03-1.행정구역정보_분석과시각화.ipynb

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    "### 행정구역 정보 분석 및 시각화\n",
    "1. pandas의 read_csv() 함수로 csv file을 읽어서 DataFrame 객체로 생성하고 데이터 확인하기\n",
    "2. Data Selection : loc[] / iloc[] 를 사용해서 특정 Row와 Column 선택하기\n",
    "3. 컬럼명 변경하기\n",
    "4. DataFrame 객체를 Excel file로 저장하기\n",
    "5. Data Grouping : groupby() 함수를 사용해서 그룹핑하기\n",
    "6. 상관관계 분석 : corr() 함수를 사용해서 인구수와 면적간의 상관관계 \n",
    "7. 시각화 : seaborn의 barplot() 함수를 사용해서 Plot 그리기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "72fd066e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.2\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "print(pd.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e1285455-f095-4aa3-87a5-2358e7146388",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "컬럼명 = Index(['Unnamed: 0', '인구수', 'shortName', 'x', 'y', '면적', '광역시도', '행정구역'], dtype='object')\n",
      "인덱스 = RangeIndex(start=0, stop=229, step=1)\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 229 entries, 0 to 228\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  229 non-null    int64  \n",
      " 1   인구수         229 non-null    int64  \n",
      " 2   shortName   229 non-null    object \n",
      " 3   x           229 non-null    int64  \n",
      " 4   y           229 non-null    int64  \n",
      " 5   면적          229 non-null    float64\n",
      " 6   광역시도        229 non-null    object \n",
      " 7   행정구역        229 non-null    object \n",
      "dtypes: float64(1), int64(4), object(3)\n",
      "memory usage: 14.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('data/data_draw_korea.csv')\n",
    "\n",
    "# print(type(data)) # ->  <class 'pandas.core.frame.DataFrame'>\n",
    "# print(data.shape) # -> (229, 8) = 229행 8열\n",
    "\n",
    "# data.head(3) -> 앞에 3개의 행만 출력\n",
    "# data.tail(5) -> 뒤에 5개의 행만 출력\n",
    "# data.sample(3) -> 랜덤한 3개의 행을 출력\n",
    "\n",
    "print(f'컬럼명 = {data.columns}')\n",
    "print(f'인덱스 = {data.index}')\n",
    "\n",
    "# DataFrame 의 values\n",
    "print(type(data.values)) # -> <class 'numpy.ndarray'>\n",
    "data.values\n",
    "\n",
    "# DataFrame 의 information 모두 확인\n",
    "data.info()"
   ]
  },
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       "Unnamed: 0      int64\n",
       "인구수             int64\n",
       "shortName      object\n",
       "x               int64\n",
       "y               int64\n",
       "면적            float64\n",
       "광역시도           object\n",
       "행정구역           object\n",
       "dtype: object"
      ]
     },
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   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "983a241f-8663-47a5-9e5f-4cd22f9de7f9",
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "<class 'pandas.core.series.Series'>\n",
      "0      202520\n",
      "1       25589\n",
      "2       86747\n",
      "3       63986\n",
      "4       76733\n",
      "        ...  \n",
      "224    127462\n",
      "225     34480\n",
      "226     62809\n",
      "227    790216\n",
      "228    198077\n",
      "Name: 인구수, Length: 229, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 특정 컬럼의 Data Type 확인\n",
    "print(type(data))\n",
    "print(type(data['인구수'])) # -> <class 'pandas.core.series.Series'>\n",
    "print(data['인구수'])"
   ]
  },
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       "      <td>229.000000</td>\n",
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       "      <td>229.000000</td>\n",
       "      <td>229.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>114.000000</td>\n",
       "      <td>2.110898e+05</td>\n",
       "      <td>6.117904</td>\n",
       "      <td>11.262009</td>\n",
       "      <td>435.419795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
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       "      <td>2.800000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
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       "      <td>6.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
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       "      <td>1.401590e+05</td>\n",
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       "      <td>11.000000</td>\n",
       "      <td>436.400000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>171.000000</td>\n",
       "      <td>3.216180e+05</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>692.800000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>228.000000</td>\n",
       "      <td>1.125461e+06</td>\n",
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       "      <td>25.000000</td>\n",
       "      <td>1817.940000</td>\n",
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       "       Unnamed: 0           인구수           x           y           면적\n",
       "count  229.000000  2.290000e+02  229.000000  229.000000   229.000000\n",
       "mean   114.000000  2.110898e+05    6.117904   11.262009   435.419795\n",
       "std     66.250786  2.074378e+05    3.320469    6.040898   381.612549\n",
       "min      0.000000  8.392000e+03    0.000000    0.000000     2.800000\n",
       "25%     57.000000  4.955900e+04    3.000000    6.000000    54.000000\n",
       "50%    114.000000  1.401590e+05    6.000000   11.000000   436.400000\n",
       "75%    171.000000  3.216180e+05    9.000000   16.000000   692.800000\n",
       "max    228.000000  1.125461e+06   13.000000   25.000000  1817.940000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe() #숫자 데이터에 대한 집계함수"
   ]
  },
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   "cell_type": "code",
   "execution_count": 36,
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       "      <th></th>\n",
       "      <th>shortName</th>\n",
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       "       shortName 광역시도 행정구역\n",
       "count        229  229  229\n",
       "unique       229   17  206\n",
       "top           강릉  경기도   동구\n",
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    "data.describe(include=[object]) # 문자 데이터에 대한 집계함수"
   ]
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       "      <td>6.117904</td>\n",
       "      <td>11.262009</td>\n",
       "      <td>435.419795</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>std</th>\n",
       "      <td>66.250786</td>\n",
       "      <td>2.074378e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.320469</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>min</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>25%</th>\n",
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       "      <td>4.955900e+04</td>\n",
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       "      <td>3.000000</td>\n",
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       "      <th>50%</th>\n",
       "      <td>114.000000</td>\n",
       "      <td>1.401590e+05</td>\n",
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       "      <td>6.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>436.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>75%</th>\n",
       "      <td>171.000000</td>\n",
       "      <td>3.216180e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>692.800000</td>\n",
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       "      <td>1.125461e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1817.940000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "        Unnamed: 0           인구수 shortName           x           y  \\\n",
       "count   229.000000  2.290000e+02       229  229.000000  229.000000   \n",
       "unique         NaN           NaN       229         NaN         NaN   \n",
       "top            NaN           NaN        강릉         NaN         NaN   \n",
       "freq           NaN           NaN         1         NaN         NaN   \n",
       "mean    114.000000  2.110898e+05       NaN    6.117904   11.262009   \n",
       "std      66.250786  2.074378e+05       NaN    3.320469    6.040898   \n",
       "min       0.000000  8.392000e+03       NaN    0.000000    0.000000   \n",
       "25%      57.000000  4.955900e+04       NaN    3.000000    6.000000   \n",
       "50%     114.000000  1.401590e+05       NaN    6.000000   11.000000   \n",
       "75%     171.000000  3.216180e+05       NaN    9.000000   16.000000   \n",
       "max     228.000000  1.125461e+06       NaN   13.000000   25.000000   \n",
       "\n",
       "                 면적 광역시도 행정구역  \n",
       "count    229.000000  229  229  \n",
       "unique          NaN   17  206  \n",
       "top             NaN  경기도   동구  \n",
       "freq            NaN   31    6  \n",
       "mean     435.419795  NaN  NaN  \n",
