2.4.데이터 정렬.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 정렬\n",
    "### Index 기준 정렬\n",
    "    df.sort_index() : df 객체 인덱스 기준으로 정렬 \n",
    "    default : 오름차순 \n",
    "    ascending = False : 내림차순 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
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       "  </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",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <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",
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       "      <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",
       "    </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>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",
       "    </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",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "<p>891 rows × 11 columns</p>\n",
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      ],
      "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  \n",
       "0          True  Southampton  \n",
       "1         False    Cherbourg  \n",
       "2         False  Southampton  \n",
       "3         False  Southampton  \n",
       "4          True  Southampton  \n",
       "..          ...          ...  \n",
       "886        True  Southampton  \n",
       "887       False  Southampton  \n",
       "888       False  Southampton  \n",
       "889        True    Cherbourg  \n",
       "890        True   Queenstown  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t = pd.read_csv(\"../../data/titanic.csv\")\n",
    "df_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>S</td>\n",
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       "      <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",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "\n",
       "     adult_male  embark_town  \n",
       "890        True   Queenstown  \n",
       "889        True    Cherbourg  \n",
       "888       False  Southampton  \n",
       "887       False  Southampton  \n",
       "886        True  Southampton  \n",
       "..          ...          ...  \n",
       "4          True  Southampton  \n",
       "3         False  Southampton  \n",
       "2         False  Southampton  \n",
       "1         False    Cherbourg  \n",
       "0          True  Southampton  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t.sort_index(axis=0,ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <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",
       "    </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>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",
       "    <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>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>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>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "\n",
       "     adult_male  embark_town  \n",
       "890        True   Queenstown  \n",
       "889        True    Cherbourg  \n",
       "888       False  Southampton  \n",
       "887       False  Southampton  \n",
       "886        True  Southampton  \n",
       "..          ...          ...  \n",
       "4          True  Southampton  \n",
       "3         False  Southampton  \n",
       "2         False  Southampton  \n",
       "1         False    Cherbourg  \n",
       "0          True  Southampton  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t.sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### column 기준 정렬  \n",
    "\n",
    "    df.sort_index(axis=1) : df 객체 column 명으로 정렬 \n",
    "    default : 오름차순 \n",
    "    ascending = False : 내림차순 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>sex</th>\n",
       "      <th>pclass</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>embark_town</th>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>S</td>\n",
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       "      <td>Third</td>\n",
       "      <td>26.0</td>\n",
       "      <td>False</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>First</td>\n",
       "      <td>35.0</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Second</td>\n",
       "      <td>27.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>First</td>\n",
       "      <td>19.0</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Third</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>First</td>\n",
       "      <td>26.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>Third</td>\n",
       "      <td>32.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  sibsp     sex  pclass  parch     fare embarked  embark_town  \\\n",
       "0           0      1    male       3      0   7.2500        S  Southampton   \n",
       "1           1      1  female       1      0  71.2833        C    Cherbourg   \n",
       "2           1      0  female       3      0   7.9250        S  Southampton   \n",
       "3           1      1  female       1      0  53.1000        S  Southampton   \n",
       "4           0      0    male       3      0   8.0500        S  Southampton   \n",
       "..        ...    ...     ...     ...    ...      ...      ...          ...   \n",
       "886         0      0    male       2      0  13.0000        S  Southampton   \n",
       "887         1      0  female       1      0  30.0000        S  Southampton   \n",
       "888         0      1  female       3      2  23.4500        S  Southampton   \n",
       "889         1      0    male       1      0  30.0000        C    Cherbourg   \n",
       "890         0      0    male       3      0   7.7500        Q   Queenstown   \n",
       "\n",
       "      class   age  adult_male  \n",
