testeando_argendolar_mgobea.ipynb

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      "source": [
        "# Prueba de usabilidad de Argendolar por Mariano Gobea Alcoba"
      ],
      "metadata": {
        "id": "1I9rUeTH3H8q"
      }
    },
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          "text": [
            "Collecting argendolar==1.1.1\n",
            "  Downloading argendolar-1.1.1-py3-none-any.whl (5.1 kB)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from argendolar==1.1.1) (2.0.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from argendolar==1.1.1) (2.31.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->argendolar==1.1.1) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->argendolar==1.1.1) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->argendolar==1.1.1) (2024.1)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas->argendolar==1.1.1) (1.25.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->argendolar==1.1.1) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->argendolar==1.1.1) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->argendolar==1.1.1) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->argendolar==1.1.1) (2024.2.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->argendolar==1.1.1) (1.16.0)\n",
            "Installing collected packages: argendolar\n",
            "  Attempting uninstall: argendolar\n",
            "    Found existing installation: argendolar 1.1.0\n",
            "    Uninstalling argendolar-1.1.0:\n",
            "      Successfully uninstalled argendolar-1.1.0\n",
            "Successfully installed argendolar-1.1.1\n"
          ]
        }
      ],
      "source": [
        "# @title Instalo Argendolar en mi entorno virtual\n",
        "! pip install argendolar==1.1.1"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Importo las clases necesarias para traer la información\n",
        "from argendolar import Argendolar, TipoDivisas"
      ],
      "metadata": {
        "id": "mgobayF4ltDK"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Instancio un objeto de la clase Argendolar y uso el método que me devuelve la cotizacion de cada dolar al momento.\n",
        "argendolar = Argendolar()\n",
        "argendolar.get_dolar()"
      ],
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          "height": 269
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        "id": "kJnP5ajWyTPS",
        "outputId": "1352b604-66a5-47f0-b682-578d0efa4c77"
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      "execution_count": 11,
      "outputs": [
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              "  moneda             casa                   nombre  compra   venta        fechaActualizacion\n",
              "0    USD          oficial                  Oficial   911.5   911.5  2024-05-28T14:59:00.000Z\n",
              "1    USD             blue                     Blue  1175.0  1195.0  2024-05-28T14:59:00.000Z\n",
              "2    USD            bolsa                    Bolsa  1197.6  1201.7  2024-05-28T14:59:00.000Z\n",
              "3    USD  contadoconliqui  Contado con liquidación  1220.5  1223.6  2024-05-28T14:59:00.000Z\n",
              "4    USD        mayorista                Mayorista   890.5   893.5  2024-05-28T14:59:00.000Z\n",
              "5    USD           cripto                   Cripto  1208.0  1232.0  2024-05-28T14:59:00.000Z\n",
              "6    USD          tarjeta                  Tarjeta  1458.4  1458.4  2024-05-28T14:59:00.000Z"
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      "source": [
        "# @title Método que me devuelve la cotización del blue al momento\n",
        "argendolar.get_blue()"
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            "text/plain": [
              "  moneda  casa nombre  compra  venta        fechaActualizacion\n",
              "0    USD  blue   Blue    1175   1195  2024-05-28T14:59:00.000Z"
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "summary": "{\n  \"name\": \"argendolar\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"moneda\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"USD\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"casa\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"blue\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nombre\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Blue\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"compra\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1175,\n        \"max\": 1175,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1175\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"venta\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1195,\n        \"max\": 1195,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1195\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fechaActualizacion\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2024-05-28T14:59:00.000Z\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Método que me devuelve la cotización del Real Brasilero al momento\n",
        "argendolar.get_real()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "Z1W_wq2m08wE",
        "outputId": "cb820cf3-df80-405a-af05-57c5a8290a4e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  moneda     casa          nombre  compra   venta        fechaActualizacion\n",
              "0    BRL  oficial  Real Brasileño  173.84  173.95  2024-05-28T13:58:00.000Z"
            ],
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              "          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",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"argendolar\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"moneda\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"BRL\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"casa\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"oficial\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nombre\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Real Brasile\\u00f1o\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"compra\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 173.84,\n        \"max\": 173.84,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          173.84\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"venta\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 173.95,\n        \"max\": 173.95,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          173.95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fechaActualizacion\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2024-05-28T13:58:00.000Z\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 13
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      ]
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    {
      "cell_type": "code",
      "source": [
        "# @title Método que me devuelve la cotización del Euro al momento\n",
        "argendolar.get_euro()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
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        "id": "KxWlt7gv0_Gb",
        "outputId": "ed23c6af-2a1d-4ccb-81a9-6e9dc8006410"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  moneda     casa nombre  compra   venta        fechaActualizacion\n",