       "std      381.612549  NaN  NaN  \n",
       "min        2.800000  NaN  NaN  \n",
       "25%       54.000000  NaN  NaN  \n",
       "50%      436.400000  NaN  NaN  \n",
       "75%      692.800000  NaN  NaN  \n",
       "max     1817.940000  NaN  NaN  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include= 'all' ) # 모든 컬럼의 데이터에 대한 집계함수"
   ]
  },
  {
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   "id": "35f9ade5-6c5d-4f3d-995c-b53aa09c4e2e",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>인구수</th>\n",
       "      <th>shortName</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>면적</th>\n",
       "      <th>광역시도</th>\n",
       "      <th>행정구역</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>92</td>\n",
       "      <td>97571</td>\n",
       "      <td>광주동구</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>48.86</td>\n",
       "      <td>광주광역시</td>\n",
       "      <td>동구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>98</td>\n",
       "      <td>330830</td>\n",
       "      <td>대구동구</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>182.35</td>\n",
       "      <td>대구광역시</td>\n",
       "      <td>동구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>104</td>\n",
       "      <td>236524</td>\n",
       "      <td>대전동구</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>136.80</td>\n",
       "      <td>대전광역시</td>\n",
       "      <td>동구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>112</td>\n",
       "      <td>85952</td>\n",
       "      <td>부산동구</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>9.77</td>\n",
       "      <td>부산광역시</td>\n",
       "      <td>동구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>151</td>\n",
       "      <td>164738</td>\n",
       "      <td>울산동구</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>35.77</td>\n",
       "      <td>울산광역시</td>\n",
       "      <td>동구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>159</td>\n",
       "      <td>68950</td>\n",
       "      <td>인천동구</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>7.05</td>\n",
       "      <td>인천광역시</td>\n",
       "      <td>동구</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0     인구수 shortName   x   y      면적   광역시도 행정구역\n",
       "92           92   97571      광주동구   3  20   48.86  광주광역시   동구\n",
       "98           98  330830      대구동구  11  13  182.35  대구광역시   동구\n",
       "104         104  236524      대전동구   5  13  136.80  대전광역시   동구\n",
       "112         112   85952      부산동구  10  17    9.77  부산광역시   동구\n",
       "151         151  164738      울산동구  12  14   35.77  울산광역시   동구\n",
       "159         159   68950      인천동구   1   5    7.05  인천광역시   동구"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data['행정구역'] == '동구'  -> 229행 모두 출력\n",
    "data.loc[data['행정구역'] == '동구'] # -> data.loc[] 을 사용해, 229행 중 조건에 맞는 row 만 출력하게끔 설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7accd987-37ae-4a8e-9449-d4e399577a20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['강원도', '경기도', '경상남도', '경상북도', '광주광역시', '대구광역시', '대전광역시', '부산광역시',\n",
       "       '서울특별시', '세종특별자치시', '울산광역시', '인천광역시', '전라남도', '전라북도', '제주특별자치도',\n",
       "       '충청남도', '충청북도'], dtype=object)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['광역시도'].unique() # 특정 컬럼의 유니크한 값을 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "947a76da-1cb1-4db8-9b7a-9584c442393c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>행정구역</th>\n",
       "      <th>인구수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>수원시</td>\n",
       "      <td>1125461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>고양시</td>\n",
       "      <td>955384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>용인시</td>\n",
       "      <td>915005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>성남시</td>\n",
       "      <td>911628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>부천시</td>\n",
       "      <td>805473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>안산시</td>\n",
       "      <td>668089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>남양주시</td>\n",
       "      <td>610300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>안양시</td>\n",
       "      <td>566558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>화성시</td>\n",
       "      <td>555129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>평택시</td>\n",
       "      <td>423763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>의정부시</td>\n",
       "      <td>408220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>파주시</td>\n",
       "      <td>385528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>시흥시</td>\n",
       "      <td>381412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>광명시</td>\n",
       "      <td>327111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>김포시</td>\n",
       "      <td>326523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>광주시</td>\n",
       "      <td>290210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>군포시</td>\n",
       "      <td>273302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>오산시</td>\n",
       "      <td>200793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>양주시</td>\n",
       "      <td>191266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>이천시</td>\n",
       "      <td>190537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>구리시</td>\n",
       "      <td>175676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>안성시</td>\n",
       "      <td>173061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>하남시</td>\n",
       "      <td>150061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>의왕시</td>\n",
       "      <td>149080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>포천시</td>\n",
       "      <td>140159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>여주시</td>\n",
       "      <td>100669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>양평군</td>\n",
       "      <td>95614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>동두천시</td>\n",
       "      <td>90398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>과천시</td>\n",
       "      <td>63227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>가평군</td>\n",
       "      <td>53497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>연천군</td>\n",
       "      <td>41076</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    행정구역      인구수\n",
       "0    수원시  1125461\n",
       "1    고양시   955384\n",
       "2    용인시   915005\n",
       "3    성남시   911628\n",
       "4    부천시   805473\n",
       "5    안산시   668089\n",
       "6   남양주시   610300\n",
       "7    안양시   566558\n",
       "8    화성시   555129\n",
       "9    평택시   423763\n",
       "10  의정부시   408220\n",
       "11   파주시   385528\n",
       "12   시흥시   381412\n",
       "13   광명시   327111\n",
       "14   김포시   326523\n",
       "15   광주시   290210\n",
       "16   군포시   273302\n",
       "17   오산시   200793\n",
       "18   양주시   191266\n",
       "19   이천시   190537\n",
       "20   구리시   175676\n",
       "21   안성시   173061\n",
       "22   하남시   150061\n",
       "23   의왕시   149080\n",
       "24   포천시   140159\n",
       "25   여주시   100669\n",
       "26   양평군    95614\n",
       "27  동두천시    90398\n",
       "28   과천시    63227\n",
       "29   가평군    53497\n",
       "30   연천군    41076"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # 경기도에 속한 행정구역 출력\n",
    "data.loc[data['광역시도'] == '경기도', '행정구역'] # -> '경기도' '행정구역' 의 series\n",
    "data.loc[data['광역시도'] == '경기도', ['행정구역']] # -> '경기도' '행정구역' 의 dataframe\n",
    "data.loc[data['광역시도'] == '경기도', ['행정구역']].reset_index(drop=True) # -> dataframe 의 index를 초기화해 출력\n",
    "data.loc[data['광역시도'] == '경기도', ['행정구역','인구수']].sort_values(by='인구수', ascending=False).reset_index(drop=True) # -> dataframe 의 '인구수' series 의 데이터를 내림차순으로 정리하고, index를 초기화해 출력"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45b5cb63-f71e-42ec-a943-dc84807a3415",
   "metadata": {},
   "source": [
    "### Data selection\n",
    "* loc[row, col]\n",
    "    - row : 조건식, index\n",
    "    - col : 컬럼명\n",
    "* iloc[row, col]\n",
    "    - row : index\n",
    "    - col : 컬럼index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8e8f593f-26e8-4478-b5cd-68fc91aae31c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "인구수 최대값 1,125,461\n",
      "인구수 최소값 8,392\n",
      "인구수 평균값 211,090\n",
      "인구수 표준편차 207,438\n",
      "인구수 중간값 140,159\n",
      "인구수 25%, 75% 0.25     49559.0\n",
      "0.75    321618.0\n",
      "Name: 인구수, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "data['인구수'] # -> series\n",
    "\n",
    "popSeries = data['인구수']\n",
    "print(f'인구수 최대값 {popSeries.max():,}')\n",
    "print(f'인구수 최소값 {popSeries.min():,}')\n",
