6. 통계, 정렬.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pandas, numpy module 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 값의 이상치가 있다면 평균 쓰면 안됨\n",
    "# 집단을 잘 표현할 수 있는 값\n",
    "# 1 2 3 4 5\n",
    "# 1 2 3 4 100\n",
    "# 회귀분석 ...\n",
    "# 분산 : 얼마나 퍼져있는가"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7-1. 통계 함수\n",
    "## 주요 통계 함수 목록\n",
    "<img src=\"img/7강/통계함수.jpg\" alt=\"통계함수\" style=\"width: 400px;\"/>\n",
    "\n",
    "## 주요 함수 옵션\n",
    "<img src=\"img/7강/주요함수옵션.jpg\" alt=\"주요옵션\" style=\"width: 400px;\"/>\n",
    "\n",
    "### ** axis는 통계함수 뿐만 아니라, 많은 함수에서 동일하게 적용됨. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th>연도</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2016</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2017</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>과목</th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>학생명</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Kim</td>\n",
       "      <td>76</td>\n",
       "      <td>81</td>\n",
       "      <td>91</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Park</td>\n",
       "      <td>67</td>\n",
       "      <td>90</td>\n",
       "      <td>76</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Lee</td>\n",
       "      <td>87</td>\n",
       "      <td>58</td>\n",
       "      <td>69</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Jung</td>\n",
       "      <td>51</td>\n",
       "      <td>77</td>\n",
       "      <td>76</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Moon</td>\n",
       "      <td>93</td>\n",
       "      <td>81</td>\n",
       "      <td>60</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "연도   2016     2017    \n",
       "과목     영어  수학   영어  수학\n",
       "학생명                   \n",
       "Kim    76  81   91  58\n",
       "Park   67  90   76  74\n",
       "Lee    87  58   69  71\n",
       "Jung   51  77   76  63\n",
       "Moon   93  81   60  87"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 샘플 데이터 생성\n",
    "np.random.seed(321)\n",
    "\n",
    "df = pd.DataFrame(np.random.randint(50, 100, (5, 4)), \n",
    "                  columns=[np.repeat([2016, 2017],2),['영어', '수학']*2],\n",
    "                  index = ['Kim','Park','Lee','Jung','Moon'])\n",
    "\n",
    "df.index.set_names('학생명', inplace=True)\n",
    "df.columns.set_names(['연도','과목'], inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
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       "      <th colspan=\"2\" halign=\"left\">2016</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2017</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>과목</th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>학생명</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Kim</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81</td>\n",
       "      <td>91</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Park</td>\n",
       "      <td>67.0</td>\n",
       "      <td>90</td>\n",
       "      <td>76</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Lee</td>\n",
       "      <td>87.0</td>\n",
       "      <td>58</td>\n",
       "      <td>69</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Jung</td>\n",
       "      <td>51.0</td>\n",
       "      <td>77</td>\n",
       "      <td>76</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Moon</td>\n",
       "      <td>93.0</td>\n",
       "      <td>81</td>\n",
       "      <td>60</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "연도    2016     2017    \n",
       "과목      영어  수학   영어  수학\n",
       "학생명                    \n",
       "Kim    NaN  81   91  58\n",
       "Park  67.0  90   76  74\n",
       "Lee   87.0  58   69  71\n",
       "Jung  51.0  77   76  63\n",
       "Moon  93.0  81   60  87"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Kim 학생의 2016년 영어 성적을 NA로 처리하기\n",
    "df.loc['Kim', (2016, '영어')]=np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th>연도</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2016</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2017</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>과목</th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>학생명</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Kim</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81</td>\n",
       "      <td>91</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Park</td>\n",
       "      <td>67.0</td>\n",
       "      <td>90</td>\n",
       "      <td>76</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Lee</td>\n",
       "      <td>87.0</td>\n",
       "      <td>58</td>\n",
       "      <td>69</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Jung</td>\n",
       "      <td>51.0</td>\n",
       "      <td>77</td>\n",
       "      <td>76</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Moon</td>\n",
       "      <td>93.0</td>\n",
       "      <td>81</td>\n",
       "      <td>60</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "연도    2016     2017    \n",
       "과목      영어  수학   영어  수학\n",
       "학생명                    \n",
       "Kim    NaN  81   91  58\n",
       "Park  67.0  90   76  74\n",
       "Lee   87.0  58   69  71\n",
       "Jung  51.0  77   76  63\n",
       "Moon  93.0  81   60  87"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df 확인\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2016년 성적만 선택해서 df2016에 저장\n",
    "df2016 = df[2016]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "과목\n",
       "영어    5\n",
       "수학    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1) count() - NaN 값을 제외한 데이터의 갯수 \n",
    "df2016.count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 원래 값으로 되돌려 놓기\n",
