I5912A_9.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/SnowTrash/pyHikethroug/blob/main/I5912A_9.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Instalando librerias"
      ],
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "BjzfbztflzZQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Librerias wonitas\n",
        "%matplotlib inline\n",
        "import sys\n",
        "import pandas as pd\n",
        "import csv\n",
        "import matplotlib.pyplot as plt\n",
        "from datetime import datetime\n",
        "import numpy as np\n",
        "from pandas.core.arrays.sparse import array\n",
        "print(sys.version)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NDStkP8Mh1jc",
        "outputId": "468fd877-ec24-4300-c3d7-c3025b0963fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3.7.14 (default, Sep  8 2022, 00:06:44) \n",
            "[GCC 7.5.0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Añadiendo el Dataset"
      ],
      "metadata": {
        "id": "TZmOJQCafRYT",
        "pycharm": {
          "name": "#%% md\n"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "> # Desde Drive"
      ],
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "IfQZ6FwohrwC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RzIuDUWoycPW",
        "outputId": "05eaca72-5b17-48ee-9c67-9a46f6cdea3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "local_path = '/content/drive/MyDrive/Clasificacion Inteligente de datos - I5912/src/A9_dataset.csv'\n",
        "fh = open(local_path)\n",
        "csv_reader = csv.reader(fh)\n",
        "fh.close()"
      ],
      "metadata": {
        "id": "sZOS7QXDh0l7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(local_path,header=None)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
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        "outputId": "de27c64d-f1cd-418c-d023-403f06f52a18"
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      "outputs": [
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          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> # Local"
      ],
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "lrWcSuNShr72"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3VQqVi-3ScBC",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [],
      "source": [
        "#import pandas as pd\n",
        "#import numpy as np\n",
        "#import matplotlib.pyplot as plt\n",
        "\n",
        "#Con estas 2 lineas creamos el archivo .csv de un .xlsx\n",
        "\n",
        "#read_file = pd.read_excel (r'src/A9_dataset.xlsx')\n",
        "#read_file.to_csv (r'src/A9_dataset.csv', index = None, header=True)\n",
        "\n",
        "#df = pd.read_csv('src/A9_dataset.csv', header=None)\n",
        "#print(len(df))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Primer contacto"
      ],
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "cbtnUZOCmL22"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g-gEvIynivLO",
        "outputId": "e8f2061d-e0b3-4da9-9471-b82a1a8c62c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 8 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   0       9997 non-null   object\n",
            " 1   1       9997 non-null   object\n",
            " 2   2       9993 non-null   object\n",
            " 3   3       43 non-null     object\n",
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            " 5   5       12 non-null     object\n",
            " 6   6       7 non-null      object\n",
            " 7   7       4 non-null      object\n",
            "dtypes: object(8)\n",
            "memory usage: 625.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 268
        },
        "id": "BRHqcdXGiu-O",
        "outputId": "f4df0030-ac3c-4c4e-f11d-e3aed5e52311"
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              "      <td>Unnamed: 4</td>\n",
              "      <td>Unnamed: 5</td>\n",
              "      <td>Unnamed: 6</td>\n",
              "      <td>Unnamed: 7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mobiliario</td>\n",
              "      <td>Mobiliario</td>\n",
              "      <td>PINTARRON 60X90 (ALTA 20 ENE 1999 MARCA ALFRA).</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Equipo</td>\n",
              "      <td>Equipo</td>\n",
              "      <td>MONITOR DE COLOR HP DE 15\", N.D. 017/2002 ALTA...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Mobiliario</td>\n",
              "      <td>Mobiliario</td>\n",
              "      <td>CESTO DE BASURA ALUMINIO N.D. 007/2005, ALTA 3...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Transporte</td>\n",
              "      <td>Transporte</td>\n",
              "      <td>AUTOMOVIL FORD ESCORT 1998, PLACAS: HZG1129, N...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-98526d9f-709e-49b9-8e18-c0f6543b4fe8')\"\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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-98526d9f-709e-49b9-8e18-c0f6543b4fe8 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-98526d9f-709e-49b9-8e18-c0f6543b4fe8');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         Tipo                                        Descripcion\n",