    "print(f'인구수 평균값 {round(popSeries.mean()):,}')\n",
    "print(f'인구수 표준편차 {round(popSeries.std()):,}')\n",
    "print(f'인구수 중간값 {round(popSeries.median()):,}')\n",
    "print(f'인구수 25%, 75% {popSeries.quantile([0.25,0.75])}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "40f6aee8-a6a0-4ad8-a8f6-596047c7dbfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Unnamed: 0</th>\n",
       "      <th>인구수</th>\n",
       "      <th>shortName</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>면적</th>\n",
       "      <th>광역시도</th>\n",
       "      <th>행정구역</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>1125461</td>\n",
       "      <td>수원</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>121.09</td>\n",
       "      <td>경기도</td>\n",
       "      <td>수원시</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0      인구수 shortName  x  y      면적 광역시도 행정구역\n",
       "30          30  1125461        수원  6  9  121.09  경기도  수원시"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 인구수가 가장 많은 행정구역\n",
    "pop_max_val = data['인구수'].max()\n",
    "data.loc[data['인구수'] == pop_max_val]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcbcfb22-78e1-4f23-82e6-f2e52206cf49",
   "metadata": {},
   "source": [
    "#### 상관계수 (Correlation Coefficient)\n",
    "* 인구수와 면적 데이터간에 관련성이 있는지 살펴보기 위해서 상관계수 구하기\n",
    "* 상관계수 값은 -1 ~ 1 사이의 값이며, 0에 가까울 수록 관련성이 낮고, 1에 가까울 수록 관련성이 높다. \n",
    "* 음수는 반비례 (면적이 넓은 반면 인구수는 적은 경우), 양수는 비례 (면적이 넓고, 인구수도 높은 경우)\n",
    "* corr() 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "186c8167-0b66-4fef-bf70-b2d75a335e06",
   "metadata": {},
   "outputs": [
    {
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       "        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>Unnamed: 0</th>\n",
       "      <th>인구수</th>\n",
       "      <th>shortName</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>면적</th>\n",
       "      <th>광역시도</th>\n",
       "      <th>행정구역</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>83</td>\n",
       "      <td>8392</td>\n",
       "      <td>울릉</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>72.82</td>\n",
       "      <td>경상북도</td>\n",
       "      <td>울릉군</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0   인구수 shortName   x  y     면적  광역시도 행정구역\n",
       "83          83  8392        울릉  13  6  72.82  경상북도  울릉군"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 인구수가 가장 적은 행정구역\n",
    "pop_min_val = data['인구수'].min()\n",
    "data.loc[data['인구수'] == pop_min_val]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "414cf53c-41cd-40ae-83d1-5996eb42b8b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 평균 인구수보다 작은 행정구역 , 광역시도, 행정구역, 인구수, index 를 리셋하고, 인구가 많은 순서대로 sort\n",
    "pop_mean_val = data['인구수'].mean()\n",
    "pop_mean_lt_df = data.loc[data['인구수'] < pop_mean_val, ['광역시도', '행정구역', '인구수']].sort_values(by='인구수', ascending=False).reset_index(drop=True)\n",
    "pop_mean_lt_df.tail(3)\n",
    "pop_mean_lt_df.to_excel('data/평균인구수미만지역.xlsx') # Exel 파일로  저장"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f1857f26-3027-4624-a950-a1af11a3a4d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "광역시도\n",
       "경기도        31\n",
       "서울특별시      25\n",
       "경상북도       23\n",
       "전라남도       22\n",
       "강원도        18\n",
       "경상남도       18\n",
       "부산광역시      16\n",
       "충청남도       15\n",
       "전라북도       14\n",
       "충청북도       11\n",
       "인천광역시      10\n",
       "대구광역시       8\n",
       "대전광역시       5\n",
       "광주광역시       5\n",
       "울산광역시       5\n",
       "제주특별자치도     2\n",
       "세종특별자치시     1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 광역시도별 row 개수 세기 value_counts()\n",
    "data['광역시도'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "6972efd4-f6ea-4d04-b18b-981839e76750",
   "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>행정구역</th>\n",
       "      <th>인구수</th>\n",
       "      <th>인구수2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>수원시</td>\n",
       "      <td>1125461</td>\n",
       "      <td>1,125,461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>고양시</td>\n",
       "      <td>955384</td>\n",
       "      <td>955,384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  행정구역      인구수       인구수2\n",
       "0  수원시  1125461  1,125,461\n",
       "1  고양시   955384    955,384"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gy_df = data.loc[data['광역시도'] == '경기도', ['행정구역','인구수']].sort_values(by='인구수', ascending=False)\\\n",
    ".reset_index(drop=True) # 역슬래쉬 \\ 를 사용하면, 줄 바꿈을 문법에 포함시키지 않음\n",
    "\n",
    "gy_df['인구수2'] = gy_df['인구수'].map('{:,}'.format) # 인구수 데이터에 , 를 추가한 새로운 컬럼 '인구수2' 생성\n",
    "gy_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b0790c24-cae4-45ab-90c2-f756b6c706f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "경기도 인구수 평균 378,845\n",
      "경기도 인구수 표준편차 304,548\n"
     ]
    }
   ],
   "source": [
    "print(f'경기도 인구수 평균 {round(gy_df['인구수'].mean()):,}')\n",
    "print(f'경기도 인구수 표준편차 {round(gy_df['인구수'].std()):,}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "f6f35b95-ccae-447a-a92c-d8863f01caf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 광역시도명 인자로 받아서 해당 광역시의 인구수의 평균과 편차 구하기\n",
    "import numpy as np \n",
    "\n",
    "def pop_mean_std_print(sido_name):\n",
    "    sido_df = data.loc[data['광역시도'] == sido_name, ['행정구역','인구수']]\n",
    "    print(f'{sido_name} 인구수 평균 {round(sido_df['인구수'].mean()):,}')\n",
    "    if np.isnan(sido_df['인구수'].std()):\n",
    "        print(f'{sido_name} 인구수 표준편차 {0}')\n",
    "    else:    \n",
    "        print(f'{sido_name} 인구수 표준편차 {round(sido_df['인구수'].std()):,}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "de57783d-75a3-48f8-a800-6eb19d0ade4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "서울특별시 인구수 평균 375,792\n",
      "서울특별시 인구수 표준편차 125,231\n"
     ]
    }
   ],
   "source": [
    "pop_mean_std_print('서울특별시')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "1f6dfeb0-f93a-4deb-a0f5-05d30a550591",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "강원도 인구수 평균 79,413\n",
      "강원도 인구수 표준편차 87,316\n",
      "경기도 인구수 평균 378,845\n",
      "경기도 인구수 표준편차 304,548\n",
      "경상남도 인구수 평균 174,694\n",
      "경상남도 인구수 표준편차 245,796\n",
      "경상북도 인구수 평균 109,085\n",
      "경상북도 인구수 표준편차 124,955\n",
      "광주광역시 인구수 평균 287,642\n",
      "광주광역시 인구수 표준편차 137,077\n",
      "대구광역시 인구수 평균 297,306\n",
      "대구광역시 인구수 표준편차 174,375\n",
      "대전광역시 인구수 평균 293,535\n",
      "대전광역시 인구수 표준편차 111,854\n",
      "부산광역시 인구수 평균 207,724\n",
      "부산광역시 인구수 표준편차 106,246\n",
      "서울특별시 인구수 평균 375,792\n",
      "서울특별시 인구수 표준편차 125,231\n",
      "세종특별자치시 인구수 평균 191,233\n",
      "세종특별자치시 인구수 표준편차 0\n",
      "울산광역시 인구수 평균 221,117\n",
      "울산광역시 인구수 표준편차 64,163\n",
      "인천광역시 인구수 평균 276,658\n",
      "인천광역시 인구수 표준편차 199,354\n",
      "전라남도 인구수 평균 77,298\n",
      "전라남도 인구수 표준편차 74,413\n",
      "전라북도 인구수 평균 124,218\n",
      "전라북도 인구수 표준편차 168,327\n",
      "제주특별자치도 인구수 평균 287,936\n",
      "제주특별자치도 인구수 표준편차 201,621\n",
      "충청남도 인구수 평균 129,742\n",
      "충청남도 인구수 표준편차 141,125\n",
      "충청북도 인구수 평균 134,975\n",
      "충청북도 인구수 표준편차 223,460\n"
     ]
    }
   ],
   "source": [
    "for sido_name in data['광역시도'].unique():\n",
    "    pop_mean_std_print(sido_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "b4e70a09-ec65-40c2-992f-7ca03e4345d8",
   "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>Unnamed: 0</th>\n",
       "      <th>인구수</th>\n",
       "      <th>shortName</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>면적</th>\n",
       "      <th>광역시도</th>\n",
       "      <th>행정구역</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>63227</td>\n",
       "      <td>과천</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>35.813</td>\n",
       "      <td>경기도</td>\n",
       "      <td>과천시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>327111</td>\n",
       "      <td>광명</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>38.500</td>\n",
       "      <td>경기도</td>\n",
       "      <td>광명시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>290210</td>\n",
       "      <td>광주</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>431.840</td>\n",
       "      <td>경기도</td>\n",
       "      <td>광주시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>175676</td>\n",
       "      <td>구리</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>33.300</td>\n",
       "      <td>경기도</td>\n",
       "      <td>구리시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>273302</td>\n",
       "      <td>군포</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>36.350</td>\n",
       "      <td>경기도</td>\n",
