lecture3_1.ipynb

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    "colab": {
      "name": "lecture3_1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNPsdKDaFd05S+Foc/YZEYn",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KxxHyoRim/Data-Mining/blob/main/lecture3_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        },
        "id": "N7jexl347YxU",
        "outputId": "5be7c7b3-e3b3-49b6-bc97-2f840a904e5b"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from google.colab import drive\n",
        "# from sklearn.neighbors import KNeighborsClassifier\n",
        "#불러올 파일의 경로를 filename 변수에 저장\n",
        "drive.mount('/content/drive')\n",
        "file_name = '/content/drive/My Drive/Data Mining/seoul_1.csv'\n",
        "\n",
        "# seoul_1 -> 0데이터에서 성북구 데이터만 읽어옴\n",
        "\n",
        "#pandas read_csv로 불러오기\n",
        "#같은 directory 레벨에 있는 csv 읽어옴\n",
        "df = pd.read_csv(file_name, encoding='cp949')\n",
        "# print(df.shape)\n",
        "df.head(3)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        },
        {
          "output_type": "execute_result",
          "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>실거래가아이디</th>\n",
              "      <th>시군구코드</th>\n",
              "      <th>자치구명</th>\n",
              "      <th>법정동코드</th>\n",
              "      <th>법정동명</th>\n",
              "      <th>신고년도</th>\n",
              "      <th>건물면적</th>\n",
              "      <th>층정보</th>\n",
              "      <th>건물주용도코드</th>\n",
              "      <th>건물주용도</th>\n",
              "      <th>물건금액</th>\n",
              "      <th>건축년도</th>\n",
              "      <th>건물명</th>\n",
              "      <th>선택</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11290-2011-4-0006693-8</td>\n",
              "      <td>11290</td>\n",
              "      <td>성북구</td>\n",
              "      <td>1129013300</td>\n",
              "      <td>정릉동</td>\n",
              "      <td>2011</td>\n",
              "      <td>61.89</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2002</td>\n",
              "      <td>연립주택</td>\n",
              "      <td>182000000</td>\n",
              "      <td>2009</td>\n",
              "      <td>정릉마이더스빌</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>11290-2011-4-0011056-1</td>\n",
              "      <td>11290</td>\n",
              "      <td>성북구</td>\n",
              "      <td>1129012300</td>\n",
              "      <td>안암동3가</td>\n",
              "      <td>2011</td>\n",
              "      <td>52.89</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1001</td>\n",
              "      <td>단독주택</td>\n",
              "      <td>220000000</td>\n",
              "      <td>1980</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11290-2011-4-0011054-1</td>\n",
              "      <td>11290</td>\n",
              "      <td>성북구</td>\n",
              "      <td>1129013600</td>\n",
              "      <td>하월곡동</td>\n",
              "      <td>2011</td>\n",
              "      <td>144.03</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1001</td>\n",
              "      <td>단독주택</td>\n",
              "      <td>390000000</td>\n",
              "      <td>1993</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  실거래가아이디  시군구코드 자치구명       법정동코드  ...       물건금액  건축년도      건물명  선택\n",
              "0  11290-2011-4-0006693-8  11290  성북구  1129013300  ...  182000000  2009  정릉마이더스빌   4\n",
              "1  11290-2011-4-0011056-1  11290  성북구  1129012300  ...  220000000  1980      NaN   3\n",
              "2  11290-2011-4-0011054-1  11290  성북구  1129013600  ...  390000000  1993      NaN   2\n",
              "\n",
              "[3 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 245
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UGMdH2NGNdeJ",
        "outputId": "06b13c3f-9c97-4a1d-ecec-c4d967f66438"
      },
      "source": [
