Taller_automatizacion_con_python.ipynb

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      "source": [
        "<div align=\"center\"><a href=\"https://colab.research.google.com/github/institutohumai/talleres/blob/master/Taller_automatizacion_con_python.ipynb\"> <img src='https://colab.research.google.com/assets/colab-badge.svg'/> </a> <br> Recordá abrir en una nueva pestaña </div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IxFuuYnCgcud"
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      "source": [
        "<h1><center><b>✨ Taller de automatización con Python ✨</b></center></h1>\n",
        "\n",
        "## [Instituto Humai](https://humai.com.ar/)\n",
        "### Conocé lo que hacemos y nuestra oferta de cursos entrando a https://humai.com.ar/\n",
        "\n",
        "#### Dictado por [Gustavo Juantorena](https://www.linkedin.com/in/gustavo-juantorena/?)\n",
        "\n",
        "# Automatización II: outputs, envío de mail, volcado a GSheets, scheduling\n",
        "\n",
        "### Hoja de ruta\n",
        "\n",
        "-   Ejemplo scrapeo sencillo\t\t\t\t\n",
        "-   Uso de la API de *Google Sheets*.\t\t\t\t\t\t\t\n",
        "-   Ejemplo envío automático de mail con *smtplib*.\n",
        "-   Generación de reportes automáticos con python-docx\n",
        "-   Extraccion de datos y generación de PDFs\n",
        "-   Scheduling: cron para Mac y linux, GUI en Windows.\t\t\t\t\t"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "erSI36S0gcuh"
      },
      "source": [
        "## Scrapping básico\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "JvR6tQDwgcuh"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import requests\n",
        "from bs4 import BeautifulSoup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WK3H7595gcuj"
      },
      "outputs": [],
      "source": [
        "url = \"https://investing.com/crypto/\"\n",
        "\n",
        "# Hacemos un pedido a la pagina\n",
        "res = requests.get(url)\n",
        "\n",
        "# \"Parseamos\" la infomacion recibida\n",
        "soup = BeautifulSoup(res.text, 'html.parser')\n",
        "\n",
        "# Extraemos la tabla con datos\n",
        "tabla = soup.find_all('table')\n",
        "tabla[0]\n",
        "\n",
        "# Creamos un DataFrame de Pandas\n",
        "df = pd.read_html(str(tabla))[0]\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nbYhvJfGLrYw"
      },
      "outputs": [],
      "source": [
        "# Limpieza\n",
        "df.drop(['#',\t'Unnamed: 1'], axis=1, inplace=True)\n",
        "df['Price (USD)'] = df['Price (USD)'].astype(float).round(2)\n",
        "\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RqTCvsST1Rf0"
      },
      "outputs": [],
      "source": [
        "# Grafico\n",
        "df.sort_values(by=['Price (USD)'], inplace=True)\n",
        "plt.barh(df['Symbol'], df['Price (USD)'])\n",
        "plt.xscale('log')\n",
        "plt.title('Precio de criptos')\n",
        "plt.xlabel('Precio (USD). Escala logaritmica')\n",
        "plt.savefig('precio_criptos.png')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S6QbKfFFKyQn"
      },
      "source": [
        "## Limpiamos y convertimos a DataFrame"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "faZLkTxqgcuo"
      },
      "source": [
        "## **Vinculación con Google Drive**\n",
        "\n",
        "Para poder escribir/leer un archivo que se encuentra en *Google Drive*, será necesario primero, contar con un archivo de *autenticación* a su vez que compartir el archivo pertinente con el servicio creado desde Python."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7VlPFf5Dgcuo"
      },
      "source": [
        "#### Generación de archivo de autenticación. (Conexión local)\n",
        "\n",
        "Para ello entraremos en el siguiente [link](https://console.cloud.google.com/apis/) ingresando con la cuenta de google que querramos vincular. Una vez dentro de la plataforma de Google Cloud, crearemos un proyecto. Una vez dentro del proyecto entraremos en la sección **API y servicios** y daremos click en la opción **habilitar API y servicio**.\n",
        "\n",
        "  <img src=\"https://unket.s3.sa-east-1.amazonaws.com/static/gcp1.png\" alt=\"drawing\" width=\"500\"/>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ivf3DvOygcup"
      },
      "source": [
        "Allí dentro seleccionaremos el tipo de API que estemos necesitando. En este caso la de Google Drive y Google Sheets. Una vez habilitada la API dentro de nuestro proyecto, iremos a la sección de **credenciales**, y dentro de la misma daremos click a **crear credenciales**. Seleccionamos la opción de *Cuenta de servicio*. Una vez que la *Cuenta de servicio* haya sido generada, será posible acceder a sus configuraciones y generar una clave en formato json dentro de la misma.\n",
        "\n",
        "<img src=\"https://unket.s3.sa-east-1.amazonaws.com/static/gcp2.png\" alt=\"drawing\" width=\"500\" height=\"300\"/> <img src=\"https://unket.s3.sa-east-1.amazonaws.com/static/clave2.png\" alt=\"drawing\" width=\"500\"/>\n",
        "\n",
        "Más información sobre como crear un proyecto y habilitar una API [aquí](https://developers.google.com/workspace/guides/create-project)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "il5zrv9OI_QS"
      },
      "outputs": [],
      "source": [
        "# Esta funcion incluye todo lo que hicios antes, para poder actualizar nuestro DataFrame\n",
        "def cripto_scraper():\n",
        "  \"\"\"Scraper de la pagina \n",
        "  Obtiene los datos, los limpia y los devuelve como un DataFrame de Pandas.\n",
        "  \"\"\"\n",
        "  url = \"https://investing.com/crypto/\"\n",
        "  res = requests.get(url)\n",
        "  soup = BeautifulSoup(res.text, 'html.parser')\n",
        "\n",
        "  tabla = soup.find_all('table')\n",
        "  tabla[0]\n",
        "  df = pd.read_html(str(tabla))[0]\n",
        "\n",
        "  # limpieza\n",
        "  df.drop(['#',\t'Unnamed: 1'], axis=1, inplace=True)\n",
        "  df['Price (USD)'] = df['Price (USD)'].astype(float).round(2)\n",
        "\n",
        "  # Grafica\n",
        "  df.sort_values(by=['Price (USD)'], inplace=True)\n",
        "  plt.barh(df['Symbol'], df['Price (USD)'])\n",