    "df.loc['Kim', (2016, '영어')] = 76"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>연도</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2016</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2017</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>과목</th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>74.800000</td>\n",
       "      <td>77.40000</td>\n",
       "      <td>74.400000</td>\n",
       "      <td>70.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>16.649324</td>\n",
       "      <td>11.84483</td>\n",
       "      <td>11.371016</td>\n",
       "      <td>11.148991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>58.00000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>58.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>77.00000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>81.00000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>71.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>81.00000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>90.00000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>87.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "연도          2016                 2017           \n",
       "과목            영어        수학         영어         수학\n",
       "count   5.000000   5.00000   5.000000   5.000000\n",
       "mean   74.800000  77.40000  74.400000  70.600000\n",
       "std    16.649324  11.84483  11.371016  11.148991\n",
       "min    51.000000  58.00000  60.000000  58.000000\n",
       "25%    67.000000  77.00000  69.000000  63.000000\n",
       "50%    76.000000  81.00000  76.000000  71.000000\n",
       "75%    87.000000  81.00000  76.000000  74.000000\n",
       "max    93.000000  90.00000  91.000000  87.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2) describe() - 각 컬럼에 대한 요약 통계량을 확인해주는 함수\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3) sum() - 값의 합을 계산\n",
    "# 기본적으로 함수는 각 컬럼에 있는 로우(row)들의 값들로 수행 (default: axis =0) \n",
    "df2016.sum(axis=0, skipna = True)\n",
    "#skipna = True : 디폴트 = 아무것도 안적어도 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "과목\n",
       "영어    374.0\n",
       "수학    387.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2016.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "학생명\n",
       "Kim     78.5\n",
       "Park    78.5\n",
       "Lee     72.5\n",
       "Jung    64.0\n",
       "Moon    87.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4) mean() - 값의 평균을 계산\n",
    "# 각 학생들의 과목 평균 구하기\n",
    "df2016.mean(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>과목</th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <td>Kim</td>\n",
       "      <td>76.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Park</td>\n",
       "      <td>143.0</td>\n",
       "      <td>171.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Lee</td>\n",
       "      <td>230.0</td>\n",
       "      <td>229.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Jung</td>\n",
       "      <td>281.0</td>\n",
       "      <td>306.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Moon</td>\n",
       "      <td>374.0</td>\n",
       "      <td>387.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "과목       영어     수학\n",
       "학생명               \n",
       "Kim    76.0   81.0\n",
       "Park  143.0  171.0\n",
       "Lee   230.0  229.0\n",
       "Jung  281.0  306.0\n",
       "Moon  374.0  387.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5) cumsum() - cumulative sum, 누적합\n",
    "df2016.cumsum()\n",
    "# default : axis = 0, 아래로 더함 - 최종값은 sum과 같음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>과목</th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>학생명</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Kim</td>\n",
       "      <td>76.0</td>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Park</td>\n",
       "      <td>67.0</td>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Lee</td>\n",
       "      <td>87.0</td>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Jung</td>\n",
       "      <td>51.0</td>\n",
       "      <td>128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Moon</td>\n",
       "      <td>93.0</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "과목      영어     수학\n",
       "학생명              \n",
       "Kim   76.0  157.0\n",
       "Park  67.0  157.0\n",
       "Lee   87.0  145.0\n",
       "Jung  51.0  128.0\n",
       "Moon  93.0  174.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2016.cumsum(axis=1)\n",
    "# 오른쪽으로 더함"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7-2. 정렬\n",
    "## * 데이터 정렬 : sort_values()\n",
    "<img src=\"img/7강/데이터프레임정렬.jpg\" alt=\"DataFrame.sort_values\" style=\"width: 600px;\"/>\n",
    "## * 인덱스 정렬 : sort_index() - 인자는 sort_values()와 동일"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Series 정렬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    3.0\n",
       "c    5.0\n",
       "a    2.0\n",
       "f    1.0\n",
       "e    7.0\n",
       "d    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Series 정렬을 위한 샘플 데이터\n",
    "# column 기준과 마찬가지로 sort_values 사용\n",
    "# 계산하는 mean, sum만 axis 반대라고 생각\n",
    "# R은 기본이 descending\n",
    "# 저장할거면 inplace = True\n",
    "# quicksort, bubblesort .. 코딩테스트에서 쓰이는 자료구조 정렬 알고리즘"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "sr = pd.Series([3,5,2,1,7, np.nan], index = list('bcafed'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    3.0\n",
       "c    5.0\n",
       "a    2.0\n",
       "f    1.0\n",
       "e    7.0\n",
       "d    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A. index 정렬 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    2.0\n",
       "b    3.0\n",
       "c    5.0\n",
       "d    NaN\n",
       "e    7.0\n",