        "df['건축년도'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['2009', '1980', '1993', '2007', '1992', '2006', '1991', '1976',\n",
              "       '2010', '2003', '1998', '0', '2001', '1961', '2002', '1999',\n",
              "       '1987', '2005', '1969', '1975', '1994', '2004', '1979', '1964',\n",
              "       '1989', '2000', '1986', '1983', '2008', '1996', '1968', '2011',\n",
              "       '1943', '1997', '1973', '1995', '1974', '1971', '1941', '1982',\n",
              "       '1981', '1972', '1990', '1962', '1967', '1985', '1984', '1988',\n",
              "       '1977', '1942', '1978', '1970', '1938', '1965', '1960', '1966',\n",
              "       '1958', '1955', '1940', '1950', '1959', '1956', '1946',\n",
              "       '20+L258501', nan, '1952', '1939', '1963', '1944', '1947'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 246
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LKOCBl9xBl0s",
        "outputId": "3da6f270-9f00-4f76-8fc3-c6188df007a9"
      },
      "source": [
        "# str -> int\n",
        "df['건축년도'] = pd.to_numeric(df['건축년도'], errors = 'coerce')\n",
        "df['건축년도'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2009., 1980., 1993., 2007., 1992., 2006., 1991., 1976., 2010.,\n",
              "       2003., 1998.,    0., 2001., 1961., 2002., 1999., 1987., 2005.,\n",
              "       1969., 1975., 1994., 2004., 1979., 1964., 1989., 2000., 1986.,\n",
              "       1983., 2008., 1996., 1968., 2011., 1943., 1997., 1973., 1995.,\n",
              "       1974., 1971., 1941., 1982., 1981., 1972., 1990., 1962., 1967.,\n",
              "       1985., 1984., 1988., 1977., 1942., 1978., 1970., 1938., 1965.,\n",
              "       1960., 1966., 1958., 1955., 1940., 1950., 1959., 1956., 1946.,\n",
              "         nan, 1952., 1939., 1963., 1944., 1947.])"
            ]
          },
          "metadata": {},
          "execution_count": 247
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zw9GuHB9BAQg",
        "outputId": "622290b4-a62c-4c46-a7ee-a29d5d2d9f7c"
      },
      "source": [
        "no_year_df = df[df['건축년도']==0].index\n",
        "df = df.drop(no_year_df)\t\n",
        "df['건축년도'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2009., 1980., 1993., 2007., 1992., 2006., 1991., 1976., 2010.,\n",
              "       2003., 1998., 2001., 1961., 2002., 1999., 1987., 2005., 1969.,\n",
              "       1975., 1994., 2004., 1979., 1964., 1989., 2000., 1986., 1983.,\n",
              "       2008., 1996., 1968., 2011., 1943., 1997., 1973., 1995., 1974.,\n",
              "       1971., 1941., 1982., 1981., 1972., 1990., 1962., 1967., 1985.,\n",
              "       1984., 1988., 1977., 1942., 1978., 1970., 1938., 1965., 1960.,\n",
              "       1966., 1958., 1955., 1940., 1950., 1959., 1956., 1946.,   nan,\n",
              "       1952., 1939., 1963., 1944., 1947.])"
            ]
          },
          "metadata": {},
          "execution_count": 248
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1OhlhG0uPYI9",
        "outputId": "52fca26f-dcd7-48b2-d1a7-916fd545d6d4"
      },
      "source": [
        "nan_idx = df[df['건축년도'].isnull()].index\n",
        "df = df.drop(nan_idx)\t\n",
        "df['건축년도'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2009., 1980., 1993., 2007., 1992., 2006., 1991., 1976., 2010.,\n",
              "       2003., 1998., 2001., 1961., 2002., 1999., 1987., 2005., 1969.,\n",
              "       1975., 1994., 2004., 1979., 1964., 1989., 2000., 1986., 1983.,\n",
              "       2008., 1996., 1968., 2011., 1943., 1997., 1973., 1995., 1974.,\n",
              "       1971., 1941., 1982., 1981., 1972., 1990., 1962., 1967., 1985.,\n",
              "       1984., 1988., 1977., 1942., 1978., 1970., 1938., 1965., 1960.,\n",
              "       1966., 1958., 1955., 1940., 1950., 1959., 1956., 1946., 1952.,\n",
              "       1939., 1963., 1944., 1947.])"
