2022_hack_data_이상치확인.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPsYwKsDjKewzosOWMTbjY5",
      "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/thumbzzero/2022-cse-hackathon/blob/main/2022_hack_data_%EC%9D%B4%EC%83%81%EC%B9%98%ED%99%95%EC%9D%B8.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2022 해커톤 은행나무 DATA\n",
        "\n"
      ],
      "metadata": {
        "id": "IVBDLhf0gfXA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datascience import *\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "Svi9nbETgraH"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "allTree = Table.read_table('대구광역시 은행나무 좌표.csv')\n",
        "allTree.show(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "9_MW60KUgi7s",
        "outputId": "5c7d0916-67e4-48ee-ac82-21ae71852ba0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "    <thead>\n",
              "        <tr>\n",
              "            <th>시작</th> <th>시작위도</th> <th>시작경도</th> <th>끝</th> <th>끝위도</th> <th>끝경도</th> <th>밀도</th> <th>Unnamed: 7</th> <th>Unnamed: 8</th> <th>Unnamed: 9</th> <th>Unnamed: 10</th> <th>Unnamed: 11</th> <th>Unnamed: 12</th> <th>Unnamed: 13</th> <th>Unnamed: 14</th> <th>Unnamed: 15</th> <th>Unnamed: 16</th> <th>Unnamed: 17</th> <th>Unnamed: 18</th> <th>Unnamed: 19</th> <th>Unnamed: 20</th> <th>Unnamed: 21</th> <th>Unnamed: 22</th> <th>Unnamed: 23</th> <th>Unnamed: 24</th> <th>Unnamed: 25</th>\n",
              "        </tr>\n",
              "    </thead>\n",
              "    <tbody>\n",
              "        <tr>\n",
              "            <td>대곡역      </td> <td>35.8095</td> <td>128.513</td> <td>한우아파트     </td> <td>35.8085</td> <td>128.508</td> <td>156.098</td> <td>nan       </td> <td>nan       </td> <td>nan       </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>한우아파트    </td> <td>35.8085</td> <td>128.508</td> <td>화원,옥포Ic네거리</td> <td>35.7921</td> <td>128.476</td> <td>157.353</td> <td>nan       </td> <td>nan       </td> <td>nan       </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>화원옥포Ic네거리</td> <td>35.7921</td> <td>128.476</td> <td>88올림픽고속도로 </td> <td>35.7895</td> <td>128.465</td> <td>154    </td> <td>nan       </td> <td>nan       </td> <td>nan       </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td>\n",
              "        </tr>\n",
              "    </tbody>\n",
              "</table>\n",
              "<p>... (200 rows omitted)</p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "현재 : 공공데이터포털 대구광역시_음향신호기_잔여시간표시기_벤치_가로수 설치현황_20210826.csv 에서 은행나무 데이터의 주소, 거리(시작위치~끝위치)를 경도, 위도 좌표로 변환한 상태\n",
        "\n",
        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74Y+QiE+GsKM3ogRuD44TzgA+0fXs8SO9YneUZVoA9pmsOHWUQpnnFex+H3mSQxkxOC9vejJdqv6xarBV7WbxeRDuEKm2037WZZAWNtkITdd1seGELF2g6FGD3jXZXFFJtN3aKiVLfa/QsbqCI6nrhqzbx+yRrXuiM7lUcwxra7B5UbiRptlGj5uqAZDmmYKFAf2kyZ15yNq3jVYAYc30sc+L79YOhYBsOtofZwZ9+MNT4kMWzHx9zecn9dzEkRIFj0H49GOAF0yDuiSAICiCBhvH9Pr62u6NsoFnYh3PHr1W+Jd28X7MCEOC5DUEzUcMbAFlZ44iRoEeUfccajfv6LD4ym54v1zuaDSvw9Qa3ZpxAHfXZfOjokm/VNyHHPQfm/YF0N8imMP3gdSYUc3iR1boyfHYnKJUDZnb3qyDjIfx5Ax+pThVu5MYoLJlU2PLPneImvUB7Jtqz4h2re+rNDtZjGhsGgaqt2sLhDSKUmq/wvKWgQYY6zpJAlL5RGQgpTSYo+ilRmGRofYSPwHcxyro6h1qWsatAU1uVIpCB4PHTWmDtzVfgVAFhJswgIACuMudwTrCwvqncYYO7fppFC0nqPjb4YZ+8AuAV0SuiQAoEgSed5+fHFCF49e0fDb+/rKtlCeqNaFTNCKDxtsu13f0zYGTv7VHYnPqMD+63NEfAKoVZRxbPPYsbXuuVI2ncZybB72Lu40qDWuCz1znv44hg4nwZ7JY6HUtgd6Z1L/2l6kuXBvUMkm0tcvf46P65raDdc7t5t1r5E0iQECniwRRmKwQuC4pTpaOabWrfFciLy47F28lBwxd5o04vFyw3GqTMxx1j1Sl7OfdPBmQ2rweCjunWlMBcCIDn+ETNMAlI83pNPbU3OcoZ/1nB451p7KKZbZO3+Lft3jMDs6aZYxOZbBg3Hp5JjZYQbEAF1yY6BLAgA2Jd54+/UtDS6ITnrf0tZNtzI+ap0G5+E7voueczeRCKnuBJ0gAVQQP5xE+5l5ty2guKhJNwpWNm/InR7RZT9Qns4p9emYpu48XYZY6WnLn29RT8YBGqh7bHl2WW4r5nhKftbc3AjUgyODEuVILuEsNqMpQ4McE620mwmdkSvqfKMq9z3LBwOxEMorYV4F2LReE2RoXpUqKcPFE7NpuLQobCTL9K83spxWjxYyZmnKMRWAEDw537KuhGOTAJQOH1t2etKT9m4ebaq8F9KAG55xPp6EIZysWvjMSNpuOXGz+KfohLXq+LmBFQ9iDI0ZgC65IdAlAQCbQvT/AxsrhiUOl5xvAAAAAElFTkSuQmCC)"
      ],
      "metadata": {
        "id": "ocWLx_AKhhPL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "좌표를 naver map API를 통해 지도에 찍기 전 이상치 확인"
      ],
      "metadata": {
        "id": "59sHpgCFiZPy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "latlon = allTree.drop(\"시작\").drop(\"끝\").drop(\"밀도\")\n",
        "latlon.show(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "fkNnMv6VgqFQ",
        "outputId": "9dc3bce1-58a7-4671-adb7-1347a0b3b0df"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "    <thead>\n",
              "        <tr>\n",
              "            <th>시작위도</th> <th>시작경도</th> <th>끝위도</th> <th>끝경도</th> <th>Unnamed: 7</th> <th>Unnamed: 8</th> <th>Unnamed: 9</th> <th>Unnamed: 10</th> <th>Unnamed: 11</th> <th>Unnamed: 12</th> <th>Unnamed: 13</th> <th>Unnamed: 14</th> <th>Unnamed: 15</th> <th>Unnamed: 16</th> <th>Unnamed: 17</th> <th>Unnamed: 18</th> <th>Unnamed: 19</th> <th>Unnamed: 20</th> <th>Unnamed: 21</th> <th>Unnamed: 22</th> <th>Unnamed: 23</th> <th>Unnamed: 24</th> <th>Unnamed: 25</th>\n",
              "        </tr>\n",
              "    </thead>\n",
              "    <tbody>\n",
              "        <tr>\n",
              "            <td>35.8095</td> <td>128.513</td> <td>35.8085</td> <td>128.508</td> <td>nan       </td> <td>nan       </td> <td>nan       </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>35.8085</td> <td>128.508</td> <td>35.7921</td> <td>128.476</td> <td>nan       </td> <td>nan       </td> <td>nan       </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>35.7921</td> <td>128.476</td> <td>35.7895</td> <td>128.465</td> <td>nan       </td> <td>nan       </td> <td>nan       </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td> <td>nan        </td>\n",
              "        </tr>\n",
              "    </tbody>\n",
              "</table>\n",
              "<p>... (200 rows omitted)</p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trydata= latlon.drop(np.arange(4,23))\n",
        "trydata.show(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "3mAXJSZKjppp",
        "outputId": "671fd178-bd7c-4682-9791-ac38e7347577"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/datascience/tables.py:1340: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
            "  if i not in exclude and c not in exclude])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "    <thead>\n",
              "        <tr>\n",