       "      <td>군포시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>326523</td>\n",
       "      <td>김포</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>276.640</td>\n",
       "      <td>경기도</td>\n",
       "      <td>김포시</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0     인구수 shortName  x  y       면적 광역시도 행정구역\n",
       "20          20   63227        과천  7  7   35.813  경기도  과천시\n",
       "21          21  327111        광명  3  3   38.500  경기도  광명시\n",
       "22          22  290210        광주  8  7  431.840  경기도  광주시\n",
       "23          23  175676        구리  9  4   33.300  경기도  구리시\n",
       "24          24  273302        군포  5  8   36.350  경기도  군포시\n",
       "25          25  326523        김포  2  3  276.640  경기도  김포시"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loc[20:25] index가 20 ~ 25 \n",
    "data.loc[20:25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "13b21974-9ce3-4cdf-9815-787092be4bbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>63227</td>\n",
       "      <td>과천</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>35.813</td>\n",
       "      <td>경기도</td>\n",
       "      <td>과천시</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>327111</td>\n",
       "      <td>광명</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>38.500</td>\n",
       "      <td>경기도</td>\n",
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       "      <td>22</td>\n",
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       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>431.840</td>\n",
       "      <td>경기도</td>\n",
       "      <td>광주시</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>175676</td>\n",
       "      <td>구리</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>33.300</td>\n",
       "      <td>경기도</td>\n",
       "      <td>구리시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>273302</td>\n",
       "      <td>군포</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>36.350</td>\n",
       "      <td>경기도</td>\n",
       "      <td>군포시</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0     인구수 shortName  x  y       면적 광역시도 행정구역\n",
       "20          20   63227        과천  7  7   35.813  경기도  과천시\n",
       "21          21  327111        광명  3  3   38.500  경기도  광명시\n",
       "22          22  290210        광주  8  7  431.840  경기도  광주시\n",
       "23          23  175676        구리  9  4   33.300  경기도  구리시\n",
       "24          24  273302        군포  5  8   36.350  경기도  군포시"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# iloc[20:25] index가 20 ~ 24\n",
    "data.iloc[20:25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b9b1eb3b-7aae-483b-b358-9d37a384cf1b",
   "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>인구수</th>\n",
       "      <th>shortName</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>면적</th>\n",
       "      <th>광역시도</th>\n",
       "      <th>행정구역</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>63227</td>\n",
       "      <td>과천</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>35.813</td>\n",
       "      <td>경기도</td>\n",
       "      <td>과천시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>327111</td>\n",
       "      <td>광명</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>38.500</td>\n",
       "      <td>경기도</td>\n",
       "      <td>광명시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>290210</td>\n",
       "      <td>광주</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>431.840</td>\n",
       "      <td>경기도</td>\n",
       "      <td>광주시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>175676</td>\n",
       "      <td>구리</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>33.300</td>\n",
       "      <td>경기도</td>\n",
       "      <td>구리시</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       인구수 shortName  x  y       면적 광역시도 행정구역\n",
       "20   63227        과천  7  7   35.813  경기도  과천시\n",
       "21  327111        광명  3  3   38.500  경기도  광명시\n",
       "22  290210        광주  8  7  431.840  경기도  광주시\n",
       "23  175676        구리  9  4   33.300  경기도  구리시"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[20:23,'인구수':'행정구역']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "d1520cad-7def-421b-90aa-7501ac0f8ca3",
   "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>인구수</th>\n",
       "      <th>shortName</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>면적</th>\n",
       "      <th>광역시도</th>\n",
       "      <th>행정구역</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>63227</td>\n",
       "      <td>과천</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>35.813</td>\n",
       "      <td>경기도</td>\n",
       "      <td>과천시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>327111</td>\n",
       "      <td>광명</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>38.500</td>\n",
       "      <td>경기도</td>\n",
       "      <td>광명시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>290210</td>\n",
       "      <td>광주</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>431.840</td>\n",
       "      <td>경기도</td>\n",
       "      <td>광주시</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       인구수 shortName  x  y       면적 광역시도 행정구역\n",
       "20   63227        과천  7  7   35.813  경기도  과천시\n",
       "21  327111        광명  3  3   38.500  경기도  광명시\n",
       "22  290210        광주  8  7  431.840  경기도  광주시"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[20:23,1:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "d94ae984-35f2-419f-83fb-efbdfb654b41",
   "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>인구수</th>\n",
       "      <th>행정구역</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25589</td>\n",
       "      <td>고성군</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63986</td>\n",
       "      <td>삼척시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>24551</td>\n",
       "      <td>양양군</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>34966</td>\n",
       "      <td>영월군</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     인구수 행정구역\n",
       "1  25589  고성군\n",
       "3  63986  삼척시\n",
       "6  24551  양양군\n",
       "7  34966  영월군"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loc[[1,3,6,7],['인구수','행정구역']] 특정 index와 컬럼을 선택\n",
    "data.loc[[1,3,6,7],['인구수','행정구역']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "afada351-5230-4b5f-bbe2-47484dd32eb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Unnamed: 0          0\n",
       "인구수            202520\n",
       "shortName          강릉\n",
       "x                  11\n",
       "y                   4\n",
       "면적            1040.07\n",
       "광역시도              강원도\n",
       "행정구역              강릉시\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 인덱스가 0인 행 하나만 선택\n",
    "print(type(data.loc[0]))\n",
    "data.loc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad00ca0c-eb91-4023-9af7-f548d7856593",
   "metadata": {},
   "source": [
    "### 컬럼명 변경하기, 컬럼을 인덱스로 전환하기\r",
    "* \n",
    "rename() 함수* \r\n",
    "inplace=False(default) 원본 DataFrame 객체를 변경하지 않고, 변경된 결과만 출력한다* .\r\n",
    "inplace=True 원본 DataFrame 객체를 변경하고, 결과는 출력하지 않는다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "a81a80dd-2fdb-4917-83ee-02dd9e3a9adc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', '인구수', 'shortName', 'x', 'y', '면적', '광역시도', '행정구역'], dtype='object')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "206dc00b-e8dc-4b18-b174-238fe3f92877",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.rename(columns={'Unnamed: 0':'seq'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "cb2f3d1d-c698-4a4b-aa06-c2a6f5f9f8f6",
   "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>seq</th>\n",
       "      <th>인구수</th>\n",
       "      <th>shortName</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>면적</th>\n",
       "      <th>광역시도</th>\n",
       "      <th>행정구역</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>202520</td>\n",
       "      <td>강릉</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>1040.07</td>\n",
       "      <td>강원도</td>\n",
       "      <td>강릉시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>25589</td>\n",
       "      <td>고성(강원)</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>664.19</td>\n",
       "      <td>강원도</td>\n",
       "      <td>고성군</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   seq     인구수 shortName   x  y       면적 광역시도 행정구역\n",
       "0    0  202520        강릉  11  4  1040.07  강원도  강릉시\n",
       "1    1   25589    고성(강원)   9  0   664.19  강원도  고성군"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "00e294ae-4556-4663-bf22-651684875545",
   "metadata": {},
   "outputs": [],
