-07"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp1 = emp.loc[emp['sal']>=10000,['name','sal','hire_date']]\n",
    "emp1.sort_values(by='sal',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#csv\n",
    "\n",
    "import  csv\n",
    "\n",
    "file = open(\"c:\\data\\emp.csv\",'r')\n",
    "emp_csv = csv.reader(file)\n",
    "emp_list = []\n",
    "for i in emp_csv:\n",
    "     if (float(i[5]) >= 10000):\n",
    "         emp_list.append(i)\n",
    "\n",
    "emp_list_sorted = sorted(emp_list, reverse=True, key=lambda x: x[5])\n",
    "\n",
    "for i in emp_list_sorted:\n",
    "    print (i[1],i[5],i[4])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[문제136] 급여를 많이 받는 순으로 10위 까지를 구하세요. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas import Series,DataFrame\n",
    "\n",
    "emp = pd.read_csv(\"c:\\python\\emp1.csv\",\n",
    "                   names=[\"empid\",\"name\",\"job\",\"mgr\",\"hire_date\",\"sal\",\"comm\",\"deptno\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>King</td>\n",
       "      <td>24000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kochhar</td>\n",
       "      <td>17000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>De Haan</td>\n",
       "      <td>17000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Russell</td>\n",
       "      <td>14000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Partners</td>\n",
       "      <td>13500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Greenberg</td>\n",
       "      <td>13208.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Hartstein</td>\n",
       "      <td>13000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Higgins</td>\n",
       "      <td>12008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Errazuriz</td>\n",
       "      <td>12000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Ozer</td>\n",
       "      <td>11500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Raphaely</td>\n",
       "      <td>11000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Cambrault</td>\n",
       "      <td>11000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Abel</td>\n",
       "      <td>11000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          name      sal\n",
       "0         King  24000.0\n",
       "1      Kochhar  17000.0\n",
       "2      De Haan  17000.0\n",
       "45     Russell  14000.0\n",
       "46    Partners  13500.0\n",
       "8    Greenberg  13208.8\n",
       "101  Hartstein  13000.0\n",
       "105    Higgins  12008.0\n",
       "47   Errazuriz  12000.0\n",
       "68        Ozer  11500.0\n",
       "14    Raphaely  11000.0\n",
       "48   Cambrault  11000.0\n",
       "74        Abel  11000.0"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp['rank'] = emp['sal'].rank(ascending=False, method = 'dense')   # 'rank' 컬럼 추가\n",
    "emp.loc[emp['rank']<=10,['name','sal']].sort_values(by='sal',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[문제137] 직업이 AD_VP, AD_PRES 인 사원들의 이름, 급여, 직업을 출력하세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sal</th>\n",
       "      <th>job</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>King</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>AD_PRES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kochhar</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>AD_VP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>De Haan</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>AD_VP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name      sal      job\n",
       "0     King  24000.0  AD_PRES\n",
       "1  Kochhar  17000.0    AD_VP\n",
       "2  De Haan  17000.0    AD_VP"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas import Series,DataFrame\n",
    "\n",
    "emp = pd.read_csv(\"c:\\python\\emp1.csv\",\n",
    "                   names=[\"empid\",\"name\",\"job\",\"mgr\",\"hire_date\",\"sal\",\"comm\",\"deptno\"])\n",
    "\n",
    "emp.loc[emp['job'].isin(['AD_VP','AD_PRES']),['name','sal','job']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[문제138] 직업이 AD_VP ,AD_PRES 아닌 사원들의 이름, 급여, 직업을 출력하세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sal</th>\n",
       "      <th>job</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hunold</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>IT_PROG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ernst</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>IT_PROG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Austin</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>IT_PROG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Pataballa</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>IT_PROG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lorentz</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>IT_PROG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Greenberg</td>\n",
       "      <td>13208.8</td>\n",
       "      <td>FI_MGR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Faviet</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>FI_ACCOUNT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Chen</td>\n",
       "      <td>8200.0</td>\n",
       "      <td>FI_ACCOUNT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Sciarra</td>\n",
       "      <td>7700.0</td>\n",
       "      <td>FI_ACCOUNT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Urman</td>\n",
       "      <td>7800.0</td>\n",
       "      <td>FI_ACCOUNT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Popp</td>\n",
       "      <td>6900.0</td>\n",