        "  plt.xscale('log')\n",
        "\n",
        "  return df"
      ]
    },
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              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Symbol</th>\n",
              "      <th>Price (USD)</th>\n",
              "      <th>Market Cap</th>\n",
              "      <th>Vol (24H)</th>\n",
              "      <th>Total Vol</th>\n",
              "      <th>Chg (24H)</th>\n",
              "      <th>Chg (7D)</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>DOGE</td>\n",
              "      <td>0.06</td>\n",
              "      <td>$8.39B</td>\n",
              "      <td>$283.11M</td>\n",
              "      <td>0.43%</td>\n",
              "      <td>-0.58%</td>\n",
              "      <td>-8.32%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>XRP</td>\n",
              "      <td>XRP</td>\n",
              "      <td>0.33</td>\n",
              "      <td>$16.33B</td>\n",
              "      <td>$875.33M</td>\n",
              "      <td>1.34%</td>\n",
              "      <td>+0.47%</td>\n",
              "      <td>-4.42%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Cardano</td>\n",
              "      <td>ADA</td>\n",
              "      <td>0.45</td>\n",
              "      <td>$15.24B</td>\n",
              "      <td>$521.95M</td>\n",
              "      <td>0.80%</td>\n",
              "      <td>+1.66%</td>\n",
              "      <td>-2.93%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Tether</td>\n",
              "      <td>USDT</td>\n",
              "      <td>1.00</td>\n",
              "      <td>$67.56B</td>\n",
              "      <td>$45.28B</td>\n",
              "      <td>69.42%</td>\n",
              "      <td>0%</td>\n",
              "      <td>+0.01%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>USD Coin</td>\n",
              "      <td>USDC</td>\n",
              "      <td>1.00</td>\n",
              "      <td>$52.19B</td>\n",
              "      <td>$6.47B</td>\n",
              "      <td>9.92%</td>\n",
              "      <td>-0.02%</td>\n",
              "      <td>-0.02%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Binance USD</td>\n",
              "      <td>BUSD</td>\n",
              "      <td>1.00</td>\n",
              "      <td>$19.21B</td>\n",
              "      <td>$6.37B</td>\n",
              "      <td>9.77%</td>\n",
              "      <td>-0.01%</td>\n",
              "      <td>+0.03%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Solana</td>\n",
              "      <td>SOL</td>\n",
              "      <td>32.09</td>\n",
              "      <td>$11.28B</td>\n",
              "      <td>$769.12M</td>\n",
              "      <td>1.18%</td>\n",
              "      <td>+1.09%</td>\n",
              "      <td>-10.05%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BNB</td>\n",
              "      <td>BNB</td>\n",
              "      <td>286.00</td>\n",
              "      <td>$46.33B</td>\n",
              "      <td>$939.41M</td>\n",
              "      <td>1.44%</td>\n",
              "      <td>+1.06%</td>\n",
              "      <td>-4.13%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Ethereum</td>\n",
              "      <td>ETH</td>\n",
              "      <td>1554.35</td>\n",
              "      <td>$190.87B</td>\n",
              "      <td>$18.79B</td>\n",
              "      <td>28.81%</td>\n",
              "      <td>+2.88%</td>\n",
              "      <td>-4.97%</td>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bitcoin</td>\n",
              "      <td>BTC</td>\n",
              "      <td>20129.60</td>\n",
              "      <td>$386.82B</td>\n",
              "      <td>$32.10B</td>\n",
              "      <td>49.21%</td>\n",
              "      <td>+0.22%</td>\n",
              "      <td>-6.36%</td>\n",
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            "text/plain": [
              "          Name Symbol  Price (USD) Market Cap Vol (24H) Total Vol Chg (24H)  \\\n",
              "9     Dogecoin   DOGE         0.06     $8.39B  $283.11M     0.43%    -0.58%   \n",
              "6          XRP    XRP         0.33    $16.33B  $875.33M     1.34%    +0.47%   \n",
              "7      Cardano    ADA         0.45    $15.24B  $521.95M     0.80%    +1.66%   \n",
              "2       Tether   USDT         1.00    $67.56B   $45.28B    69.42%        0%   \n",
              "3     USD Coin   USDC         1.00    $52.19B    $6.47B     9.92%    -0.02%   \n",
              "5  Binance USD   BUSD         1.00    $19.21B    $6.37B     9.77%    -0.01%   \n",
              "8       Solana    SOL        32.09    $11.28B  $769.12M     1.18%    +1.09%   \n",
              "4          BNB    BNB       286.00    $46.33B  $939.41M     1.44%    +1.06%   \n",
              "1     Ethereum    ETH      1554.35   $190.87B   $18.79B    28.81%    +2.88%   \n",
              "0      Bitcoin    BTC     20129.60   $386.82B   $32.10B    49.21%    +0.22%   \n",
              "\n",
              "  Chg (7D)  \n",
              "9   -8.32%  \n",
              "6   -4.42%  \n",
              "7   -2.93%  \n",
              "2   +0.01%  \n",
              "3   -0.02%  \n",
              "5   +0.03%  \n",
              "8  -10.05%  \n",
              "4   -4.13%  \n",
              "1   -4.97%  \n",
              "0   -6.36%  "
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df = cripto_scraper()\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "id": "X43nSXhGNdrj",
        "outputId": "7f0e321a-842b-420b-e689-69ca8477fe2e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-038d293f-4547-46d1-bffe-693701f6a9d6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>Name</th>\n",
              "      <th>Symbol</th>\n",
              "      <th>Price (USD)</th>\n",
              "      <th>Market Cap</th>\n",
              "      <th>Vol (24H)</th>\n",
              "      <th>Total Vol</th>\n",
              "      <th>Chg (24H)</th>\n",
              "      <th>Chg (7D)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>DOGE</td>\n",
              "      <td>0.06</td>\n",
              "      <td>$8.39B</td>\n",