       "f    1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 오름차순 정렬, 기본값 = True\n",
    "sr.sort_index(ascending = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "f    1.0\n",
       "e    7.0\n",
       "d    NaN\n",
       "c    5.0\n",
       "b    3.0\n",
       "a    2.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 내림차순 정렬\n",
    "sr.sort_index(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## B. value 정렬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "f    1.0\n",
       "a    2.0\n",
       "b    3.0\n",
       "c    5.0\n",
       "e    7.0\n",
       "d    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NA값을 맨 마지막으로 오름차순 정렬, 기본값 = True\n",
    "sr.sort_values(ascending = True, na_position='last')\n",
    "# na 위치를 last로"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    NaN\n",
       "e    7.0\n",
       "c    5.0\n",
       "b    3.0\n",
       "a    2.0\n",
       "f    1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NA값을 맨 처음으로 내림차순 정렬\n",
    "sr.sort_values(ascending = False, na_position = 'first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# series에서는 열이 하나이기 때문에 by를 쓸 필요가 없음\n",
    "# dataframe은 열이 많아서 by로 지정 필요"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) DataFrame 정렬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>영어</th>\n",
       "      <th>수학</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>70</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>90</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   영어  수학\n",
       "0  10  90\n",
       "1  60  80\n",
       "2  80  70\n",
       "3  30  60\n",
       "4  40  50\n",
       "5  50  50\n",
       "6  20  40\n",
       "7  70  30\n",
       "8  30  30\n",
       "9  90  20"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sample data 생성\n",
    "dic = {'영어':[10, 60, 80, 30, 40, 50, 20, 70, 30, 90],\n",
    "       '수학':[90, 80, 70, 60, 50, 50, 40, 30, 30, 20]}\n",
    "\n",
    "df2 = pd.DataFrame(dic)\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A. index 정렬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>90</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>70</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>90</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   영어  수학\n",
       "0  10  90\n",
       "1  60  80\n",
       "2  80  70\n",
       "3  30  60\n",
       "4  40  50\n",
       "5  50  50\n",
       "6  20  40\n",
       "7  70  30\n",
       "8  30  30\n",
       "9  90  20"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# row index 정렬 (default: axis=0) \n",
    "df2.sort_index(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>수학</th>\n",
       "      <th>영어</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
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       "      <td>80</td>\n",
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       "      <td>2</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   수학  영어\n",
       "0  90  10\n",
       "1  80  60\n",
       "2  70  80\n",
       "3  60  30\n",
       "4  50  40\n",
       "5  50  50\n",
       "6  40  20\n",
       "7  30  70\n",
       "8  30  30\n",
       "9  20  90"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# column index 정렬\n",
    "df2.sort_index(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## B. value 정렬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
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       "  </thead>\n",
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       "      <td>9</td>\n",
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       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   영어  수학\n",
       "9  90  20\n",
       "7  70  30\n",
       "8  30  30\n",
       "6  20  40\n",
       "4  40  50\n",
       "5  50  50\n",
       "3  30  60\n",
       "2  80  70\n",
       "1  60  80\n",
       "0  10  90"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 수학 성적으로 오름차순 정렬\n",
    "df2.sort_values(by= '수학', ascending = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   영어  수학\n",
       "9  90  20\n",
       "2  80  70\n",
       "7  70  30\n",
       "1  60  80\n",
       "5  50  50\n",
       "4  40  50\n",
       "3  30  60\n",
       "8  30  30\n",
       "6  20  40\n",
       "0  10  90"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 영어 성적으로 내림차순 정렬\n",
    "df2.sort_values(by='영어', ascending =False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>30</td>\n",
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       "      <td>90</td>\n",
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      ],
      "text/plain": [
       "   수학  영어\n",
       "0  90  10\n",
       "1  80  60\n",
       "2  70  80\n",
       "3  60  30\n",
       "4  50  40\n",
       "5  50  50\n",
       "6  40  20\n",
       "7  30  70\n",
       "8  30  30\n",
       "9  20  90"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# column index 정렬하고 수학을 기준으로 내림차순 정렬\n",
    "df2.sort_index(axis = 1, inplace = True) #columns\n",
    "df2.sort_values(by = '수학', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>20</td>\n",
       "      <td>90</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   수학  영어\n",
       "0  90  10\n",
       "1  80  60\n",
       "2  70  80\n",
       "3  60  30\n",
       "5  50  50\n",
       "4  50  40\n",
       "6  40  20\n",
       "7  30  70\n",
       "8  30  30\n",
       "9  20  90"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2개의 기준으로 정렬\n",
    "# 수학을 기준으로 내림차순 정렬하고 수학 점수가 같은 경우 영어 성적으로 내림차순 정렬\n",
    "df2.sort_values(by=['수학', '영어'], ascending = False)\n",
    "# 앞 뒤가 매칭이 되어야 함, 같을 때는 [False, False] 안써도 됨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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      ],