       "      <td>FI_ACCOUNT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Raphaely</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>PU_MAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Khoo</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>PU_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Baida</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>PU_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Tobias</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>PU_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Himuro</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>PU_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Colmeres</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>PU_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Weiss</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>ST_MAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Fripp</td>\n",
       "      <td>8200.0</td>\n",
       "      <td>ST_MAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Kaufling</td>\n",
       "      <td>7900.0</td>\n",
       "      <td>ST_MAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Vollman</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>ST_MAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Mourgos</td>\n",
       "      <td>5800.0</td>\n",
       "      <td>ST_MAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>yer</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Mikkilineni</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Landry</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Markle</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Bissot</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Atkinson</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Marlow</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Olson</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>ST_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Livingston</td>\n",
       "      <td>8400.0</td>\n",
       "      <td>SA_REP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Grant</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>SA_REP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Johnson</td>\n",
       "      <td>6200.0</td>\n",
       "      <td>SA_REP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Taylor</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>SH_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Fleaur</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>SH_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Sullivan</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>SH_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Geoni</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>SH_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Sarchand</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>SH_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Bull</td>\n",
       "      <td>4100.0</td>\n",
       "      <td>SH_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Dellinger</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>SH_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Cabrio</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>SH_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Chung</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>SH_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Dilly</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>SH_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Gates</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>SH_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Perkins</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>SH_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Bell</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>SH_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Everett</td>\n",
       "      <td>3900.0</td>\n",
       "      <td>SH_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>McCain</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>SH_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Jones</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>SH_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Walsh</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>SH_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Feeney</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>SH_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>OConnell</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>SH_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Grant</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>SH_CLERK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Whalen</td>\n",
       "      <td>4400.0</td>\n",
       "      <td>AD_ASST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Hartstein</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>MK_MAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Fay</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>MK_REP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Mavris</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>HR_REP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Baer</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>PR_REP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Higgins</td>\n",
       "      <td>12008.0</td>\n",
       "      <td>AC_MGR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Gietz</td>\n",
       "      <td>8300.0</td>\n",
       "      <td>AC_ACCOUNT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>104 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            name      sal         job\n",
       "3         Hunold   9000.0     IT_PROG\n",
       "4          Ernst   6000.0     IT_PROG\n",
       "5         Austin   4800.0     IT_PROG\n",
       "6      Pataballa   4800.0     IT_PROG\n",
       "7        Lorentz   4200.0     IT_PROG\n",
       "8      Greenberg  13208.8      FI_MGR\n",