              "      <td>$283.11M</td>\n",
              "      <td>0.43%</td>\n",
              "      <td>-0.55%</td>\n",
              "      <td>-8.32%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>XRP</td>\n",
              "      <td>XRP</td>\n",
              "      <td>0.33</td>\n",
              "      <td>$16.33B</td>\n",
              "      <td>$875.33M</td>\n",
              "      <td>1.34%</td>\n",
              "      <td>+0.49%</td>\n",
              "      <td>-4.42%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Cardano</td>\n",
              "      <td>ADA</td>\n",
              "      <td>0.45</td>\n",
              "      <td>$15.24B</td>\n",
              "      <td>$521.95M</td>\n",
              "      <td>0.80%</td>\n",
              "      <td>+0.92%</td>\n",
              "      <td>-2.93%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Tether</td>\n",
              "      <td>USDT</td>\n",
              "      <td>1.00</td>\n",
              "      <td>$67.56B</td>\n",
              "      <td>$45.28B</td>\n",
              "      <td>69.42%</td>\n",
              "      <td>+0.01%</td>\n",
              "      <td>+0.01%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>USD Coin</td>\n",
              "      <td>USDC</td>\n",
              "      <td>1.00</td>\n",
              "      <td>$52.19B</td>\n",
              "      <td>$6.47B</td>\n",
              "      <td>9.92%</td>\n",
              "      <td>-0.02%</td>\n",
              "      <td>-0.02%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Binance USD</td>\n",
              "      <td>BUSD</td>\n",
              "      <td>1.00</td>\n",
              "      <td>$19.21B</td>\n",
              "      <td>$6.37B</td>\n",
              "      <td>9.77%</td>\n",
              "      <td>-0.01%</td>\n",
              "      <td>+0.03%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Solana</td>\n",
              "      <td>SOL</td>\n",
              "      <td>32.09</td>\n",
              "      <td>$11.28B</td>\n",
              "      <td>$769.12M</td>\n",
              "      <td>1.18%</td>\n",
              "      <td>+1.09%</td>\n",
              "      <td>-10.05%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BNB</td>\n",
              "      <td>BNB</td>\n",
              "      <td>286.00</td>\n",
              "      <td>$46.33B</td>\n",
              "      <td>$939.41M</td>\n",
              "      <td>1.44%</td>\n",
              "      <td>+1.06%</td>\n",
              "      <td>-4.13%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Ethereum</td>\n",
              "      <td>ETH</td>\n",
              "      <td>1554.35</td>\n",
              "      <td>$190.87B</td>\n",
              "      <td>$18.79B</td>\n",
              "      <td>28.81%</td>\n",
              "      <td>+2.88%</td>\n",
              "      <td>-4.97%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bitcoin</td>\n",
              "      <td>BTC</td>\n",
              "      <td>20129.60</td>\n",
              "      <td>$386.82B</td>\n",
              "      <td>$32.10B</td>\n",
              "      <td>49.21%</td>\n",
              "      <td>+0.22%</td>\n",
              "      <td>-6.36%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-038d293f-4547-46d1-bffe-693701f6a9d6')\"\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",
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              "  </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-038d293f-4547-46d1-bffe-693701f6a9d6 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-038d293f-4547-46d1-bffe-693701f6a9d6');\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",
              "  "
            ],
            "text/plain": [
              "          Name Symbol  Price (USD) Market Cap Vol (24H) Total Vol Chg (24H)  \\\n",
              "9     Dogecoin   DOGE         0.06     $8.39B  $283.11M     0.43%    -0.55%   \n",
              "6          XRP    XRP         0.33    $16.33B  $875.33M     1.34%    +0.49%   \n",
              "7      Cardano    ADA         0.45    $15.24B  $521.95M     0.80%    +0.92%   \n",
              "2       Tether   USDT         1.00    $67.56B   $45.28B    69.42%    +0.01%   \n",
              "3     USD Coin   USDC         1.00    $52.19B    $6.47B     9.92%    -0.02%   \n",
              "5  Binance USD   BUSD         1.00    $19.21B    $6.37B     9.77%    -0.01%   \n",
              "8       Solana    SOL        32.09    $11.28B  $769.12M     1.18%    +1.09%   \n",
              "4          BNB    BNB       286.00    $46.33B  $939.41M     1.44%    +1.06%   \n",
              "1     Ethereum    ETH      1554.35   $190.87B   $18.79B    28.81%    +2.88%   \n",
              "0      Bitcoin    BTC     20129.60   $386.82B   $32.10B    49.21%    +0.22%   \n",
              "\n",
              "  Chg (7D)  \n",
              "9   -8.32%  \n",
              "6   -4.42%  \n",
              "7   -2.93%  \n",
              "2   +0.01%  \n",
              "3   -0.02%  \n",
              "5   +0.03%  \n",
              "8  -10.05%  \n",
              "4   -4.13%  \n",
              "1   -4.97%  \n",
              "0   -6.36%  "
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df = cripto_scraper()\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BNFaGKa6A16r"
      },
      "source": [
        "### Usando gspread para interactuar con las hojas de cálculo de Google\n",
        "\n",
        "Documentación: https://docs.gspread.org/en/v5.3.0/\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "vlerF2Qmi4RU"
      },
      "outputs": [],
      "source": [
        "# Instalamos y hacemos un upgrade de gspread porque la funcion que necesitamos esta a partir de la version 3.6\n",
        "!pip install gspread --upgrade -q"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P4JEwDoqDbxP"
      },
      "source": [
        "Importamos la libreria gspread y chequeamos la version "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w2rDkvdLDYS_",
        "outputId": "99f59df5-f298-4fac-c908-4f153fdd0c21"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Version de gspread:5.4.0\n"
          ]
        }
      ],
      "source": [
        "import gspread\n",
