0312_그룹화groupby(), 필터링filter(). 멀티 인덱스.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 그룹화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
      "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
      "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
      "\n",
      "     who  adult_male deck  embark_town alive  alone  \n",
      "1  woman       False    C    Cherbourg   yes  False  \n",
      "3  woman       False    C  Southampton   yes  False  \n",
      "Second\n",
      "    survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
      "9          1       2  female  14.0      1      0  30.0708        C  Second   \n",
      "15         1       2  female  55.0      0      0  16.0000        S  Second   \n",
      "\n",
      "      who  adult_male deck  embark_town alive  alone  \n",
      "9   child       False  NaN    Cherbourg   yes  False  \n",
      "15  woman       False  NaN  Southampton   yes   True  \n",
      "Third\n",
      "   survived  pclass     sex   age  sibsp  parch   fare embarked  class    who  \\\n",
      "0         0       3    male  22.0      1      0  7.250        S  Third    man   \n",
      "2         1       3  female  26.0      0      0  7.925        S  Third  woman   \n",
      "\n",
      "   adult_male deck  embark_town alive  alone  \n",
      "0        True  NaN  Southampton    no  False  \n",
      "2       False  NaN  Southampton   yes   True  \n",
      "        survived  pclass        age     sibsp     parch       fare  \\\n",
      "class                                                                \n",
      "First   0.629630     1.0  38.233441  0.416667  0.356481  84.154687   \n",
      "Second  0.472826     2.0  29.877630  0.402174  0.380435  20.662183   \n",
      "Third   0.242363     3.0  25.140620  0.615071  0.393075  13.675550   \n",
      "\n",
      "        adult_male     alone  \n",
      "class                         \n",
      "First     0.550926  0.504630  \n",
      "Second    0.538043  0.565217  \n",
      "Third     0.649695  0.659878  \n",
      "\n",
      "              survived                                              pclass  \\\n",
      "                 count      mean       std  min  25%  50%  75%  max  count   \n",
      "class  sex                                                                   \n",
      "First  female     94.0  0.968085  0.176716  0.0  1.0  1.0  1.0  1.0   94.0   \n",
      "       male      122.0  0.368852  0.484484  0.0  0.0  0.0  1.0  1.0  122.0   \n",
      "Second female     76.0  0.921053  0.271448  0.0  1.0  1.0  1.0  1.0   76.0   \n",
      "       male      108.0  0.157407  0.365882  0.0  0.0  0.0  0.0  1.0  108.0   \n",
      "Third  female    144.0  0.500000  0.501745  0.0  0.0  0.5  1.0  1.0  144.0   \n",
      "       male      347.0  0.135447  0.342694  0.0  0.0  0.0  0.0  1.0  347.0   \n",
      "\n",
      "                    ... parch        fare                                  \\\n",
      "              mean  ...   75%  max  count        mean        std      min   \n",
      "class  sex          ...                                                     \n",
      "First  female  1.0  ...   1.0  2.0   94.0  106.125798  74.259988  25.9292   \n",
      "       male    1.0  ...   0.0  4.0  122.0   67.226127  77.548021   0.0000   \n",
      "Second female  2.0  ...   1.0  3.0   76.0   21.970121  10.891796  10.5000   \n",
      "       male    2.0  ...   0.0  2.0  108.0   19.741782  14.922235   0.0000   \n",
      "Third  female  3.0  ...   1.0  6.0  144.0   16.118810  11.690314   6.7500   \n",
      "       male    3.0  ...   0.0  5.0  347.0   12.661633  11.681696   0.0000   \n",
      "\n",
      "                                                         \n",
      "                    25%       50%         75%       max  \n",
      "class  sex                                               \n",
      "First  female  57.24480  82.66455  134.500000  512.3292  \n",
      "       male    27.72810  41.26250   78.459375  512.3292  \n",
      "Second female  13.00000  22.00000   26.062500   65.0000  \n",
      "       male    12.33125  13.00000   26.000000   73.5000  \n",
      "Third  female   7.85420  12.47500   20.221875   69.5500  \n",
      "       male     7.75000   7.92500   10.008300   69.5500  \n",
      "\n",
      "[6 rows x 48 columns]\n"
     ]
    }
   ],
   "source": [
    "#필요한 패키지들 가져오기 \n",
    "import numpy as np #수치연산, 선형대수, ndarray라는 자료 구조를 가진 패키지\n",
    "import pandas as pd #Series, DF 자료구조를 가진 패키지\n",
    "import seaborn as sns #샘플데이터와 시각화\n",
    "\n",
    "#1. 타이타닉 데이터 가져오기\n",
    "titanic = sns.load_dataset('titanic')\n",
    "titanic\n",
    "\n",
    "#2. 클래스 별로 그룹화 하기\n",
    "group = titanic.groupby('class')\n",
    "group\n",
    "\n",
    "for key,data in group:\n",
    "    print(key)\n",
    "    print(data.head(2)) #first그룹, second그룹, third그룹으로 나눠서 볼 수 있음\n",
    "    \n",
    "\n",
    "#3. 3등급 객실 데이터만 가져와보기\n",
    "third = group.get_group('Third')\n",