      "text/plain": [
       "   수학  영어\n",
       "0  90  10\n",
       "1  80  60\n",
       "2  70  80\n",
       "3  60  30\n",
       "4  50  40\n",
       "5  50  50\n",
       "6  40  20\n",
       "8  30  30\n",
       "7  30  70\n",
       "9  20  90"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 수학을 기준으로 내림차순 정렬하고 수학 점수가 같은 경우 영어 성적으로 오름차순 정렬\n",
    "df2.sort_values(by = ['수학', '영어'], ascending = [False, True])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) 계층 색인 DataFrame 정렬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr>\n",
       "      <th>연도</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2016</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2017</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>과목</th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
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       "    <tr>\n",
       "      <th>학생명</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Kim</td>\n",
       "      <td>76.0</td>\n",
       "      <td>81</td>\n",
       "      <td>91</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Park</td>\n",
       "      <td>67.0</td>\n",
       "      <td>90</td>\n",
       "      <td>76</td>\n",
       "      <td>74</td>\n",
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       "    <tr>\n",
       "      <td>Lee</td>\n",
       "      <td>87.0</td>\n",
       "      <td>58</td>\n",
       "      <td>69</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Jung</td>\n",
       "      <td>51.0</td>\n",
       "      <td>77</td>\n",
       "      <td>76</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Moon</td>\n",
       "      <td>93.0</td>\n",
       "      <td>81</td>\n",
       "      <td>60</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "연도    2016     2017    \n",
       "과목      영어  수학   영어  수학\n",
       "학생명                    \n",
       "Kim   76.0  81   91  58\n",
       "Park  67.0  90   76  74\n",
       "Lee   87.0  58   69  71\n",
       "Jung  51.0  77   76  63\n",
       "Moon  93.0  81   60  87"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data frame확인\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>Park</td>\n",
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       "      <td>74</td>\n",
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       "      <td>Lee</td>\n",
       "      <td>87.0</td>\n",
       "      <td>58</td>\n",
       "      <td>69</td>\n",
       "      <td>71</td>\n",
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       "    <tr>\n",
       "      <td>Jung</td>\n",
       "      <td>51.0</td>\n",
       "      <td>77</td>\n",
       "      <td>76</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Kim</td>\n",
       "      <td>76.0</td>\n",
       "      <td>81</td>\n",
       "      <td>91</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "연도    2016     2017    \n",
       "과목      영어  수학   영어  수학\n",
       "학생명                    \n",
       "Moon  93.0  81   60  87\n",
       "Park  67.0  90   76  74\n",
       "Lee   87.0  58   69  71\n",
       "Jung  51.0  77   76  63\n",
       "Kim   76.0  81   91  58"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2017년 수학 성적 기준으로 내림차순 정렬\n",
    "df.sort_values (by=(2017, '수학'), ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 2016년 평균을 구하고 2016년 평균을 기준으로 내림차순 정렬\n",
    "df2016 = df[2016].mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "학생명\n",
       "Moon    87.0\n",
       "Park    78.5\n",
       "Kim     78.5\n",
       "Lee     72.5\n",
       "Jung    64.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2016.sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>연도</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2016</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2017</th>\n",
       "      <th>2016</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>과목</th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
       "      <th>영어</th>\n",
       "      <th>수학</th>\n",
       "      <th>평균</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>학생명</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",
       "      <td>Jung</td>\n",
       "      <td>51.0</td>\n",
       "      <td>77</td>\n",
       "      <td>76</td>\n",
       "      <td>63</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Lee</td>\n",
       "      <td>87.0</td>\n",
       "      <td>58</td>\n",
       "      <td>69</td>\n",
       "      <td>71</td>\n",
       "      <td>72.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Kim</td>\n",
       "      <td>76.0</td>\n",
       "      <td>81</td>\n",
       "      <td>91</td>\n",
       "      <td>58</td>\n",
       "      <td>78.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Park</td>\n",
       "      <td>67.0</td>\n",
       "      <td>90</td>\n",
       "      <td>76</td>\n",
       "      <td>74</td>\n",
       "      <td>78.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Moon</td>\n",
       "      <td>93.0</td>\n",
       "      <td>81</td>\n",
       "      <td>60</td>\n",
       "      <td>87</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "연도    2016     2017      2016\n",
       "과목      영어  수학   영어  수학    평균\n",
       "학생명                          \n",
       "Jung  51.0  77   76  63  64.0\n",
       "Lee   87.0  58   69  71  72.5\n",
       "Kim   76.0  81   91  58  78.5\n",
       "Park  67.0  90   76  74  78.5\n",
       "Moon  93.0  81   60  87  87.0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(2016, '평균')] =  df[2016].mean(axis=1)\n",
    "df.sort_values(by=(2016,'평균'), ascending =True)"
   ]
  }
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