        "print(f'Version de gspread:{gspread.__version__}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aRIaqSkwQt_8"
      },
      "source": [
        "#### Interactuamos con Google Sheets.\n",
        "\n",
        "Creamos una nueva hoja de calculo con su debido título y la compartimos con la cuenta desde la cual querramos acceder."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5LhPtj0cCWRW"
      },
      "source": [
        "**Autenticación local**\n",
        "* Documentación: https://docs.gspread.org/en/latest/oauth2.html"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "fPjjW7uBFLN5"
      },
      "outputs": [],
      "source": [
        "# Para no dejar en el codigo mis credenciales\n",
        "# Leo mis credenciales de un archivo con primera fila \"usuario\" y segunda fila \"contraseña\"\n",
        "\n",
        "with open('credenciales.txt') as f:\n",
        "  lines = f.readlines()\n",
        "\n",
        "usuario = lines[0].strip()\n",
        "contrasena = lines[1].strip()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "SYqJrKbfMacH"
      },
      "outputs": [],
      "source": [
        "# Paso 1: Accedemos a nuestra cuenta y creamos la hoja de calculo\n",
        "MAIL = usuario # asd@mail.com\n",
        "NOMBRE_DE_CLAVE = 'clave_taller.json'\n",
        "gc = gspread.service_account(filename=f'/content/{NOMBRE_DE_CLAVE}')\n",
        "\n",
        "nombre = 'prueba_taller_2'\n",
        "hoja_de_calculo = gc.create(nombre)\n",
        "\n",
        "# Para hacer visible el archivo es necesario compartirlo\n",
        "hoja_de_calculo.share(MAIL, perm_type='user', role='writer')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "pExEMGiGM0nr"
      },
      "outputs": [],
      "source": [
        "# Paso 2: Compartir la hoja con el 'client_email' que viene en el json\n",
        "# Para eso simplemente abrimos nuestra hoja, vamos a 'Compartir' y ahi agregamos el mail que encontramos \n",
        "#en el json como si fuera un usuario mas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "UTVSTxNEFwOQ",
        "outputId": "7ea09297-9da6-40a6-c4b9-888a19bd1a7a"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "  <div id=\"df-b3dda3e9-a675-4071-bd87-b950ff8e16f0\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>#</th>\n",
              "      <th>Unnamed: 1</th>\n",
              "      <th>Name</th>\n",
              "      <th>Symbol</th>\n",
              "      <th>Price (USD)</th>\n",
              "      <th>Market Cap</th>\n",
              "      <th>Vol (24H)</th>\n",
              "      <th>Total Vol</th>\n",
              "      <th>Chg (24H)</th>\n",
              "      <th>Chg (7D)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>DOGE</td>\n",
              "      <td>0.063098</td>\n",
              "      <td>$8.42B</td>\n",
              "      <td>$286.67M</td>\n",
              "      <td>0.44%</td>\n",
              "      <td>-0.13%</td>\n",
              "      <td>-7.78%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>XRP</td>\n",
              "      <td>XRP</td>\n",
              "      <td>0.328630</td>\n",
              "      <td>$16.37B</td>\n",
              "      <td>$877.85M</td>\n",
              "      <td>1.34%</td>\n",
              "      <td>+0.71%</td>\n",
              "      <td>-4.02%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cardano</td>\n",
              "      <td>ADA</td>\n",
              "      <td>0.451000</td>\n",
              "      <td>$15.27B</td>\n",
              "      <td>$526.62M</td>\n",
              "      <td>0.80%</td>\n",
              "      <td>+2.07%</td>\n",
              "      <td>-2.27%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>USD Coin</td>\n",
              "      <td>USDC</td>\n",
              "      <td>0.999900</td>\n",
              "      <td>$52.19B</td>\n",
              "      <td>$6.50B</td>\n",
              "      <td>9.91%</td>\n",
              "      <td>-0.01%</td>\n",
              "      <td>-0.02%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Binance USD</td>\n",
              "      <td>BUSD</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>$19.21B</td>\n",
              "      <td>$6.39B</td>\n",
              "      <td>9.74%</td>\n",
              "      <td>0%</td>\n",
              "      <td>-0.01%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tether</td>\n",
              "      <td>USDT</td>\n",
              "      <td>1.000100</td>\n",
              "      <td>$67.55B</td>\n",
              "      <td>$45.49B</td>\n",
              "      <td>69.33%</td>\n",
              "      <td>0%</td>\n",
              "      <td>+0.01%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Solana</td>\n",
              "      <td>SOL</td>\n",
              "      <td>32.220000</td>\n",
              "      <td>$11.32B</td>\n",
              "      <td>$781.69M</td>\n",
              "      <td>1.19%</td>\n",
              "      <td>+1.82%</td>\n",
              "      <td>-9.03%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BNB</td>\n",
              "      <td>BNB</td>\n",
              "      <td>286.700000</td>\n",
              "      <td>$46.48B</td>\n",
              "      <td>$945.09M</td>\n",
              "      <td>1.44%</td>\n",
              "      <td>+1.31%</td>\n",
              "      <td>-3.38%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Ethereum</td>\n",
              "      <td>ETH</td>\n",
              "      <td>1558.340000</td>\n",
              "      <td>$191.74B</td>\n",
              "      <td>$18.98B</td>\n",
              "      <td>28.92%</td>\n",
              "      <td>+4.10%</td>\n",
              "      <td>-3.84%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Bitcoin</td>\n",
              "      <td>BTC</td>\n",
              "      <td>20178.400000</td>\n",
              "      <td>$388.74B</td>\n",
              "      <td>$32.23B</td>\n",
              "      <td>49.13%</td>\n",
              "      <td>+0.46%</td>\n",
