2.3.조건에 따른row,column선택 및 삭제.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 조건에 따른 row/column 선택 및 삭제 \n",
    "### 조건에 따른 row 선택  \n",
    "<b> Series boolean selection 활용하기</b>\n",
    "    \n",
    "    조건이 하나일 때 : df[df['컬럼명'] >= 40]\n",
    "    조건이 두개 이상 : df[(조건1) & (조건2) & ((조건3) | (조건4)) ]\n",
    "    ** 관계연산자의 우선순위를 모른다면 그냥 괄호를 많이 쓸 것 \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>embark_town</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>False</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "   adult_male  embark_town  \n",
       "0        True  Southampton  \n",
       "1       False    Cherbourg  \n",
       "2       False  Southampton  \n",
       "3       False  Southampton  \n",
       "4        True  Southampton  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "df_t = pd.read_csv(\"../../data/titanic.csv\")\n",
    "df_t.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>embark_town</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>False</td>\n",
       "      <td>Cherbourg</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "      <td>Southampton</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>C</td>\n",
       "      <td>Second</td>\n",
       "      <td>False</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>C</td>\n",
       "      <td>Third</td>\n",
       "      <td>False</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>False</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>True</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>342 rows × 11 columns</p>\n",
       "</div>"
      ],
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       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "8           1       3  female  27.0      0      2  11.1333        S   Third   \n",
       "9           1       2  female  14.0      1      0  30.0708        C  Second   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "875         1       3  female  15.0      0      0   7.2250        C   Third   \n",
       "879         1       1  female  56.0      0      1  83.1583        C   First   \n",
       "880         1       2  female  25.0      0      1  26.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "\n",
       "     adult_male  embark_town  \n",
       "1         False    Cherbourg  \n",
       "2         False  Southampton  \n",
       "3         False  Southampton  \n",
       "8         False  Southampton  \n",
       "9         False    Cherbourg  \n",
       "..          ...          ...  \n",
       "875       False    Cherbourg  \n",
       "879       False    Cherbourg  \n",
       "880       False  Southampton  \n",
       "887       False  Southampton  \n",
       "889        True    Cherbourg  \n",
       "\n",
       "[342 rows x 11 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t[df_t['survived']==1] ## 하나의 조건문 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1       True\n",
       "2       True\n",
       "3       True\n",
       "4      False\n",
       "       ...  \n",
       "886    False\n",
       "887     True\n",
       "888    False\n",
       "889     True\n",
       "890    False\n",
       "Name: survived, Length: 891, dtype: bool"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t['survived']==1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
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       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
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       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
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       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>C</td>\n",
       "      <td>Third</td>\n",
       "      <td>False</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>Cherbourg</td>\n",
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       "      <td>1</td>\n",
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       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>True</td>\n",
       "      <td>Cherbourg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>396 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "6           0       1    male  54.0      0      0  51.8625        S   First   \n",
       "8           1       3  female  27.0      0      2  11.1333        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "875         1       3  female  15.0      0      0   7.2250        C   Third   \n",
       "879         1       1  female  56.0      0      1  83.1583        C   First   \n",
       "880         1       2  female  25.0      0      1  26.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "\n",
       "     adult_male  embark_town  \n",
       "1         False    Cherbourg  \n",
       "2         False  Southampton  \n",
       "3         False  Southampton  \n",
       "6          True  Southampton  \n",
       "8         False  Southampton  \n",
       "..          ...          ...  \n",
       "875       False    Cherbourg  \n",
       "879       False    Cherbourg  \n",
       "880       False  Southampton  \n",
       "887       False  Southampton  \n",
       "889        True    Cherbourg  \n",
       "\n",
       "[396 rows x 11 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 조건이 여러개  &가 | 보다 우선순위를 가짐 , 조건연산자의 순위를 모를 때에는 괄호로 묶자! \n",
    "\n",
    "df_t[ (df_t[\"survived\"]==1) | (df_t['fare']>30) & (df_t['class']==\"First\") ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 조건에 따른 column 추가, 변경"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>False</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>99999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>99999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>99999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>99999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>True</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>99999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>True</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "\n",
       "     adult_male  embark_town     temp  \n",
       "0          True  Southampton      NaN  \n",
       "1         False    Cherbourg  99999.0  \n",
       "2         False  Southampton  99999.0  \n",
       "3         False  Southampton  99999.0  \n",
       "4          True  Southampton      NaN  \n",
       "..          ...          ...      ...  \n",
       "886        True  Southampton      NaN  \n",
       "887       False  Southampton  99999.0  \n",
       "888       False  Southampton      NaN  \n",
       "889        True    Cherbourg  99999.0  \n",
       "890        True   Queenstown      NaN  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t.loc[df_t[\"survived\"]==1,'temp'] =99999\n",
    "df_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      False\n",
       "3      False\n",
       "4      False\n",
       "6      False\n",
       "11     False\n",
       "       ...  \n",
       "873    False\n",
       "879    False\n",
       "881    False\n",
       "885    False\n",
       "890    False\n",
       "Name: temp2, Length: 305, dtype: bool"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t['temp2']=False\n",
    "df_t.loc[df_t.age>30,'temp2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_t.loc[df_t.age>30,'temp2'] = \"30대 이상입니다.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