            ]
          },
          "metadata": {},
          "execution_count": 249
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h8EGQIfFQ2bD"
      },
      "source": [
        "df = df.drop(df[df['건축년도'] < 1990].index)\n",
        "df = df.drop(df[df['건축년도'] > 2010].index)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i9zBbkULA3PA"
      },
      "source": [
        "df['면적당가격'] = df['물건금액'] / df['건물면적']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "GcjymeGUM9IO",
        "outputId": "a6b51c3e-22ae-4729-d205-e6370534eac8"
      },
      "source": [
        "import seaborn as sns\n",
        "\n",
        "apt_df = df[df['건물주용도코드'] == 2001]\n",
        "\n",
        "plt.figure(figsize=(10,10)) # 그래프 사이즈 지정\n",
        "sns.regplot(apt_df['건축년도'], apt_df['면적당가격'], fit_reg=True)\n",
        "plt.title('Price Trends based on Years of Build for Apartments')\n",
        "plt.xlabel('year') \n",
        "plt.ylabel('price')\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_6TgUxyB8e6S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c84b604f-f0e0-4d6f-ecc6-123aebe98119"
      },
      "source": [
        "df['건물주용도코드'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2001     3184\n",
              "2002     1043\n",
              "1001      211\n",
              "14202     105\n",
              "Name: 건물주용도코드, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 253
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pdq8ILPD9-vg",
        "outputId": "9d115842-bad2-43c0-caa3-ec63860cea70"
      },
      "source": [
        "df['건물주용도'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "아파트     3184\n",
              "연립주택    1043\n",
              "단독주택     211\n",
              "오피스텔     105\n",
              "Name: 건물주용도, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 254
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jVoP-fvVfBjB",
        "outputId": "f0a3d8d2-2891-43f0-a594-e0fbf32b4c26"
      },
      "source": [
        "\"\"\" Figure 2 그리기 \"\"\"\n",
        "df = pd.read_csv(file_name, encoding='cp949')\n",
        "\n",
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "실거래가아이디     object\n",
              "시군구코드        int64\n",
              "자치구명        object\n",
              "법정동코드        int64\n",
              "법정동명        object\n",
              "신고년도         int64\n",
              "건물면적       float64\n",
              "층정보        float64\n",
              "건물주용도코드      int64\n",
              "건물주용도       object\n",
              "물건금액         int64\n",
              "건축년도        object\n",
              "건물명         object\n",
              "선택           int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 255
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hDVF6TysfSbh"
      },
      "source": [
        "df_2011 = df_2011['건물주용도코드'].astype(str)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "dbKgrQ4oauk3",
        "outputId": "ebc50383-9207-4fc9-ebac-238ab0d3e858"
      },
      "source": [
        "df = pd.read_csv(file_name, encoding='cp949')\n",
        "\n",
        "df_2011 = df[df['건축년도'] == '2011']\n",
        "df_2011['건축년도'].astype(int)\n",
        "df_2011.head()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>실거래가아이디</th>\n",
              "      <th>시군구코드</th>\n",
              "      <th>자치구명</th>\n",
              "      <th>법정동코드</th>\n",
              "      <th>법정동명</th>\n",
              "      <th>신고년도</th>\n",
              "      <th>건물면적</th>\n",
              "      <th>층정보</th>\n",
              "      <th>건물주용도코드</th>\n",
              "      <th>건물주용도</th>\n",
              "      <th>물건금액</th>\n",
              "      <th>건축년도</th>\n",
              "      <th>건물명</th>\n",
              "      <th>선택</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>11290-2011-4-0010864-1</td>\n",
              "      <td>11290</td>\n",
              "      <td>성북구</td>\n",
              "      <td>1129013300</td>\n",
              "      <td>정릉동</td>\n",
              "      <td>2011</td>\n",
              "      <td>29.58</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2002</td>\n",
              "      <td>연립주택</td>\n",
              "      <td>158000000</td>\n",
              "      <td>2011</td>\n",
              "      <td>(716-67)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>11290-2011-4-0010835-1</td>\n",