            "0  Mobiliario  SILLON EJECUTIVO RESPALDO MEDIO COLOR NEGRO. N...\n",
            "1  Mobiliario    PINTARRON 60X90 (ALTA 20 ENE 1999 MARCA ALFRA).\n",
            "2      Equipo  MONITOR DE COLOR HP DE 15\", N.D. 017/2002 ALTA...\n",
            "3  Mobiliario  CESTO DE BASURA ALUMINIO N.D. 007/2005, ALTA 3...\n",
            "4  Transporte  AUTOMOVIL FORD ESCORT 1998, PLACAS: HZG1129, N...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "y =df[[0,2]]\n",
        "df_sb = pd.DataFrame(y)\n",
        "df_sb.columns = ['Tipo','Descripcion']\n",
        "print(df_sb.head())\n",
        "type(df_sb)"
      ],
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F17phe2REcQ_",
        "outputId": "bb1ee4de-7427-4dd7-b95d-9d42b9110eeb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# nltlk - Leyendo el inventario"
      ],
      "metadata": {
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "tXijljxuicj0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* nltk.download('punkt')\n",
        "* nltk.download('stopwords')\n",
        "* nltk.download('otraslibrerias')"
      ],
      "metadata": {
        "id": "Aq_yxH4H4gb3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "#Importamos las herramientas para procesar texto (se necesitan descargar)\n",
        "import nltk\n",
        "\n",
        "#nltk.download('punkt')\n",
        "#nltk.download('stopwords')\n",
        "\n",
        "\n",
        "#Despues de buen rato de pelear con la funcion, entendi que:\n",
        "#Toma NaN como valor flotante en la opeacion asi que\n",
        "df_sb = df_sb.dropna()"
      ],
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "O414RCu_ickc"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "#definimos el stopwords en espaniol\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.snowball import SnowballStemmer\n",
        "\n",
        "# function to clean data\n",
        "def clean_data(df, col, clean_col):\n",
        "      \n",
        "    #df[str(clean_col)] = df[str(clean_col)].apply(lambda x: x.lower())\n",
        "\n",
        "    # Tokenizamos el texto --->tokens = \n",
        "    df[str(clean_col)] = df.apply(lambda row: nltk.word_tokenize(row[str(col)]), axis=1)\n",
        "        \n",
        "    #df[str(clean_col)] = df.apply(lambda row: row[(col)].lower(), axis=1)\n",
        "\n",
        "    # remove extra spaces in between\n",
        "    #df[str(clean_col)] = df[str(clean_col)].apply(lambda x: nltk.re.sub('+', ' ', x))\n",
        "    \n",
        "    # remove punctuation\n",
        "    #df[str(clean_col)] = df.apply(lambda x: nltk.re.sub('[^a-zA-Z]', ' ', x))\n",
        "    \n",
        "    #remove stopwords and get the stem\n",
        "    #df[\"stopworded\"] = df[str(clean_col)].apply(lambda x: ' '.join(SnowballStemmer('spanish').stem(text) for text in x if text not in stopwords.words('spanish') ))\n",
        "    return df"
      ],
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "ZHgkPUdnickc"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Tipo', 'Descripcion']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/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"
          ]
        }
      ],
      "source": [
        "print(df_sb.columns.tolist())\n",
        "res = clean_data(df_sb.head(70),'Descripcion','stopworded')"
      ],
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "outputId": "1df79e4a-b85a-47e1-c521-5a8cc5c25fd6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jp7A6aIYickd"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Tipo                                        Descripcion  \\\n",
              "0  Mobiliario  SILLON EJECUTIVO RESPALDO MEDIO COLOR NEGRO. N...   \n",
              "1  Mobiliario    PINTARRON 60X90 (ALTA 20 ENE 1999 MARCA ALFRA).   \n",
              "2      Equipo  MONITOR DE COLOR HP DE 15\", N.D. 017/2002 ALTA...   \n",
              "3  Mobiliario  CESTO DE BASURA ALUMINIO N.D. 007/2005, ALTA 3...   \n",
              "4  Transporte  AUTOMOVIL FORD ESCORT 1998, PLACAS: HZG1129, N...   \n",
              "\n",
              "                                          stopworded  \n",
              "0  [SILLON, EJECUTIVO, RESPALDO, MEDIO, COLOR, NE...  \n",
              "1  [PINTARRON, 60X90, (, ALTA, 20, ENE, 1999, MAR...  \n",
              "2  [MONITOR, DE, COLOR, HP, DE, 15, '', ,, N.D., ...  \n",
              "3  [CESTO, DE, BASURA, ALUMINIO, N.D., 007/2005, ...  \n",
              "4  [AUTOMOVIL, FORD, ESCORT, 1998, ,, PLACAS, :, ...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1316d4e8-b986-4ce5-9f38-09b2190b3c45\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Tipo</th>\n",
              "      <th>Descripcion</th>\n",
              "      <th>stopworded</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mobiliario</td>\n",
              "      <td>SILLON EJECUTIVO RESPALDO MEDIO COLOR NEGRO. N...</td>\n",