       "9         Faviet   9900.0  FI_ACCOUNT\n",
       "10          Chen   8200.0  FI_ACCOUNT\n",
       "11       Sciarra   7700.0  FI_ACCOUNT\n",
       "12         Urman   7800.0  FI_ACCOUNT\n",
       "13          Popp   6900.0  FI_ACCOUNT\n",
       "14      Raphaely  11000.0      PU_MAN\n",
       "15          Khoo   3100.0    PU_CLERK\n",
       "16         Baida   2900.0    PU_CLERK\n",
       "17        Tobias   2800.0    PU_CLERK\n",
       "18        Himuro   2600.0    PU_CLERK\n",
       "19      Colmeres   2500.0    PU_CLERK\n",
       "20         Weiss   8000.0      ST_MAN\n",
       "21         Fripp   8200.0      ST_MAN\n",
       "22      Kaufling   7900.0      ST_MAN\n",
       "23       Vollman   6500.0      ST_MAN\n",
       "24       Mourgos   5800.0      ST_MAN\n",
       "25           yer   3200.0    ST_CLERK\n",
       "26   Mikkilineni   2700.0    ST_CLERK\n",
       "27        Landry   2400.0    ST_CLERK\n",
       "28        Markle   2200.0    ST_CLERK\n",
       "29        Bissot   3300.0    ST_CLERK\n",
       "30      Atkinson   2800.0    ST_CLERK\n",
       "31        Marlow   2500.0    ST_CLERK\n",
       "32         Olson   2100.0    ST_CLERK\n",
       "..           ...      ...         ...\n",
       "77    Livingston   8400.0      SA_REP\n",
       "78         Grant   7000.0      SA_REP\n",
       "79       Johnson   6200.0      SA_REP\n",
       "80        Taylor   3200.0    SH_CLERK\n",
       "81        Fleaur   3100.0    SH_CLERK\n",
       "82      Sullivan   2500.0    SH_CLERK\n",
       "83         Geoni   2800.0    SH_CLERK\n",
       "84      Sarchand   4200.0    SH_CLERK\n",
       "85          Bull   4100.0    SH_CLERK\n",
       "86     Dellinger   3400.0    SH_CLERK\n",
       "87        Cabrio   3000.0    SH_CLERK\n",
       "88         Chung   3800.0    SH_CLERK\n",
       "89         Dilly   3600.0    SH_CLERK\n",
       "90         Gates   2900.0    SH_CLERK\n",
       "91       Perkins   2500.0    SH_CLERK\n",
       "92          Bell   4000.0    SH_CLERK\n",
       "93       Everett   3900.0    SH_CLERK\n",
       "94        McCain   3200.0    SH_CLERK\n",
       "95         Jones   2800.0    SH_CLERK\n",
       "96         Walsh   3100.0    SH_CLERK\n",
       "97        Feeney   3000.0    SH_CLERK\n",
       "98      OConnell   2600.0    SH_CLERK\n",
       "99         Grant   2600.0    SH_CLERK\n",
       "100       Whalen   4400.0     AD_ASST\n",
       "101    Hartstein  13000.0      MK_MAN\n",
       "102          Fay   6000.0      MK_REP\n",
       "103       Mavris   6500.0      HR_REP\n",
       "104         Baer  10000.0      PR_REP\n",
       "105      Higgins  12008.0      AC_MGR\n",
       "106        Gietz   8300.0  AC_ACCOUNT\n",
       "\n",
       "[104 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas import Series,DataFrame\n",
    "\n",
    "emp = pd.read_csv(\"c:\\python\\emp1.csv\",\n",
    "                 names=[\"empid\",\"name\",\"job\",\"mgr\",\"hire_date\",\"sal\",\"comm\",\"deptno\"])\n",
    "\n",
    "emp.loc[~ emp['job'].isin(['AD_VP','AD_PRES']),['name','sal','job']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ㅁ null 값 확인하기   \n",
    "### null은 None으로 또는 numpy모듈의 np.nan 으로 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    3.0\n",
       "3    NaN\n",
       "4    5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series,DataFrame\n",
    "\n",
    "obj = Series([1,2,3,None,5])\n",
    "obj = Series([1,2,3,np.nan,5])\n",
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3     True\n",
       "4    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3     True\n",
       "4    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3   NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#값 확인\n",
    "obj[obj.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3   NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[pd.isnull(obj)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    3.0\n",
       "3    0.0\n",
       "4    5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#na인 값을 채워주는 메소드\n",
    "obj.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>1</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "     0    1    2\n",
       "0  1.0  2.0  3.0\n",
       "1  1.0  NaN  NaN\n",
       "2  NaN  NaN  NaN\n",
       "3  NaN  2.0  3.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = DataFrame([[1,2,3],[1,None,None],[None,None,None],[None,2,3]])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
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       "</div>"
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      "text/plain": [
       "     0    1    2\n",
       "0  1.0  2.0  3.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna()  # NaN 이 하나라도 있으면 제외"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "     0    1    2\n",
       "0  1.0  2.0  3.0\n",
       "1  1.0  NaN  NaN\n",
       "3  NaN  2.0  3.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(how='all') # 행의 모든값이 NaN이면 drop "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "     0    1    2     4\n",
       "0  1.0  2.0  3.0  None\n",
       "1  1.0  NaN  NaN  None\n",
       "2  NaN  NaN  NaN  None\n",
       "3  NaN  2.0  3.0  None"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df[4] = None\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 26,
     "metadata": {},
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   "source": [
    "df.dropna(how='all',axis = 1) #컬럼의 모든 값이 NaN으로 되어있는 컬럼만 제외"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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       "     0    1    2  4\n",
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       "2  0.0  0.0  0.0  0\n",
       "3  0.0  2.0  3.0  0"