              "      <td>11290</td>\n",
              "      <td>성북구</td>\n",
              "      <td>1129013500</td>\n",
              "      <td>종암동</td>\n",
              "      <td>2011</td>\n",
              "      <td>47.10</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2002</td>\n",
              "      <td>연립주택</td>\n",
              "      <td>187500000</td>\n",
              "      <td>2011</td>\n",
              "      <td>평화</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>11290-2011-4-0010804-1</td>\n",
              "      <td>11290</td>\n",
              "      <td>성북구</td>\n",
              "      <td>1129013500</td>\n",
              "      <td>종암동</td>\n",
              "      <td>2011</td>\n",
              "      <td>114.98</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2001</td>\n",
              "      <td>아파트</td>\n",
              "      <td>685000000</td>\n",
              "      <td>2011</td>\n",
              "      <td>래미안라센트</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>11290-2011-4-0010686-1</td>\n",
              "      <td>11290</td>\n",
              "      <td>성북구</td>\n",
              "      <td>1129013400</td>\n",
              "      <td>길음동</td>\n",
              "      <td>2011</td>\n",
              "      <td>29.20</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2002</td>\n",
              "      <td>연립주택</td>\n",
              "      <td>130000000</td>\n",
              "      <td>2011</td>\n",
              "      <td>비전타운</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>11290-2011-4-0010674-1</td>\n",
              "      <td>11290</td>\n",
              "      <td>성북구</td>\n",
              "      <td>1129013300</td>\n",
              "      <td>정릉동</td>\n",
              "      <td>2011</td>\n",
              "      <td>71.61</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2002</td>\n",
              "      <td>연립주택</td>\n",
              "      <td>240000000</td>\n",
              "      <td>2011</td>\n",
              "      <td>(716-67)</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    실거래가아이디  시군구코드 자치구명  ...  건축년도       건물명  선택\n",
              "99   11290-2011-4-0010864-1  11290  성북구  ...  2011  (716-67)   1\n",
              "117  11290-2011-4-0010835-1  11290  성북구  ...  2011        평화   1\n",
              "139  11290-2011-4-0010804-1  11290  성북구  ...  2011    래미안라센트  10\n",
              "193  11290-2011-4-0010686-1  11290  성북구  ...  2011      비전타운   8\n",
              "199  11290-2011-4-0010674-1  11290  성북구  ...  2011  (716-67)  10\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 257
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "bOwLOAapfdX6",
        "outputId": "960056ba-46f5-4ca1-d674-b2a0b7e24e09"
      },
      "source": [
        "df_2011['면적당가격'] = df_2011['물건금액']/df_2011['건물면적']\n",
        "df_2011.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>실거래가아이디</th>\n",
              "      <th>시군구코드</th>\n",
              "      <th>자치구명</th>\n",
              "      <th>법정동코드</th>\n",
              "      <th>법정동명</th>\n",
              "      <th>신고년도</th>\n",
              "      <th>건물면적</th>\n",
              "      <th>층정보</th>\n",
              "      <th>건물주용도코드</th>\n",
              "      <th>건물주용도</th>\n",
              "      <th>물건금액</th>\n",
              "      <th>건축년도</th>\n",
              "      <th>건물명</th>\n",
              "      <th>선택</th>\n",
              "      <th>면적당가격</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>11290-2011-4-0010864-1</td>\n",
              "      <td>11290</td>\n",
              "      <td>성북구</td>\n",
              "      <td>1129013300</td>\n",
              "      <td>정릉동</td>\n",
              "      <td>2011</td>\n",
              "      <td>29.58</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2002</td>\n",
              "      <td>연립주택</td>\n",
              "      <td>158000000</td>\n",
              "      <td>2011</td>\n",
              "      <td>(716-67)</td>\n",
              "      <td>1</td>\n",
              "      <td>5.341447e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>11290-2011-4-0010835-1</td>\n",
              "      <td>11290</td>\n",
              "      <td>성북구</td>\n",
              "      <td>1129013500</td>\n",