              "      <td>-5.59%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b3dda3e9-a675-4071-bd87-b950ff8e16f0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      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-b3dda3e9-a675-4071-bd87-b950ff8e16f0 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-b3dda3e9-a675-4071-bd87-b950ff8e16f0');\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",
              "  "
            ],
            "text/plain": [
              "    #  Unnamed: 1         Name Symbol   Price (USD) Market Cap Vol (24H)  \\\n",
              "9  10         NaN     Dogecoin   DOGE      0.063098     $8.42B  $286.67M   \n",
              "6   7         NaN          XRP    XRP      0.328630    $16.37B  $877.85M   \n",
              "7   8         NaN      Cardano    ADA      0.451000    $15.27B  $526.62M   \n",
              "3   4         NaN     USD Coin   USDC      0.999900    $52.19B    $6.50B   \n",
              "5   6         NaN  Binance USD   BUSD      1.000000    $19.21B    $6.39B   \n",
              "2   3         NaN       Tether   USDT      1.000100    $67.55B   $45.49B   \n",
              "8   9         NaN       Solana    SOL     32.220000    $11.32B  $781.69M   \n",
              "4   5         NaN          BNB    BNB    286.700000    $46.48B  $945.09M   \n",
              "1   2         NaN     Ethereum    ETH   1558.340000   $191.74B   $18.98B   \n",
              "0   1         NaN      Bitcoin    BTC  20178.400000   $388.74B   $32.23B   \n",
              "\n",
              "  Total Vol Chg (24H) Chg (7D)  \n",
              "9     0.44%    -0.13%   -7.78%  \n",
              "6     1.34%    +0.71%   -4.02%  \n",
              "7     0.80%    +2.07%   -2.27%  \n",
              "3     9.91%    -0.01%   -0.02%  \n",
              "5     9.74%        0%   -0.01%  \n",
              "2    69.33%        0%   +0.01%  \n",
              "8     1.19%    +1.82%   -9.03%  \n",
              "4     1.44%    +1.31%   -3.38%  \n",
              "1    28.92%    +4.10%   -3.84%  \n",
              "0    49.13%    +0.46%   -5.59%  "
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s7gVr09NNOwA",
        "outputId": "4632ff20-021b-40e1-afc1-9a707ba14665"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'spreadsheetId': '1bmsJdJX3iAyurjGb82Ti4gkpJb_6ZefClH-KBaYIPaU',\n",
              " 'updatedRange': 'Sheet1!A1:I11',\n",
              " 'updatedRows': 11,\n",
              " 'updatedColumns': 9,\n",
              " 'updatedCells': 99}"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Paso 3: Abrimos accedemos al documento \n",
        "# Abrimos el documento\n",
        "hoja_de_calculo = gc.open(nombre)\n",
        "\n",
        "# Agarramos la primera de las hojas \n",
        "worksheet = hoja_de_calculo.sheet1\n",
        "\n",
        "# Actualizo la hoja\n",
        "worksheet.update([df.columns.values.tolist()] + df.values.tolist())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xvh4gWFPGpER"
      },
      "outputs": [],
      "source": [
        "# Para testear que se actualiza con nuevos datos\n",
        "df['pruebas'] = 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "UOsN8_YhQz7A",
        "outputId": "6012daed-0639-4db0-8f9c-e3a0611daf57"
      },
      "outputs": [
        {
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Name</td>\n",
              "      <td>Symbol</td>\n",
              "      <td>Price (USD)</td>\n",
              "      <td>Market Cap</td>\n",
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              "      <td>Total Vol</td>\n",
              "      <td>Chg (24H)</td>\n",
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              "      <th>1</th>\n",
              "      <td>Dogecoin</td>\n",
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              "      <td>$283.11M</td>\n",
              "      <td>0.43%</td>\n",
              "      <td>-0.55%</td>\n",
              "      <td>-8.32%</td>\n",
              "      <td>2</td>\n",
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              "      <th>2</th>\n",
              "      <td>XRP</td>\n",
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              "      <td>0.33</td>\n",
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              "      <td>$875.33M</td>\n",
              "      <td>1.34%</td>\n",
              "      <td>+0.49%</td>\n",
              "      <td>-4.42%</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Cardano</td>\n",
              "      <td>ADA</td>\n",
              "      <td>0.45</td>\n",
              "      <td>$15.24B</td>\n",
              "      <td>$521.95M</td>\n",
              "      <td>0.80%</td>\n",
              "      <td>+0.92%</td>\n",
              "      <td>-2.93%</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Tether</td>\n",
              "      <td>USDT</td>\n",
              "      <td>1</td>\n",
              "      <td>$67.56B</td>\n",
              "      <td>$45.28B</td>\n",
              "      <td>69.42%</td>\n",
              "      <td>+0.01%</td>\n",
              "      <td>+0.01%</td>\n",
              "      <td>2</td>\n",
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              "\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-06cfdb4f-48f4-4803-a5dd-308127dfab90 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-06cfdb4f-48f4-4803-a5dd-308127dfab90');\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",
              "  "
            ],
            "text/plain": [
              "          0       1            2           3          4          5          6  \\\n",
              "0      Name  Symbol  Price (USD)  Market Cap  Vol (24H)  Total Vol  Chg (24H)   \n",
              "1  Dogecoin    DOGE         0.06      $8.39B   $283.11M      0.43%     -0.55%   \n",
              "2       XRP     XRP         0.33     $16.33B   $875.33M      1.34%     +0.49%   \n",
              "3   Cardano     ADA         0.45     $15.24B   $521.95M      0.80%     +0.92%   \n",
              "4    Tether    USDT            1     $67.56B    $45.28B     69.42%     +0.01%   \n",
              "\n",
              "          7        8  \n",
              "0  Chg (7D)  pruebas  \n",
              "1    -8.32%        2  \n",
              "2    -4.42%        2  \n",
              "3    -2.93%        2  \n",
              "4    +0.01%        2  "