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   "source": [
    "df.fillna(0)    #모든 na값은 0으로 채워짐"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    1.0\n",
       "2    0.0\n",
       "3    0.0\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#특정 열에만 널값 입력\n",
    "df[0].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "# 각각의 지정된 열에 na값 수정\n",
    "df.fillna({0:0,1:1,4:4})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "# null 값 체워주면서 기존 값을 변경하는 메소드 : inplace=True\n",
    "df.fillna(0,inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 35,
     "metadata": {},
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   ],
   "source": [
    "df = DataFrame([[1,2,5],[None,None,4],[3,2,None],[2,None,3]])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "     0    1    2\n",
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     "execution_count": 36,
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   "source": [
    "df.fillna(method='ffill')  #앞에 값으로 na를 채워줌"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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       "     0    1    2\n",
       "0  1.0  2.0  5.0\n",
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       "2  3.0  2.0  3.0\n",
       "3  2.0  NaN  3.0"
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     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.fillna(method='bfill')  #뒤에 값으로 na를 채워줌"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[문제139]커미션이 null 인 사원들의 이름과 커미션을 출력하세요 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>Lorentz</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>8</th>\n",
       "      <td>Greenberg</td>\n",
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       "      <th>9</th>\n",
       "      <td>Faviet</td>\n",
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       "      <th>10</th>\n",
       "      <td>Chen</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>11</th>\n",
       "      <td>Sciarra</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>12</th>\n",
       "      <td>Urman</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Popp</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Raphaely</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Khoo</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Baida</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Tobias</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Himuro</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Colmeres</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Weiss</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Fripp</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Kaufling</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Vollman</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Mourgos</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>yer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Mikkilineni</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Landry</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Markle</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Bissot</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Davies</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Matos</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Vargas</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Taylor</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Fleaur</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Sullivan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Geoni</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Sarchand</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Bull</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Dellinger</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Cabrio</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Chung</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Dilly</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Gates</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Perkins</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Bell</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Everett</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>McCain</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Jones</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Walsh</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Feeney</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>OConnell</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Grant</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Whalen</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Hartstein</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Fay</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Mavris</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Baer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Higgins</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Gietz</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            name  comm\n",
       "0           King   NaN\n",
       "1        Kochhar   NaN\n",
       "2        De Haan   NaN\n",
       "3         Hunold   NaN\n",
       "4          Ernst   NaN\n",
       "5         Austin   NaN\n",
       "6      Pataballa   NaN\n",
       "7        Lorentz   NaN\n",
       "8      Greenberg   NaN\n",
       "9         Faviet   NaN\n",
       "10          Chen   NaN\n",
       "11       Sciarra   NaN\n",
       "12         Urman   NaN\n",
       "13          Popp   NaN\n",
       "14      Raphaely   NaN\n",
       "15          Khoo   NaN\n",
       "16         Baida   NaN\n",
       "17        Tobias   NaN\n",
       "18        Himuro   NaN\n",
       "19      Colmeres   NaN\n",
       "20         Weiss   NaN\n",