              "            <th>시작위도</th> <th>시작경도</th> <th>끝위도</th> <th>끝경도</th>\n",
              "        </tr>\n",
              "    </thead>\n",
              "    <tbody>\n",
              "        <tr>\n",
              "            <td>35.8095</td> <td>128.513</td> <td>35.8085</td> <td>128.508</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>35.8085</td> <td>128.508</td> <td>35.7921</td> <td>128.476</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>35.7921</td> <td>128.476</td> <td>35.7895</td> <td>128.465</td>\n",
              "        </tr>\n",
              "    </tbody>\n",
              "</table>\n",
              "<p>... (200 rows omitted)</p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "startD = trydata.drop(\"끝위도\").drop(\"끝경도\")"
      ],
      "metadata": {
        "id": "uw9sZQNdmCXO"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "goalD = trydata.drop(\"시작위도\").drop(\"시작경도\")\n",
        "goalD.show(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "6nD7CK_bngo2",
        "outputId": "4d6092eb-cd84-4a4d-a3ea-88a3c11e7d17"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "    <thead>\n",
              "        <tr>\n",
              "            <th>끝위도</th> <th>끝경도</th>\n",
              "        </tr>\n",
              "    </thead>\n",
              "    <tbody>\n",
              "        <tr>\n",
              "            <td>35.8085</td> <td>128.508</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>35.7921</td> <td>128.476</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <td>35.7895</td> <td>128.465</td>\n",
              "        </tr>\n",
              "    </tbody>\n",
              "</table>\n",
              "<p>... (200 rows omitted)</p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import matplotlib.pyplot as plots\n"
      ],
      "metadata": {
        "id": "hw_psg1unlmw"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "visual = startD.scatter(\"시작위도\",\"시작경도\") #이상치의 경우 한곳에 몰리지 않음"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "ofc3wlNxnyst",
        "outputId": "829e51af-2db9-464e-bdf4-105c65198ec8"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "시작 위치가 위도 128.5근처에 몰려있어 이부분이 우리가 변환한 대구 위치임을 알 수 있음. 시작 데이터에서 위도가 129 이상, 127 이상을 지우면 온전한 대구 가로수 데이터만 얻을 수 있다. "
      ],
      "metadata": {
        "id": "JuGiFI0woZdI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "visual = startD.scatter(\"시작위도\",\"시작경도\") #이상치의 경우 한곳에 몰리지 않음"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "d9iolDlJoBuT",
        "outputId": "06f65069-7bb4-4cb8-d833-c3e86b4f5d28"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "대구의 경우는 35.5~36.0 이내 일텐데 그 외 지역이 들어있음. 이 경우 가로수 데이터에서 위 범위 이상의 값을 제거해야함"
      ],
      "metadata": {
        "id": "c-KnsuNHoNur"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 결론\n",
        "\n",
        "- 대구 가로수 데이터를 데이터베이스에 업데이트하기전에 마지막으로 이상치, 결측치를 확인하였다. scatter()로 visualiztion 해본 결과 대구 주소를 Google API로 변환하였음에도 대구 지역이 아닌 다른 위치로 저장된 것을 확인하였다."
      ],
      "metadata": {
        "id": "OrY7QUFno00c"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2mUuoNzApJ_r"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}