              "      <td>종암동</td>\n",
              "      <td>2011</td>\n",
              "      <td>47.10</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2002</td>\n",
              "      <td>연립주택</td>\n",
              "      <td>187500000</td>\n",
              "      <td>2011</td>\n",
              "      <td>평화</td>\n",
              "      <td>1</td>\n",
              "      <td>3.980892e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>11290-2011-4-0010804-1</td>\n",
              "      <td>11290</td>\n",
              "      <td>성북구</td>\n",
              "      <td>1129013500</td>\n",
              "      <td>종암동</td>\n",
              "      <td>2011</td>\n",
              "      <td>114.98</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2001</td>\n",
              "      <td>아파트</td>\n",
              "      <td>685000000</td>\n",
              "      <td>2011</td>\n",
              "      <td>래미안라센트</td>\n",
              "      <td>10</td>\n",
              "      <td>5.957558e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>11290-2011-4-0010686-1</td>\n",
              "      <td>11290</td>\n",
              "      <td>성북구</td>\n",
              "      <td>1129013400</td>\n",
              "      <td>길음동</td>\n",
              "      <td>2011</td>\n",
              "      <td>29.20</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2002</td>\n",
              "      <td>연립주택</td>\n",
              "      <td>130000000</td>\n",
              "      <td>2011</td>\n",
              "      <td>비전타운</td>\n",
              "      <td>8</td>\n",
              "      <td>4.452055e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>11290-2011-4-0010674-1</td>\n",
              "      <td>11290</td>\n",
              "      <td>성북구</td>\n",
              "      <td>1129013300</td>\n",
              "      <td>정릉동</td>\n",
              "      <td>2011</td>\n",
              "      <td>71.61</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2002</td>\n",
              "      <td>연립주택</td>\n",
              "      <td>240000000</td>\n",
              "      <td>2011</td>\n",
              "      <td>(716-67)</td>\n",
              "      <td>10</td>\n",
              "      <td>3.351487e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    실거래가아이디  시군구코드 자치구명  ...       건물명  선택         면적당가격\n",
              "99   11290-2011-4-0010864-1  11290  성북구  ...  (716-67)   1  5.341447e+06\n",
              "117  11290-2011-4-0010835-1  11290  성북구  ...        평화   1  3.980892e+06\n",
              "139  11290-2011-4-0010804-1  11290  성북구  ...    래미안라센트  10  5.957558e+06\n",
              "193  11290-2011-4-0010686-1  11290  성북구  ...      비전타운   8  4.452055e+06\n",
              "199  11290-2011-4-0010674-1  11290  성북구  ...  (716-67)  10  3.351487e+06\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "k_g3iQ2I-VG2",
        "outputId": "5149089c-a46c-44aa-b24e-984c504302ca"
      },
      "source": [
        "'''오피스텔 제거'''\n",
        "\n",
        "df_2011.drop(df_2011[df_2011['건물주용도코드'] == 14202].index)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>실거래가아이디</th>\n",
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              "      <th>층정보</th>\n",
              "      <th>건물주용도코드</th>\n",
              "      <th>건물주용도</th>\n",
              "      <th>물건금액</th>\n",
              "      <th>건축년도</th>\n",
              "      <th>건물명</th>\n",
              "      <th>선택</th>\n",
              "      <th>면적당가격</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>11290-2011-4-0010864-1</td>\n",
              "      <td>11290</td>\n",
              "      <td>성북구</td>\n",
              "      <td>1129013300</td>\n",
              "      <td>정릉동</td>\n",
              "      <td>2011</td>\n",
              "      <td>29.58</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2002</td>\n",
              "      <td>연립주택</td>\n",
              "      <td>158000000</td>\n",
              "      <td>2011</td>\n",
              "      <td>(716-67)</td>\n",
              "      <td>1</td>\n",
              "      <td>5.341447e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>11290-2011-4-0010835-1</td>\n",
              "      <td>11290</td>\n",
              "      <td>성북구</td>\n",
              "      <td>1129013500</td>\n",