       "21         Fripp   NaN\n",
       "22      Kaufling   NaN\n",
       "23       Vollman   NaN\n",
       "24       Mourgos   NaN\n",
       "25           yer   NaN\n",
       "26   Mikkilineni   NaN\n",
       "27        Landry   NaN\n",
       "28        Markle   NaN\n",
       "29        Bissot   NaN\n",
       "..           ...   ...\n",
       "42        Davies   NaN\n",
       "43         Matos   NaN\n",
       "44        Vargas   NaN\n",
       "80        Taylor   NaN\n",
       "81        Fleaur   NaN\n",
       "82      Sullivan   NaN\n",
       "83         Geoni   NaN\n",
       "84      Sarchand   NaN\n",
       "85          Bull   NaN\n",
       "86     Dellinger   NaN\n",
       "87        Cabrio   NaN\n",
       "88         Chung   NaN\n",
       "89         Dilly   NaN\n",
       "90         Gates   NaN\n",
       "91       Perkins   NaN\n",
       "92          Bell   NaN\n",
       "93       Everett   NaN\n",
       "94        McCain   NaN\n",
       "95         Jones   NaN\n",
       "96         Walsh   NaN\n",
       "97        Feeney   NaN\n",
       "98      OConnell   NaN\n",
       "99         Grant   NaN\n",
       "100       Whalen   NaN\n",
       "101    Hartstein   NaN\n",
       "102          Fay   NaN\n",
       "103       Mavris   NaN\n",
       "104         Baer   NaN\n",
       "105      Higgins   NaN\n",
       "106        Gietz   NaN\n",
       "\n",
       "[72 rows x 2 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas import Series,DataFrame\n",
    "\n",
    "emp = pd.read_csv(\"c:\\python\\emp1.csv\",\n",
    "                 names=[\"empid\",\"name\",\"job\",\"mgr\",\"hire_date\",\"sal\",\"comm\",\"deptno\"])\n",
    "\n",
    "emp.loc[emp['comm'].isnull(),['name','comm']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[문제140] 커미션이 null이 아닌 사원들의 이름과 커미션을 출력하세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>comm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Russell</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Partners</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Errazuriz</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Cambrault</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Zlotkey</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Tucker</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Bernstein</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Hall</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Olsen</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Cambrault</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Tuvault</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>King</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Sully</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>McEwen</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Smith</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Doran</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Sewall</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Vishney</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Greene</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Marvins</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Lee</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Ande</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Banda</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Ozer</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Bloom</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Fox</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Smith</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Bates</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Kumar</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Abel</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Hutton</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Taylor</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Livingston</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Grant</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Johnson</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          name  comm\n",
       "45     Russell  0.40\n",
       "46    Partners  0.30\n",
       "47   Errazuriz  0.30\n",
       "48   Cambrault  0.30\n",
       "49     Zlotkey  0.20\n",
       "50      Tucker  0.30\n",
       "51   Bernstein  0.25\n",
       "52        Hall  0.25\n",
       "53       Olsen  0.20\n",
       "54   Cambrault  0.20\n",
       "55     Tuvault  0.15\n",
       "56        King  0.35\n",
       "57       Sully  0.35\n",
       "58      McEwen  0.35\n",
       "59       Smith  0.30\n",
       "60       Doran  0.30\n",
       "61      Sewall  0.25\n",
       "62     Vishney  0.25\n",
       "63      Greene  0.15\n",
       "64     Marvins  0.10\n",
       "65         Lee  0.10\n",
       "66        Ande  0.10\n",
       "67       Banda  0.10\n",
       "68        Ozer  0.25\n",
       "69       Bloom  0.20\n",
       "70         Fox  0.20\n",
       "71       Smith  0.15\n",
       "72       Bates  0.15\n",
       "73       Kumar  0.10\n",
       "74        Abel  0.30\n",
       "75      Hutton  0.25\n",
       "76      Taylor  0.20\n",
       "77  Livingston  0.20\n",
       "78       Grant  0.15\n",
       "79     Johnson  0.10"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp = pd.read_csv(\"c:\\python\\emp1.csv\",\n",
    "                names=[\"empid\",\"name\",\"job\",\"mgr\",\"hire_date\",\"sal\",\"comm\",\"deptno\"])\n",
    "\n",
    "emp.loc[~ emp['comm'].isnull(), ['name','comm']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ㅁapply : 행,열값을 인수값으로 받아서 반복하여 그함수를 적용한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = Series([1,2,3])\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    4\n",