              "      <td>[SILLON, EJECUTIVO, RESPALDO, MEDIO, COLOR, NE...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mobiliario</td>\n",
              "      <td>PINTARRON 60X90 (ALTA 20 ENE 1999 MARCA ALFRA).</td>\n",
              "      <td>[PINTARRON, 60X90, (, ALTA, 20, ENE, 1999, MAR...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Equipo</td>\n",
              "      <td>MONITOR DE COLOR HP DE 15\", N.D. 017/2002 ALTA...</td>\n",
              "      <td>[MONITOR, DE, COLOR, HP, DE, 15, '', ,, N.D., ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Mobiliario</td>\n",
              "      <td>CESTO DE BASURA ALUMINIO N.D. 007/2005, ALTA 3...</td>\n",
              "      <td>[CESTO, DE, BASURA, ALUMINIO, N.D., 007/2005, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Transporte</td>\n",
              "      <td>AUTOMOVIL FORD ESCORT 1998, PLACAS: HZG1129, N...</td>\n",
              "      <td>[AUTOMOVIL, FORD, ESCORT, 1998, ,, PLACAS, :, ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1316d4e8-b986-4ce5-9f38-09b2190b3c45')\"\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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1316d4e8-b986-4ce5-9f38-09b2190b3c45 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-1316d4e8-b986-4ce5-9f38-09b2190b3c45');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 251
        }
      ],
      "source": [
        "res.head(5)"
      ],
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VkCxwvjYicke",
        "outputId": "d9847c8c-2859-4351-cbbb-5c89a1d0a5c5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "--------------------------\n",
        "Class_1\n",
        "* Equipo        41\n",
        "* Mobiliario    28\n",
        "* Transporte     1\n",
        "* Name: tokenized_items, dtype: int64\n",
        "--------------------------\n",
        "--------------------------\n",
        "Class_2\n",
        "* Equipo          41\n",
        "* Mobiliario      27\n",
        "* Mobiliario.1     1\n",
        "* Transporte       1\n",
        "* Name: tokenized_items, dtype: int64\n"
      ],
      "metadata": {
        "id": "7lnaRScmCMFk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('--------------------------')\n",
        "res.groupby(by='Tipo')['stopworded'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UPEDhKQtBD3i",
        "outputId": "f10609c0-18f3-4e4b-b2d4-c7bdc6d106b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Tipo\n",
              "Equipo        41\n",
              "Mobiliario    28\n",
              "Transporte     1\n",
              "Name: stopworded, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 243
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "En este punto, tratare de arreglar las stopwords, donde creo que esta el problema , pues lee las 'o' finales de una palabra como una non-noun, una marca del lenguaje\n",
        "\n",
        "\n",
        "print(stopwords.words('spanish'))"
      ],
      "metadata": {
        "id": "brTfAQsljesc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inventario = res.explode(column='stopworded')"
      ],
      "metadata": {
        "id": "5gf24-zJnJgI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Palabras totales por categoria')\n",
        "print('--------------------------')\n",
        "print(inventario.groupby(by='Tipo')['Descripcion'].count())\n",
        "print('--------------------------')\n",
        "print('--------------------------')\n",
        "print('Palabras distintas en las categorias')\n",
        "print(inventario.groupby(by='Tipo')['stopworded'].nunique())\n",
        "print('--------------------------')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "62jFClxz4QeM",
        "outputId": "308db213-fc2f-426f-c52b-59cd4a6c0bf7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Palabras totales por categoria\n",
            "--------------------------\n",
            "Tipo\n",
            "Equipo        204\n",
            "Mobiliario    166\n",
            "Transporte      7\n",
            "Name: Descripcion, dtype: int64\n",
            "--------------------------\n",
            "--------------------------\n",
            "Palabras distintas en las categorias\n",
            "Tipo\n",
            "Equipo        169\n",
            "Mobiliario    140\n",
            "Transporte      7\n",
            "Name: stopworded, dtype: int64\n",
            "--------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Obtención de listado de stopwords del espanol\n",
        "# ==============================================================================\n",
        "stop_words = list(stopwords.words('spanish'))\n",
        "stop_words = list(map(lambda x: x.upper(),stop_words))\n",
        "# Se añade la stoprword: amp, ax, ex\n",
        "stop_words.extend((\"amp\", \"xa\", \"xe\",\")\",\"(\",\"'\",\",\",\"\",\".\",\":\",\"\",\"7.1\",\"6\",\"15\",\"MARCA\",\"1\",\"ALTA\",\"N.D\"))\n",
        "stop_words.extend((\"COMPLETA\",\"256MB\",\"S/N\",\"AZUL\",\"1998\",\"N.D.\",\"COLOR\",\"X\",\"NEGRO\",\"CM\",\"MODELO\",\"S/M\",\"LCD\"))\n",