              "0    EUR  oficial   Euro  969.98  970.88  2024-05-28T14:59:00.000Z"
            ],
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              "\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        document.querySelector('#df-b0380c4c-7a84-4807-8f2d-3b285c51ff03 button.colab-df-convert');\n",
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              "        const element = document.querySelector('#df-b0380c4c-7a84-4807-8f2d-3b285c51ff03');\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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              "type": "dataframe",
              "summary": "{\n  \"name\": \"argendolar\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"moneda\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"EUR\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"casa\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"oficial\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nombre\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Euro\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"compra\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 969.98,\n        \"max\": 969.98,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          969.98\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"venta\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 970.88,\n        \"max\": 970.88,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          970.88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fechaActualizacion\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2024-05-28T14:59:00.000Z\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Método que me devuelve la cotización del MEP al momento\n",
        "argendolar.get_mep()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "usg0wGhL1Bgb",
        "outputId": "90e149b7-339e-4c4e-8e4a-9bdce03b2dc4"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  moneda   casa nombre  compra   venta        fechaActualizacion\n",
              "0    USD  bolsa  Bolsa  1224.5  1233.5  2024-05-28T13:58:00.000Z"
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"argendolar\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"moneda\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"USD\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"casa\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"bolsa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nombre\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Bolsa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"compra\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1224.5,\n        \"max\": 1224.5,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1224.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"venta\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1233.5,\n        \"max\": 1233.5,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1233.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fechaActualizacion\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2024-05-28T13:58:00.000Z\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Método que me devuelve la totalidad de las cotizaciones de un tipo de dolar solicitado desde que existe registro informático\n",
        "historical_data = argendolar.get_dolar_historia_completa(TipoDivisas.MEP)\n",
        "historical_data.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "LygYZ3uS1NLk",
        "outputId": "8263356f-b703-450e-9e96-1d3c53511cb6"
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      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       casa  compra   venta       fecha\n",
              "1982  bolsa  1225.7  1233.6  2024-05-23\n",
              "1983  bolsa  1203.4  1209.3  2024-05-24\n",
              "1984  bolsa  1203.4  1209.3  2024-05-25\n",
              "1985  bolsa  1203.4  1209.3  2024-05-26\n",
              "1986  bolsa  1224.5  1233.5  2024-05-27"
            ],
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              "      <th>1985</th>\n",
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              "      <th>1986</th>\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Uso librerías para mostrar la evolución del MEP que traje de mi clase Argendolar by Mgobea\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "PZOhJpAm2dEm"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Gráfico en serie temporal la evolución del MEP\n",
        "# historical_data['fecha'] = pd.to_datetime(historical_data['fecha']) # Descomentar la primera vez\n",
        "\n",
        "# historical_data.set_index('fecha', inplace=True) # Descomentar la primera vez\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(historical_data.index, historical_data['compra'], label='Compra')\n",
        "plt.plot(historical_data.index, historical_data['venta'], label='Venta')\n",
        "\n",
        "plt.title('Evolución del Dólar MEP a lo largo del tiempo')\n",
        "plt.xlabel('Fecha')\n",
        "plt.ylabel('Tipo de Cambio')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "id": "swkQBsUx2SaX",
        "outputId": "f2e5af49-5352-4cb4-a801-7efd364c64d0"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Traigo la cotización de un tipo de dolar especifico en una fecha especifica\n",
        "argendolar.get_dolar_historico(tipo=TipoDivisas.BLUE, fecha='2024/01/01')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "ti3STfvd226T",
        "outputId": "27faf598-343e-491f-dc68-9e444d284d2c"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   casa  compra  venta       fecha\n",
              "0  blue    1005   1025  2024-01-01"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-24243913-aecf-47e3-891b-3cfe53fb2aea\" 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>casa</th>\n",
              "      <th>compra</th>\n",
              "      <th>venta</th>\n",
              "      <th>fecha</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>blue</td>\n",
              "      <td>1005</td>\n",
              "      <td>1025</td>\n",
              "      <td>2024-01-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-24243913-aecf-47e3-891b-3cfe53fb2aea')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-24243913-aecf-47e3-891b-3cfe53fb2aea 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-24243913-aecf-47e3-891b-3cfe53fb2aea');\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",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"argendolar\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"casa\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"blue\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"compra\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1005,\n        \"max\": 1005,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1005\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"venta\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1025,\n        \"max\": 1025,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1025\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fecha\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2024-01-01\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    }
  ]
}