lecture_13(astype,unique,groupby).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[문제150]부서번호,급여를 기준으로 내림차순 정렬해서 아래 화면처럼 컬럼정보를 출력하세요.\n",
    "\n",
    "     deptno  empid         name      sal\n",
    "105   110.0    205      Higgins  12008.0\n",
    "106   110.0    206        Gietz   8300.0\n",
    "8     100.0    108    Greenberg  13208.8\n",
    "9     100.0    109       Faviet   9900.0\n",
    "10    100.0    110         Chen   8200.0\n",
    "12    100.0    112        Urman   7800.0\n",
    "11    100.0    111      Sciarra   7700.0\n",
    "13    100.0    113         Popp   6900.0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series,DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp = pd.read_csv(\"c:\\python\\emp1.csv\",\n",
    "                 names=['empid','name','job','mgr','hire_date','sal','comm','deptno'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>deptno</th>\n",
       "      <th>empid</th>\n",
       "      <th>name</th>\n",
       "      <th>sal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>510.0</td>\n",
       "      <td>205</td>\n",
       "      <td>Higgins</td>\n",
       "      <td>12008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>110.0</td>\n",
       "      <td>206</td>\n",
       "      <td>Gietz</td>\n",
       "      <td>8300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100.0</td>\n",
       "      <td>108</td>\n",
       "      <td>Greenberg</td>\n",
       "      <td>13208.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>100.0</td>\n",
       "      <td>109</td>\n",
       "      <td>Faviet</td>\n",
       "      <td>9900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>100.0</td>\n",
       "      <td>110</td>\n",
       "      <td>Chen</td>\n",
       "      <td>8200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>100.0</td>\n",
       "      <td>112</td>\n",
       "      <td>Urman</td>\n",
       "      <td>7800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100.0</td>\n",
       "      <td>111</td>\n",
       "      <td>Sciarra</td>\n",
       "      <td>7700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>100.0</td>\n",
       "      <td>113</td>\n",
       "      <td>Popp</td>\n",
       "      <td>6900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90.0</td>\n",
       "      <td>100</td>\n",
       "      <td>King</td>\n",
       "      <td>24000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90.0</td>\n",
       "      <td>101</td>\n",
       "      <td>Kochhar</td>\n",
       "      <td>17000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90.0</td>\n",
       "      <td>102</td>\n",
       "      <td>De Haan</td>\n",
       "      <td>17000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>80.0</td>\n",
       "      <td>145</td>\n",
       "      <td>Russell</td>\n",
       "      <td>14000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>80.0</td>\n",
       "      <td>146</td>\n",
       "      <td>Partners</td>\n",
       "      <td>13500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>80.0</td>\n",
       "      <td>147</td>\n",
       "      <td>Errazuriz</td>\n",
       "      <td>12000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>80.0</td>\n",
       "      <td>168</td>\n",
       "      <td>Ozer</td>\n",
       "      <td>11500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>80.0</td>\n",
       "      <td>148</td>\n",
       "      <td>Cambrault</td>\n",
       "      <td>11000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>80.0</td>\n",
       "      <td>174</td>\n",
       "      <td>Abel</td>\n",
       "      <td>11000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>80.0</td>\n",
       "      <td>149</td>\n",
       "      <td>Zlotkey</td>\n",
       "      <td>10500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>80.0</td>\n",
       "      <td>162</td>\n",
       "      <td>Vishney</td>\n",
       "      <td>10500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>80.0</td>\n",
       "      <td>150</td>\n",
       "      <td>Tucker</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>80.0</td>\n",
       "      <td>156</td>\n",
       "      <td>King</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>80.0</td>\n",
       "      <td>169</td>\n",
       "      <td>Bloom</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>80.0</td>\n",
       "      <td>170</td>\n",
       "      <td>Fox</td>\n",
       "      <td>9600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>80.0</td>\n",
       "      <td>151</td>\n",
       "      <td>Bernstein</td>\n",
       "      <td>9500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>80.0</td>\n",
       "      <td>157</td>\n",
       "      <td>Sully</td>\n",
       "      <td>9500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>80.0</td>\n",
       "      <td>163</td>\n",
       "      <td>Greene</td>\n",
       "      <td>9500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>80.0</td>\n",
       "      <td>152</td>\n",
       "      <td>Hall</td>\n",
       "      <td>9000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>80.0</td>\n",
       "      <td>158</td>\n",
       "      <td>McEwen</td>\n",
       "      <td>9000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>80.0</td>\n",
       "      <td>175</td>\n",
       "      <td>Hutton</td>\n",
       "      <td>8800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>80.0</td>\n",
       "      <td>176</td>\n",
       "      <td>Taylor</td>\n",
       "      <td>8600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>50.0</td>\n",
       "      <td>190</td>\n",
       "      <td>Gates</td>\n",
       "      <td>2900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>50.0</td>\n",
       "      <td>130</td>\n",
       "      <td>Atkinson</td>\n",
       "      <td>2800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>50.0</td>\n",
       "      <td>183</td>\n",
       "      <td>Geoni</td>\n",
       "      <td>2800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>50.0</td>\n",
       "      <td>195</td>\n",
       "      <td>Jones</td>\n",
       "      <td>2800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>50.0</td>\n",
       "      <td>126</td>\n",
       "      <td>Mikkilineni</td>\n",
       "      <td>2700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>50.0</td>\n",
       "      <td>139</td>\n",
       "      <td>Seo</td>\n",
       "      <td>2700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>50.0</td>\n",
       "      <td>143</td>\n",
       "      <td>Matos</td>\n",
       "      <td>2600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>50.0</td>\n",
       "      <td>198</td>\n",
       "      <td>OConnell</td>\n",
       "      <td>2600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>50.0</td>\n",
       "      <td>199</td>\n",
       "      <td>Grant</td>\n",
       "      <td>2600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>50.0</td>\n",
       "      <td>131</td>\n",
       "      <td>Marlow</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>50.0</td>\n",
       "      <td>140</td>\n",
       "      <td>Patel</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>50.0</td>\n",
       "      <td>144</td>\n",
       "      <td>Vargas</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>50.0</td>\n",
       "      <td>182</td>\n",
       "      <td>Sullivan</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>50.0</td>\n",
       "      <td>191</td>\n",
       "      <td>Perkins</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>50.0</td>\n",
       "      <td>127</td>\n",
       "      <td>Landry</td>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>50.0</td>\n",
       "      <td>135</td>\n",
       "      <td>Gee</td>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>50.0</td>\n",
       "      <td>128</td>\n",
       "      <td>Markle</td>\n",
       "      <td>2200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>50.0</td>\n",
       "      <td>136</td>\n",
       "      <td>Philtanker</td>\n",
       "      <td>2200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>50.0</td>\n",
       "      <td>132</td>\n",
       "      <td>Olson</td>\n",
       "      <td>2100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>40.0</td>\n",
       "      <td>203</td>\n",
       "      <td>Mavris</td>\n",
       "      <td>6500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>30.0</td>\n",
       "      <td>114</td>\n",
       "      <td>Raphaely</td>\n",
       "      <td>11000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>30.0</td>\n",
       "      <td>115</td>\n",
       "      <td>Khoo</td>\n",
       "      <td>3100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>30.0</td>\n",
       "      <td>116</td>\n",
       "      <td>Baida</td>\n",
       "      <td>2900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>30.0</td>\n",
       "      <td>117</td>\n",
       "      <td>Tobias</td>\n",
       "      <td>2800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>30.0</td>\n",
       "      <td>118</td>\n",
       "      <td>Himuro</td>\n",
       "      <td>2600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>30.0</td>\n",
       "      <td>119</td>\n",
       "      <td>Colmeres</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>20.0</td>\n",
       "      <td>201</td>\n",
       "      <td>Hartstein</td>\n",
       "      <td>13000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>20.0</td>\n",
       "      <td>202</td>\n",
       "      <td>Fay</td>\n",
       "      <td>6000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>10.0</td>\n",
       "      <td>200</td>\n",
       "      <td>Whalen</td>\n",
       "      <td>4400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>NaN</td>\n",
       "      <td>178</td>\n",
       "      <td>Grant</td>\n",
       "      <td>7000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     deptno  empid         name      sal\n",
       "105   510.0    205      Higgins  12008.0\n",
       "106   110.0    206        Gietz   8300.0\n",
       "8     100.0    108    Greenberg  13208.8\n",
       "9     100.0    109       Faviet   9900.0\n",
       "10    100.0    110         Chen   8200.0\n",
       "12    100.0    112        Urman   7800.0\n",
       "11    100.0    111      Sciarra   7700.0\n",
       "13    100.0    113         Popp   6900.0\n",
       "0      90.0    100         King  24000.0\n",
       "1      90.0    101      Kochhar  17000.0\n",
       "2      90.0    102      De Haan  17000.0\n",
       "45     80.0    145      Russell  14000.0\n",
       "46     80.0    146     Partners  13500.0\n",
       "47     80.0    147    Errazuriz  12000.0\n",
       "68     80.0    168         Ozer  11500.0\n",
       "48     80.0    148    Cambrault  11000.0\n",
       "74     80.0    174         Abel  11000.0\n",
       "49     80.0    149      Zlotkey  10500.0\n",
       "62     80.0    162      Vishney  10500.0\n",
       "50     80.0    150       Tucker  10000.0\n",
       "56     80.0    156         King  10000.0\n",
       "69     80.0    169        Bloom  10000.0\n",
       "70     80.0    170          Fox   9600.0\n",
       "51     80.0    151    Bernstein   9500.0\n",
       "57     80.0    157        Sully   9500.0\n",
       "63     80.0    163       Greene   9500.0\n",
       "52     80.0    152         Hall   9000.0\n",
       "58     80.0    158       McEwen   9000.0\n",
       "75     80.0    175       Hutton   8800.0\n",
       "76     80.0    176       Taylor   8600.0\n",
       "..      ...    ...          ...      ...\n",
       "90     50.0    190        Gates   2900.0\n",
       "30     50.0    130     Atkinson   2800.0\n",
       "83     50.0    183        Geoni   2800.0\n",
       "95     50.0    195        Jones   2800.0\n",
       "26     50.0    126  Mikkilineni   2700.0\n",
       "39     50.0    139          Seo   2700.0\n",
       "43     50.0    143        Matos   2600.0\n",
       "98     50.0    198     OConnell   2600.0\n",
       "99     50.0    199        Grant   2600.0\n",
       "31     50.0    131       Marlow   2500.0\n",
       "40     50.0    140        Patel   2500.0\n",
       "44     50.0    144       Vargas   2500.0\n",
       "82     50.0    182     Sullivan   2500.0\n",
       "91     50.0    191      Perkins   2500.0\n",
       "27     50.0    127       Landry   2400.0\n",
       "35     50.0    135          Gee   2400.0\n",
       "28     50.0    128       Markle   2200.0\n",
       "36     50.0    136   Philtanker   2200.0\n",
       "32     50.0    132        Olson   2100.0\n",
       "103    40.0    203       Mavris   6500.0\n",
       "14     30.0    114     Raphaely  11000.0\n",
       "15     30.0    115         Khoo   3100.0\n",
       "16     30.0    116        Baida   2900.0\n",
       "17     30.0    117       Tobias   2800.0\n",
       "18     30.0    118       Himuro   2600.0\n",
       "19     30.0    119     Colmeres   2500.0\n",
       "101    20.0    201    Hartstein  13000.0\n",
       "102    20.0    202          Fay   6000.0\n",
       "100    10.0    200       Whalen   4400.0\n",
       "78      NaN    178        Grant   7000.0\n",
       "\n",
       "[107 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp[['deptno','empid','name','sal']].sort_values(by = ['deptno','sal'], ascending= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[문제151] index 번호 0부터 50까지 부서번호, 급여를 기준으로 내림차순 정렬한 후 아래결과처럼 출력하세요.\n",
    "\n",
    "\n",
    "    deptno  empid         name      sal\n",
    "8    100.0    108    Greenberg  13208.8\n",
    "9    100.0    109       Faviet   9900.0\n",
    "10   100.0    110         Chen   8200.0\n",
    "12   100.0    112        Urman   7800.0\n",
    "11   100.0    111      Sciarra   7700.0\n",
    "13   100.0    113         Popp   6900.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp =  pd.read_csv(\"c:\\python\\emp1.csv\",\n",
    "                  names = ['empid','name','job','mgr','hire_date','sal','comm','deptno'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>deptno</th>\n",
       "      <th>empid</th>\n",
       "      <th>name</th>\n",
       "      <th>sal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100.0</td>\n",
       "      <td>108</td>\n",
       "      <td>Greenberg</td>\n",
       "      <td>13208.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>100.0</td>\n",