        "stop_words.extend((\"2002\",\"GRIS\",\"3FABP1136WR109452\",\"SERIE\",\"AGOSTO\",\"''\",\"ALTURA\",\"CAOBA\",\"GHZ\"))\n",
        "\n",
        "print(stop_words[:10])\n",
        "inventario = inventario[~(inventario[\"stopworded\"].isin(stop_words))]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RhnifmkUDLS0",
        "outputId": "f9d4f446-1c81-45c8-d190-c9592879f94f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['DE', 'LA', 'QUE', 'EL', 'EN', 'Y', 'A', 'LOS', 'DEL', 'SE']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inventario.groupby(['Tipo','stopworded'])['stopworded'] \\\n",
        " .count() \\\n",
        " .reset_index(name='count') \\\n",
        " .groupby('Tipo') \\\n",
        " .apply(lambda x: x.sort_values('count', ascending=True).tail(13)) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cDK-ui3kPXK7",
        "outputId": "1e7d187e-51b8-48fe-fb86-86dab9d3400f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Tipo   stopworded  count\n",
              "Tipo                                          \n",
              "Equipo     150      Equipo     SOFTWARE      2\n",
              "           138      Equipo        RATÓN      2\n",
              "           56       Equipo       COGNOS      2\n",
              "           61       Equipo          CPU      2\n",
              "           146      Equipo       SCROLL      2\n",
              "           60       Equipo     CORDLESS      2\n",
              "           155      Equipo     TELEFONO      3\n",
              "           91       Equipo           HP      3\n",
              "           125      Equipo      PENTIUM      3\n",
              "           165      Equipo        VIDEO      3\n",
              "           115      Equipo      MONITOR      4\n",
              "           51       Equipo          CDS      4\n",
              "           59       Equipo  COMPUTADORA      4\n",
              "Mobiliario 228  Mobiliario     CUBIERTA      2\n",
              "           225  Mobiliario      CODERAS      2\n",
              "           218  Mobiliario      CAJONES      2\n",
              "           260  Mobiliario      MAMPARA      2\n",
              "           259  Mobiliario       MADERA      2\n",
              "           262  Mobiliario      MEDIDAS      2\n",
              "           264  Mobiliario         MESA      3\n",
              "           295  Mobiliario       SILLON      3\n",
              "           234  Mobiliario   ESCRITORIO      3\n",
              "           232  Mobiliario    EJECUTIVO      3\n",
              "           293  Mobiliario        SILLA      4\n",
              "           216  Mobiliario        CAJON      4\n",
              "           300  Mobiliario         TELA      6\n",
              "Transporte 309  Transporte    AUTOMOVIL      1\n",
              "           310  Transporte        ECONÓ      1\n",
              "           311  Transporte       ESCORT      1\n",
              "           312  Transporte         FORD      1\n",
              "           313  Transporte      HZG1129      1\n",
              "           314  Transporte        MOTOR      1\n",
              "           315  Transporte       PLACAS      1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6fcf26aa-0020-42a7-8617-4f49bd9c615d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "\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></th>\n",
              "      <th>Tipo</th>\n",
              "      <th>stopworded</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tipo</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"13\" valign=\"top\">Equipo</th>\n",
              "      <th>150</th>\n",
              "      <td>Equipo</td>\n",
              "      <td>SOFTWARE</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>Equipo</td>\n",
              "      <td>RATÓN</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Equipo</td>\n",
              "      <td>COGNOS</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>Equipo</td>\n",
              "      <td>CPU</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>Equipo</td>\n",
              "      <td>SCROLL</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>Equipo</td>\n",
              "      <td>CORDLESS</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>Equipo</td>\n",
              "      <td>TELEFONO</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>Equipo</td>\n",
              "      <td>HP</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>Equipo</td>\n",
              "      <td>PENTIUM</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>Equipo</td>\n",
              "      <td>VIDEO</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>Equipo</td>\n",
              "      <td>MONITOR</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Equipo</td>\n",
              "      <td>CDS</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Equipo</td>\n",
              "      <td>COMPUTADORA</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"13\" valign=\"top\">Mobiliario</th>\n",