   "source": [
    "# seq 컬럼을 인덱스로 변경하기\n",
    "data.set_index('seq', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "c5225fbc-063d-4e86-ba38-a0c27a948f2f",
   "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>인구수</th>\n",
       "      <th>shortName</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>면적</th>\n",
       "      <th>광역시도</th>\n",
       "      <th>행정구역</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>202520</td>\n",
       "      <td>강릉</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>1040.07</td>\n",
       "      <td>강원도</td>\n",
       "      <td>강릉시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25589</td>\n",
       "      <td>고성(강원)</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>664.19</td>\n",
       "      <td>강원도</td>\n",
       "      <td>고성군</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        인구수 shortName   x  y       면적 광역시도 행정구역\n",
       "seq                                            \n",
       "0    202520        강릉  11  4  1040.07  강원도  강릉시\n",
       "1     25589    고성(강원)   9  0   664.19  강원도  고성군"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58a72d8a-789f-4b04-a5f2-4c50b907ebf8",
   "metadata": {},
   "source": [
    "#### GroupBy 기능 사용하기\n",
    "* 광역시도별 행정구역의 인구수 합계\n",
    "* ~별에 컬럼명이나 컬럼값을 groupby() 함수의 인자로 전달합니다.\n",
    "* Series 객체 series.groupby(data['광역시도']) 함수는 컬럼값을 전달합니다.\n",
    "* DataFrame 객체 dataframe.groupby('광역시도') 함수는 컬럼명을 전달합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "a2e084e5-b316-4709-8533-8c40301d85c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "광역시도\n",
       "경기도        11744210\n",
       "서울특별시       9394807\n",
       "부산광역시       3323591\n",
       "경상남도        3144487\n",
       "인천광역시       2766575\n",
       "경상북도        2508964\n",
       "대구광역시       2378450\n",
       "충청남도        1946129\n",
       "전라북도        1739048\n",
       "전라남도        1700563\n",
       "충청북도        1484720\n",
       "대전광역시       1467677\n",
       "광주광역시       1438209\n",
       "강원도         1429438\n",
       "울산광역시       1105585\n",
       "제주특별자치도      575873\n",
       "세종특별자치시      191233\n",
       "Name: 인구수, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Series 객체사용 - 광역시도별 행정구역의 인구수 합계\n",
    "# pandas.core.groupby.generic.SeriesGroupBy object\n",
    "data['인구수'].groupby(data['광역시도']).sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "960dce3a-6ea8-40d5-944f-c469bf5f7fbc",
   "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>인구수</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>면적</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>광역시도</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>강원도</th>\n",
       "      <td>1429438</td>\n",
       "      <td>173</td>\n",
       "      <td>68</td>\n",
       "      <td>16874.810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경기도</th>\n",
       "      <td>11744210</td>\n",
       "      <td>182</td>\n",
       "      <td>162</td>\n",
       "      <td>10179.743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경상남도</th>\n",
       "      <td>3144487</td>\n",
       "      <td>123</td>\n",
       "      <td>313</td>\n",
       "      <td>10437.460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경상북도</th>\n",
       "      <td>2508964</td>\n",
       "      <td>229</td>\n",
       "      <td>240</td>\n",
       "      <td>19021.850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>광주광역시</th>\n",
       "      <td>1438209</td>\n",
       "      <td>12</td>\n",
       "      <td>99</td>\n",
       "      <td>501.280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대구광역시</th>\n",
       "      <td>2378450</td>\n",
       "      <td>74</td>\n",
       "      <td>111</td>\n",
       "      <td>885.690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대전광역시</th>\n",
       "      <td>1467677</td>\n",
       "      <td>23</td>\n",
       "      <td>66</td>\n",
       "      <td>539.640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>부산광역시</th>\n",
       "      <td>3323591</td>\n",
       "      <td>162</td>\n",
       "      <td>276</td>\n",
       "      <td>763.240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울특별시</th>\n",
       "      <td>9394807</td>\n",
       "      <td>142</td>\n",
       "      <td>111</td>\n",
       "      <td>605.390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>세종특별자치시</th>\n",
       "      <td>191233</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>465.230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>울산광역시</th>\n",
       "      <td>1105585</td>\n",
       "      <td>58</td>\n",
       "      <td>71</td>\n",
       "      <td>980.220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천광역시</th>\n",
       "      <td>2766575</td>\n",
       "      <td>13</td>\n",
       "      <td>61</td>\n",
       "      <td>1022.910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전라남도</th>\n",
       "      <td>1700563</td>\n",
       "      <td>44</td>\n",
       "      <td>438</td>\n",
       "      <td>12008.390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전라북도</th>\n",
       "      <td>1739048</td>\n",
       "      <td>46</td>\n",
       "      <td>219</td>\n",
       "      <td>7907.220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>제주특별자치도</th>\n",
       "      <td>575873</td>\n",
       "      <td>8</td>\n",
       "      <td>49</td>\n",
       "      <td>1848.480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충청남도</th>\n",
       "      <td>1946129</td>\n",
       "      <td>39</td>\n",
       "      <td>160</td>\n",
       "      <td>8272.270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충청북도</th>\n",
       "      <td>1484720</td>\n",
       "      <td>69</td>\n",
       "      <td>124</td>\n",
       "      <td>7397.310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              인구수    x    y         면적\n",
       "광역시도                                  \n",
       "강원도       1429438  173   68  16874.810\n",
       "경기도      11744210  182  162  10179.743\n",
       "경상남도      3144487  123  313  10437.460\n",
       "경상북도      2508964  229  240  19021.850\n",
       "광주광역시     1438209   12   99    501.280\n",
       "대구광역시     2378450   74  111    885.690\n",
       "대전광역시     1467677   23   66    539.640\n",
       "부산광역시     3323591  162  276    763.240\n",
       "서울특별시     9394807  142  111    605.390\n",
       "세종특별자치시    191233    4   11    465.230\n",
       "울산광역시     1105585   58   71    980.220\n",
       "인천광역시     2766575   13   61   1022.910\n",
       "전라남도      1700563   44  438  12008.390\n",
       "전라북도      1739048   46  219   7907.220\n",
       "제주특별자치도    575873    8   49   1848.480\n",
       "충청남도      1946129   39  160   8272.270\n",
       "충청북도      1484720   69  124   7397.310"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame 객체사용 - 광역시도별 행정구역의 인구수 합계\n",
    "# pandas.core.groupby.generic.DataFrameGroupBy object\n",
    "data.groupby('광역시도').sum(numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "882c10f3-531c-43a0-adb1-bb6ea4f1cd7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "광역시도\n",
       "경기도        11744210\n",
       "서울특별시       9394807\n",
       "부산광역시       3323591\n",
       "경상남도        3144487\n",
       "인천광역시       2766575\n",
       "경상북도        2508964\n",
       "대구광역시       2378450\n",
       "충청남도        1946129\n",
       "전라북도        1739048\n",
       "전라남도        1700563\n",
       "충청북도        1484720\n",
       "대전광역시       1467677\n",
       "광주광역시       1438209\n",
       "강원도         1429438\n",
       "울산광역시       1105585\n",
       "제주특별자치도      575873\n",
       "세종특별자치시      191233\n",
       "Name: 인구수, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('광역시도')['인구수'].sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "d8b0cb82-e989-4049-95d4-81875378cd6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 광역시도별, 행정구역별 인구수의 합계\n",
    "group_series = data.groupby(['광역시도','행정구역'])['인구수'].sum()\n",
    "group_series.to_excel('data/광역시별행정구역별인구수합계.xlsx',sheet_name='인구수')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "803e6244-4322-4353-9dc0-1331b13aa415",
   "metadata": {},
   "source": [
    "### Excel 수치 데이터에 콤마 찍기\n",
    "* [Excelwriter](https://xlsxwriter.readthedocs.io/working_with_pandas.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "1d4ed7a4-8236-474e-8503-43fd1ffb5455",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xlsxwriter\n",
      "  Downloading XlsxWriter-3.2.0-py3-none-any.whl.metadata (2.6 kB)\n",
      "Downloading XlsxWriter-3.2.0-py3-none-any.whl (159 kB)\n",
      "   ---------------------------------------- 0.0/159.9 kB ? eta -:--:--\n",