       "      <td>109</td>\n",
       "      <td>Faviet</td>\n",
       "      <td>9900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>100.0</td>\n",
       "      <td>110</td>\n",
       "      <td>Chen</td>\n",
       "      <td>8200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>100.0</td>\n",
       "      <td>112</td>\n",
       "      <td>Urman</td>\n",
       "      <td>7800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100.0</td>\n",
       "      <td>111</td>\n",
       "      <td>Sciarra</td>\n",
       "      <td>7700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>100.0</td>\n",
       "      <td>113</td>\n",
       "      <td>Popp</td>\n",
       "      <td>6900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90.0</td>\n",
       "      <td>100</td>\n",
       "      <td>King</td>\n",
       "      <td>24000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90.0</td>\n",
       "      <td>101</td>\n",
       "      <td>Kochhar</td>\n",
       "      <td>17000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90.0</td>\n",
       "      <td>102</td>\n",
       "      <td>De Haan</td>\n",
       "      <td>17000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>80.0</td>\n",
       "      <td>145</td>\n",
       "      <td>Russell</td>\n",
       "      <td>14000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>80.0</td>\n",
       "      <td>146</td>\n",
       "      <td>Partners</td>\n",
       "      <td>13500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>80.0</td>\n",
       "      <td>147</td>\n",
       "      <td>Errazuriz</td>\n",
       "      <td>12000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>80.0</td>\n",
       "      <td>148</td>\n",
       "      <td>Cambrault</td>\n",
       "      <td>11000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>80.0</td>\n",
       "      <td>149</td>\n",
       "      <td>Zlotkey</td>\n",
       "      <td>10500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>80.0</td>\n",
       "      <td>150</td>\n",
       "      <td>Tucker</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60.0</td>\n",
       "      <td>103</td>\n",
       "      <td>Hunold</td>\n",
       "      <td>9000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60.0</td>\n",
       "      <td>104</td>\n",
       "      <td>Ernst</td>\n",
       "      <td>6000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60.0</td>\n",
       "      <td>105</td>\n",
       "      <td>Austin</td>\n",
       "      <td>4800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>60.0</td>\n",
       "      <td>106</td>\n",
       "      <td>Pataballa</td>\n",
       "      <td>4800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>60.0</td>\n",
       "      <td>107</td>\n",
       "      <td>Lorentz</td>\n",
       "      <td>4200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>50.0</td>\n",
       "      <td>121</td>\n",
       "      <td>Fripp</td>\n",
       "      <td>8200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>50.0</td>\n",
       "      <td>120</td>\n",
       "      <td>Weiss</td>\n",
       "      <td>8000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>50.0</td>\n",
       "      <td>122</td>\n",
       "      <td>Kaufling</td>\n",
       "      <td>7900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>50.0</td>\n",
       "      <td>123</td>\n",
       "      <td>Vollman</td>\n",
       "      <td>6500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>50.0</td>\n",
       "      <td>124</td>\n",
       "      <td>Mourgos</td>\n",
       "      <td>5800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>50.0</td>\n",
       "      <td>137</td>\n",
       "      <td>Ladwig</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>50.0</td>\n",
       "      <td>141</td>\n",
       "      <td>Rajs</td>\n",
       "      <td>3500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>50.0</td>\n",
       "      <td>129</td>\n",
       "      <td>Bissot</td>\n",
       "      <td>3300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>50.0</td>\n",
       "      <td>133</td>\n",
       "      <td>Mallin</td>\n",
       "      <td>3300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>50.0</td>\n",
       "      <td>125</td>\n",
       "      <td>yer</td>\n",
       "      <td>3200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>50.0</td>\n",
       "      <td>138</td>\n",
       "      <td>Stiles</td>\n",
       "      <td>3200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>50.0</td>\n",
       "      <td>142</td>\n",
       "      <td>Davies</td>\n",
       "      <td>3100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>50.0</td>\n",
       "      <td>134</td>\n",
       "      <td>Rogers</td>\n",
       "      <td>2900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>50.0</td>\n",
       "      <td>130</td>\n",
       "      <td>Atkinson</td>\n",
       "      <td>2800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>50.0</td>\n",
       "      <td>126</td>\n",
       "      <td>Mikkilineni</td>\n",
       "      <td>2700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>50.0</td>\n",
       "      <td>139</td>\n",
       "      <td>Seo</td>\n",
       "      <td>2700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>50.0</td>\n",
       "      <td>143</td>\n",
       "      <td>Matos</td>\n",
       "      <td>2600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>50.0</td>\n",
       "      <td>131</td>\n",
       "      <td>Marlow</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>50.0</td>\n",
       "      <td>140</td>\n",
       "      <td>Patel</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>50.0</td>\n",
       "      <td>144</td>\n",
       "      <td>Vargas</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>50.0</td>\n",
       "      <td>127</td>\n",
       "      <td>Landry</td>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>50.0</td>\n",
       "      <td>135</td>\n",
       "      <td>Gee</td>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>50.0</td>\n",
       "      <td>128</td>\n",
       "      <td>Markle</td>\n",
       "      <td>2200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>50.0</td>\n",
       "      <td>136</td>\n",
       "      <td>Philtanker</td>\n",
       "      <td>2200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>50.0</td>\n",
       "      <td>132</td>\n",
       "      <td>Olson</td>\n",
       "      <td>2100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>30.0</td>\n",
       "      <td>114</td>\n",
       "      <td>Raphaely</td>\n",
       "      <td>11000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>30.0</td>\n",
       "      <td>115</td>\n",
       "      <td>Khoo</td>\n",
       "      <td>3100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>30.0</td>\n",
       "      <td>116</td>\n",
       "      <td>Baida</td>\n",
       "      <td>2900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>30.0</td>\n",
       "      <td>117</td>\n",
       "      <td>Tobias</td>\n",
       "      <td>2800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>30.0</td>\n",
       "      <td>118</td>\n",
       "      <td>Himuro</td>\n",
       "      <td>2600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>30.0</td>\n",
       "      <td>119</td>\n",
       "      <td>Colmeres</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    deptno  empid         name      sal\n",
       "8    100.0    108    Greenberg  13208.8\n",
       "9    100.0    109       Faviet   9900.0\n",
       "10   100.0    110         Chen   8200.0\n",
       "12   100.0    112        Urman   7800.0\n",
       "11   100.0    111      Sciarra   7700.0\n",
       "13   100.0    113         Popp   6900.0\n",
       "0     90.0    100         King  24000.0\n",
       "1     90.0    101      Kochhar  17000.0\n",
       "2     90.0    102      De Haan  17000.0\n",
       "45    80.0    145      Russell  14000.0\n",
       "46    80.0    146     Partners  13500.0\n",
       "47    80.0    147    Errazuriz  12000.0\n",
       "48    80.0    148    Cambrault  11000.0\n",
       "49    80.0    149      Zlotkey  10500.0\n",
       "50    80.0    150       Tucker  10000.0\n",
       "3     60.0    103       Hunold   9000.0\n",
       "4     60.0    104        Ernst   6000.0\n",
       "5     60.0    105       Austin   4800.0\n",
       "6     60.0    106    Pataballa   4800.0\n",
       "7     60.0    107      Lorentz   4200.0\n",
       "21    50.0    121        Fripp   8200.0\n",
       "20    50.0    120        Weiss   8000.0\n",
       "22    50.0    122     Kaufling   7900.0\n",
       "23    50.0    123      Vollman   6500.0\n",
       "24    50.0    124      Mourgos   5800.0\n",
       "37    50.0    137       Ladwig   3600.0\n",
       "41    50.0    141         Rajs   3500.0\n",
       "29    50.0    129       Bissot   3300.0\n",
       "33    50.0    133       Mallin   3300.0\n",
       "25    50.0    125          yer   3200.0\n",
       "38    50.0    138       Stiles   3200.0\n",
       "42    50.0    142       Davies   3100.0\n",
       "34    50.0    134       Rogers   2900.0\n",
       "30    50.0    130     Atkinson   2800.0\n",
       "26    50.0    126  Mikkilineni   2700.0\n",
       "39    50.0    139          Seo   2700.0\n",
       "43    50.0    143        Matos   2600.0\n",
       "31    50.0    131       Marlow   2500.0\n",
       "40    50.0    140        Patel   2500.0\n",
       "44    50.0    144       Vargas   2500.0\n",
       "27    50.0    127       Landry   2400.0\n",
       "35    50.0    135          Gee   2400.0\n",
       "28    50.0    128       Markle   2200.0\n",
       "36    50.0    136   Philtanker   2200.0\n",
       "32    50.0    132        Olson   2100.0\n",
       "14    30.0    114     Raphaely  11000.0\n",
       "15    30.0    115         Khoo   3100.0\n",
       "16    30.0    116        Baida   2900.0\n",
       "17    30.0    117       Tobias   2800.0\n",
       "18    30.0    118       Himuro   2600.0\n",
       "19    30.0    119     Colmeres   2500.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp[['deptno','empid','name','sal']][0:51].sort_values(by=['deptno','sal'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[문제152] 50번 부서 사원들의 정보를 급여를 기준으로 내림차순 정렬해서 해서 아래 화면처럼 컬럼정보를 출력하세요.\n",
    "\n",
    "   empid         name  deptno     sal\n",
    "21    121        Fripp    50.0  8200.0\n",
    "20    120        Weiss    50.0  8000.0\n",
    "22    122     Kaufling    50.0  7900.0\n",
    "23    123      Vollman    50.0  6500.0\n",
    "24    124      Mourgos    50.0  5800.0\n",
    "84    184     Sarchand    50.0  4200.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp = pd.read_csv(\"c:\\python\\emp1.csv\",\n",
    "                  names = ['empid','name','job','mgr','hire_date','sal','comm','deptno'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>empid</th>\n",
       "      <th>name</th>\n",
       "      <th>deptno</th>\n",
       "      <th>sal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>121</td>\n",
       "      <td>Fripp</td>\n",
       "      <td>50.0</td>\n",
       "      <td>8200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>120</td>\n",
       "      <td>Weiss</td>\n",
       "      <td>50.0</td>\n",
       "      <td>8000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>122</td>\n",
       "      <td>Kaufling</td>\n",
       "      <td>50.0</td>\n",
       "      <td>7900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>123</td>\n",
       "      <td>Vollman</td>\n",
       "      <td>50.0</td>\n",
       "      <td>6500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>124</td>\n",
       "      <td>Mourgos</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>184</td>\n",
       "      <td>Sarchand</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>185</td>\n",
       "      <td>Bull</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>192</td>\n",
       "      <td>Bell</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>193</td>\n",
       "      <td>Everett</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>188</td>\n",
       "      <td>Chung</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>137</td>\n",
       "      <td>Ladwig</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>189</td>\n",
       "      <td>Dilly</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>141</td>\n",
       "      <td>Rajs</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>186</td>\n",
       "      <td>Dellinger</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>129</td>\n",
       "      <td>Bissot</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>133</td>\n",
       "      <td>Mallin</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>194</td>\n",
       "      <td>McCain</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>125</td>\n",
       "      <td>yer</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>138</td>\n",
       "      <td>Stiles</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>180</td>\n",
       "      <td>Taylor</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>196</td>\n",
       "      <td>Walsh</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>181</td>\n",
       "      <td>Fleaur</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>142</td>\n",
       "      <td>Davies</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>197</td>\n",
       "      <td>Feeney</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>187</td>\n",
       "      <td>Cabrio</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>190</td>\n",
       "      <td>Gates</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>134</td>\n",
       "      <td>Rogers</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>183</td>\n",
       "      <td>Geoni</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>195</td>\n",
       "      <td>Jones</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>130</td>\n",
       "      <td>Atkinson</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>139</td>\n",
       "      <td>Seo</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>126</td>\n",
       "      <td>Mikkilineni</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>143</td>\n",
       "      <td>Matos</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>198</td>\n",