              "      <th>228</th>\n",
              "      <td>Mobiliario</td>\n",
              "      <td>CUBIERTA</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>225</th>\n",
              "      <td>Mobiliario</td>\n",
              "      <td>CODERAS</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>218</th>\n",
              "      <td>Mobiliario</td>\n",
              "      <td>CAJONES</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>260</th>\n",
              "      <td>Mobiliario</td>\n",
              "      <td>MAMPARA</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>259</th>\n",
              "      <td>Mobiliario</td>\n",
              "      <td>MADERA</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>262</th>\n",
              "      <td>Mobiliario</td>\n",
              "      <td>MEDIDAS</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>264</th>\n",
              "      <td>Mobiliario</td>\n",
              "      <td>MESA</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>295</th>\n",
              "      <td>Mobiliario</td>\n",
              "      <td>SILLON</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>234</th>\n",
              "      <td>Mobiliario</td>\n",
              "      <td>ESCRITORIO</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>232</th>\n",
              "      <td>Mobiliario</td>\n",
              "      <td>EJECUTIVO</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>293</th>\n",
              "      <td>Mobiliario</td>\n",
              "      <td>SILLA</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>216</th>\n",
              "      <td>Mobiliario</td>\n",
              "      <td>CAJON</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>Mobiliario</td>\n",
              "      <td>TELA</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"7\" valign=\"top\">Transporte</th>\n",
              "      <th>309</th>\n",
              "      <td>Transporte</td>\n",
              "      <td>AUTOMOVIL</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>310</th>\n",
              "      <td>Transporte</td>\n",
              "      <td>ECONÓ</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>311</th>\n",
              "      <td>Transporte</td>\n",
              "      <td>ESCORT</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>312</th>\n",
              "      <td>Transporte</td>\n",
              "      <td>FORD</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>313</th>\n",
              "      <td>Transporte</td>\n",
              "      <td>HZG1129</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>314</th>\n",
              "      <td>Transporte</td>\n",
              "      <td>MOTOR</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>315</th>\n",
              "      <td>Transporte</td>\n",
              "      <td>PLACAS</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "                                                     [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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 273
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axs = plt.subplots(nrows=4, ncols=1,figsize=(10, 22))\n",
        "for i, Tipo in enumerate(inventario.Tipo.unique()):\n",
        "    df_temp = inventario[inventario.Tipo == Tipo]\n",
        "    counts  = df_temp['stopworded'].value_counts(ascending=False).head(20)\n",
        "    counts.plot(kind='barh', color='firebrick', ax=axs[i])\n",
        "    axs[i].invert_yaxis()\n",
        "    axs[i].set_title(Tipo)\n",
        "fig.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "CFxNvAOsDfrb",
        "outputId": "9fb90455-cc13-417c-85b5-a57cf114eb31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x1584 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "file_name = \"/content/drive/MyDrive/Clasificacion Inteligente de datos - I5912/src/A9_final_data.csv\"\n",
        "inventario.to_csv(file_name, sep='\\t', encoding='utf-8')"
      ],
      "metadata": {
        "id": "INz_ILONdn9_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conclusion\n",
        "> # Existen 3 categorias obtenidas de las clases:\n",
        "estas categorias podrian quedas solo en transporte ye mobiliario\n",
        "> 1. Transporte\n",
        "  >> * Automobil Ford Escort -- UNO\n",
        "> 2. Equipo\n",
        "  >> * Monitor --\n",
        "  >> * Cds --\n",
        "  >> * Computadoras\n",
        "  >> * Monitores\n",
        "  >> * Telefono\n",
        "  >> * Mouse\n",
        "  >> * Teclado\n",
        "  >> * Portatil\n",
        "  >> * USB\n",
        "  \n",
        "> 3. Mobiliario\n",
        "  >> * Cajon\n",
        "  >> * Silla \n",
        "  >> * Sillon \n",
        "  >> * Escritorio\n",
        "  >> * mesa\n",
        "  >> * archivero\n",
        "  >> * mamparas"
      ],
      "metadata": {
        "id": "PMJNHLwxZ37H"
      }
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "lrWcSuNShr72"
      ],
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}