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Paso 4: Obtenemos los valores desde nuestra hoja de calculo\n",
        "\n",
        "nuestra_hoja = worksheet.get_all_values()\n",
        "\n",
        "# Cargamos con Pandas\n",
        "df_aux = pd.DataFrame(nuestra_hoja)\n",
        "# df_aux.columns = df_aux.iloc[0,:]\n",
        "# df_aux = df_aux.iloc[1:,:]\n",
        "df_aux.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "id": "36K-sEEILwYU",
        "outputId": "6f767240-2be1-4808-b65d-c7a533a5af81"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Symbol</th>\n",
              "      <th>Price (USD)</th>\n",
              "      <th>Market Cap</th>\n",
              "      <th>Vol (24H)</th>\n",
              "      <th>Total Vol</th>\n",
              "      <th>Chg (24H)</th>\n",
              "      <th>Chg (7D)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Dogecoin</td>\n",
              "      <td>DOGE</td>\n",
              "      <td>0.06</td>\n",
              "      <td>$8.41B</td>\n",
              "      <td>$286.10M</td>\n",
              "      <td>0.44%</td>\n",
              "      <td>+2.90%</td>\n",
              "      <td>-7.65%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>XRP</td>\n",
              "      <td>XRP</td>\n",
              "      <td>0.33</td>\n",
              "      <td>$16.38B</td>\n",
              "      <td>$932.44M</td>\n",
              "      <td>1.45%</td>\n",
              "      <td>+3.15%</td>\n",
              "      <td>-3.31%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Cardano</td>\n",
              "      <td>ADA</td>\n",
              "      <td>0.45</td>\n",
              "      <td>$15.10B</td>\n",
              "      <td>$514.46M</td>\n",
              "      <td>0.80%</td>\n",
              "      <td>+3.89%</td>\n",
              "      <td>-2.64%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Tether</td>\n",
              "      <td>USDT</td>\n",
              "      <td>1.00</td>\n",
              "      <td>$67.56B</td>\n",
              "      <td>$44.70B</td>\n",
              "      <td>69.48%</td>\n",
              "      <td>-0.01%</td>\n",
              "      <td>+0.02%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>USD Coin</td>\n",
              "      <td>USDC</td>\n",
              "      <td>1.00</td>\n",
              "      <td>$52.29B</td>\n",
              "      <td>$7.99B</td>\n",
              "      <td>12.41%</td>\n",
              "      <td>0%</td>\n",
              "      <td>+0.01%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Binance USD</td>\n",
              "      <td>BUSD</td>\n",
              "      <td>1.00</td>\n",
              "      <td>$19.20B</td>\n",
              "      <td>$5.65B</td>\n",
              "      <td>8.79%</td>\n",
              "      <td>-0.01%</td>\n",
              "      <td>-0.03%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Solana</td>\n",
              "      <td>SOL</td>\n",
              "      <td>32.10</td>\n",
              "      <td>$11.18B</td>\n",
              "      <td>$845.61M</td>\n",
              "      <td>1.31%</td>\n",
              "      <td>+5.09%</td>\n",
              "      <td>-9.03%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BNB</td>\n",
              "      <td>BNB</td>\n",
              "      <td>287.00</td>\n",
              "      <td>$46.10B</td>\n",
              "      <td>$987.31M</td>\n",
              "      <td>1.53%</td>\n",
              "      <td>+2.94%</td>\n",
              "      <td>-3.70%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Ethereum</td>\n",
              "      <td>ETH</td>\n",
              "      <td>1535.36</td>\n",
              "      <td>$187.00B</td>\n",
              "      <td>$17.40B</td>\n",
              "      <td>27.05%</td>\n",
              "      <td>+6.15%</td>\n",
              "      <td>-5.56%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bitcoin</td>\n",
              "      <td>BTC</td>\n",
              "      <td>20193.40</td>\n",
              "      <td>$385.86B</td>\n",
              "      <td>$32.04B</td>\n",
              "      <td>49.81%</td>\n",
              "      <td>+2.17%</td>\n",
              "      <td>-5.33%</td>\n",
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              "  </tbody>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e29e4828-c933-4878-8937-7da2dd930f1c');\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",
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            "text/plain": [
              "          Name Symbol  Price (USD) Market Cap Vol (24H) Total Vol Chg (24H)  \\\n",
              "9     Dogecoin   DOGE         0.06     $8.41B  $286.10M     0.44%    +2.90%   \n",
              "6          XRP    XRP         0.33    $16.38B  $932.44M     1.45%    +3.15%   \n",
              "7      Cardano    ADA         0.45    $15.10B  $514.46M     0.80%    +3.89%   \n",
              "2       Tether   USDT         1.00    $67.56B   $44.70B    69.48%    -0.01%   \n",
              "3     USD Coin   USDC         1.00    $52.29B    $7.99B    12.41%        0%   \n",
              "5  Binance USD   BUSD         1.00    $19.20B    $5.65B     8.79%    -0.01%   \n",
              "8       Solana    SOL        32.10    $11.18B  $845.61M     1.31%    +5.09%   \n",
              "4          BNB    BNB       287.00    $46.10B  $987.31M     1.53%    +2.94%   \n",
              "1     Ethereum    ETH      1535.36   $187.00B   $17.40B    27.05%    +6.15%   \n",
              "0      Bitcoin    BTC     20193.40   $385.86B   $32.04B    49.81%    +2.17%   \n",
              "\n",
              "  Chg (7D)  \n",
              "9   -7.65%  \n",
              "6   -3.31%  \n",
              "7   -2.64%  \n",
              "2   +0.02%  \n",
              "3   +0.01%  \n",
              "5   -0.03%  \n",
              "8   -9.03%  \n",
              "4   -3.70%  \n",
              "1   -5.56%  \n",
              "0   -5.33%  "
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# actualizamos el df\n",
        "df = cripto_scraper()\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uuq2jr7_gcuq"
      },
      "source": [