       "      <td>OConnell</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>199</td>\n",
       "      <td>Grant</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>144</td>\n",
       "      <td>Vargas</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>191</td>\n",
       "      <td>Perkins</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>182</td>\n",
       "      <td>Sullivan</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>140</td>\n",
       "      <td>Patel</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>131</td>\n",
       "      <td>Marlow</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>127</td>\n",
       "      <td>Landry</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>135</td>\n",
       "      <td>Gee</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>128</td>\n",
       "      <td>Markle</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>136</td>\n",
       "      <td>Philtanker</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>132</td>\n",
       "      <td>Olson</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    empid         name  deptno     sal\n",
       "21    121        Fripp    50.0  8200.0\n",
       "20    120        Weiss    50.0  8000.0\n",
       "22    122     Kaufling    50.0  7900.0\n",
       "23    123      Vollman    50.0  6500.0\n",
       "24    124      Mourgos    50.0  5800.0\n",
       "84    184     Sarchand    50.0  4200.0\n",
       "85    185         Bull    50.0  4100.0\n",
       "92    192         Bell    50.0  4000.0\n",
       "93    193      Everett    50.0  3900.0\n",
       "88    188        Chung    50.0  3800.0\n",
       "37    137       Ladwig    50.0  3600.0\n",
       "89    189        Dilly    50.0  3600.0\n",
       "41    141         Rajs    50.0  3500.0\n",
       "86    186    Dellinger    50.0  3400.0\n",
       "29    129       Bissot    50.0  3300.0\n",
       "33    133       Mallin    50.0  3300.0\n",
       "94    194       McCain    50.0  3200.0\n",
       "25    125          yer    50.0  3200.0\n",
       "38    138       Stiles    50.0  3200.0\n",
       "80    180       Taylor    50.0  3200.0\n",
       "96    196        Walsh    50.0  3100.0\n",
       "81    181       Fleaur    50.0  3100.0\n",
       "42    142       Davies    50.0  3100.0\n",
       "97    197       Feeney    50.0  3000.0\n",
       "87    187       Cabrio    50.0  3000.0\n",
       "90    190        Gates    50.0  2900.0\n",
       "34    134       Rogers    50.0  2900.0\n",
       "83    183        Geoni    50.0  2800.0\n",
       "95    195        Jones    50.0  2800.0\n",
       "30    130     Atkinson    50.0  2800.0\n",
       "39    139          Seo    50.0  2700.0\n",
       "26    126  Mikkilineni    50.0  2700.0\n",
       "43    143        Matos    50.0  2600.0\n",
       "98    198     OConnell    50.0  2600.0\n",
       "99    199        Grant    50.0  2600.0\n",
       "44    144       Vargas    50.0  2500.0\n",
       "91    191      Perkins    50.0  2500.0\n",
       "82    182     Sullivan    50.0  2500.0\n",
       "40    140        Patel    50.0  2500.0\n",
       "31    131       Marlow    50.0  2500.0\n",
       "27    127       Landry    50.0  2400.0\n",
       "35    135          Gee    50.0  2400.0\n",
       "28    128       Markle    50.0  2200.0\n",
       "36    136   Philtanker    50.0  2200.0\n",
       "32    132        Olson    50.0  2100.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp[['empid','name','deptno','sal']][emp['deptno']==50].sort_values(by=['sal'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ㅁ~.astype()\n",
    "\n",
    "##문자열이라 소트 제대로 안됨 -> ~.astype()으로 숫자형으로 바꾸어줌\n",
    "## 실제 데이터에 형변환 하지는 않음\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'12'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = Series(['1','2'])\n",
    "s.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.astype(int).sum()   #~.astype()으로 숫자형으로 바꾸어줌"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    12.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = DataFrame(['1','2'])\n",
    "df.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.astype(int).sum()   #~.astype()으로 숫자형으로 바꾸어줌"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = Series(['1','2','10'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "2    10\n",
       "1     2\n",
       "dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     2\n",
       "2    10\n",
       "0     1\n",
       "dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1     2\n",
       "2    10\n",
       "dtype: int32"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.astype(int).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    10\n",
       "1     2\n",
       "0     1\n",
       "dtype: int32"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.astype(int).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "[문제153] 10,20,30,40,50번 부서 사원들의 급여의 총액을 출력하세요.\n",
    "\n",
    "<화면출력>\n",
    "\n",
    "10 4400.0\n",
    "20 19000.0\n",
    "30 24900.0\n",
    "40 6500.0\n",
    "50 156400.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp = pd.read_csv(\"c:\\python\\emp1.csv\",\n",
    "                  names = ['empid','name','job','mgr','hire_date','sal','comm','deptno'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 4400.0\n",
      "20 19000.0\n",
      "30 24900.0\n",
      "40 6500.0\n",
      "50 156400.0\n"
     ]
    }
   ],
   "source": [
    "for i in [10,20,30,40,50]:\n",
    "    print(i, emp['sal'][emp['deptno']==i].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 4400.0\n",
      "20 19000.0\n",
      "30 24900.0\n",
      "40 6500.0\n",
      "50 156400.0\n"
     ]
    }
   ],
   "source": [
    "#[선생님 풀이]\n",
    "deptno_dict={}\n",
    "\n",
    "for i in [10,20,30,40,50]:\n",
    "\tdept_sum=emp.loc[emp['deptno']==i,'sal'].sum()\n",
    "\tdeptno_dict[i]=dept_sum\n",
    "\n",
    "for k,v in deptno_dict.items():\n",
    "\tprint(k,v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ㅁ  유일한 값만 찾기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = Series([1,2,3,4,1,2,3,4,1,2,5,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6], dtype=int64)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = Series(['a','b','c','c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['a', 'b', 'c'], dtype=object)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = Series([1,2,3,4,1,2,3,4,1,2,5,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    3\n",
       "1    3\n",
       "4    2\n",
       "3    2\n",
       "6    1\n",
       "5    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "s.value_counts()  #유니크해서 각 항목이 몇개인지 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    0.250000\n",
       "1    0.250000\n",
       "4    0.166667\n",
       "3    0.166667\n",
       "6    0.083333\n",
       "5    0.083333\n",
       "dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.value_counts(normalize=True) #유니크해서 각 항목간 상대비율확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    3\n",
       "1    3\n",
       "4    2\n",
       "3    2\n",
       "6    1\n",
       "5    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.value_counts(sort=True) #기본값, count된 값을 기준으로 소트"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    3\n",
       "2    3\n",
       "3    2\n",
       "4    2\n",
       "5    1\n",
       "6    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.value_counts(sort=False)  #유니크 된 항목을 기준으로 소트 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = DataFrame({'a' : ['a1','a1','a1','a2','a2','a3'],\n",
    "               'b' : ['b1','b1','b2','b2','b3',np.nan]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['a1', 'a2', 'a3'], dtype=object)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['a'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['b1', 'b2', 'b3', nan], dtype=object)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['b'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b1    2\n",
       "b2    2\n",
       "b3    1\n",
       "Name: b, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['b'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b1    2\n",
       "b2    2\n",
       "b3    1\n",
       "Name: b, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['b'].value_counts(dropna=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b1     2\n",
       "b2     2\n",
       "b3     1\n",
       "NaN    1\n",
       "Name: b, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['b'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[문제154] 부서별로 급여 총액을 출력하세요.\n",
    "\n",
    "<화면 출력>\n",
    "\n",
    "10 4400.0\n",
    "20 19000.0\n",
    "30 24900.0\n",
    "40 6500.0\n",
    "50 156400.0\n",
    "60 28800.0\n",
    "70 10000.0\n",
    "80 304500.0\n",
    "90 58000.0\n",
    "100 53708.8\n",
    "110 20308.0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp = pd.read_csv(\"c:\\python\\emp1.csv\",\n",
    "                  names = ['empid','name','job','mgr','hire_date','sal','comm','deptno'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 10.,  20.,  30.,  40.,  50.,  60.,  70.,  80.,  90., 100., 110.,\n",
       "       510.])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dept_list = emp['deptno'].sort_values().dropna().unique()\n",
    "dept_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{10.0: 4400.0,\n",
       " 20.0: 19000.0,\n",
       " 30.0: 24900.0,\n",
       " 40.0: 6500.0,\n",
       " 50.0: 156400.0,\n",
       " 60.0: 28800.0,\n",
       " 70.0: 10000.0,\n",
       " 80.0: 304500.0,\n",
       " 90.0: 58000.0,\n",
       " 100.0: 53708.8,\n",
       " 110.0: 8300.0,\n",
       " 510.0: 12008.0}"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = {}\n",
    "for i in dept_list:\n",
    "    a[i] = emp['sal'][emp['deptno']==i].sum()\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.0 4400.0\n",
      "20.0 19000.0\n",
      "30.0 24900.0\n",
      "40.0 6500.0\n",
      "50.0 156400.0\n",
      "60.0 28800.0\n",
      "70.0 10000.0\n",
      "80.0 304500.0\n",
      "90.0 58000.0\n",
      "100.0 53708.8\n",
      "110.0 8300.0\n",
      "510.0 12008.0\n"
     ]
    }
   ],
   "source": [
    "for k,v in a.items():\n",
    "    print(k,v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 4400.0\n",
      "20 19000.0\n",
      "30 24900.0\n",
      "40 6500.0\n",
      "50 156400.0\n",
      "60 28800.0\n",
      "70 10000.0\n",
      "80 304500.0\n",
      "90 58000.0\n",
      "100 53708.8\n",
      "110 8300.0\n",
      "510 12008.0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#[선생님]\n",
    "\n",
    "from pandas import Series, DataFrame\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import operator\n",
    "import math\n",
    "\n",
    "emp = pd.read_csv(\"c:\\python\\emp1.csv\", names = ['empid','name','job','mgr','hire_date','sal','comm','deptno'])\n",
    "\n",
    "deptno_unique = Series(emp['deptno'].unique()) #numpy.array형식이됨\n",
    "deptno_unique = deptno_unique.dropna()\n",
    "\n",
    "deptno_dict = {}\n",
    "\n",
    "for i in deptno_unique:\n",
    "    dept_sum = emp.loc[emp['deptno'] == i,'sal'].sum()\n",
    "    deptno_dict[i] = dept_sum\n",
    "\n",
    "for k,v in sorted(deptno_dict.items(), key=operator.itemgetter(0)):\n",
    "    print(math.trunc(k),v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##############################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(107, 8)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['empid', 'name', 'job', 'mgr', 'hire_date', 'sal', 'comm', 'deptno'], dtype='object')"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'dtype'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-144-68d829043e4d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0memp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5065\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5066\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5067\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5068\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5069\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'dtype'"
     ]
    }
   ],
   "source": [
    "emp.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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>empid</th>\n",
       "      <th>name</th>\n",
       "      <th>job</th>\n",
       "      <th>deptno</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>King</td>\n",
       "      <td>AD_PRES</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101</td>\n",
       "      <td>Kochhar</td>\n",
       "      <td>AD_VP</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102</td>\n",
       "      <td>De Haan</td>\n",
       "      <td>AD_VP</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>103</td>\n",
       "      <td>Hunold</td>\n",
       "      <td>IT_PROG</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104</td>\n",
       "      <td>Ernst</td>\n",
       "      <td>IT_PROG</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>105</td>\n",
       "      <td>Austin</td>\n",
       "      <td>IT_PROG</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>106</td>\n",
       "      <td>Pataballa</td>\n",
       "      <td>IT_PROG</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>107</td>\n",
       "      <td>Lorentz</td>\n",
       "      <td>IT_PROG</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>108</td>\n",
       "      <td>Greenberg</td>\n",
       "      <td>FI_MGR</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>109</td>\n",
       "      <td>Faviet</td>\n",
       "      <td>FI_ACCOUNT</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>110</td>\n",
       "      <td>Chen</td>\n",
       "      <td>FI_ACCOUNT</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>111</td>\n",
       "      <td>Sciarra</td>\n",
       "      <td>FI_ACCOUNT</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>112</td>\n",
       "      <td>Urman</td>\n",
       "      <td>FI_ACCOUNT</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>113</td>\n",
       "      <td>Popp</td>\n",
       "      <td>FI_ACCOUNT</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>114</td>\n",
       "      <td>Raphaely</td>\n",
       "      <td>PU_MAN</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>115</td>\n",
       "      <td>Khoo</td>\n",
       "      <td>PU_CLERK</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>116</td>\n",