      "   ---------------------------------------- 0.0/159.9 kB ? eta -:--:--\n",
      "   ------- ------------------------------- 30.7/159.9 kB 660.6 kB/s eta 0:00:01\n",
      "   -------------------------------------- - 153.6/159.9 kB 1.5 MB/s eta 0:00:01\n",
      "   ---------------------------------------- 159.9/159.9 kB 1.6 MB/s eta 0:00:00\n",
      "Installing collected packages: xlsxwriter\n",
      "Successfully installed xlsxwriter-3.2.0\n"
     ]
    }
   ],
   "source": [
    "!pip install xlsxwriter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "20dc51e0-eb7f-4d1d-ad27-6b16fe7a00ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: XlsxWriter\n",
      "Version: 3.2.0\n",
      "Summary: A Python module for creating Excel XLSX files.\n",
      "Home-page: https://github.com/jmcnamara/XlsxWriter\n",
      "Author: John McNamara\n",
      "Author-email: jmcnamara@cpan.org\n",
      "License: BSD-2-Clause\n",
      "Location: C:\\Users\\kjm99\\anaconda3\\Lib\\site-packages\n",
      "Requires: \n",
      "Required-by: \n"
     ]
    }
   ],
   "source": [
    "!pip show xlsxwriter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "1a134604-fb8d-47d6-a295-4835b2f6627b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Pandas Excel writer using XlsxWriter as the engine.\n",
    "writer = pd.ExcelWriter('data/광역시도별인구수의합계1.xlsx', engine='xlsxwriter')\n",
    "\n",
    "# Convert the dataframe to an XlsxWriter Excel object.\n",
    "group_series.to_excel(writer, sheet_name='인구수합계')\n",
    "\n",
    "# Get the xlsxwriter workbook and worksheet objects.\n",
    "workbook  = writer.book\n",
    "worksheet = writer.sheets['인구수합계']\n",
    "\n",
    "# Set a currency number format for a column.\n",
    "num_format = workbook.add_format({'num_format': '#,###'})\n",
    "worksheet.set_column('C:C', None, num_format)\n",
    "\n",
    "\n",
    "# Close the Pandas Excel writer and output the Excel file.\n",
    "writer.close() #_save()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2cc852d-91a4-4039-9b89-8524b3beb89c",
   "metadata": {},
   "source": [
    "### 상관계수(Correlation Coefficient)\r\n",
    "* 인구수와 면적 데이터간에 관련성이 있는지 살펴보기 위해서 상관계수 구하기\r\n",
    "* 상관계수 값은 -1 ~ 1 사이의 값이며, 0에 가까울 수록 관련성이 낮고, 1에 가까울 수록 관련성이 높다\r\n",
    "* 음수는 반비례 (면적이 넓은 반면 인구수는 적은 경우), 양수는 비례 (면적이 넓고, 인구수도 높은 경우)\r\n",
    "* corr() 함수rr() 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "7da8bb9e-f530-4dcb-ad6f-2fdd71e3a9c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.3460351605045771"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['면적'].corr(data['인구수'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "1966263f-5825-4793-8b2a-011ef9be8d2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.664426818890978"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seoul_df = data.loc[data['광역시도'] == '서울특별시']\n",
    "seoul_df['면적'].corr(seoul_df['인구수'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "3dca519b-4898-4534-baeb-014141759bbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.2157248507159082"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gy_df = data.loc[data['광역시도'] == '경기도']\n",
    "gy_df['면적'].corr(gy_df['인구수'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4160e162-5485-49ff-9f54-a8ee6a4fca2c",
   "metadata": {},
   "source": [
    "### Grouping 된 DataFrame 에 집계함수를 여러개 설정하기\n",
    "* agg() 함수\n",
    "* agg(['max', 'std'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "bc020175-4c82-4f2d-91ea-a26b035f8492",
   "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>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>광역시도</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>강원도</th>\n",
       "      <td>313851</td>\n",
       "      <td>79413.222222</td>\n",
       "      <td>87316.383238</td>\n",
       "      <td>20809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경기도</th>\n",
       "      <td>1125461</td>\n",
       "      <td>378845.483871</td>\n",
       "      <td>304548.395142</td>\n",
       "      <td>41076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경상남도</th>\n",
       "      <td>1010040</td>\n",
       "      <td>174693.722222</td>\n",
       "      <td>245796.465077</td>\n",
       "      <td>25091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경상북도</th>\n",
       "      <td>488395</td>\n",
       "      <td>109085.391304</td>\n",
       "      <td>124954.740885</td>\n",
       "      <td>8392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>광주광역시</th>\n",
       "      <td>439683</td>\n",
       "      <td>287641.800000</td>\n",
       "      <td>137076.683206</td>\n",
       "      <td>97571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대구광역시</th>\n",
       "      <td>584517</td>\n",
       "      <td>297306.250000</td>\n",
       "      <td>174375.394380</td>\n",
       "      <td>74950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대전광역시</th>\n",
       "      <td>475094</td>\n",
       "      <td>293535.400000</td>\n",
       "      <td>111853.571882</td>\n",
       "      <td>190815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>부산광역시</th>\n",
       "      <td>397134</td>\n",
       "      <td>207724.437500</td>\n",
       "      <td>106246.041384</td>\n",
       "      <td>43105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울특별시</th>\n",
       "      <td>615487</td>\n",
       "      <td>375792.280000</td>\n",
       "      <td>125231.247975</td>\n",
       "      <td>114967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>세종특별자치시</th>\n",
       "      <td>191233</td>\n",
       "      <td>191233.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>191233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>울산광역시</th>\n",
       "      <td>327270</td>\n",
       "      <td>221117.000000</td>\n",
       "      <td>64163.286582</td>\n",
       "      <td>164738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천광역시</th>\n",
       "      <td>527153</td>\n",
       "      <td>276657.500000</td>\n",
       "      <td>199353.596491</td>\n",
       "      <td>17735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전라남도</th>\n",
       "      <td>263166</td>\n",
       "      <td>77298.318182</td>\n",
       "      <td>74413.253230</td>\n",
       "      <td>23189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전라북도</th>\n",
       "      <td>630708</td>\n",
       "      <td>124217.714286</td>\n",
       "      <td>168327.134821</td>\n",
       "      <td>20492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>제주특별자치도</th>\n",
       "      <td>430504</td>\n",
       "      <td>287936.500000</td>\n",
       "      <td>201620.892054</td>\n",
       "      <td>145369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충청남도</th>\n",
       "      <td>584912</td>\n",
       "      <td>129741.933333</td>\n",
       "      <td>141125.108080</td>\n",
       "      <td>29671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충청북도</th>\n",
       "      <td>790216</td>\n",
       "      <td>134974.545455</td>\n",
       "      <td>223460.356657</td>\n",
       "      <td>27130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             max           mean            std     min\n",
       "광역시도                                                  \n",
       "강원도       313851   79413.222222   87316.383238   20809\n",
       "경기도      1125461  378845.483871  304548.395142   41076\n",
       "경상남도     1010040  174693.722222  245796.465077   25091\n",
       "경상북도      488395  109085.391304  124954.740885    8392\n",
       "광주광역시     439683  287641.800000  137076.683206   97571\n",
       "대구광역시     584517  297306.250000  174375.394380   74950\n",
       "대전광역시     475094  293535.400000  111853.571882  190815\n",
       "부산광역시     397134  207724.437500  106246.041384   43105\n",
       "서울특별시     615487  375792.280000  125231.247975  114967\n",
       "세종특별자치시   191233  191233.000000       0.000000  191233\n",
       "울산광역시     327270  221117.000000   64163.286582  164738\n",
       "인천광역시     527153  276657.500000  199353.596491   17735\n",
       "전라남도      263166   77298.318182   74413.253230   23189\n",
       "전라북도      630708  124217.714286  168327.134821   20492\n",
       "제주특별자치도   430504  287936.500000  201620.892054  145369\n",
       "충청남도      584912  129741.933333  141125.108080   29671\n",
       "충청북도      790216  134974.545455  223460.356657   27130"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('광역시도')['인구수'].agg(['max', 'mean', 'std', 'min']).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "96456553-b993-4271-bf51-dc1c3d688615",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>광역시도</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>경기도</th>\n",