        "**Otorgar acceso desde Google Colab**\n",
        "\n",
        "* En caso de acceder desde Google Colab resulta más sencillo autenticarse dado que lo hace automaticamente con la cuenta linkeada.\n",
        "\n",
        "* Una vez que ya tenemos nuestro objeto ```gc``` podemos trabajar usando los mismos métodos que en el caso local."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QLgRvYUcgcuq"
      },
      "outputs": [],
      "source": [
        "# Autenticarse con Colab\n",
        "import gspread\n",
        "from google.auth import default\n",
        "creds, _ = default()\n",
        "gc = gspread.authorize(creds)\n",
        "gc"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pW5srOZKb5MK"
      },
      "source": [
        "## **Envio automatico de e-mails**\n",
        "\n",
        "En esta sección haremos un ejemplo de como enviar mails desde Python \n",
        "\n",
        "### **Protocolo SMTP**\n",
        "El protocolo para transferencia simple de correo (en inglés: Simple Mail Transfer Protocol o SMTP) es un protocolo de red utilizado para el intercambio de mensajes de correo electrónico entre computadoras u otros dispositivos (PDA, teléfonos móviles, impresoras, etcétera).\n",
        "\n",
        "### Usando Python\n",
        "\n",
        "1. Ir a https://myaccount.google.com/apppasswords y crear una contraseña de app\n",
        "2. En la parte inferior, hacer clic en **Seleccionar aplicación** y elegir la aplicación que estás usando. \n",
        "3. Haga clic en Seleccionar dispositivo y elegir el dispositivo que está utilizando. También se puede poner un nombre personalizado.\n",
        "4. Hacer click en Generar.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5rvOKtA1gcut",
        "outputId": "ea451843-191d-4dca-c8ab-237c1ed73c05"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mail enviado\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import smtplib\n",
        "from email.message import EmailMessage\n",
        "\n",
        "\n",
        "DESTINATARIO = usuario # mail@mail.com\n",
        "CUERPO_DEL_MAIL = 'Este es un mail enviado con Python en el taller de Humai! =D'\n",
        "\n",
        "msg = EmailMessage()\n",
        "\n",
        "# Contenido\n",
        "msg['From']=\"curso_de_automatizacion_de_humai@gmail.com\"\n",
        "msg['To']= DESTINATARIO\n",
        "msg['Subject']= \"Probando mandar mails!\"\n",
        "msg.set_content(CUERPO_DEL_MAIL)\n",
        "\n",
        "# No se queden en los detalles aquí, pero pueden leer más sobre\n",
        "#el protocolo SMTP acá: https://es.wikipedia.org/wiki/Protocolo_para_transferencia_simple_de_correo \n",
        "server = smtplib.SMTP('smtp.gmail.com', 587)\n",
        "server.starttls()\n",
        "\n",
        "# Usuario y contraseña\n",
        "USER = usuario\n",
        "PASSWORD = contrasena\n",
        "\n",
        "server.login(USER, PASSWORD)\n",
        "\n",
        "\n",
        "# enviar\n",
        "server.send_message(msg)\n",
        "server.quit();\n",
        "print('Mail enviado')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7aVTvlIU3Xxu"
      },
      "source": [
        "### Enviar más de un mail\n",
        "\n",
        "Podría existir el caso de uso donde querramos enviar más de un mail. Por ejemplo a todes nuestres alumnes con la nota de su parcial."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eYUhxoI_qwio",
        "outputId": "7b98cfe0-b48f-44f3-f7d1-c4f042564701"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mail enviado a Lupe\n",
            "mail enviado a Juan\n",
            "mail enviado a Sofia\n"
          ]
        }
      ],
      "source": [
        "import smtplib\n",
        "from email.message import EmailMessage\n",
        "import time\n",
        "\n",
        "notas  = [10, 8 , 7]\n",
        "alumnes = ['Lupe', 'Juan', 'Sofia']\n",
        "mails = ['Juan@mail.com', 'Sofia@mail.com', 'Lupe@mail.com']\n",
        "\n",
        "with smtplib.SMTP('smtp.gmail.com', 587) as server:\n",
        "  for i in range(len(notas)):\n",
        "    # Contenido\n",
        "    msg = EmailMessage()\n",
        "\n",
        "    msg['From']=\"curso_de_automatizacion_de_humai@gmail.com\"\n",
        "    msg['To']= usuario# Obviamente habria que ir variando los mails, aca no lo voya hacer pero seria poner mails[i]\n",
        "    msg['Subject']= \"Probando mandar mails!\"\n",
        "    cuerpo_del_mail = f'Hola {alumnes[i]}, tu nota en el parcial fue de {notas[i]}.\\n\\nSaludos!'\n",
        "    msg.set_content(cuerpo_del_mail)\n",
        "    server = smtplib.SMTP('smtp.gmail.com', 587)\n",
        "    server.starttls()\n",
        "    server.login(USER, PASSWORD)\n",
        "\n",
        "    # enviar\n",
        "    server.send_message(msg)\n",
        "    time.sleep(3)\n",
        "    print(f'mail enviado a {alumnes[i]}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G5Gd9hvQ30mz"
      },
      "source": [
        "### Enviar archivos adjuntos\n",
        "\n",
        "Podemos agregar archivos adjuntos como por ejemplo imágenes o PDFs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dW_Ol6JvuhpK",
        "outputId": "e168b6d0-86ef-46a4-cb25-6f0c0549eacd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2022-08-30 14:15:28--  https://humai.com.ar/static/logos/ISOTIPO.png\n",
            "Resolving humai.com.ar (humai.com.ar)... 18.190.92.144\n",
            "Connecting to humai.com.ar (humai.com.ar)|18.190.92.144|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 17855 (17K) [image/png]\n",
            "Saving to: ‘humai_logo.png’\n",
            "\n",
            "humai_logo.png      100%[===================>]  17.44K  --.-KB/s    in 0s      \n",
            "\n",
            "2022-08-30 14:15:28 (227 MB/s) - ‘humai_logo.png’ saved [17855/17855]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget -O humai_logo.png https://humai.com.ar/static/logos/ISOTIPO.png"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EcChj5Qyx49l",
        "outputId": "bd86ce71-3891-4c10-e89a-8d200be021b4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mail enviado\n"
          ]
        }
      ],
      "source": [
        "# Enviar archivos adjuntos\n",