       "      <td>Baida</td>\n",
       "      <td>PU_CLERK</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>117</td>\n",
       "      <td>Tobias</td>\n",
       "      <td>PU_CLERK</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>118</td>\n",
       "      <td>Himuro</td>\n",
       "      <td>PU_CLERK</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>119</td>\n",
       "      <td>Colmeres</td>\n",
       "      <td>PU_CLERK</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>120</td>\n",
       "      <td>Weiss</td>\n",
       "      <td>ST_MAN</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>121</td>\n",
       "      <td>Fripp</td>\n",
       "      <td>ST_MAN</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>122</td>\n",
       "      <td>Kaufling</td>\n",
       "      <td>ST_MAN</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>123</td>\n",
       "      <td>Vollman</td>\n",
       "      <td>ST_MAN</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>124</td>\n",
       "      <td>Mourgos</td>\n",
       "      <td>ST_MAN</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>125</td>\n",
       "      <td>yer</td>\n",
       "      <td>ST_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>126</td>\n",
       "      <td>Mikkilineni</td>\n",
       "      <td>ST_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>127</td>\n",
       "      <td>Landry</td>\n",
       "      <td>ST_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>128</td>\n",
       "      <td>Markle</td>\n",
       "      <td>ST_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>129</td>\n",
       "      <td>Bissot</td>\n",
       "      <td>ST_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>177</td>\n",
       "      <td>Livingston</td>\n",
       "      <td>SA_REP</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>178</td>\n",
       "      <td>Grant</td>\n",
       "      <td>SA_REP</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>179</td>\n",
       "      <td>Johnson</td>\n",
       "      <td>SA_REP</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>180</td>\n",
       "      <td>Taylor</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>181</td>\n",
       "      <td>Fleaur</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>182</td>\n",
       "      <td>Sullivan</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>183</td>\n",
       "      <td>Geoni</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>184</td>\n",
       "      <td>Sarchand</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>185</td>\n",
       "      <td>Bull</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>186</td>\n",
       "      <td>Dellinger</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>187</td>\n",
       "      <td>Cabrio</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>188</td>\n",
       "      <td>Chung</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>189</td>\n",
       "      <td>Dilly</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>190</td>\n",
       "      <td>Gates</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>191</td>\n",
       "      <td>Perkins</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>192</td>\n",
       "      <td>Bell</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>193</td>\n",
       "      <td>Everett</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>194</td>\n",
       "      <td>McCain</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>195</td>\n",
       "      <td>Jones</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>196</td>\n",
       "      <td>Walsh</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>197</td>\n",
       "      <td>Feeney</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>198</td>\n",
       "      <td>OConnell</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>199</td>\n",
       "      <td>Grant</td>\n",
       "      <td>SH_CLERK</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>200</td>\n",
       "      <td>Whalen</td>\n",
       "      <td>AD_ASST</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>201</td>\n",
       "      <td>Hartstein</td>\n",
       "      <td>MK_MAN</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>202</td>\n",
       "      <td>Fay</td>\n",
       "      <td>MK_REP</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>203</td>\n",
       "      <td>Mavris</td>\n",
       "      <td>HR_REP</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>204</td>\n",
       "      <td>Baer</td>\n",
       "      <td>PR_REP</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>205</td>\n",
       "      <td>Higgins</td>\n",
       "      <td>AC_MGR</td>\n",
       "      <td>510.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>206</td>\n",
       "      <td>Gietz</td>\n",
       "      <td>AC_ACCOUNT</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     empid         name         job  deptno\n",
       "0      100         King     AD_PRES    90.0\n",
       "1      101      Kochhar       AD_VP    90.0\n",
       "2      102      De Haan       AD_VP    90.0\n",
       "3      103       Hunold     IT_PROG    60.0\n",
       "4      104        Ernst     IT_PROG    60.0\n",
       "5      105       Austin     IT_PROG    60.0\n",
       "6      106    Pataballa     IT_PROG    60.0\n",
       "7      107      Lorentz     IT_PROG    60.0\n",
       "8      108    Greenberg      FI_MGR   100.0\n",
       "9      109       Faviet  FI_ACCOUNT   100.0\n",
       "10     110         Chen  FI_ACCOUNT   100.0\n",
       "11     111      Sciarra  FI_ACCOUNT   100.0\n",
       "12     112        Urman  FI_ACCOUNT   100.0\n",
       "13     113         Popp  FI_ACCOUNT   100.0\n",
       "14     114     Raphaely      PU_MAN    30.0\n",
       "15     115         Khoo    PU_CLERK    30.0\n",
       "16     116        Baida    PU_CLERK    30.0\n",
       "17     117       Tobias    PU_CLERK    30.0\n",
       "18     118       Himuro    PU_CLERK    30.0\n",
       "19     119     Colmeres    PU_CLERK    30.0\n",
       "20     120        Weiss      ST_MAN    50.0\n",
       "21     121        Fripp      ST_MAN    50.0\n",
       "22     122     Kaufling      ST_MAN    50.0\n",
       "23     123      Vollman      ST_MAN    50.0\n",
       "24     124      Mourgos      ST_MAN    50.0\n",
       "25     125          yer    ST_CLERK    50.0\n",
       "26     126  Mikkilineni    ST_CLERK    50.0\n",
       "27     127       Landry    ST_CLERK    50.0\n",
       "28     128       Markle    ST_CLERK    50.0\n",
       "29     129       Bissot    ST_CLERK    50.0\n",
       "..     ...          ...         ...     ...\n",
       "77     177   Livingston      SA_REP    80.0\n",
       "78     178        Grant      SA_REP     NaN\n",
       "79     179      Johnson      SA_REP    80.0\n",
       "80     180       Taylor    SH_CLERK    50.0\n",
       "81     181       Fleaur    SH_CLERK    50.0\n",
       "82     182     Sullivan    SH_CLERK    50.0\n",
       "83     183        Geoni    SH_CLERK    50.0\n",
       "84     184     Sarchand    SH_CLERK    50.0\n",
       "85     185         Bull    SH_CLERK    50.0\n",
       "86     186    Dellinger    SH_CLERK    50.0\n",
       "87     187       Cabrio    SH_CLERK    50.0\n",
       "88     188        Chung    SH_CLERK    50.0\n",
       "89     189        Dilly    SH_CLERK    50.0\n",
       "90     190        Gates    SH_CLERK    50.0\n",
       "91     191      Perkins    SH_CLERK    50.0\n",
       "92     192         Bell    SH_CLERK    50.0\n",
       "93     193      Everett    SH_CLERK    50.0\n",
       "94     194       McCain    SH_CLERK    50.0\n",
       "95     195        Jones    SH_CLERK    50.0\n",
       "96     196        Walsh    SH_CLERK    50.0\n",
       "97     197       Feeney    SH_CLERK    50.0\n",
       "98     198     OConnell    SH_CLERK    50.0\n",
       "99     199        Grant    SH_CLERK    50.0\n",
       "100    200       Whalen     AD_ASST    10.0\n",
       "101    201    Hartstein      MK_MAN    20.0\n",
       "102    202          Fay      MK_REP    20.0\n",
       "103    203       Mavris      HR_REP    40.0\n",
       "104    204         Baer      PR_REP    70.0\n",
       "105    205      Higgins      AC_MGR   510.0\n",
       "106    206        Gietz  AC_ACCOUNT   110.0\n",
       "\n",
       "[107 rows x 4 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp2 = emp[['empid','name','job','deptno']]\n",
    "emp2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "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>empid</th>\n",
       "      <th>name</th>\n",
       "      <th>job</th>\n",
       "      <th>deptno</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>King</td>\n",
       "      <td>AD_PRES</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101</td>\n",
       "      <td>Kochhar</td>\n",
       "      <td>AD_VP</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102</td>\n",
       "      <td>De Haan</td>\n",
       "      <td>AD_VP</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>103</td>\n",
       "      <td>Hunold</td>\n",
       "      <td>IT_PROG</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104</td>\n",
       "      <td>Ernst</td>\n",
       "      <td>IT_PROG</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   empid     name      job  deptno\n",
       "0    100     King  AD_PRES    90.0\n",
       "1    101  Kochhar    AD_VP    90.0\n",
       "2    102  De Haan    AD_VP    90.0\n",
       "3    103   Hunold  IT_PROG    60.0\n",
       "4    104    Ernst  IT_PROG    60.0"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "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>empid</th>\n",
       "      <th>name</th>\n",
       "      <th>job</th>\n",
       "      <th>deptno</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>202</td>\n",
       "      <td>Fay</td>\n",
       "      <td>MK_REP</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>203</td>\n",
       "      <td>Mavris</td>\n",
       "      <td>HR_REP</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>204</td>\n",
       "      <td>Baer</td>\n",
       "      <td>PR_REP</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>205</td>\n",
       "      <td>Higgins</td>\n",
       "      <td>AC_MGR</td>\n",
       "      <td>510.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>206</td>\n",
       "      <td>Gietz</td>\n",
       "      <td>AC_ACCOUNT</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     empid     name         job  deptno\n",
       "102    202      Fay      MK_REP    20.0\n",
       "103    203   Mavris      HR_REP    40.0\n",
       "104    204     Baer      PR_REP    70.0\n",
       "105    205  Higgins      AC_MGR   510.0\n",
       "106    206    Gietz  AC_ACCOUNT   110.0"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp2.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for 문 안돌리고 그룹함수 사용하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp2 = emp[['empid','name','job','deptno','sal']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.SeriesGroupBy object at 0x0000024D072E64E0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deptno_group=emp['sal'].groupby(emp['deptno'])\n",
    "deptno_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deptno\n",
       "10.0       4400.0\n",
       "20.0      19000.0\n",
       "30.0      24900.0\n",
       "40.0       6500.0\n",
       "50.0     156400.0\n",
       "60.0      28800.0\n",
       "70.0      10000.0\n",
       "80.0     304500.0\n",
       "90.0      58000.0\n",
       "100.0     53708.8\n",
       "110.0      8300.0\n",
       "510.0     12008.0\n",
       "Name: sal, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deptno_group.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deptno\n",
       "10.0      4400.000000\n",
       "20.0      9500.000000\n",
       "30.0      4150.000000\n",
       "40.0      6500.000000\n",
       "50.0      3475.555556\n",
       "60.0      5760.000000\n",
       "70.0     10000.000000\n",
       "80.0      8955.882353\n",
       "90.0     19333.333333\n",
       "100.0     8951.466667\n",
       "110.0     8300.000000\n",
       "510.0    12008.000000\n",
       "Name: sal, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deptno_group.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deptno\n",
       "10.0              NaN\n",
       "20.0     2.450000e+07\n",
       "30.0     1.130700e+07\n",
       "40.0              NaN\n",
       "50.0     2.214162e+06\n",
       "60.0     3.708000e+06\n",
       "70.0              NaN\n",
       "80.0     4.135873e+06\n",
       "90.0     1.633333e+07\n",
       "100.0    5.337973e+06\n",
       "110.0             NaN\n",
       "510.0             NaN\n",
       "Name: sal, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deptno_group.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deptno\n",
       "10.0             NaN\n",
       "20.0     4949.747468\n",
       "30.0     3362.588289\n",
       "40.0             NaN\n",
       "50.0     1488.005919\n",
       "60.0     1925.616784\n",
       "70.0             NaN\n",
       "80.0     2033.684695\n",
       "90.0     4041.451884\n",
       "100.0    2310.405355\n",
       "110.0            NaN\n",
       "510.0            NaN\n",
       "Name: sal, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deptno_group.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deptno\n",
       "10.0      1\n",
       "20.0      2\n",
       "30.0      6\n",
       "40.0      1\n",
       "50.0     45\n",
       "60.0      5\n",
       "70.0      1\n",
       "80.0     34\n",
       "90.0      3\n",
       "100.0     6\n",
       "110.0     1\n",
       "510.0     1\n",
       "Name: sal, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deptno_group.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deptno\n",
       "10.0      4400.0\n",
       "20.0     13000.0\n",
       "30.0     11000.0\n",
       "40.0      6500.0\n",
       "50.0      8200.0\n",
       "60.0      9000.0\n",
       "70.0     10000.0\n",
       "80.0     14000.0\n",
       "90.0     24000.0\n",
       "100.0    13208.8\n",
       "110.0     8300.0\n",
       "510.0    12008.0\n",
       "Name: sal, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deptno_group.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deptno\n",