              "      <td>종암동</td>\n",
              "      <td>2011</td>\n",
              "      <td>47.10</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2002</td>\n",
              "      <td>연립주택</td>\n",
              "      <td>187500000</td>\n",
              "      <td>2011</td>\n",
              "      <td>평화</td>\n",
              "      <td>1</td>\n",
              "      <td>3.980892e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>11290-2011-4-0010804-1</td>\n",
              "      <td>11290</td>\n",
              "      <td>성북구</td>\n",
              "      <td>1129013500</td>\n",
              "      <td>종암동</td>\n",
              "      <td>2011</td>\n",
              "      <td>114.98</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2001</td>\n",
              "      <td>아파트</td>\n",
              "      <td>685000000</td>\n",
              "      <td>2011</td>\n",
              "      <td>래미안라센트</td>\n",
              "      <td>10</td>\n",
              "      <td>5.957558e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>11290-2011-4-0010686-1</td>\n",
              "      <td>11290</td>\n",
              "      <td>성북구</td>\n",
              "      <td>1129013400</td>\n",
              "      <td>길음동</td>\n",
              "      <td>2011</td>\n",
              "      <td>29.20</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2002</td>\n",
              "      <td>연립주택</td>\n",
              "      <td>130000000</td>\n",
              "      <td>2011</td>\n",
              "      <td>비전타운</td>\n",
              "      <td>8</td>\n",
              "      <td>4.452055e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>11290-2011-4-0010674-1</td>\n",
              "      <td>11290</td>\n",
              "      <td>성북구</td>\n",
              "      <td>1129013300</td>\n",
              "      <td>정릉동</td>\n",
              "      <td>2011</td>\n",
              "      <td>71.61</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2002</td>\n",
              "      <td>연립주택</td>\n",
              "      <td>240000000</td>\n",
              "      <td>2011</td>\n",
              "      <td>(716-67)</td>\n",
              "      <td>10</td>\n",
              "      <td>3.351487e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5107</th>\n",
              "      <td>11290-2011-4-0006423-6</td>\n",
              "      <td>11290</td>\n",
              "      <td>성북구</td>\n",
              "      <td>1129013600</td>\n",
              "      <td>하월곡동</td>\n",
              "      <td>2011</td>\n",
              "      <td>34.65</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2002</td>\n",
              "      <td>연립주택</td>\n",
              "      <td>112000000</td>\n",
              "      <td>2011</td>\n",
              "      <td>진형쉐르빌A동</td>\n",
              "      <td>4</td>\n",
              "      <td>3.232323e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5108</th>\n",
              "      <td>11290-2011-4-0006423-7</td>\n",
              "      <td>11290</td>\n",
              "      <td>성북구</td>\n",
              "      <td>1129013600</td>\n",
              "      <td>하월곡동</td>\n",
              "      <td>2011</td>\n",
              "      <td>44.80</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2002</td>\n",
              "      <td>연립주택</td>\n",
              "      <td>144500000</td>\n",
              "      <td>2011</td>\n",
              "      <td>진형쉐르빌A동</td>\n",
              "      <td>3</td>\n",
              "      <td>3.225446e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5109</th>\n",
              "      <td>11290-2011-4-0006423-8</td>\n",
              "      <td>11290</td>\n",
              "      <td>성북구</td>\n",
              "      <td>1129013600</td>\n",
              "      <td>하월곡동</td>\n",
              "      <td>2011</td>\n",
              "      <td>62.18</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2002</td>\n",
              "      <td>연립주택</td>\n",
              "      <td>200500000</td>\n",
              "      <td>2011</td>\n",
              "      <td>진형쉐르빌A동</td>\n",
              "      <td>2</td>\n",
              "      <td>3.224509e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5110</th>\n",
              "      <td>11290-2011-4-0006423-9</td>\n",