       "      <td>1125461</td>\n",
       "      <td>378845.483871</td>\n",
       "      <td>304548.395142</td>\n",
       "      <td>41076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경상남도</th>\n",
       "      <td>1010040</td>\n",
       "      <td>174693.722222</td>\n",
       "      <td>245796.465077</td>\n",
       "      <td>25091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충청북도</th>\n",
       "      <td>790216</td>\n",
       "      <td>134974.545455</td>\n",
       "      <td>223460.356657</td>\n",
       "      <td>27130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>제주특별자치도</th>\n",
       "      <td>430504</td>\n",
       "      <td>287936.500000</td>\n",
       "      <td>201620.892054</td>\n",
       "      <td>145369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천광역시</th>\n",
       "      <td>527153</td>\n",
       "      <td>276657.500000</td>\n",
       "      <td>199353.596491</td>\n",
       "      <td>17735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대구광역시</th>\n",
       "      <td>584517</td>\n",
       "      <td>297306.250000</td>\n",
       "      <td>174375.394380</td>\n",
       "      <td>74950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전라북도</th>\n",
       "      <td>630708</td>\n",
       "      <td>124217.714286</td>\n",
       "      <td>168327.134821</td>\n",
       "      <td>20492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충청남도</th>\n",
       "      <td>584912</td>\n",
       "      <td>129741.933333</td>\n",
       "      <td>141125.108080</td>\n",
       "      <td>29671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>광주광역시</th>\n",
       "      <td>439683</td>\n",
       "      <td>287641.800000</td>\n",
       "      <td>137076.683206</td>\n",
       "      <td>97571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울특별시</th>\n",
       "      <td>615487</td>\n",
       "      <td>375792.280000</td>\n",
       "      <td>125231.247975</td>\n",
       "      <td>114967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경상북도</th>\n",
       "      <td>488395</td>\n",
       "      <td>109085.391304</td>\n",
       "      <td>124954.740885</td>\n",
       "      <td>8392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대전광역시</th>\n",
       "      <td>475094</td>\n",
       "      <td>293535.400000</td>\n",
       "      <td>111853.571882</td>\n",
       "      <td>190815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>부산광역시</th>\n",
       "      <td>397134</td>\n",
       "      <td>207724.437500</td>\n",
       "      <td>106246.041384</td>\n",
       "      <td>43105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강원도</th>\n",
       "      <td>313851</td>\n",
       "      <td>79413.222222</td>\n",
       "      <td>87316.383238</td>\n",
       "      <td>20809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전라남도</th>\n",
       "      <td>263166</td>\n",
       "      <td>77298.318182</td>\n",
       "      <td>74413.253230</td>\n",
       "      <td>23189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>울산광역시</th>\n",
       "      <td>327270</td>\n",
       "      <td>221117.000000</td>\n",
       "      <td>64163.286582</td>\n",
       "      <td>164738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>세종특별자치시</th>\n",
       "      <td>191233</td>\n",
       "      <td>191233.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>191233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             max           mean            std     min\n",
       "광역시도                                                  \n",
       "경기도      1125461  378845.483871  304548.395142   41076\n",
       "경상남도     1010040  174693.722222  245796.465077   25091\n",
       "충청북도      790216  134974.545455  223460.356657   27130\n",
       "제주특별자치도   430504  287936.500000  201620.892054  145369\n",
       "인천광역시     527153  276657.500000  199353.596491   17735\n",
       "대구광역시     584517  297306.250000  174375.394380   74950\n",
       "전라북도      630708  124217.714286  168327.134821   20492\n",
       "충청남도      584912  129741.933333  141125.108080   29671\n",
       "광주광역시     439683  287641.800000  137076.683206   97571\n",
       "서울특별시     615487  375792.280000  125231.247975  114967\n",
       "경상북도      488395  109085.391304  124954.740885    8392\n",
       "대전광역시     475094  293535.400000  111853.571882  190815\n",
       "부산광역시     397134  207724.437500  106246.041384   43105\n",
       "강원도       313851   79413.222222   87316.383238   20809\n",
       "전라남도      263166   77298.318182   74413.253230   23189\n",
       "울산광역시     327270  221117.000000   64163.286582  164738\n",
       "세종특별자치시   191233  191233.000000       0.000000  191233"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_agg_df = data.groupby('광역시도')['인구수'].agg(['max', 'mean', 'std', 'min']).fillna(0).sort_values(by= 'std', ascending=False)\n",
    "group_agg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "bd1c00d6-63da-4926-b5ed-515905584772",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>광역시도</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>경기도</th>\n",
       "      <td>1125461</td>\n",
       "      <td>378845.48</td>\n",
       "      <td>304548.40</td>\n",
       "      <td>41076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경상남도</th>\n",
       "      <td>1010040</td>\n",
       "      <td>174693.72</td>\n",
       "      <td>245796.47</td>\n",
       "      <td>25091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충청북도</th>\n",
       "      <td>790216</td>\n",
       "      <td>134974.55</td>\n",
       "      <td>223460.36</td>\n",
       "      <td>27130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>제주특별자치도</th>\n",
       "      <td>430504</td>\n",
       "      <td>287936.50</td>\n",
       "      <td>201620.89</td>\n",
       "      <td>145369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천광역시</th>\n",
       "      <td>527153</td>\n",
       "      <td>276657.50</td>\n",
       "      <td>199353.60</td>\n",
       "      <td>17735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대구광역시</th>\n",
       "      <td>584517</td>\n",
       "      <td>297306.25</td>\n",
       "      <td>174375.39</td>\n",
       "      <td>74950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전라북도</th>\n",
       "      <td>630708</td>\n",
       "      <td>124217.71</td>\n",
       "      <td>168327.13</td>\n",
       "      <td>20492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충청남도</th>\n",
       "      <td>584912</td>\n",
       "      <td>129741.93</td>\n",
       "      <td>141125.11</td>\n",
       "      <td>29671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>광주광역시</th>\n",
       "      <td>439683</td>\n",
       "      <td>287641.80</td>\n",
       "      <td>137076.68</td>\n",
       "      <td>97571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울특별시</th>\n",
       "      <td>615487</td>\n",
       "      <td>375792.28</td>\n",
       "      <td>125231.25</td>\n",
       "      <td>114967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경상북도</th>\n",
       "      <td>488395</td>\n",
       "      <td>109085.39</td>\n",
       "      <td>124954.74</td>\n",
       "      <td>8392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대전광역시</th>\n",
       "      <td>475094</td>\n",
       "      <td>293535.40</td>\n",
       "      <td>111853.57</td>\n",
       "      <td>190815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>부산광역시</th>\n",
       "      <td>397134</td>\n",
       "      <td>207724.44</td>\n",
       "      <td>106246.04</td>\n",
       "      <td>43105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강원도</th>\n",
       "      <td>313851</td>\n",
       "      <td>79413.22</td>\n",
       "      <td>87316.38</td>\n",
       "      <td>20809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전라남도</th>\n",
       "      <td>263166</td>\n",
       "      <td>77298.32</td>\n",
       "      <td>74413.25</td>\n",
       "      <td>23189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>울산광역시</th>\n",
       "      <td>327270</td>\n",
       "      <td>221117.00</td>\n",
       "      <td>64163.29</td>\n",
       "      <td>164738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>세종특별자치시</th>\n",
       "      <td>191233</td>\n",
       "      <td>191233.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>191233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             max       mean        std     min\n",
       "광역시도                                          \n",