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       "      <th>0</th>\n",
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       "      <td>Cherbourg</td>\n",
       "      <td>99999.0</td>\n",
       "      <td>30대 이상입니다.</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
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       "      <td>99999.0</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
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       "      <td>First</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>99999.0</td>\n",
       "      <td>30대 이상입니다.</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30대 이상입니다.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>886</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>13.0000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>99999.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>True</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>99999.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>True</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30대 이상입니다.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "\n",
       "     adult_male  embark_town     temp       temp2  \n",
       "0          True  Southampton      NaN       False  \n",
       "1         False    Cherbourg  99999.0  30대 이상입니다.  \n",
       "2         False  Southampton  99999.0       False  \n",
       "3         False  Southampton  99999.0  30대 이상입니다.  \n",
       "4          True  Southampton      NaN  30대 이상입니다.  \n",
       "..          ...          ...      ...         ...  \n",
       "886        True  Southampton      NaN       False  \n",
       "887       False  Southampton  99999.0       False  \n",
       "888       False  Southampton      NaN       False  \n",
       "889        True    Cherbourg  99999.0       False  \n",
       "890        True   Queenstown      NaN  30대 이상입니다.  \n",
       "\n",
       "[891 rows x 13 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jjang\\anaconda3\\envs\\ADP_Class\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "## 데이터 프레임으로 접근하여 값을 대입하면 오류가 남 // 데이터 프레임 접근은 복사본이므로 데이터에 직접 접근할 때에는 loc를 써야함 \n",
    "df_t[df_t[\"age\"]>30]['temp2'] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      30대 이상입니다.\n",
       "3      30대 이상입니다.\n",
       "4      30대 이상입니다.\n",
       "6      30대 이상입니다.\n",
       "11     30대 이상입니다.\n",
       "          ...    \n",
       "873    30대 이상입니다.\n",
       "879    30대 이상입니다.\n",
       "881    30대 이상입니다.\n",
       "885    30대 이상입니다.\n",
       "890    30대 이상입니다.\n",
       "Name: temp2, Length: 305, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t[df_t[\"age\"]>30].temp2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_t.drop(columns=['temp','temp2'],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### np.where()\n",
    "* 데이터 프레임 or 리스트 내 필드에 접근하고 수정하는 방법 . (loc or iloc를 두번 사용하는 것과 동일)\n",
    "* np.where(condition, x, y)는 조건이 충족되면x를, 그렇지 않으면y를 반환합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>False</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>True</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>C</td>\n",
       "      <td>Second</td>\n",
       "      <td>False</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "5         0       3    male   NaN      0      0   8.4583        Q   Third   \n",
       "6         0       1    male  54.0      0      0  51.8625        S   First   \n",
       "7         0       3    male   2.0      3      1  21.0750        S   Third   \n",
       "8         1       3  female  27.0      0      2  11.1333        S   Third   \n",
       "9         1       2  female  14.0      1      0  30.0708        C  Second   \n",
       "\n",
       "   adult_male  embark_town alive  \n",
       "0        True  Southampton    No  \n",
       "1       False    Cherbourg   Yes  \n",
       "2       False  Southampton   Yes  \n",
       "3       False  Southampton   Yes  \n",
       "4        True  Southampton    No  \n",
       "5        True   Queenstown    No  \n",
       "6        True  Southampton    No  \n",
       "7       False  Southampton    No  \n",
       "8       False  Southampton   Yes  \n",
       "9       False    Cherbourg   Yes  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t['alive'] = np.where(df_t['survived']==1, 'Yes', 'No')\n",
    "df_t.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### np.select()\n",
    "* 데이터 프레임 or 리스트 내 필드에 접근하고 수정하는 방법 . \n",
    "* 조건 목록과 선택 목록을 입력으로 받아서 조건에 따라 선택 목록의 요소로 구성된 배열을 반환합니다. \n",
    "* 이 방법을 사용하면 두 개 이상의 조건이있을 때 Pandas의 주어진 조건을 기반으로 DataFrame 열을 만들 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>No</td>\n",
       "      <td>성인</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>False</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>Yes</td>\n",
       "      <td>성인</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Yes</td>\n",
       "      <td>성인</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Yes</td>\n",
       "      <td>성인</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>No</td>\n",
       "      <td>성인</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>True</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>No</td>\n",
       "      <td>Not_Specified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>No</td>\n",
       "      <td>성인</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>No</td>\n",
       "      <td>미취학아동</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Yes</td>\n",
       "      <td>성인</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>C</td>\n",
       "      <td>Second</td>\n",
       "      <td>False</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>Yes</td>\n",
       "      <td>초중고등학생</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "5         0       3    male   NaN      0      0   8.4583        Q   Third   \n",
       "6         0       1    male  54.0      0      0  51.8625        S   First   \n",
       "7         0       3    male   2.0      3      1  21.0750        S   Third   \n",
       "8         1       3  female  27.0      0      2  11.1333        S   Third   \n",
       "9         1       2  female  14.0      1      0  30.0708        C  Second   \n",
       "\n",
       "   adult_male  embark_town alive         status  \n",
       "0        True  Southampton    No             성인  \n",
       "1       False    Cherbourg   Yes             성인  \n",
       "2       False  Southampton   Yes             성인  \n",
       "3       False  Southampton   Yes             성인  \n",
       "4        True  Southampton    No             성인  \n",
       "5        True   Queenstown    No  Not_Specified  \n",
       "6        True  Southampton    No             성인  \n",
       "7       False  Southampton    No          미취학아동  \n",
       "8       False  Southampton   Yes             성인  \n",
       "9       False    Cherbourg   Yes         초중고등학생  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conditionlist = [\n",
    "    (df_t['age'] >= 19) ,\n",
    "    (df_t['age'] >= 8) & (df_t['age'] <19),\n",
    "    df_t['age'] < 8]\n",
    "choicelist = ['성인', '초중고등학생', '미취학아동']\n",
    "\n",
    "df_t['status'] = np.select(conditionlist, choicelist,default='Not_Specified')\n",
    "\n",
    "df_t.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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