       "10.0      4400.0\n",
       "20.0      6000.0\n",
       "30.0      2500.0\n",
       "40.0      6500.0\n",
       "50.0      2100.0\n",
       "60.0      4200.0\n",
       "70.0     10000.0\n",
       "80.0      6100.0\n",
       "90.0     17000.0\n",
       "100.0     6900.0\n",
       "110.0     8300.0\n",
       "510.0    12008.0\n",
       "Name: sal, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deptno_group.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2개 그룹을 그룹핑해서 그룹함수 적용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deptno  job       \n",
       "10.0    AD_ASST         4400.0\n",
       "20.0    MK_MAN         13000.0\n",
       "        MK_REP          6000.0\n",
       "30.0    PU_CLERK       13900.0\n",
       "        PU_MAN         11000.0\n",
       "40.0    HR_REP          6500.0\n",
       "50.0    SH_CLERK       64300.0\n",
       "        ST_CLERK       55700.0\n",
       "        ST_MAN         36400.0\n",
       "60.0    IT_PROG        28800.0\n",
       "70.0    PR_REP         10000.0\n",
       "80.0    SA_MAN         61000.0\n",
       "        SA_REP        243500.0\n",
       "90.0    AD_PRES        24000.0\n",
       "        AD_VP          34000.0\n",
       "100.0   FI_ACCOUNT     40500.0\n",
       "        FI_MGR         13208.8\n",
       "110.0   AC_ACCOUNT      8300.0\n",
       "510.0   AC_MGR         12008.0\n",
       "Name: sal, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp['sal'].groupby([emp['deptno'],emp['job']]).sum()\n",
    "                            #각각 입력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.SeriesGroupBy object at 0x0000024D06C1B5C0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dept_group = emp['sal'].groupby([emp['deptno'],emp['job']])\n",
    "dept_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deptno  job       \n",
       "10.0    AD_ASST        4400.000000\n",
       "20.0    MK_MAN        13000.000000\n",
       "        MK_REP         6000.000000\n",
       "30.0    PU_CLERK       2780.000000\n",
       "        PU_MAN        11000.000000\n",
       "40.0    HR_REP         6500.000000\n",
       "50.0    SH_CLERK       3215.000000\n",
       "        ST_CLERK       2785.000000\n",
       "        ST_MAN         7280.000000\n",
       "60.0    IT_PROG        5760.000000\n",
       "70.0    PR_REP        10000.000000\n",
       "80.0    SA_MAN        12200.000000\n",
       "        SA_REP         8396.551724\n",
       "90.0    AD_PRES       24000.000000\n",
       "        AD_VP         17000.000000\n",
       "100.0   FI_ACCOUNT     8100.000000\n",
       "        FI_MGR        13208.800000\n",
       "110.0   AC_ACCOUNT     8300.000000\n",
       "510.0   AC_MGR        12008.000000\n",
       "Name: sal, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#변수에 집어넣고 \n",
    "dept_mean = dept_group.mean()\n",
    "dept_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#위 결과를 deptno, job의 크로스 테이블로 만들기\n",
    "ㅁ ~.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>job</th>\n",
       "      <th>AC_ACCOUNT</th>\n",
       "      <th>AC_MGR</th>\n",
       "      <th>AD_ASST</th>\n",
       "      <th>AD_PRES</th>\n",
       "      <th>AD_VP</th>\n",
       "      <th>FI_ACCOUNT</th>\n",
       "      <th>FI_MGR</th>\n",
       "      <th>HR_REP</th>\n",
       "      <th>IT_PROG</th>\n",
       "      <th>MK_MAN</th>\n",
       "      <th>MK_REP</th>\n",
       "      <th>PR_REP</th>\n",
       "      <th>PU_CLERK</th>\n",
       "      <th>PU_MAN</th>\n",
       "      <th>SA_MAN</th>\n",
       "      <th>SA_REP</th>\n",
       "      <th>SH_CLERK</th>\n",
       "      <th>ST_CLERK</th>\n",
       "      <th>ST_MAN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deptno</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2780.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3215.0</td>\n",
       "      <td>2785.0</td>\n",
       "      <td>7280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12200.0</td>\n",
       "      <td>8396.551724</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8100.0</td>\n",
       "      <td>13208.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110.0</th>\n",
       "      <td>8300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12008.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "job     AC_ACCOUNT   AC_MGR  AD_ASST  AD_PRES    AD_VP  FI_ACCOUNT   FI_MGR  \\\n",
       "deptno                                                                        \n",
       "10.0           NaN      NaN   4400.0      NaN      NaN         NaN      NaN   \n",
       "20.0           NaN      NaN      NaN      NaN      NaN         NaN      NaN   \n",
       "30.0           NaN      NaN      NaN      NaN      NaN         NaN      NaN   \n",
       "40.0           NaN      NaN      NaN      NaN      NaN         NaN      NaN   \n",
       "50.0           NaN      NaN      NaN      NaN      NaN         NaN      NaN   \n",
       "60.0           NaN      NaN      NaN      NaN      NaN         NaN      NaN   \n",
       "70.0           NaN      NaN      NaN      NaN      NaN         NaN      NaN   \n",
       "80.0           NaN      NaN      NaN      NaN      NaN         NaN      NaN   \n",
       "90.0           NaN      NaN      NaN  24000.0  17000.0         NaN      NaN   \n",
       "100.0          NaN      NaN      NaN      NaN      NaN      8100.0  13208.8   \n",
       "110.0       8300.0      NaN      NaN      NaN      NaN         NaN      NaN   \n",
       "510.0          NaN  12008.0      NaN      NaN      NaN         NaN      NaN   \n",
       "\n",
       "job     HR_REP  IT_PROG   MK_MAN  MK_REP   PR_REP  PU_CLERK   PU_MAN   SA_MAN  \\\n",
       "deptno                                                                          \n",
       "10.0       NaN      NaN      NaN     NaN      NaN       NaN      NaN      NaN   \n",
       "20.0       NaN      NaN  13000.0  6000.0      NaN       NaN      NaN      NaN   \n",
       "30.0       NaN      NaN      NaN     NaN      NaN    2780.0  11000.0      NaN   \n",
       "40.0    6500.0      NaN      NaN     NaN      NaN       NaN      NaN      NaN   \n",
       "50.0       NaN      NaN      NaN     NaN      NaN       NaN      NaN      NaN   \n",
       "60.0       NaN   5760.0      NaN     NaN      NaN       NaN      NaN      NaN   \n",
       "70.0       NaN      NaN      NaN     NaN  10000.0       NaN      NaN      NaN   \n",
       "80.0       NaN      NaN      NaN     NaN      NaN       NaN      NaN  12200.0   \n",
       "90.0       NaN      NaN      NaN     NaN      NaN       NaN      NaN      NaN   \n",
       "100.0      NaN      NaN      NaN     NaN      NaN       NaN      NaN      NaN   \n",
       "110.0      NaN      NaN      NaN     NaN      NaN       NaN      NaN      NaN   \n",
       "510.0      NaN      NaN      NaN     NaN      NaN       NaN      NaN      NaN   \n",
       "\n",
       "job          SA_REP  SH_CLERK  ST_CLERK  ST_MAN  \n",
       "deptno                                           \n",
       "10.0            NaN       NaN       NaN     NaN  \n",
       "20.0            NaN       NaN       NaN     NaN  \n",
       "30.0            NaN       NaN       NaN     NaN  \n",
       "40.0            NaN       NaN       NaN     NaN  \n",
       "50.0            NaN    3215.0    2785.0  7280.0  \n",
       "60.0            NaN       NaN       NaN     NaN  \n",
       "70.0            NaN       NaN       NaN     NaN  \n",
       "80.0    8396.551724       NaN       NaN     NaN  \n",
       "90.0            NaN       NaN       NaN     NaN  \n",
       "100.0           NaN       NaN       NaN     NaN  \n",
       "110.0           NaN       NaN       NaN     NaN  \n",
       "510.0           NaN       NaN       NaN     NaN  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dept_mean.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>job</th>\n",
       "      <th>AC_ACCOUNT</th>\n",
       "      <th>AC_MGR</th>\n",
       "      <th>AD_ASST</th>\n",
       "      <th>AD_PRES</th>\n",
       "      <th>AD_VP</th>\n",
       "      <th>FI_ACCOUNT</th>\n",
       "      <th>FI_MGR</th>\n",
       "      <th>HR_REP</th>\n",
       "      <th>IT_PROG</th>\n",
       "      <th>MK_MAN</th>\n",
       "      <th>MK_REP</th>\n",
       "      <th>PR_REP</th>\n",
       "      <th>PU_CLERK</th>\n",
       "      <th>PU_MAN</th>\n",
       "      <th>SA_MAN</th>\n",
       "      <th>SA_REP</th>\n",
       "      <th>SH_CLERK</th>\n",
       "      <th>ST_CLERK</th>\n",
       "      <th>ST_MAN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deptno</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2780.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3215.0</td>\n",
       "      <td>2785.0</td>\n",
       "      <td>7280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12200.0</td>\n",
       "      <td>8396.551724</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8100.0</td>\n",
       "      <td>13208.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110.0</th>\n",
       "      <td>8300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>12008.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "job     AC_ACCOUNT   AC_MGR  AD_ASST  AD_PRES    AD_VP  FI_ACCOUNT   FI_MGR  \\\n",
       "deptno                                                                        \n",
       "10.0           0.0      0.0   4400.0      0.0      0.0         0.0      0.0   \n",
       "20.0           0.0      0.0      0.0      0.0      0.0         0.0      0.0   \n",
       "30.0           0.0      0.0      0.0      0.0      0.0         0.0      0.0   \n",
       "40.0           0.0      0.0      0.0      0.0      0.0         0.0      0.0   \n",
       "50.0           0.0      0.0      0.0      0.0      0.0         0.0      0.0   \n",
       "60.0           0.0      0.0      0.0      0.0      0.0         0.0      0.0   \n",
       "70.0           0.0      0.0      0.0      0.0      0.0         0.0      0.0   \n",
       "80.0           0.0      0.0      0.0      0.0      0.0         0.0      0.0   \n",
       "90.0           0.0      0.0      0.0  24000.0  17000.0         0.0      0.0   \n",
       "100.0          0.0      0.0      0.0      0.0      0.0      8100.0  13208.8   \n",
       "110.0       8300.0      0.0      0.0      0.0      0.0         0.0      0.0   \n",
       "510.0          0.0  12008.0      0.0      0.0      0.0         0.0      0.0   \n",
       "\n",
       "job     HR_REP  IT_PROG   MK_MAN  MK_REP   PR_REP  PU_CLERK   PU_MAN   SA_MAN  \\\n",
       "deptno                                                                          \n",
       "10.0       0.0      0.0      0.0     0.0      0.0       0.0      0.0      0.0   \n",
       "20.0       0.0      0.0  13000.0  6000.0      0.0       0.0      0.0      0.0   \n",
       "30.0       0.0      0.0      0.0     0.0      0.0    2780.0  11000.0      0.0   \n",
       "40.0    6500.0      0.0      0.0     0.0      0.0       0.0      0.0      0.0   \n",
       "50.0       0.0      0.0      0.0     0.0      0.0       0.0      0.0      0.0   \n",
       "60.0       0.0   5760.0      0.0     0.0      0.0       0.0      0.0      0.0   \n",
       "70.0       0.0      0.0      0.0     0.0  10000.0       0.0      0.0      0.0   \n",
       "80.0       0.0      0.0      0.0     0.0      0.0       0.0      0.0  12200.0   \n",
       "90.0       0.0      0.0      0.0     0.0      0.0       0.0      0.0      0.0   \n",
       "100.0      0.0      0.0      0.0     0.0      0.0       0.0      0.0      0.0   \n",
       "110.0      0.0      0.0      0.0     0.0      0.0       0.0      0.0      0.0   \n",
       "510.0      0.0      0.0      0.0     0.0      0.0       0.0      0.0      0.0   \n",
       "\n",
       "job          SA_REP  SH_CLERK  ST_CLERK  ST_MAN  \n",
       "deptno                                           \n",
       "10.0       0.000000       0.0       0.0     0.0  \n",
       "20.0       0.000000       0.0       0.0     0.0  \n",
       "30.0       0.000000       0.0       0.0     0.0  \n",
       "40.0       0.000000       0.0       0.0     0.0  \n",
       "50.0       0.000000    3215.0    2785.0  7280.0  \n",
       "60.0       0.000000       0.0       0.0     0.0  \n",
       "70.0       0.000000       0.0       0.0     0.0  \n",
       "80.0    8396.551724       0.0       0.0     0.0  \n",
       "90.0       0.000000       0.0       0.0     0.0  \n",
       "100.0      0.000000       0.0       0.0     0.0  \n",
       "110.0      0.000000       0.0       0.0     0.0  \n",
       "510.0      0.000000       0.0       0.0     0.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dept_mean.unstack().fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "ㅁ groupby 사용하는 두가지 문법"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deptno  job       \n",
       "10.0    AD_ASST         4400.0\n",
       "20.0    MK_MAN         13000.0\n",
       "        MK_REP          6000.0\n",
       "30.0    PU_CLERK       13900.0\n",
       "        PU_MAN         11000.0\n",
       "40.0    HR_REP          6500.0\n",
       "50.0    SH_CLERK       64300.0\n",
       "        ST_CLERK       55700.0\n",
       "        ST_MAN         36400.0\n",
       "60.0    IT_PROG        28800.0\n",
       "70.0    PR_REP         10000.0\n",
       "80.0    SA_MAN         61000.0\n",
       "        SA_REP        243500.0\n",
       "90.0    AD_PRES        24000.0\n",
       "        AD_VP          34000.0\n",
       "100.0   FI_ACCOUNT     40500.0\n",
       "        FI_MGR         13208.8\n",
       "110.0   AC_ACCOUNT      8300.0\n",
       "510.0   AC_MGR         12008.0\n",
       "Name: sal, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1.\n",
    "emp['sal'].groupby([emp['deptno'],emp['job']]).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deptno  job       \n",
       "10.0    AD_ASST         4400.0\n",
       "20.0    MK_MAN         13000.0\n",
       "        MK_REP          6000.0\n",
       "30.0    PU_CLERK       13900.0\n",
       "        PU_MAN         11000.0\n",
       "40.0    HR_REP          6500.0\n",
       "50.0    SH_CLERK       64300.0\n",
       "        ST_CLERK       55700.0\n",
       "        ST_MAN         36400.0\n",
       "60.0    IT_PROG        28800.0\n",
       "70.0    PR_REP         10000.0\n",
       "80.0    SA_MAN         61000.0\n",
       "        SA_REP        243500.0\n",