    "third\n",
    "\n",
    "print(group.mean())\n",
    "print(\"\")\n",
    "\n",
    "#4. 2개의 특성으로 그룹화 해서 집계\n",
    "group = titanic.groupby(['class', 'sex']) #이 범주형 데이터 순서대로 분할을 계속하니까 정리하고 싶은 결과를 먼저 생각해야 할 듯!\n",
    "print(group.describe())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 그룹화 후 원하는 함수를 적용하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        survived  pclass    age  sibsp  parch      fare\n",
      "class                                                  \n",
      "First          1       0  79.08      3      4  512.3292\n",
      "Second         1       0  69.33      3      3   73.5000\n",
      "Third          1       0  73.58      8      6   69.5500\n",
      "       survived               pclass            age                  sibsp  \\\n",
      "            max      mean min    max mean min   max       mean   min   max   \n",
      "class                                                                        \n",
      "First         1  0.629630   0      1    1   1  80.0  38.233441  0.92     3   \n",
      "Second        1  0.472826   0      2    2   2  70.0  29.877630  0.67     3   \n",
      "Third         1  0.242363   0      3    3   3  74.0  25.140620  0.42     8   \n",
      "\n",
      "        ... parch      fare                 adult_male                  alone  \\\n",
      "        ...   min       max       mean  min        max      mean    min   max   \n",
      "class   ...                                                                     \n",
      "First   ...     0  512.3292  84.154687  0.0       True  0.550926  False  True   \n",
      "Second  ...     0   73.5000  20.662183  0.0       True  0.538043  False  True   \n",
      "Third   ...     0   69.5500  13.675550  0.0       True  0.649695  False  True   \n",
      "\n",
      "                         \n",
      "            mean    min  \n",
      "class                    \n",
      "First   0.504630  False  \n",
      "Second  0.565217  False  \n",
      "Third   0.659878  False  \n",
      "\n",
      "[3 rows x 24 columns]\n"
     ]
    }
   ],
   "source": [
    "def func(x):\n",
    "    return x.max() - x.min()\n",
    "\n",
    "gr = titanic.groupby(['class'])\n",
    "print(gr.agg(func))\n",
    "\n",
    "#여러개의 함수를 적용하고 싶다면 agg에다가 (['max', 'min'])과 같이 리스트로 나열해버리면 됨\n",
    "print(gr.agg(['max','mean','min']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 필터링 : filter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 216\n",
      "Second 184\n",
      "Third 491\n",
      "\n",
      "First 38.233440860215055\n",
      "Second 29.87763005780347\n",
      "Third 25.14061971830986\n",
      "\n"
     ]
    },
    {
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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",
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       "      <td>1</td>\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>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
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       "      <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",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
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       "      <td>6</td>\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>man</td>\n",
       "      <td>True</td>\n",
       "      <td>E</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
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       "      <td>11</td>\n",
       "      <td>1</td>\n",
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       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
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       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>A</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>871</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>D</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>872</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <td>879</td>\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>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
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       "      <td>887</td>\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>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <td>889</td>\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>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
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       "<p>216 rows × 15 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",
       "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",
       "11          1       1  female  58.0      0      0  26.5500        S  First   \n",
       "23          1       1    male  28.0      0      0  35.5000        S  First   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...    ...   \n",
       "871         1       1  female  47.0      1      1  52.5542        S  First   \n",