              "      <td>11290</td>\n",
              "      <td>성북구</td>\n",
              "      <td>1129013600</td>\n",
              "      <td>하월곡동</td>\n",
              "      <td>2011</td>\n",
              "      <td>38.86</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2002</td>\n",
              "      <td>연립주택</td>\n",
              "      <td>123000000</td>\n",
              "      <td>2011</td>\n",
              "      <td>진형쉐르빌A동</td>\n",
              "      <td>2</td>\n",
              "      <td>3.165208e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5133</th>\n",
              "      <td>11290-2011-4-0006490-1</td>\n",
              "      <td>11290</td>\n",
              "      <td>성북구</td>\n",
              "      <td>1129010100</td>\n",
              "      <td>성북동</td>\n",
              "      <td>2011</td>\n",
              "      <td>588.14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1001</td>\n",
              "      <td>단독주택</td>\n",
              "      <td>6280000000</td>\n",
              "      <td>2011</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>1.067773e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>89 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                     실거래가아이디  시군구코드 자치구명  ...       건물명  선택         면적당가격\n",
              "99    11290-2011-4-0010864-1  11290  성북구  ...  (716-67)   1  5.341447e+06\n",
              "117   11290-2011-4-0010835-1  11290  성북구  ...        평화   1  3.980892e+06\n",
              "139   11290-2011-4-0010804-1  11290  성북구  ...    래미안라센트  10  5.957558e+06\n",
              "193   11290-2011-4-0010686-1  11290  성북구  ...      비전타운   8  4.452055e+06\n",
              "199   11290-2011-4-0010674-1  11290  성북구  ...  (716-67)  10  3.351487e+06\n",
              "...                      ...    ...  ...  ...       ...  ..           ...\n",
              "5107  11290-2011-4-0006423-6  11290  성북구  ...   진형쉐르빌A동   4  3.232323e+06\n",
              "5108  11290-2011-4-0006423-7  11290  성북구  ...   진형쉐르빌A동   3  3.225446e+06\n",
              "5109  11290-2011-4-0006423-8  11290  성북구  ...   진형쉐르빌A동   2  3.224509e+06\n",
              "5110  11290-2011-4-0006423-9  11290  성북구  ...   진형쉐르빌A동   2  3.165208e+06\n",
              "5133  11290-2011-4-0006490-1  11290  성북구  ...       NaN   4  1.067773e+07\n",
              "\n",
              "[89 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 259
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lSXyWb41gCn2",
        "outputId": "ede2b578-d854-40cc-ea5d-c3b2fbd7969e"
      },
      "source": [
        "df_2011['건물주용도코드'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2002    66\n",
              "2001    22\n",
              "1001     1\n",
              "Name: 건물주용도코드, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 260
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jNlynIopgvca",
        "outputId": "bf1edaff-c0a2-400e-ea61-60c7660c5a46"
      },
      "source": [
        "'''타입에 따른 건물 분류'''\n",
        "\n",
        "a = df_2011[df_2011['건물주용도코드'] == 2001]\n",
        "b = df_2011[df_2011['건물주용도코드'] == 2002]\n",
        "c = df_2011[df_2011['건물주용도코드'] == 1001]\n",
        "\n",
        "''' 데이터 타입 변환 '''\n",
        "\n",
        "a['건물주용도코드'] = a['건물주용도코드'].astype(str)\n",
        "b['건물주용도코드'] = b['건물주용도코드'].astype(str)\n",
        "c['건물주용도코드'] = c['건물주용도코드'].astype(str)\n",
        "\n",
        "a['물건금액'] = a['물건금액'].astype(str)\n",
        "b['물건금액'] = b['물건금액'].astype(str)\n",
        "c['물건금액'] = c['물건금액'].astype(str)\n",
        "\n",
        "\n",
        "''' 그래프 시각화 '''\n",
        "\n",
        "\n",
        "plt.figure(figsize=(10,10)) # 그래프 사이즈 지정\n",
        "plt.scatter(a['건물주용도코드'], a['면적당가격'])  # 그래프 그리기\n",
        "plt.scatter(b['건물주용도코드'], b['면적당가격'])  # 그래프 그리기\n",
        "plt.scatter(c['건물주용도코드'], c['면적당가격'])  # 그래프 그리기\n",
        "\n",
        "plt.xlabel('types') \n",
        "plt.ylabel('price')\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  # Remove the CWD from sys.path while we load stuff.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  # This is added back by InteractiveShellApp.init_path()\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  del sys.path[0]\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  from ipykernel import kernelapp as app\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 930