       "90.0    AD_PRES        24000.0\n",
       "        AD_VP          34000.0\n",
       "100.0   FI_ACCOUNT     40500.0\n",
       "        FI_MGR         13208.8\n",
       "110.0   AC_ACCOUNT      8300.0\n",
       "510.0   AC_MGR         12008.0\n",
       "Name: sal, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2.\n",
    "emp.groupby(['deptno','job'])['sal'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deptno  job       \n",
       "10.0    AD_ASST         4400.0\n",
       "20.0    MK_MAN         13000.0\n",
       "        MK_REP          6000.0\n",
       "30.0    PU_CLERK       13900.0\n",
       "        PU_MAN         11000.0\n",
       "40.0    HR_REP          6500.0\n",
       "50.0    SH_CLERK       64300.0\n",
       "        ST_CLERK       55700.0\n",
       "        ST_MAN         36400.0\n",
       "60.0    IT_PROG        28800.0\n",
       "70.0    PR_REP         10000.0\n",
       "80.0    SA_MAN         61000.0\n",
       "        SA_REP        243500.0\n",
       "90.0    AD_PRES        24000.0\n",
       "        AD_VP          34000.0\n",
       "100.0   FI_ACCOUNT     40500.0\n",
       "        FI_MGR         13208.8\n",
       "110.0   AC_ACCOUNT      8300.0\n",
       "510.0   AC_MGR         12008.0\n",
       "Name: sal, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2.\n",
    "emp.groupby(['deptno','job'])['sal'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000024D09AC2C88>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp.groupby('deptno')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.0\n",
      "     empid    name      job    mgr   hire_date     sal  comm  deptno\n",
      "100    200  Whalen  AD_ASST  101.0  2003-09-17  4400.0   NaN    10.0\n",
      "20.0\n",
      "     empid       name     job    mgr   hire_date      sal  comm  deptno\n",
      "101    201  Hartstein  MK_MAN  100.0  2004-02-17  13000.0   NaN    20.0\n",
      "102    202        Fay  MK_REP  201.0  2005-08-17   6000.0   NaN    20.0\n",
      "30.0\n",
      "    empid      name       job    mgr   hire_date      sal  comm  deptno\n",
      "14    114  Raphaely    PU_MAN  100.0  2002-12-07  11000.0   NaN    30.0\n",
      "15    115      Khoo  PU_CLERK  114.0  2003-05-18   3100.0   NaN    30.0\n",
      "16    116     Baida  PU_CLERK  114.0  2005-12-24   2900.0   NaN    30.0\n",
      "17    117    Tobias  PU_CLERK  114.0  2005-07-24   2800.0   NaN    30.0\n",
      "18    118    Himuro  PU_CLERK  114.0  2006-11-15   2600.0   NaN    30.0\n",
      "19    119  Colmeres  PU_CLERK  114.0  2007-08-10   2500.0   NaN    30.0\n",
      "40.0\n",
      "     empid    name     job    mgr   hire_date     sal  comm  deptno\n",
      "103    203  Mavris  HR_REP  101.0  2002-06-07  6500.0   NaN    40.0\n",
      "50.0\n",
      "    empid         name       job    mgr   hire_date     sal  comm  deptno\n",
      "20    120        Weiss    ST_MAN  100.0  2004-07-18  8000.0   NaN    50.0\n",
      "21    121        Fripp    ST_MAN  100.0  2005-04-10  8200.0   NaN    50.0\n",
      "22    122     Kaufling    ST_MAN  100.0  2003-05-01  7900.0   NaN    50.0\n",
      "23    123      Vollman    ST_MAN  100.0  2005-10-10  6500.0   NaN    50.0\n",
      "24    124      Mourgos    ST_MAN  100.0  2007-11-16  5800.0   NaN    50.0\n",
      "25    125          yer  ST_CLERK  120.0  2005-07-16  3200.0   NaN    50.0\n",
      "26    126  Mikkilineni  ST_CLERK  120.0  2006-09-28  2700.0   NaN    50.0\n",
      "27    127       Landry  ST_CLERK  120.0  2007-01-14  2400.0   NaN    50.0\n",
      "28    128       Markle  ST_CLERK  120.0  2008-03-08  2200.0   NaN    50.0\n",
      "29    129       Bissot  ST_CLERK  121.0  2005-08-20  3300.0   NaN    50.0\n",
      "30    130     Atkinson  ST_CLERK  121.0  2005-10-30  2800.0   NaN    50.0\n",
      "31    131       Marlow  ST_CLERK  121.0  2005-02-16  2500.0   NaN    50.0\n",
      "32    132        Olson  ST_CLERK  121.0  2007-04-10  2100.0   NaN    50.0\n",
      "33    133       Mallin  ST_CLERK  122.0  2004-06-14  3300.0   NaN    50.0\n",
      "34    134       Rogers  ST_CLERK  122.0  2006-08-26  2900.0   NaN    50.0\n",
      "35    135          Gee  ST_CLERK  122.0  2007-12-12  2400.0   NaN    50.0\n",
      "36    136   Philtanker  ST_CLERK  122.0  2008-02-06  2200.0   NaN    50.0\n",
      "37    137       Ladwig  ST_CLERK  123.0  2003-07-14  3600.0   NaN    50.0\n",
      "38    138       Stiles  ST_CLERK  123.0  2005-10-26  3200.0   NaN    50.0\n",
      "39    139          Seo  ST_CLERK  123.0  2006-02-12  2700.0   NaN    50.0\n",
      "40    140        Patel  ST_CLERK  123.0  2006-04-06  2500.0   NaN    50.0\n",
      "41    141         Rajs  ST_CLERK  124.0  2003-10-17  3500.0   NaN    50.0\n",
      "42    142       Davies  ST_CLERK  124.0  2005-01-29  3100.0   NaN    50.0\n",
      "43    143        Matos  ST_CLERK  124.0  2006-03-15  2600.0   NaN    50.0\n",
      "44    144       Vargas  ST_CLERK  124.0  2006-07-09  2500.0   NaN    50.0\n",
      "80    180       Taylor  SH_CLERK  120.0  2006-01-24  3200.0   NaN    50.0\n",
      "81    181       Fleaur  SH_CLERK  120.0  2006-02-23  3100.0   NaN    50.0\n",
      "82    182     Sullivan  SH_CLERK  120.0  2007-06-21  2500.0   NaN    50.0\n",
      "83    183        Geoni  SH_CLERK  120.0  2008-02-03  2800.0   NaN    50.0\n",
      "84    184     Sarchand  SH_CLERK  121.0  2004-01-27  4200.0   NaN    50.0\n",
      "85    185         Bull  SH_CLERK  121.0  2005-02-20  4100.0   NaN    50.0\n",
      "86    186    Dellinger  SH_CLERK  121.0  2006-06-24  3400.0   NaN    50.0\n",
      "87    187       Cabrio  SH_CLERK  121.0  2007-02-07  3000.0   NaN    50.0\n",
      "88    188        Chung  SH_CLERK  122.0  2005-06-14  3800.0   NaN    50.0\n",
      "89    189        Dilly  SH_CLERK  122.0  2005-08-13  3600.0   NaN    50.0\n",
      "90    190        Gates  SH_CLERK  122.0  2006-07-11  2900.0   NaN    50.0\n",
      "91    191      Perkins  SH_CLERK  122.0  2007-12-19  2500.0   NaN    50.0\n",
      "92    192         Bell  SH_CLERK  123.0  2004-02-04  4000.0   NaN    50.0\n",
      "93    193      Everett  SH_CLERK  123.0  2005-03-03  3900.0   NaN    50.0\n",
      "94    194       McCain  SH_CLERK  123.0  2006-07-01  3200.0   NaN    50.0\n",
      "95    195        Jones  SH_CLERK  123.0  2007-03-17  2800.0   NaN    50.0\n",
      "96    196        Walsh  SH_CLERK  124.0  2006-04-24  3100.0   NaN    50.0\n",
      "97    197       Feeney  SH_CLERK  124.0  2006-05-23  3000.0   NaN    50.0\n",
      "98    198     OConnell  SH_CLERK  124.0  2007-06-21  2600.0   NaN    50.0\n",
      "99    199        Grant  SH_CLERK  124.0  2008-01-13  2600.0   NaN    50.0\n",
      "60.0\n",
      "   empid       name      job    mgr   hire_date     sal  comm  deptno\n",
      "3    103     Hunold  IT_PROG  102.0  2006-01-03  9000.0   NaN    60.0\n",
      "4    104      Ernst  IT_PROG  103.0  2007-05-21  6000.0   NaN    60.0\n",
      "5    105     Austin  IT_PROG  103.0  2005-06-25  4800.0   NaN    60.0\n",
      "6    106  Pataballa  IT_PROG  103.0  2006-02-05  4800.0   NaN    60.0\n",
      "7    107    Lorentz  IT_PROG  103.0  2007-02-07  4200.0   NaN    60.0\n",
      "70.0\n",
      "     empid  name     job    mgr   hire_date      sal  comm  deptno\n",
      "104    204  Baer  PR_REP  101.0  2002-06-07  10000.0   NaN    70.0\n",
      "80.0\n",
      "    empid        name     job    mgr   hire_date      sal  comm  deptno\n",
      "45    145     Russell  SA_MAN  100.0  2004-10-01  14000.0  0.40    80.0\n",
      "46    146    Partners  SA_MAN  100.0  2005-01-05  13500.0  0.30    80.0\n",
      "47    147   Errazuriz  SA_MAN  100.0  2005-03-10  12000.0  0.30    80.0\n",
      "48    148   Cambrault  SA_MAN  100.0  2007-10-15  11000.0  0.30    80.0\n",
      "49    149     Zlotkey  SA_MAN  100.0  2008-01-29  10500.0  0.20    80.0\n",
      "50    150      Tucker  SA_REP  145.0  2005-01-30  10000.0  0.30    80.0\n",
      "51    151   Bernstein  SA_REP  145.0  2005-03-24   9500.0  0.25    80.0\n",
      "52    152        Hall  SA_REP  145.0  2005-08-20   9000.0  0.25    80.0\n",
      "53    153       Olsen  SA_REP  145.0  2006-03-30   8000.0  0.20    80.0\n",
      "54    154   Cambrault  SA_REP  145.0  2006-12-09   7500.0  0.20    80.0\n",
      "55    155     Tuvault  SA_REP  145.0  2007-11-23   7000.0  0.15    80.0\n",
      "56    156        King  SA_REP  146.0  2004-01-30  10000.0  0.35    80.0\n",
      "57    157       Sully  SA_REP  146.0  2004-03-04   9500.0  0.35    80.0\n",
      "58    158      McEwen  SA_REP  146.0  2004-08-01   9000.0  0.35    80.0\n",
      "59    159       Smith  SA_REP  146.0  2005-03-10   8000.0  0.30    80.0\n",
      "60    160       Doran  SA_REP  146.0  2005-12-15   7500.0  0.30    80.0\n",
      "61    161      Sewall  SA_REP  146.0  2006-11-03   7000.0  0.25    80.0\n",
      "62    162     Vishney  SA_REP  147.0  2005-11-11  10500.0  0.25    80.0\n",
      "63    163      Greene  SA_REP  147.0  2007-03-19   9500.0  0.15    80.0\n",
      "64    164     Marvins  SA_REP  147.0  2008-01-24   7200.0  0.10    80.0\n",
      "65    165         Lee  SA_REP  147.0  2008-02-23   6800.0  0.10    80.0\n",
      "66    166        Ande  SA_REP  147.0  2008-03-24   6400.0  0.10    80.0\n",
      "67    167       Banda  SA_REP  147.0  2008-04-21   6200.0  0.10    80.0\n",
      "68    168        Ozer  SA_REP  148.0  2005-03-11  11500.0  0.25    80.0\n",
      "69    169       Bloom  SA_REP  148.0  2006-03-23  10000.0  0.20    80.0\n",
      "70    170         Fox  SA_REP  148.0  2006-01-24   9600.0  0.20    80.0\n",
      "71    171       Smith  SA_REP  148.0  2007-02-23   7400.0  0.15    80.0\n",
      "72    172       Bates  SA_REP  148.0  2007-03-24   7300.0  0.15    80.0\n",
      "73    173       Kumar  SA_REP  148.0  2008-04-21   6100.0  0.10    80.0\n",
      "74    174        Abel  SA_REP  149.0  2004-05-11  11000.0  0.30    80.0\n",
      "75    175      Hutton  SA_REP  149.0  2005-03-19   8800.0  0.25    80.0\n",
      "76    176      Taylor  SA_REP  149.0  2006-03-24   8600.0  0.20    80.0\n",
      "77    177  Livingston  SA_REP  149.0  2006-04-23   8400.0  0.20    80.0\n",
      "79    179     Johnson  SA_REP  149.0  2008-01-04   6200.0  0.10    80.0\n",
      "90.0\n",
      "   empid     name      job    mgr   hire_date      sal  comm  deptno\n",
      "0    100     King  AD_PRES    NaN  2003-06-17  24000.0   NaN    90.0\n",
      "1    101  Kochhar    AD_VP  100.0  2005-09-21  17000.0   NaN    90.0\n",
      "2    102  De Haan    AD_VP  100.0  2001-01-13  17000.0   NaN    90.0\n",
      "100.0\n",
      "    empid       name         job    mgr   hire_date      sal  comm  deptno\n",
      "8     108  Greenberg      FI_MGR  101.0  2002-08-17  13208.8   NaN   100.0\n",
      "9     109     Faviet  FI_ACCOUNT  108.0  2002-08-16   9900.0   NaN   100.0\n",
      "10    110       Chen  FI_ACCOUNT  108.0  2005-09-28   8200.0   NaN   100.0\n",
      "11    111    Sciarra  FI_ACCOUNT  108.0  2005-09-30   7700.0   NaN   100.0\n",
      "12    112      Urman  FI_ACCOUNT  108.0  2006-03-07   7800.0   NaN   100.0\n",
      "13    113       Popp  FI_ACCOUNT  108.0  2007-12-07   6900.0   NaN   100.0\n",
      "110.0\n",
      "     empid   name         job    mgr   hire_date     sal  comm  deptno\n",
      "106    206  Gietz  AC_ACCOUNT  205.0  2002-06-07  8300.0   NaN   110.0\n",
      "510.0\n",
      "     empid     name     job    mgr   hire_date      sal  comm  deptno\n",
      "105    205  Higgins  AC_MGR  101.0  2002-06-07  12008.0   NaN   510.0\n"
     ]
    }
   ],
   "source": [
    "for name,group in emp.groupby('deptno'):\n",
    "    print(name)\n",
    "    print(group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[문제155] 년도별로 입사한 인원수 결과를 출력해주세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import time\n",
    "import datetime\n",
    "import operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = open(\"c:\\python\\emp1.csv\",\"r\")\n",
    "emp_csv = csv.reader(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2003,\n",
       " 2005,\n",
       " 2001,\n",
       " 2006,\n",
       " 2007,\n",
       " 2005,\n",
       " 2006,\n",
       " 2007,\n",
       " 2002,\n",
       " 2002,\n",
       " 2005,\n",
       " 2005,\n",
       " 2006,\n",
       " 2007,\n",
       " 2002,\n",
       " 2003,\n",
       " 2005,\n",
       " 2005,\n",
       " 2006,\n",
       " 2007,\n",
       " 2004,\n",
       " 2005,\n",
       " 2003,\n",
       " 2005,\n",
       " 2007,\n",
       " 2005,\n",
       " 2006,\n",
       " 2007,\n",
       " 2008,\n",
       " 2005,\n",
       " 2005,\n",
       " 2005,\n",
       " 2007,\n",
       " 2004,\n",
       " 2006,\n",
       " 2007,\n",
       " 2008,\n",
       " 2003,\n",
       " 2005,\n",
       " 2006,\n",
       " 2006,\n",
       " 2003,\n",
       " 2005,\n",
       " 2006,\n",
       " 2006,\n",
       " 2004,\n",
       " 2005,\n",
       " 2005,\n",
       " 2007,\n",
       " 2008,\n",
       " 2005,\n",
       " 2005,\n",
       " 2005,\n",
       " 2006,\n",
       " 2006,\n",
       " 2007,\n",
       " 2004,\n",
       " 2004,\n",
       " 2004,\n",
       " 2005,\n",
       " 2005,\n",
       " 2006,\n",
       " 2005,\n",
       " 2007,\n",
       " 2008,\n",
       " 2008,\n",
       " 2008,\n",
       " 2008,\n",
       " 2005,\n",
       " 2006,\n",
       " 2006,\n",
       " 2007,\n",
       " 2007,\n",
       " 2008,\n",
       " 2004,\n",
       " 2005,\n",
       " 2006,\n",
       " 2006,\n",
       " 2007,\n",
       " 2008,\n",
       " 2006,\n",
       " 2006,\n",
       " 2007,\n",
       " 2008,\n",
       " 2004,\n",
       " 2005,\n",
       " 2006,\n",
       " 2007,\n",
       " 2005,\n",
       " 2005,\n",
       " 2006,\n",
       " 2007,\n",
       " 2004,\n",
       " 2005,\n",
       " 2006,\n",
       " 2007,\n",
       " 2006,\n",
       " 2006,\n",
       " 2007,\n",
       " 2008,\n",
       " 2003,\n",
       " 2004,\n",
       " 2005,\n",
       " 2002,\n",
       " 2002,\n",
       " 2002,\n",
       " 2002]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = open(\"c:\\python\\emp1.csv\",\"r\")\n",
    "emp_csv = csv.reader(file)\n",
    "hire_date = []\n",
    "for i in emp_csv:\n",
    "    hire_date.append(time.strptime(i[4],'%Y-%m-%d').tm_year)\n",
    "\n",