       "경기도      1125461  378845.48  304548.40   41076\n",
       "경상남도     1010040  174693.72  245796.47   25091\n",
       "충청북도      790216  134974.55  223460.36   27130\n",
       "제주특별자치도   430504  287936.50  201620.89  145369\n",
       "인천광역시     527153  276657.50  199353.60   17735\n",
       "대구광역시     584517  297306.25  174375.39   74950\n",
       "전라북도      630708  124217.71  168327.13   20492\n",
       "충청남도      584912  129741.93  141125.11   29671\n",
       "광주광역시     439683  287641.80  137076.68   97571\n",
       "서울특별시     615487  375792.28  125231.25  114967\n",
       "경상북도      488395  109085.39  124954.74    8392\n",
       "대전광역시     475094  293535.40  111853.57  190815\n",
       "부산광역시     397134  207724.44  106246.04   43105\n",
       "강원도       313851   79413.22   87316.38   20809\n",
       "전라남도      263166   77298.32   74413.25   23189\n",
       "울산광역시     327270  221117.00   64163.29  164738\n",
       "세종특별자치시   191233  191233.00       0.00  191233"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 컬럼값에 소수점 둘째자리로 변경\n",
    "\n",
    "group_agg_df['mean'] = group_agg_df['mean'].map('{:.2f}'.format)\n",
    "group_agg_df['std'] = group_agg_df['std'].map('{:.2f}'.format)\n",
    "\n",
    "group_agg_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09e78059-5376-4a71-b5fb-793ad4ccf492",
   "metadata": {},
   "source": [
    "### 시각화\n",
    "* %matplotlib inline 설정 (jupyter 에서는 show() 함수를 호출하지 않아도 plot이 출력된다)\n",
    "* 한글폰트 설정이 필요함\n",
    "* Plot에 대한 설정은 matplotlib의 함수를 사용하고, Plot을 그려주는 기능은 seaborn()의 함수를 사용합니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "b6e21c38-fc87-47dc-9ca1-53ce4ba35160",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "6a082da0-aa22-43ab-b213-aa593b3cce17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matplotlib  3.8.4\n",
      "seaborn  0.13.2\n"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as fm\n",
    "import seaborn as sns\n",
    "\n",
    "print('matplotlib ', matplotlib.__version__)\n",
    "print('seaborn ', sns.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "590eb7db-edbc-4390-9eca-343074adac0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Malgun Gothic', 'C:\\\\Windows\\\\Fonts\\\\malgunsl.ttf'),\n",
       " ('Malgun Gothic', 'C:\\\\Windows\\\\Fonts\\\\malgunbd.ttf'),\n",
       " ('Malgun Gothic', 'C:\\\\Windows\\\\Fonts\\\\malgun.ttf')]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for font in fm.fontManager.ttflist:\n",
    "#     print((font.name, font.fname))\n",
    "# 폰트이름과 폰트파일 정보 출력 list comprehension \n",
    "[ (font.name, font.fname) for font in fm.fontManager.ttflist if 'Mal' in font.name ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "23e6c5ab-48a2-4cbb-9eba-a1f20efad874",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Malgun Gothic\n"
     ]
    }
   ],
   "source": [
    "# font name을 알고 있다면 생략가능\n",
    "#한글폰트 path 설정\n",
    "font_path = 'C:\\\\windows\\\\Fonts\\\\malgun.ttf'\n",
    "#font의 파일정보로 font name 을 알아내기\n",
    "font_prop = fm.FontProperties(fname=font_path).get_name()\n",
    "print(font_prop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "7984120e-9093-4edd-ad1f-eea19c5d4a14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 한글 폰트명을 matplotlib에 설정하기\n",
    "matplotlib.rc('font', family=font_prop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "6f377f67-036c-4d8d-93a6-f9c984786037",
   "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>인구수</th>\n",
       "      <th>shortName</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>면적</th>\n",
       "      <th>광역시도</th>\n",
       "      <th>행정구역</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seq</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>526157</td>\n",
       "      <td>서울강남</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>39.5</td>\n",
       "      <td>서울특별시</td>\n",
       "      <td>강남구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>432028</td>\n",
       "      <td>서울강동</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>24.6</td>\n",
       "      <td>서울특별시</td>\n",
       "      <td>강동구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>310292</td>\n",
       "      <td>서울강북</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>23.6</td>\n",
       "      <td>서울특별시</td>\n",
       "      <td>강북구</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        인구수 shortName  x  y    면적   광역시도 행정구역\n",
       "seq                                          \n",
       "124  526157      서울강남  6  7  39.5  서울특별시  강남구\n",
       "125  432028      서울강동  8  5  24.6  서울특별시  강동구\n",
       "126  310292      서울강북  5  2  23.6  서울특별시  강북구"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seoul_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "ed35a48a-5c1a-435b-90d2-358b20d6c624",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(1800x1200)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='행정구역', ylabel='면적'>"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure,(axes1, axes2) = plt.subplots(nrows=2, ncols=1)\n",
    "figure.set_size_inches(18,12)\n",
    "print(figure)\n",
    "# print(axes1)\n",
    "# print(axes2)\n",
    "sns.barplot(data=seoul_df.sort_values(by=\"인구수\", ascending=False), x=\"행정구역\", y=\"인구수\",ax = axes1, hue=\"행정구역\") #barplot 의 아규먼트들 실습\n",
    "sns.barplot(data=seoul_df.sort_values(by=\"면적\", ascending=False), x=\"행정구역\", y=\"면적\",ax = axes2, hue=\"행정구역\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "e670620b-8d4f-4127-a712-a53c4879b770",
   "metadata": {},
   "outputs": [],
   "source": [
    "#광역시도 이름을 인자로 받아서 행정구역별로 인구수와 면적을 barplot으로 그리는 함수 show_pop_area\n",
    "def show_pop_area(sido_name):\n",
    "    sido_df = data.loc[data['광역시도'] == sido_name]\n",
    "    sido_df.head(3)\n",
    "    figure,(axes1, axes2) = plt.subplots(nrows=2, ncols=1)\n",
    "    figure.set_size_inches(18,12)\n",
    "    print(figure)\n",
    "    pop_plot = sns.barplot(data=sido_df.sort_values(by=\"인구수\", ascending=False), x=\"행정구역\", y=\"인구수\",ax = axes1, hue=\"행정구역\") #barplot 의 아규먼트들 실습\n",
    "    pop_plot.set_title(f'{sido_name} 행정구역별 인구수')\n",
    "    area_plot = sns.barplot(data=sido_df.sort_values(by=\"면적\", ascending=False), x=\"행정구역\", y=\"면적\",ax = axes2, hue=\"행정구역\")\n",
    "    area_plot.set_title(f'{sido_name} 행정구역별 면적')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "41e5a82f-e8b6-4cfe-b623-7e9ff1bc262c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(1800x1200)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_pop_area('경기도')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "b8f0d678-a20e-4022-99a8-50b347e34d79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='광역시도', ylabel='인구수'>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axes1 = plt.subplots()\n",
    "figure.set_size_inches(18, 12)\n",
    "sns.barplot(data = data, x='광역시도', y='인구수', ax = axes1, hue=\"광역시도\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "789d95a4-ed47-4124-bbbc-1e95cf9969e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 그린 plot 을 image 파일로 저장하기\n",
    "figure.savefig('data/전국인구수.png', bbox_inches = 'tight') # bbox_inches -> 바깥쪽 여백 제거\n",
    "figure.savefig('data/전국인구수2.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "df48c0c2-daaa-40b8-baef-9b4e12fb5862",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'zip'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<zip at 0x1a09f8fd240>"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(type(seoul_df['인구수'].items())) # -> <class 'zip'>   : zip 객체는 Iterable 하지 않습니다.\n",
    "seoul_df['인구수'].items() # -> <zip at 0x1a09f8e5d00>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9376f2d2-cb18-43a9-8c7d-9b0e7076bdd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "for v in seoul_df['인구수'].items():\n",
    "    print(type(v))\n",
    "    print(v[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "b2beb0fa-89b5-4660-bdc0-7a83625bd8fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, ax1 = plt.subplots(nrows=1, ncols=1)\n",
    "figure.set_size_inches(18,12)\n",
    "sns.barplot(data=seoul_df, x=\"행정구역\", y=\"인구수\", ax=ax1,hue='행정구역')\n",
    "#y축의 label값에 ,(콤마) 출력하기\n",
    "ax1.get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,}\".format(int(x))))\n",
    "#ax1.set(ylabel='인구수')\n",
    "\n",
    "for item in ax1.get_xticklabels(): \n",
    "    item.set_rotation(90)\n",
    "for i, v in enumerate(seoul_df[\"인구수\"].items()):\n",
    "    ax1.text(i ,v[1], \"{:,}\".format(v[1]), color='r', va ='bottom', rotation=45)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "361f158f-d001-454f-9d15-9df136248328",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}