       "0     Third  22.0        True  \n",
       "1     First  38.0       False  \n",
       "2     Third  26.0       False  \n",
       "3     First  35.0       False  \n",
       "4     Third  35.0        True  \n",
       "..      ...   ...         ...  \n",
       "886  Second  27.0        True  \n",
       "887   First  19.0       False  \n",
       "888   Third   NaN       False  \n",
       "889   First  26.0        True  \n",
       "890   Third  32.0        True  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t.sort_index(axis=1,ascending =False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 값 기준 정렬 \n",
    "    obj.sort_values() :시리즈 객체 값 기준으로 오름차순 정렬 \n",
    "    df.sort_values(by='컬럼명') : df객체를 지정한 컬럼의 값 기준으로 정렬 \n",
    "    df.sort_values(by=['컬럼명1','컬럼명2]) : df객체를 지정한 컬럼들의 값 기준으로 정렬\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>630</th>\n",
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       "      <th>851</th>\n",
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       "      <td>True</td>\n",
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       "      <th>493</th>\n",
       "      <td>0</td>\n",
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       "      <th>96</th>\n",
       "      <td>0</td>\n",
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       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>True</td>\n",
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       "      <th>633</th>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
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       "      <td>S</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
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       "      <td>Second</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
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       "      <th>815</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
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       "      <td>First</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass   sex   age  sibsp  parch     fare embarked   class  \\\n",
       "630         1       1  male  80.0      0      0  30.0000        S   First   \n",
       "851         0       3  male  74.0      0      0   7.7750        S   Third   \n",
       "493         0       1  male  71.0      0      0  49.5042        C   First   \n",
       "96          0       1  male  71.0      0      0  34.6542        C   First   \n",
       "116         0       3  male  70.5      0      0   7.7500        Q   Third   \n",
       "..        ...     ...   ...   ...    ...    ...      ...      ...     ...   \n",
       "481         0       2  male   NaN      0      0   0.0000        S  Second   \n",
       "633         0       1  male   NaN      0      0   0.0000        S   First   \n",
       "674         0       2  male   NaN      0      0   0.0000        S  Second   \n",
       "732         0       2  male   NaN      0      0   0.0000        S  Second   \n",
       "815         0       1  male   NaN      0      0   0.0000        S   First   \n",
       "\n",
       "     adult_male  embark_town  \n",
       "630        True  Southampton  \n",
       "851        True  Southampton  \n",
       "493        True    Cherbourg  \n",
       "96         True    Cherbourg  \n",
       "116        True   Queenstown  \n",
       "..          ...          ...  \n",
       "481        True  Southampton  \n",
       "633        True  Southampton  \n",
       "674        True  Southampton  \n",
       "732        True  Southampton  \n",
       "815        True  Southampton  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t.sort_values(by=['age','fare'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       "     survived  pclass     sex  age  sibsp  parch     fare embarked  class  \\\n",
       "164         0       3    male  1.0      4      1  39.6875        S  Third   \n",
       "386         0       3    male  1.0      5      2  46.9000        S  Third   \n",
       "7           0       3    male  2.0      3      1  21.0750        S  Third   \n",
       "16          0       3    male  2.0      4      1  29.1250        Q  Third   \n",
       "119         0       3  female  2.0      4      2  31.2750        S  Third   \n",
       "..        ...     ...     ...  ...    ...    ...      ...      ...    ...   \n",
       "727         1       3  female  NaN      0      0   7.7375        Q  Third   \n",
       "740         1       1    male  NaN      0      0  30.0000        S  First   \n",
       "828         1       3    male  NaN      0      0   7.7500        Q  Third   \n",
       "839         1       1    male  NaN      0      0  29.7000        C  First   \n",
       "849         1       1  female  NaN      1      0  89.1042        C  First   \n",
       "\n",
       "     adult_male  embark_town  \n",
       "164       False  Southampton  \n",
       "386       False  Southampton  \n",
       "7         False  Southampton  \n",
       "16        False   Queenstown  \n",
       "119       False  Southampton  \n",
       "..          ...          ...  \n",
       "727       False   Queenstown  \n",
       "740        True  Southampton  \n",
       "828        True   Queenstown  \n",
       "839        True    Cherbourg  \n",
       "849       False    Cherbourg  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
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   "source": [
    "df_t.sort_values( by=['survived','age'] )"
   ]
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  {
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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