    "hire_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2003: 6, 2005: 29, 2001: 1, 2006: 24, 2007: 19, 2002: 7, 2004: 10, 2008: 11}"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = open(\"c:\\python\\emp1.csv\",\"r\")\n",
    "emp_csv = csv.reader(file)\n",
    "\n",
    "dept_dic={}\n",
    "year=0\n",
    "for i in emp_csv:\n",
    "    year=int(time.strptime(i[4],'%Y-%m-%d').tm_year)\n",
    "    if year in dept_dic:\n",
    "        dept_dic[year] = dept_dic[year]+1\n",
    "    else:\n",
    "        dept_dic[year] = 1\n",
    "\n",
    "dept_dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2001 1\n",
      "2002 7\n",
      "2003 6\n",
      "2004 10\n",
      "2005 29\n",
      "2006 24\n",
      "2007 19\n",
      "2008 11\n"
     ]
    }
   ],
   "source": [
    "for k,v in sorted(dept_dic.items(),key=operator.itemgetter(0)):\n",
    "    print(k,v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pandas 사용 \n",
    "\n",
    "from pandas import Series,DataFrame\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "emp = pd.read_csv(\"c:\\python\\emp1.csv\",\n",
    "                 names=['empid', 'name', 'job', 'mgr', 'hire_date', 'sal', 'comm', 'deptno'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hire_date\n",
       "2001     1\n",
       "2002     7\n",
       "2003     6\n",
       "2004    10\n",
       "2005    29\n",
       "2006    24\n",
       "2007    19\n",
       "2008    11\n",
       "Name: empid, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp.groupby(emp['hire_date'].apply(lambda x : x[0:4]))['empid'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hire_date\n",
       "2001     1\n",
       "2002     7\n",
       "2003     6\n",
       "2004    10\n",
       "2005    29\n",
       "2006    24\n",
       "2007    19\n",
       "2008    11\n",
       "Name: empid, dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#[선생님]\n",
    "\n",
    "from pandas import Series,DataFrame\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "emp = pd.read_csv(\"c:\\python\\emp1.csv\",\n",
    "                 names=['empid', 'name', 'job', 'mgr', 'hire_date', 'sal', 'comm', 'deptno'])\n",
    "\n",
    "emp.groupby(emp['hire_date'].str.slice(0,4))['empid'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[문제156] dept.csv 를 dept 리스트 변수안에 아래 모양과 같은 딕셔너리 데이터 유형에 데이트를 입력한 후 출력하세요.\n",
    "\n",
    "\n",
    "{'deptno': '10', 'dname': 'Administration', 'mgr': '200', 'loc': '1700'}\n",
    "    ↑            ↑\n",
    "    키           값 \n",
    "\n",
    "10 Administration 200 1700\n",
    "20 Marketing 201 1800\n",
    "30 Purchasing 114 1700\n",
    "40 Human Resources 203 2400\n",
    "50 Shipping 121 1500\n",
    "60 IT 103 1400\n",
    "70 Public Relations 204 2700"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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>deptno</th>\n",
       "      <th>dname</th>\n",
       "      <th>mgr</th>\n",
       "      <th>loc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>Administration</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>Purchasing</td>\n",
       "      <td>114.0</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>203.0</td>\n",
       "      <td>2400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>Shipping</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60</td>\n",
       "      <td>IT</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>70</td>\n",
       "      <td>Public Relations</td>\n",
       "      <td>204.0</td>\n",
       "      <td>2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>80</td>\n",
       "      <td>Sales</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>90</td>\n",
       "      <td>Executive</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>100</td>\n",
       "      <td>Finance</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>110</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>205.0</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>120</td>\n",
       "      <td>Treasury</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>130</td>\n",
       "      <td>Corporate Tax</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>140</td>\n",
       "      <td>Control And Credit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>150</td>\n",
       "      <td>Shareholder Services</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>160</td>\n",
       "      <td>Benefits</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>170</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>180</td>\n",
       "      <td>Construction</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>190</td>\n",
       "      <td>Contracting</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>200</td>\n",
       "      <td>Operations</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>210</td>\n",
       "      <td>IT Support</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>220</td>\n",
       "      <td>NOC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>230</td>\n",
       "      <td>IT Helpdesk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>240</td>\n",
       "      <td>Government Sales</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>250</td>\n",
       "      <td>Retail Sales</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>260</td>\n",
       "      <td>Recruiting</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>270</td>\n",
       "      <td>Payroll</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    deptno                 dname    mgr   loc\n",
       "0       10        Administration  200.0  1700\n",
       "1       20             Marketing  201.0  1800\n",
       "2       30            Purchasing  114.0  1700\n",
       "3       40       Human Resources  203.0  2400\n",
       "4       50              Shipping  121.0  1500\n",
       "5       60                    IT  103.0  1400\n",
       "6       70      Public Relations  204.0  2700\n",
       "7       80                 Sales  145.0  2500\n",
       "8       90             Executive  100.0  1700\n",
       "9      100               Finance  108.0  1700\n",
       "10     110            Accounting  205.0  1700\n",
       "11     120              Treasury    NaN  1700\n",
       "12     130         Corporate Tax    NaN  1700\n",
       "13     140    Control And Credit    NaN  1700\n",
       "14     150  Shareholder Services    NaN  1700\n",
       "15     160              Benefits    NaN  1700\n",
       "16     170         Manufacturing    NaN  1700\n",
       "17     180          Construction    NaN  1700\n",
       "18     190           Contracting    NaN  1700\n",
       "19     200            Operations    NaN  1700\n",
       "20     210            IT Support    NaN  1700\n",
       "21     220                   NOC    NaN  1700\n",
       "22     230           IT Helpdesk    NaN  1700\n",
       "23     240      Government Sales    NaN  1700\n",
       "24     250          Retail Sales    NaN  1700\n",
       "25     260            Recruiting    NaN  1700\n",
       "26     270               Payroll    NaN  1700"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp = pd.read_csv(\"c:\\python\\dept.csv\",\n",
    "                 names = ['deptno','dname','mgr','loc'])\n",
    "emp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'deptno': '10', 'dname': 'Administration', 'mgr': '200', 'loc': '1700'},\n",
       " {'deptno': '20', 'dname': 'Marketing', 'mgr': '201', 'loc': '1800'},\n",
       " {'deptno': '30', 'dname': 'Purchasing', 'mgr': '114', 'loc': '1700'},\n",
       " {'deptno': '40', 'dname': 'Human Resources', 'mgr': '203', 'loc': '2400'},\n",
       " {'deptno': '50', 'dname': 'Shipping', 'mgr': '121', 'loc': '1500'},\n",
       " {'deptno': '60', 'dname': 'IT', 'mgr': '103', 'loc': '1400'},\n",
       " {'deptno': '70', 'dname': 'Public Relations', 'mgr': '204', 'loc': '2700'},\n",
       " {'deptno': '80', 'dname': 'Sales', 'mgr': '145', 'loc': '2500'},\n",
       " {'deptno': '90', 'dname': 'Executive', 'mgr': '100', 'loc': '1700'},\n",
       " {'deptno': '100', 'dname': 'Finance', 'mgr': '108', 'loc': '1700'},\n",
       " {'deptno': '110', 'dname': 'Accounting', 'mgr': '205', 'loc': '1700'},\n",
       " {'deptno': '120', 'dname': 'Treasury', 'mgr': '', 'loc': '1700'},\n",
       " {'deptno': '130', 'dname': 'Corporate Tax', 'mgr': '', 'loc': '1700'},\n",
       " {'deptno': '140', 'dname': 'Control And Credit', 'mgr': '', 'loc': '1700'},\n",
       " {'deptno': '150', 'dname': 'Shareholder Services', 'mgr': '', 'loc': '1700'},\n",
       " {'deptno': '160', 'dname': 'Benefits', 'mgr': '', 'loc': '1700'},\n",
       " {'deptno': '170', 'dname': 'Manufacturing', 'mgr': '', 'loc': '1700'},\n",
       " {'deptno': '180', 'dname': 'Construction', 'mgr': '', 'loc': '1700'},\n",
       " {'deptno': '190', 'dname': 'Contracting', 'mgr': '', 'loc': '1700'},\n",
       " {'deptno': '200', 'dname': 'Operations', 'mgr': '', 'loc': '1700'},\n",
       " {'deptno': '210', 'dname': 'IT Support', 'mgr': '', 'loc': '1700'},\n",
       " {'deptno': '220', 'dname': 'NOC', 'mgr': '', 'loc': '1700'},\n",
       " {'deptno': '230', 'dname': 'IT Helpdesk', 'mgr': '', 'loc': '1700'},\n",
       " {'deptno': '240', 'dname': 'Government Sales', 'mgr': '', 'loc': '1700'},\n",
       " {'deptno': '250', 'dname': 'Retail Sales', 'mgr': '', 'loc': '1700'},\n",
       " {'deptno': '260', 'dname': 'Recruiting', 'mgr': '', 'loc': '1700'},\n",
       " {'deptno': '270', 'dname': 'Payroll', 'mgr': '', 'loc': '1700'}]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import csv\n",
    "\n",
    "file = open('c:\\python\\dept.csv','r')\n",
    "dept_csv = csv.reader(file)\n",
    "\n",
    "dept = []\n",
    "for i in dept_csv:\n",
    "    if not i[0] in dept:\n",
    "        dept.append({'deptno': i[0],'dname': i[1],'mgr' : i[2],'loc' : i[3]})\n",
    "dept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 Administration 200 1700\n",
      "20 Marketing 201 1800\n",
      "30 Purchasing 114 1700\n",
      "40 Human Resources 203 2400\n",
      "50 Shipping 121 1500\n",
      "60 IT 103 1400\n",
      "70 Public Relations 204 2700\n",
      "80 Sales 145 2500\n",
      "90 Executive 100 1700\n",
      "100 Finance 108 1700\n",
      "110 Accounting 205 1700\n",
      "120 Treasury  1700\n",
      "130 Corporate Tax  1700\n",
      "140 Control And Credit  1700\n",
      "150 Shareholder Services  1700\n",
      "160 Benefits  1700\n",
      "170 Manufacturing  1700\n",
      "180 Construction  1700\n",
      "190 Contracting  1700\n",
      "200 Operations  1700\n",
      "210 IT Support  1700\n",
      "220 NOC  1700\n",
      "230 IT Helpdesk  1700\n",
      "240 Government Sales  1700\n",
      "250 Retail Sales  1700\n",
      "260 Recruiting  1700\n",
      "270 Payroll  1700\n"
     ]
    }
   ],
   "source": [
    "for i in dept:\n",
    "    print(i['deptno'],i['dname'],i['mgr'],i['loc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[문제157] emp.csv 를 emp리스트 변수안에 넣되 아래 모양과 같은 딕셔너리 데이터 유형에 데이타를 입력한 후 출력하세요.\n",
    "\n",
    "{'empno': 100, 'ename': King, 'job': AD_PRES, 'mgr': '', 'hiredate': 2003-06-17, 'sal': 24000, 'comm': '', 'deptno': 90}\n",
    "\n",
    "100 King AD_PRES  2003-06-17 24000  90\n",
    "101 Kochhar AD_VP 100 2005-09-21 17000  90\n",
    "102 De Haan AD_VP 100 2001-01-13 17000  90\n",
    "103 Hunold IT_PROG 102 2006-01-03 9000  60\n",
    "104 Ernst IT_PROG 103 2007-05-21 6000  60\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "file = open('c:\\python\\emp1.csv','r')\n",
    "emp_csv = csv.reader(file)\n",
    "\n",
    "emp = []\n",
    "for i in emp_csv:\n",
    "    emp.append({'empno':i[0],'ename':i[1],'job':i[2],'mgr':i[3],'hiredate':i[4],'sal':i[5],'comm':i[6],'deptno':i[7]})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 King 2003-06-17 24000 90\n",
      "101 Kochhar 2005-09-21 17000 90\n",
      "102 De Haan 2001-01-13 17000 90\n",
      "103 Hunold 2006-01-03 9000 60\n",
      "104 Ernst 2007-05-21 6000 60\n",
      "105 Austin 2005-06-25 4800 60\n",
      "106 Pataballa 2006-02-05 4800 60\n",
      "107 Lorentz 2007-02-07 4200 60\n",
      "108 Greenberg 2002-08-17 13208.8 100\n",
      "109 Faviet 2002-08-16 9900 100\n",
      "110 Chen 2005-09-28 8200 100\n",
      "111 Sciarra 2005-09-30 7700 100\n",
      "112 Urman 2006-03-07 7800 100\n",
      "113 Popp 2007-12-07 6900 100\n",
      "114 Raphaely 2002-12-07 11000 30\n",
      "115 Khoo 2003-05-18 3100 30\n",
      "116 Baida 2005-12-24 2900 30\n",
      "117 Tobias 2005-07-24 2800 30\n",
      "118 Himuro 2006-11-15 2600 30\n",
      "119 Colmeres 2007-08-10 2500 30\n",
      "120 Weiss 2004-07-18 8000 50\n",
      "121 Fripp 2005-04-10 8200 50\n",
      "122 Kaufling 2003-05-01 7900 50\n",
      "123 Vollman 2005-10-10 6500 50\n",
      "124 Mourgos 2007-11-16 5800 50\n",
      "125 yer 2005-07-16 3200 50\n",
      "126 Mikkilineni 2006-09-28 2700 50\n",
      "127 Landry 2007-01-14 2400 50\n",
      "128 Markle 2008-03-08 2200 50\n",
      "129 Bissot 2005-08-20 3300 50\n",
      "130 Atkinson 2005-10-30 2800 50\n",
      "131 Marlow 2005-02-16 2500 50\n",
      "132 Olson 2007-04-10 2100 50\n",
      "133 Mallin 2004-06-14 3300 50\n",
      "134 Rogers 2006-08-26 2900 50\n",
      "135 Gee 2007-12-12 2400 50\n",
      "136 Philtanker 2008-02-06 2200 50\n",
      "137 Ladwig 2003-07-14 3600 50\n",
      "138 Stiles 2005-10-26 3200 50\n",
      "139 Seo 2006-02-12 2700 50\n",
      "140 Patel 2006-04-06 2500 50\n",
      "141 Rajs 2003-10-17 3500 50\n",
      "142 Davies 2005-01-29 3100 50\n",
      "143 Matos 2006-03-15 2600 50\n",
      "144 Vargas 2006-07-09 2500 50\n",
      "145 Russell 2004-10-01 14000 80\n",
      "146 Partners 2005-01-05 13500 80\n",
      "147 Errazuriz 2005-03-10 12000 80\n",
      "148 Cambrault 2007-10-15 11000 80\n",
      "149 Zlotkey 2008-01-29 10500 80\n",
      "150 Tucker 2005-01-30 10000 80\n",
      "151 Bernstein 2005-03-24 9500 80\n",
      "152 Hall 2005-08-20 9000 80\n",
      "153 Olsen 2006-03-30 8000 80\n",
      "154 Cambrault 2006-12-09 7500 80\n",
      "155 Tuvault 2007-11-23 7000 80\n",
      "156 King 2004-01-30 10000 80\n",
      "157 Sully 2004-03-04 9500 80\n",
      "158 McEwen 2004-08-01 9000 80\n",
      "159 Smith 2005-03-10 8000 80\n",
      "160 Doran 2005-12-15 7500 80\n",
      "161 Sewall 2006-11-03 7000 80\n",
      "162 Vishney 2005-11-11 10500 80\n",
      "163 Greene 2007-03-19 9500 80\n",
      "164 Marvins 2008-01-24 7200 80\n",
      "165 Lee 2008-02-23 6800 80\n",
      "166 Ande 2008-03-24 6400 80\n",