       "872         0       1    male  33.0      0      0   5.0000        S  First   \n",
       "879         1       1  female  56.0      0      1  83.1583        C  First   \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",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "1    woman       False    C    Cherbourg   yes  False  \n",
       "3    woman       False    C  Southampton   yes  False  \n",
       "6      man        True    E  Southampton    no   True  \n",
       "11   woman       False    C  Southampton   yes   True  \n",
       "23     man        True    A  Southampton   yes   True  \n",
       "..     ...         ...  ...          ...   ...    ...  \n",
       "871  woman       False    D  Southampton   yes  False  \n",
       "872    man        True    B  Southampton    no   True  \n",
       "879  woman       False    C    Cherbourg   yes  False  \n",
       "887  woman       False    B  Southampton   yes   True  \n",
       "889    man        True    C    Cherbourg   yes   True  \n",
       "\n",
       "[216 rows x 15 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 각 그룹별 데이터 개수 확인\n",
    "for key, data in gr:\n",
    "    print(key,len(data))\n",
    "print(\"\")\n",
    "\n",
    "#데이터가 200개는 안되는 그룹은 제거 하는 함수 만들기\n",
    "def count200(x):\n",
    "    return len(x) >=200 #200개 이상 여부를 bool로 리턴을 받을 것\n",
    "\n",
    "gr_filter = gr.filter(count200) #filter로 True에 해당이 안되면 날아감!\n",
    "gr_filter\n",
    "\n",
    "#위에 함수를 람다로도 많이 쓴다 (한줄이니까)\n",
    "gr_filter=gr.filter(lambda x:len(x)>=200)\n",
    "gr_filter\n",
    "\n",
    "#age열의 평균이 30이 안되는 그룹을 제거해보자\n",
    "for key, data in gr:\n",
    "    print(key,data['age'].mean()) \n",
    "print(\"\")\n",
    "\n",
    "gr_filter2 = gr.filter(lambda x : x['age'].mean()>=30)\n",
    "gr_filter2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 멀티 인덱스"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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></th>\n",
       "      <th>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th>survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">First</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>44.581967</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.259740</td>\n",
       "      <td>62.894910</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.649351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.248000</td>\n",
       "      <td>0.377778</td>\n",
       "      <td>0.311111</td>\n",
       "      <td>74.637320</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">Second</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>33.369048</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>19.488965</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.022000</td>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>21.095100</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.411765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">Third</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27.255814</td>\n",
       "      <td>0.523333</td>\n",
       "      <td>0.213333</td>\n",
       "      <td>12.204469</td>\n",
       "      <td>0.936667</td>\n",
       "      <td>0.773333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.274211</td>\n",
       "      <td>0.340426</td>\n",
       "      <td>0.297872</td>\n",
       "      <td>15.579696</td>\n",
       "      <td>0.808511</td>\n",
       "      <td>0.680851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 pclass        age     sibsp     parch       fare  adult_male  \\\n",
       "class  survived                                                                 \n",
       "First  0            1.0  44.581967  0.272727  0.259740  62.894910    1.000000   \n",
       "       1            1.0  36.248000  0.377778  0.311111  74.637320    0.933333   \n",
       "Second 0            2.0  33.369048  0.307692  0.142857  19.488965    1.000000   \n",
       "       1            2.0  16.022000  0.529412  0.647059  21.095100    0.470588   \n",
       "Third  0            3.0  27.255814  0.523333  0.213333  12.204469    0.936667   \n",
       "       1            3.0  22.274211  0.340426  0.297872  15.579696    0.808511   \n",
       "\n",
       "                    alone  \n",
       "class  survived            \n",
       "First  0         0.649351  \n",
       "       1         0.555556  \n",
       "Second 0         0.714286  \n",
       "       1         0.411765  \n",
       "Third  0         0.773333  \n",
       "       1         0.680851  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr = titanic.groupby(['class','sex','survived'])\n",
    "gdf = gr.mean()\n",
    "gdf\n",
    "\n",
    "#1. 위의 멀티 인덱스에서 원하는 데이터만 접근할 땐 어떻게 해야 할까???\n",
    "gdf.loc['Third'] #loc를 이용하자!\n",
    "gdf.loc['Third','male'] #Third의 남자만 알고 싶을 때\n",
    "\n",
    "# 이렇게 하면 더이상 전체 class를 전체의 남자만 가져오는건 안됨..! >> xs 인덱서 이용\n",
    "\n",
    "gdf.xs('male', level='sex')"
   ]
  }
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