        },
        "id": "0UZlXvUdjW9l",
        "outputId": "e0fa6746-cc88-43f6-9965-944215d3d285"
      },
      "source": [
        "\n",
        "a['물건금액'] = a['물건금액'].astype(str) \n",
        "b['물건금액'] = b['물건금액'].astype(str)\n",
        "c['물건금액'] = c['물건금액'].astype(str)\n",
        "\n",
        "plt.figure(figsize=(10,10)) # 그래프 사이즈 지정\n",
        "plt.scatter(a['건물주용도코드'], a['물건금액'])  # 그래프 그리기\n",
        "plt.scatter(b['건물주용도코드'], b['물건금액'])  # 그래프 그리기\n",
        "plt.scatter(c['건물주용도코드'], c['물건금액'])  # 그래프 그리기\n",
        "\n",
        "plt.xlabel('types') \n",
        "plt.ylabel('price')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aXU8t2eApI8U"
      },
      "source": [
        "file_name = '/content/drive/My Drive/Data Mining/seoul_0.csv'\n",
        "\n",
        "df = pd.read_csv(file_name, encoding='cp949')\n",
        "# df =df[ df['건물면적'] < 500 ]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VA8th-Gin58a"
      },
      "source": [
        "apt = df[df['건물주용도코드'] == 2001]    # 아파트\n",
        "low_apt = df[df['건물주용도코드'] == 2002]    # 연립주택"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "MB-uy2t8n_fH",
        "outputId": "393641c2-7c45-47f2-9ec0-621ff4517329"
      },
      "source": [
        "plt.figure(figsize=(10,10)) # 그래프 사이즈 지정\n",
        "plt.scatter(apt['건물면적'], apt['물건금액'])  # 그래프 그리기\n",
        "plt.scatter(low_apt['건물면적'], low_apt['물건금액'])  # 그래프 그리기\n",
        "\n",
        "# centers = pd.DataFrame(model.cluster_centers_,columns=['건물면적','물건금액'])\n",
        "\n",
        "# center_x = centers['건물면적']\n",
        "\n",
        "# center_y = centers['물건금액']\n",
        "\n",
        "\n",
        "plt.xlabel('area') \n",
        "plt.axis([0, 200, 0, 5000000000])\n",
        "plt.ylabel('price')\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "OWBO7ndcDZ_S",
        "outputId": "6b8a0ba2-2cb9-4da7-b179-aeff4e1a636a"
      },
      "source": [
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(111, projection='3d')\n",
        "\n",
        "surf = ax.plot_surface(apt['건물면적'], apt['건축년도'] , apt['물건금액'], cmap='viridis')\n",
        "\n",
        "fig.colorbar(surf, shrink=0.6, aspect=8)\n",
        "surf.set_clim(-1.0, 1.0)\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "# # plt.figure(figsize=(10,10)) # 그래프 사이즈 지정\n",
        "# plt3.scatter(apt['건물면적'], apt['물건금액'], apt['건축년도'])  # 그래프 그리기\n",
        "# plt3.scatter(low_apt['건물면적'], low_apt['물건금액'], low_apt['건축년도'])  # 그래프 그리기\n",
        "\n",
        "# # centers = pd.DataFrame(model.cluster_centers_,columns=['건물면적','물건금액'])\n",
        "\n",
        "# # center_x = centers['건물면적']\n",
        "\n",
        "# # center_y = centers['물건금액']\n",
        "\n",
        "\n",
        "# plt3.xlabel('area') \n",
        "# # plt3.axis([0, 200, 0, 5000000000])\n",
        "# plt3.ylabel('price')\n",
        "# plt3.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-275-4e11439b8ee9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0msurf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_surface\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'건물면적'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'물건금액'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'건축년도'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'viridis'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msurf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshrink\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maspect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/mpl_toolkits/mplot3d/axes3d.py\u001b[0m in \u001b[0;36mplot_surface\u001b[0;34m(self, X, Y, Z, norm, vmin, vmax, lightsource, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1495\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1496\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mZ\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1497\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Argument Z must be 2-dimensional.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1498\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mZ\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1499\u001b[0m             cbook._warn_external(\n",
            "\u001b[0;31mValueError\u001b[0m: Argument Z must be 2-dimensional."
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}