      "167 Banda 2008-04-21 6200 80\n",
      "168 Ozer 2005-03-11 11500 80\n",
      "169 Bloom 2006-03-23 10000 80\n",
      "170 Fox 2006-01-24 9600 80\n",
      "171 Smith 2007-02-23 7400 80\n",
      "172 Bates 2007-03-24 7300 80\n",
      "173 Kumar 2008-04-21 6100 80\n",
      "174 Abel 2004-05-11 11000 80\n",
      "175 Hutton 2005-03-19 8800 80\n",
      "176 Taylor 2006-03-24 8600 80\n",
      "177 Livingston 2006-04-23 8400 80\n",
      "178 Grant 2007-05-24 7000 \n",
      "179 Johnson 2008-01-04 6200 80\n",
      "180 Taylor 2006-01-24 3200 50\n",
      "181 Fleaur 2006-02-23 3100 50\n",
      "182 Sullivan 2007-06-21 2500 50\n",
      "183 Geoni 2008-02-03 2800 50\n",
      "184 Sarchand 2004-01-27 4200 50\n",
      "185 Bull 2005-02-20 4100 50\n",
      "186 Dellinger 2006-06-24 3400 50\n",
      "187 Cabrio 2007-02-07 3000 50\n",
      "188 Chung 2005-06-14 3800 50\n",
      "189 Dilly 2005-08-13 3600 50\n",
      "190 Gates 2006-07-11 2900 50\n",
      "191 Perkins 2007-12-19 2500 50\n",
      "192 Bell 2004-02-04 4000 50\n",
      "193 Everett 2005-03-03 3900 50\n",
      "194 McCain 2006-07-01 3200 50\n",
      "195 Jones 2007-03-17 2800 50\n",
      "196 Walsh 2006-04-24 3100 50\n",
      "197 Feeney 2006-05-23 3000 50\n",
      "198 OConnell 2007-06-21 2600 50\n",
      "199 Grant 2008-01-13 2600 50\n",
      "200 Whalen 2003-09-17 4400 10\n",
      "201 Hartstein 2004-02-17 13000 20\n",
      "202 Fay 2005-08-17 6000 20\n",
      "203 Mavris 2002-06-07 6500 40\n",
      "204 Baer 2002-06-07 10000 70\n",
      "205 Higgins 2002-06-07 12008 510\n",
      "206 Gietz 2002-06-07 8300 110\n"
     ]
    }
   ],
   "source": [
    "for i in emp:\n",
    "    print(i['empno'],i['ename'],i['hiredate'],i['sal'],i['deptno'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[문제158] emp.csv, dept.csv 파일을 읽어서 사원의 이름, 부서 이름을 출력하세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = open(\"c:\\python\\emp1.csv\",\"r\")\n",
    "emp_csv = csv.reader(file)\n",
    "\n",
    "emp = []\n",
    "for i in emp_csv:\n",
    "    emp.append({'deptno':i[7],'name':i[1]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = open(\"c:\\python\\dept.csv\")\n",
    "dept_csv = csv.reader(file)\n",
    "\n",
    "dept = []\n",
    "for i in dept_csv:\n",
    "    dept.append({'deptno': i[0],'dname':i[1]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Whalen Administration\n",
      "Hartstein Marketing\n",
      "Fay Marketing\n",
      "Raphaely Purchasing\n",
      "Khoo Purchasing\n",
      "Baida Purchasing\n",
      "Tobias Purchasing\n",
      "Himuro Purchasing\n",
      "Colmeres Purchasing\n",
      "Mavris Human Resources\n",
      "Weiss Shipping\n",
      "Fripp Shipping\n",
      "Kaufling Shipping\n",
      "Vollman Shipping\n",
      "Mourgos Shipping\n",
      "yer Shipping\n",
      "Mikkilineni Shipping\n",
      "Landry Shipping\n",
      "Markle Shipping\n",
      "Bissot Shipping\n",
      "Atkinson Shipping\n",
      "Marlow Shipping\n",
      "Olson Shipping\n",
      "Mallin Shipping\n",
      "Rogers Shipping\n",
      "Gee Shipping\n",
      "Philtanker Shipping\n",
      "Ladwig Shipping\n",
      "Stiles Shipping\n",
      "Seo Shipping\n",
      "Patel Shipping\n",
      "Rajs Shipping\n",
      "Davies Shipping\n",
      "Matos Shipping\n",
      "Vargas Shipping\n",
      "Taylor Shipping\n",
      "Fleaur Shipping\n",
      "Sullivan Shipping\n",
      "Geoni Shipping\n",
      "Sarchand Shipping\n",
      "Bull Shipping\n",
      "Dellinger Shipping\n",
      "Cabrio Shipping\n",
      "Chung Shipping\n",
      "Dilly Shipping\n",
      "Gates Shipping\n",
      "Perkins Shipping\n",
      "Bell Shipping\n",
      "Everett Shipping\n",
      "McCain Shipping\n",
      "Jones Shipping\n",
      "Walsh Shipping\n",
      "Feeney Shipping\n",
      "OConnell Shipping\n",
      "Grant Shipping\n",
      "Hunold IT\n",
      "Ernst IT\n",
      "Austin IT\n",
      "Pataballa IT\n",
      "Lorentz IT\n",
      "Baer Public Relations\n",
      "Russell Sales\n",
      "Partners Sales\n",
      "Errazuriz Sales\n",
      "Cambrault Sales\n",
      "Zlotkey Sales\n",
      "Tucker Sales\n",
      "Bernstein Sales\n",
      "Hall Sales\n",
      "Olsen Sales\n",
      "Cambrault Sales\n",
      "Tuvault Sales\n",
      "King Sales\n",
      "Sully Sales\n",
      "McEwen Sales\n",
      "Smith Sales\n",
      "Doran Sales\n",
      "Sewall Sales\n",
      "Vishney Sales\n",
      "Greene Sales\n",
      "Marvins Sales\n",
      "Lee Sales\n",
      "Ande Sales\n",
      "Banda Sales\n",
      "Ozer Sales\n",
      "Bloom Sales\n",
      "Fox Sales\n",
      "Smith Sales\n",
      "Bates Sales\n",
      "Kumar Sales\n",
      "Abel Sales\n",
      "Hutton Sales\n",
      "Taylor Sales\n",
      "Livingston Sales\n",
      "Johnson Sales\n",
      "King Executive\n",
      "Kochhar Executive\n",
      "De Haan Executive\n",
      "Greenberg Finance\n",
      "Faviet Finance\n",
      "Chen Finance\n",
      "Sciarra Finance\n",
      "Urman Finance\n",
      "Popp Finance\n",
      "Gietz Accounting\n"
     ]
    }
   ],
   "source": [
    "for i in dept:\n",
    "    for s in emp:\n",
    "        if i['deptno'] ==s['deptno']:\n",
    "            print(s['name'],i['dname'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[문제159] 사원들의 이름, 부서 이름을 출력하면서 소속부서가 없는 사원도 출력해주세요. 마지막에는 총건수도 출력하세요. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'deptno': '90', 'name': 'King'},\n",
       " {'deptno': '90', 'name': 'Kochhar'},\n",
       " {'deptno': '90', 'name': 'De Haan'},\n",
       " {'deptno': '60', 'name': 'Hunold'},\n",
       " {'deptno': '60', 'name': 'Ernst'},\n",
       " {'deptno': '60', 'name': 'Austin'},\n",
       " {'deptno': '60', 'name': 'Pataballa'},\n",
       " {'deptno': '60', 'name': 'Lorentz'},\n",
       " {'deptno': '100', 'name': 'Greenberg'},\n",
       " {'deptno': '100', 'name': 'Faviet'},\n",
       " {'deptno': '100', 'name': 'Chen'},\n",
       " {'deptno': '100', 'name': 'Sciarra'},\n",
       " {'deptno': '100', 'name': 'Urman'},\n",
       " {'deptno': '100', 'name': 'Popp'},\n",
       " {'deptno': '30', 'name': 'Raphaely'},\n",
       " {'deptno': '30', 'name': 'Khoo'},\n",
       " {'deptno': '30', 'name': 'Baida'},\n",
       " {'deptno': '30', 'name': 'Tobias'},\n",
       " {'deptno': '30', 'name': 'Himuro'},\n",
       " {'deptno': '30', 'name': 'Colmeres'},\n",
       " {'deptno': '50', 'name': 'Weiss'},\n",
       " {'deptno': '50', 'name': 'Fripp'},\n",
       " {'deptno': '50', 'name': 'Kaufling'},\n",
       " {'deptno': '50', 'name': 'Vollman'},\n",
       " {'deptno': '50', 'name': 'Mourgos'},\n",
       " {'deptno': '50', 'name': 'yer'},\n",
       " {'deptno': '50', 'name': 'Mikkilineni'},\n",
       " {'deptno': '50', 'name': 'Landry'},\n",
       " {'deptno': '50', 'name': 'Markle'},\n",
       " {'deptno': '50', 'name': 'Bissot'},\n",
       " {'deptno': '50', 'name': 'Atkinson'},\n",
       " {'deptno': '50', 'name': 'Marlow'},\n",
       " {'deptno': '50', 'name': 'Olson'},\n",
       " {'deptno': '50', 'name': 'Mallin'},\n",
       " {'deptno': '50', 'name': 'Rogers'},\n",
       " {'deptno': '50', 'name': 'Gee'},\n",
       " {'deptno': '50', 'name': 'Philtanker'},\n",
       " {'deptno': '50', 'name': 'Ladwig'},\n",
       " {'deptno': '50', 'name': 'Stiles'},\n",
       " {'deptno': '50', 'name': 'Seo'},\n",
       " {'deptno': '50', 'name': 'Patel'},\n",
       " {'deptno': '50', 'name': 'Rajs'},\n",
       " {'deptno': '50', 'name': 'Davies'},\n",
       " {'deptno': '50', 'name': 'Matos'},\n",
       " {'deptno': '50', 'name': 'Vargas'},\n",
       " {'deptno': '80', 'name': 'Russell'},\n",
       " {'deptno': '80', 'name': 'Partners'},\n",
       " {'deptno': '80', 'name': 'Errazuriz'},\n",
       " {'deptno': '80', 'name': 'Cambrault'},\n",
       " {'deptno': '80', 'name': 'Zlotkey'},\n",
       " {'deptno': '80', 'name': 'Tucker'},\n",
       " {'deptno': '80', 'name': 'Bernstein'},\n",
       " {'deptno': '80', 'name': 'Hall'},\n",
       " {'deptno': '80', 'name': 'Olsen'},\n",
       " {'deptno': '80', 'name': 'Cambrault'},\n",
       " {'deptno': '80', 'name': 'Tuvault'},\n",
       " {'deptno': '80', 'name': 'King'},\n",
       " {'deptno': '80', 'name': 'Sully'},\n",
       " {'deptno': '80', 'name': 'McEwen'},\n",
       " {'deptno': '80', 'name': 'Smith'},\n",
       " {'deptno': '80', 'name': 'Doran'},\n",
       " {'deptno': '80', 'name': 'Sewall'},\n",
       " {'deptno': '80', 'name': 'Vishney'},\n",
       " {'deptno': '80', 'name': 'Greene'},\n",
       " {'deptno': '80', 'name': 'Marvins'},\n",
       " {'deptno': '80', 'name': 'Lee'},\n",
       " {'deptno': '80', 'name': 'Ande'},\n",
       " {'deptno': '80', 'name': 'Banda'},\n",
       " {'deptno': '80', 'name': 'Ozer'},\n",
       " {'deptno': '80', 'name': 'Bloom'},\n",
       " {'deptno': '80', 'name': 'Fox'},\n",
       " {'deptno': '80', 'name': 'Smith'},\n",
       " {'deptno': '80', 'name': 'Bates'},\n",
       " {'deptno': '80', 'name': 'Kumar'},\n",
       " {'deptno': '80', 'name': 'Abel'},\n",
       " {'deptno': '80', 'name': 'Hutton'},\n",
       " {'deptno': '80', 'name': 'Taylor'},\n",
       " {'deptno': '80', 'name': 'Livingston'},\n",
       " {'deptno': '', 'name': 'Grant'},\n",
       " {'deptno': '80', 'name': 'Johnson'},\n",
       " {'deptno': '50', 'name': 'Taylor'},\n",
       " {'deptno': '50', 'name': 'Fleaur'},\n",
       " {'deptno': '50', 'name': 'Sullivan'},\n",
       " {'deptno': '50', 'name': 'Geoni'},\n",
       " {'deptno': '50', 'name': 'Sarchand'},\n",
       " {'deptno': '50', 'name': 'Bull'},\n",
       " {'deptno': '50', 'name': 'Dellinger'},\n",
       " {'deptno': '50', 'name': 'Cabrio'},\n",
       " {'deptno': '50', 'name': 'Chung'},\n",
       " {'deptno': '50', 'name': 'Dilly'},\n",
       " {'deptno': '50', 'name': 'Gates'},\n",
       " {'deptno': '50', 'name': 'Perkins'},\n",
       " {'deptno': '50', 'name': 'Bell'},\n",
       " {'deptno': '50', 'name': 'Everett'},\n",
       " {'deptno': '50', 'name': 'McCain'},\n",
       " {'deptno': '50', 'name': 'Jones'},\n",
       " {'deptno': '50', 'name': 'Walsh'},\n",
       " {'deptno': '50', 'name': 'Feeney'},\n",
       " {'deptno': '50', 'name': 'OConnell'},\n",
       " {'deptno': '50', 'name': 'Grant'},\n",
       " {'deptno': '10', 'name': 'Whalen'},\n",
       " {'deptno': '20', 'name': 'Hartstein'},\n",
       " {'deptno': '20', 'name': 'Fay'},\n",
       " {'deptno': '40', 'name': 'Mavris'},\n",
       " {'deptno': '70', 'name': 'Baer'},\n",
       " {'deptno': '510', 'name': 'Higgins'},\n",
       " {'deptno': '110', 'name': 'Gietz'}]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import csv\n",
    "\n",
    "file = open(\"c:\\python\\emp1.csv\",\"r\")\n",
    "emp_csv = csv.reader(file)\n",
    "\n",
    "emp = []\n",
    "for i in emp_csv:\n",
    "    emp.append({'deptno':i[7],'name':i[1]})\n",
    "emp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = open(\"c:\\python\\dept.csv\",\"r\")\n",
    "dept_csv = csv.reader(file)\n",
    "\n",
    "dept=[]\n",
    "for i in dept_csv:\n",
    "    dept.append({'deptno':i[0],'dname':i[1]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "King Executive\n",
      "Kochhar Executive\n",
      "De Haan Executive\n",
      "Hunold IT\n",
      "Ernst IT\n",
      "Austin IT\n",
      "Pataballa IT\n",
      "Lorentz IT\n",
      "Greenberg Finance\n",
      "Faviet Finance\n",
      "Chen Finance\n",
      "Sciarra Finance\n",
      "Urman Finance\n",
      "Popp Finance\n",
      "Raphaely Purchasing\n",
      "Khoo Purchasing\n",
      "Baida Purchasing\n",
      "Tobias Purchasing\n",
      "Himuro Purchasing\n",
      "Colmeres Purchasing\n",
      "Weiss Shipping\n",
      "Fripp Shipping\n",
      "Kaufling Shipping\n",
      "Vollman Shipping\n",
      "Mourgos Shipping\n",
      "yer Shipping\n",
      "Mikkilineni Shipping\n",
      "Landry Shipping\n",
      "Markle Shipping\n",
      "Bissot Shipping\n",
      "Atkinson Shipping\n",
      "Marlow Shipping\n",
      "Olson Shipping\n",
      "Mallin Shipping\n",
      "Rogers Shipping\n",
      "Gee Shipping\n",
      "Philtanker Shipping\n",
      "Ladwig Shipping\n",
      "Stiles Shipping\n",
      "Seo Shipping\n",
      "Patel Shipping\n",
      "Rajs Shipping\n",
      "Davies Shipping\n",
      "Matos Shipping\n",
      "Vargas Shipping\n",
      "Russell Sales\n",
      "Partners Sales\n",
      "Errazuriz Sales\n",
      "Cambrault Sales\n",
      "Zlotkey Sales\n",
      "Tucker Sales\n",
      "Bernstein Sales\n",
      "Hall Sales\n",
      "Olsen Sales\n",
      "Cambrault Sales\n",
      "Tuvault Sales\n",
      "King Sales\n",
      "Sully Sales\n",
      "McEwen Sales\n",
      "Smith Sales\n",
      "Doran Sales\n",
      "Sewall Sales\n",
      "Vishney Sales\n",
      "Greene Sales\n",
      "Marvins Sales\n",
      "Lee Sales\n",
      "Ande Sales\n",
      "Banda Sales\n",
      "Ozer Sales\n",
      "Bloom Sales\n",
      "Fox Sales\n",
      "Smith Sales\n",
      "Bates Sales\n",
      "Kumar Sales\n",
      "Abel Sales\n",
      "Hutton Sales\n",
      "Taylor Sales\n",
      "Livingston Sales\n",
      "Grant NAAAAAAAAAAAAAAAAAA\n",
      "Johnson Sales\n",
      "Taylor Shipping\n",
      "Fleaur Shipping\n",
      "Sullivan Shipping\n",
      "Geoni Shipping\n",
      "Sarchand Shipping\n",
      "Bull Shipping\n",
      "Dellinger Shipping\n",
      "Cabrio Shipping\n",
      "Chung Shipping\n",
      "Dilly Shipping\n",
      "Gates Shipping\n",
      "Perkins Shipping\n",
      "Bell Shipping\n",
      "Everett Shipping\n",
      "McCain Shipping\n",
      "Jones Shipping\n",
      "Walsh Shipping\n",
      "Feeney Shipping\n",
      "OConnell Shipping\n",
      "Grant Shipping\n",
      "Whalen Administration\n",
      "Hartstein Marketing\n",
      "Fay Marketing\n",
      "Mavris Human Resources\n",
      "Baer Public Relations\n",
      "Gietz Accounting\n",
      "106\n"
     ]
    }
   ],
   "source": [
    "co = 0\n",
    "for i in emp:\n",
    "    if i['deptno']=='':\n",
    "        print(i['name'],'NAAAAAAAAAAAAAAAAAA')\n",
    "        co+=1\n",
    "    for s in dept:\n",
    "        if s['deptno'] == i['deptno']:\n",
    "            co += 1\n",
    "            print(i['name'],s['dname'])\n",
    "print(co)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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