L_spell_check의_사본의_사본.ipynb

{
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KorStats/classification-project/blob/main/L_spell_check%EC%9D%98_%EC%82%AC%EB%B3%B8%EC%9D%98_%EC%82%AC%EB%B3%B8.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U3wrPXQ_ORLb"
      },
      "source": [
        "## 구글드라이브에서 파일 읽어오기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dqJbzoq_OPxK",
        "outputId": "936212e3-9a34-434e-8ad9-a370546175e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wmtIZqMGOdVX",
        "outputId": "9bdebb24-916b-4709-cf2d-1100e73dedc2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n",
            " 산업분류자동화\t\t df_C_inc.csv\t   df_K_inc.csv     spell_check\n",
            "'Colab Notebooks'\t'# df_G_inc.csv'   df_P_inc.csv\n",
            " C_ori_model.pt\t\t df_G_inc.csv\t  'My Drive'\n",
            "'답안 작성용 파일.csv'\t df_J_inc.csv\t   Q_inc_model.pt\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "!ln -s /content/gdrive/My\\ Drive/ /mydrive\n",
        "!ls /mydrive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xV_iSLl7QFgx",
        "outputId": "08d06883-70bd-479f-9943-4819798e540e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/gdrive/My Drive/산업분류자동화/실습용 자료.zip\n",
            "1. 실습용자료.txt:  mismatching \"local\" filename (1. ьЛдьК╡ьЪйьЮРыгМ.txt),\n",
            "         continuing with \"central\" filename version\n",
            "  inflating: /content/dataset/1. 실습용자료.txt  \n"
          ]
        }
      ],
      "source": [
        "!unzip /content/gdrive/'My Drive'/산업분류자동화/'실습용 자료'.zip -d /content/dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "-lGZy4fGpXlo"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "df=pd.read_csv('/content/dataset/1. 실습용자료.txt', sep = \"|\", encoding = \"euc-kr\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "geFjTXHh5-dy"
      },
      "source": [
        "## 데이터 전처리"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "5RbL6zqMdTtY"
      },
      "outputs": [],
      "source": [
        "df=df[(df['digit_1'] =='L')] #조건에 맞는 데이터 출력"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.reset_index(drop=True)\n",
        "df"
      ],
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        "colab": {
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          "height": 423
        },
        "id": "3ISvtyKhpKd2",
        "outputId": "9ef1d05c-ca87-4a5e-de5b-a336be988011"
      },
      "execution_count": 6,
      "outputs": [
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              "            AI_id digit_1  digit_2  digit_3  text_obj         text_mthd  \\\n",
              "0      id_0000099       L       68      682     사업장에서           아파트를 위해   \n",
              "1      id_0000106       L       68      682     사무실에서             계약의대리   \n",
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              "3      id_0000164       L       68      682   상가사무실에서          빌딩입주자 위해   \n",
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              "...           ...     ...      ...      ...       ...               ...   \n",
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              "40137  id_0999983       L       68      682     사업장에서            예채아파트를   \n",
              "40138  id_0999991       L       68      682  부동산중개소에서  계약및중개등을 통해 수수료받음   \n",
              "40139  id_0999995       L       68      681     사무실에서               NaN   \n",
              "\n",
              "           text_deal  \n",
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              "...              ...  \n",
              "40135          부동산중개  \n",
              "40136          부동산중개  \n",
              "40137             관리  \n",
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              "40139        비주거용 임대  \n",
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          "metadata": {},
          "execution_count": 6
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              "            AI_id digit_1  digit_2  digit_3  \\\n",
              "0      id_0000099       L       68      682   \n",
              "1      id_0000106       L       68      682   \n",
              "2      id_0000142       L       68      681   \n",
              "3      id_0000164       L       68      682   \n",
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              "...           ...     ...      ...      ...   \n",
              "40135  id_0999952       L       68      682   \n",
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              "\n",
              "                                     text  \n",
              "0                    사업장에서 아파트를 위해 아파트 관리  \n",
              "1                       사무실에서 계약의대리 부동산중개  \n",
              "2                           사무실에서 비주거용 임대  \n",
              "3                상가사무실에서 빌딩입주자 위해 건물관리서비스  \n",
              "4             사업장에서 아파트를 위해 주거용 아파트 관리서비스  \n",
              "...                                   ...  \n",
              "40135                        부동산 중개 부동산중개  \n",
              "40136                사업장에서 고객의요청에의해 부동산중개  \n",
              "40137                     사업장에서 예채아파트를 관리  \n",
              "40138  부동산중개소에서 계약및중개등을 통해 수수료받음 부동산 거래중개  \n",
              "40139                      사무실에서  비주거용 임대  \n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_0000142</td>\n",
              "      <td>L</td>\n",
              "      <td>68</td>\n",
              "      <td>681</td>\n",