       "2    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def square(x):\n",
    "    return x**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "square(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#apply 함수는 행,열값을 인수값으로 받아서 반복하여 그함수를 적용한다.\n",
    "\t#apply함수는 인수값으로 함수가 들어가야 한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    4\n",
       "2    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.apply(square)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    4\n",
       "2    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#함수대신 람다를 사용\n",
    "s1.apply(lambda x : x**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
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    "df=DataFrame([[1,2,3],[4,5,6]])\n",
    "df"
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   "source": [
    "df.apply(square)"
   ]
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   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
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       "0     1\n",
       "1    16\n",
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     "execution_count": 80,
     "metadata": {},
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   "source": [
    "df[0].apply(square)"
   ]
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   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 81,
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   "source": [
    "df.apply(square,axis=0) # 0:각 컬럼이 함수에 적용, 1:각 row가 함수에 적용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#axis method 사용의 예"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np   \t#np.sum : numpy에서 제공하는 sum함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5\n",
       "1    7\n",
       "2    9\n",
       "dtype: int64"
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     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    " df.apply(np.sum, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     6\n",
       "1    15\n",
       "dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df.apply(np.sum, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "df.apply(lambda x: x**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1    16\n",
       "Name: 0, dtype: int64"
      ]
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     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0].apply(lambda x: x**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[문제141] 이름 첫글자가 S 로 시작하는 사원들의 이름을 출력하세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp = pd.read_csv(\"c:\\python\\emp1.csv\",\n",
    "                names=[\"empid\",\"name\",\"job\",\"mgr\",\"hire_date\",\"sal\",\"comm\",\"deptno\"])\n",
    "#함수 사용\n",
    "def ini(arg):\n",
    "    return arg[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>name</th>\n",
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       "      <td>Sciarra</td>\n",
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       "      <td>Seo</td>\n",
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       "      <td>Sully</td>\n",
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       "      <th>59</th>\n",
       "      <td>Smith</td>\n",
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       "      <th>61</th>\n",
       "      <td>Sewall</td>\n",
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       "      <th>71</th>\n",
       "      <td>Smith</td>\n",
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       "      <td>Sullivan</td>\n",
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       "      <td>Sarchand</td>\n",
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      ],
      "text/plain": [
       "        name\n",
       "11   Sciarra\n",
       "38    Stiles\n",
       "39       Seo\n",
       "57     Sully\n",
       "59     Smith\n",
       "61    Sewall\n",
       "71     Smith\n",
       "82  Sullivan\n",
       "84  Sarchand"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp.loc[emp['name'].apply(ini)=='S',['name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Sciarra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Stiles</td>\n",
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       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Seo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Sully</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Smith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Sewall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Smith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Sullivan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Sarchand</td>\n",
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      ],
      "text/plain": [
       "        name\n",
       "11   Sciarra\n",
       "38    Stiles\n",
       "39       Seo\n",
       "57     Sully\n",
       "59     Smith\n",
       "61    Sewall\n",
       "71     Smith\n",
       "82  Sullivan\n",
       "84  Sarchand"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lambda 사용\n",
    "emp.loc[emp['name'].apply(lambda x: x[0])=='S',['name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#[선생님]\n",
    "def first_character(w):\n",
    "\tif w[0] == 'S':\n",
    "\t\treturn True\n",
    "\treturn False\n",
    "\n",
    "print(emp[['name','sal']][emp['name'].apply(first_character)])\n",
    "print(emp.loc[emp['name'].apply(first_character),['name','sal']])\n",
    "print(emp.ix[emp['name'].apply(first_character),['name','sal']])\n",
    "\n",
    "\n",
    "#lambda사용\n",
    "\n",
    "print(emp.ix[emp['name'].apply(lambda x : x[0]=='S'),['name','sal']])\n",
    "print(emp.loc[emp['name'].apply(lambda x : x[0]=='S'),['name','sal']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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