              "      <td>사무실에서 비주거용 임대</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_0000164</td>\n",
              "      <td>L</td>\n",
              "      <td>68</td>\n",
              "      <td>682</td>\n",
              "      <td>상가사무실에서 빌딩입주자 위해 건물관리서비스</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_0000193</td>\n",
              "      <td>L</td>\n",
              "      <td>68</td>\n",
              "      <td>682</td>\n",
              "      <td>사업장에서 아파트를 위해 주거용 아파트 관리서비스</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40135</th>\n",
              "      <td>id_0999952</td>\n",
              "      <td>L</td>\n",
              "      <td>68</td>\n",
              "      <td>682</td>\n",
              "      <td>부동산 중개 부동산중개</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40136</th>\n",
              "      <td>id_0999980</td>\n",
              "      <td>L</td>\n",
              "      <td>68</td>\n",
              "      <td>682</td>\n",
              "      <td>사업장에서 고객의요청에의해 부동산중개</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40137</th>\n",
              "      <td>id_0999983</td>\n",
              "      <td>L</td>\n",
              "      <td>68</td>\n",
              "      <td>682</td>\n",
              "      <td>사업장에서 예채아파트를 관리</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40138</th>\n",
              "      <td>id_0999991</td>\n",
              "      <td>L</td>\n",
              "      <td>68</td>\n",
              "      <td>682</td>\n",
              "      <td>부동산중개소에서 계약및중개등을 통해 수수료받음 부동산 거래중개</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40139</th>\n",
              "      <td>id_0999995</td>\n",
              "      <td>L</td>\n",
              "      <td>68</td>\n",
              "      <td>681</td>\n",
              "      <td>사무실에서  비주거용 임대</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>40140 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-23547b91-781f-4104-b1a0-8e0c6892d4a8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-23547b91-781f-4104-b1a0-8e0c6892d4a8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-23547b91-781f-4104-b1a0-8e0c6892d4a8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df['text']=df['text_obj'].map(str)+\" \"+df['text_mthd'].map(str)+\" \"+df['text_deal'].map(str)\n",
        "i=[]\n",
        "for sentence in df['text']:\n",
        "    a=sentence.replace('nan', '')\n",
        "    i.append(a)\n",
        "df['text']=pd.DataFrame(i)\n",
        "df=df.drop(['text_obj','text_mthd', 'text_deal'], axis=1) # 기존에 있던 세개의 text변수 삭제\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['digit_3'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mzEwzSHzMsc5",
        "outputId": "a7b04f9b-06e5-41bc-d6c5-4c399dca5576"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "682    33212\n",
              "681     6928\n",
              "Name: digit_3, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yzAnY0FvePIR"
      },
      "source": [
        "## 맞춤법 교정"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git+https://github.com/ssut/py-hanspell"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U0g6lgW8yFAt",
        "outputId": "617a96e5-4cf9-408d-dd1e-0eb9ab692232"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/ssut/py-hanspell\n",
            "  Cloning https://github.com/ssut/py-hanspell to /tmp/pip-req-build-nvdt7r8y\n",
            "  Running command git clone -q https://github.com/ssut/py-hanspell /tmp/pip-req-build-nvdt7r8y\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from py-hanspell==1.1) (2.23.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->py-hanspell==1.1) (2021.10.8)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->py-hanspell==1.1) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->py-hanspell==1.1) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->py-hanspell==1.1) (3.0.4)\n",
            "Building wheels for collected packages: py-hanspell\n",
            "  Building wheel for py-hanspell (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for py-hanspell: filename=py_hanspell-1.1-py3-none-any.whl size=4868 sha256=f2dc65122bb4fdaf6a2932d16b25a8698b3f355abc8b3531ffba885f213630bc\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-tw15w0cg/wheels/31/c2/c7/ddbbd9a84ddb0a67c2e52e3324d4b7b47dcf9581af9cd784de\n",
            "Successfully built py-hanspell\n",
            "Installing collected packages: py-hanspell\n",
            "Successfully installed py-hanspell-1.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from hanspell import spell_checker\n",
        "\n",
        "temp_result=[]\n",
        "for sentence in df['text']:\n",
        "  try:\n",
        "    result=spell_checker.check(sentence)\n",
        "    temp_result.append(result.checked)\n",
        "  except:\n",
        "    temp_result.append(sentence)\n",
        "\n",
        "df['text']=pd.DataFrame(temp_result)  "
      ],
      "metadata": {
        "id": "8IykblSnyRsV"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('df_L_spellcheck.csv', index=False, encoding='utf-8-sig')"
      ],
      "metadata": {
        "id": "y6d2WHbeN_23"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "4addBclrHoYL"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "background_execution": "on",
      "collapsed_sections": [],
      "machine_shape": "hm",
      "name": "L spell check의 사본의 사본",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}