        "\n",
        "import smtplib\n",
        "# El módulo imghdr determina el tipo de imagen contenida en un archivo.\n",
        "import imghdr\n",
        "from email.message import EmailMessage\n",
        "\n",
        "msg = EmailMessage()\n",
        "\n",
        "# Contenido\n",
        "msg['From']=\"curso_de_automatizacion_de_humai@gmail.com\"\n",
        "msg['To']= usuario #usuario@mail.com\n",
        "msg['Subject']= \"Probando mandar mails!\"\n",
        "cuerpo_del_mail = 'Te estoy enviando una imagen con Python! =D'\n",
        "msg.set_content(cuerpo_del_mail)\n",
        "\n",
        "path_imagen = '/content/humai_logo.png' \n",
        "\n",
        "with open(path_imagen, 'rb') as f:\n",
        "    image_data = f.read()\n",
        "    # Para saber el tipo de archivo\n",
        "    image_type = imghdr.what(f.name)\n",
        "    image_name = f.name\n",
        "\n",
        "msg.add_attachment(image_data, maintype='image', subtype=image_type, filename=image_name)\n",
        "\n",
        "# No se queden en los detalles aquí, pero pueden leer más sobre el protocolo SMTP acá: https://es.wikipedia.org/wiki/Protocolo_para_transferencia_simple_de_correo \n",
        "server = smtplib.SMTP('smtp.gmail.com', 587)\n",
        "server.starttls()\n",
        "\n",
        "# Usuario y contraseña\n",
        "USER = usuario\n",
        "PASSWORD = contrasena\n",
        "\n",
        "server.login(USER, PASSWORD)\n",
        "\n",
        "# enviar\n",
        "server.send_message(msg)\n",
        "print('Mail enviado')\n",
        "server.quit();\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "poPoBoT_UOFT"
      },
      "source": [
        "## Reportes automatizados en formato .docx"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yfhuue_7UtVM",
        "outputId": "39815733-d5bc-49ac-9a79-cf8f88582017"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 5.6 MB 31.5 MB/s \n",
            "\u001b[?25h  Building wheel for python-docx (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install python-docx -q\n",
        "from docx import Document\n",
        "# from docx.shared import Inches\n",
        "from docx.enum.text import WD_ALIGN_PARAGRAPH\n",
        "\n",
        "\n",
        "from datetime import date"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "GmZOuqCYUWqt"
      },
      "outputs": [],
      "source": [
        "def add_df_to_doc(df, doc, name='default_name'):\n",
        "    \"\"\"Agrega una tabla al archivo .docx\"\"\"\n",
        "    \n",
        "    # fila extra para poder agregar el \"header\"\n",
        "    t = doc.add_table(df.shape[0]+1, df.shape[1])\n",
        "\n",
        "    # Agreagar el header\n",
        "    for j in range(df.shape[-1]):\n",
        "        t.cell(0,j).text = df.columns[j]\n",
        "\n",
        "    # Agregar el resto del dataframe\n",
        "    for i in range(df.shape[0]):\n",
        "        for j in range(df.shape[-1]):\n",
        "            t.cell(i+1,j).text = str(df.values[i,j])\n",
        "\n",
        "    # save the doc\n",
        "    doc.save(name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "uEqjxhU7UNhy"
      },
      "outputs": [],
      "source": [
        "# plt.rcParams.update({'font.size': 16}) # must set in top\n",
        "NOMBRE_DE_ARCHIVO = 'prueba.docx'\n",
        "FECHA_ACTUAL = str(date.today().strftime('%d/%m/%Y'))\n",
        "MONEDA_MAS_CARA = df.sort_values('Price (USD)',ascending=False)['Name'][0]\n",
        "\n",
        "document = Document()\n",
        "\n",
        "document.add_heading('Tabla de precios cripto', 0)\n",
        "\n",
        "PARRAFO = f\"\"\"\n",
        "Reporte generado automáticamente usando Python el {FECHA_ACTUAL} con datos de https://investing.com/crypto/\n",
        "\"\"\"\n",
        "p = document.add_paragraph(PARRAFO)\n",
        "p.alignment = WD_ALIGN_PARAGRAPH.CENTER\n",
        "\n",
        "item1 = document.add_paragraph(\n",
        "    'La moneda con mayor valor es ', style='List Bullet'\n",
        ")\n",
        "\n",
        "item1.add_run(f'{MONEDA_MAS_CARA}.').italic = True\n",
        "\n",
        "item2 = document.add_paragraph(\n",
        "    f'Se obtuvo el valor de {df.shape[0]} monedas', style='List Bullet'\n",
        ")\n",
        "\n",
        "document.add_picture('precio_criptos.png')\n",
        "document.add_page_break()\n",
        "\n",
        "\n",
        "document.save(NOMBRE_DE_ARCHIVO)\n",
        "add_df_to_doc(df, document, NOMBRE_DE_ARCHIVO)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MH_PqONRwNn-"
      },
      "source": [
        " ## ¿Y si queremos enviar por mail este archivo?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P6w-wOTmrBgV",
        "outputId": "3c50fa23-d549-46c9-bd86-4ffe1569ffc8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mail con archivo adjunto enviado\n"
          ]
        }
      ],
      "source": [
        "# Enviar archivos adjuntos\n",
        "\n",
        "import smtplib\n",
        "from email.message import EmailMessage\n",
        "\n",
        "msg = EmailMessage()\n",
        "\n",
        "# Contenido\n",
        "msg['From']=\"curso_de_automatizacion_de_humai@gmail.com\"\n",
        "msg['To']= usuario\n",
        "msg['Subject']= \"Probando mandar mails!\"\n",
        "cuerpo_del_mail = 'Te estoy enviando una imagen con Python! =D'\n",
        "msg.set_content(cuerpo_del_mail)\n",
        "\n",
        "path_doc = '/content/prueba.docx' \n",
        "\n",
        "with open(path_doc, 'rb') as f:\n",
        "    doc_data = f.read()\n",
        "    doc_name = f.name\n",
        "\n",
        "msg.add_attachment(doc_data, maintype='application', subtype='docx', filename=doc_name)\n",
        "\n",
        "# No se queden en los detalles aquí, pero pueden leer más sobre el protocolo SMTP acá: https://es.wikipedia.org/wiki/Protocolo_para_transferencia_simple_de_correo \n",
        "server = smtplib.SMTP('smtp.gmail.com', 587)\n",
        "server.starttls()\n",
        "\n",
        "# Usuario y contraseña\n",
        "USER = usuario\n",
        "PASSWORD = contrasena\n",
        "\n",
        "server.login(USER, PASSWORD)\n",
        "\n",
        "# enviar\n",
        "server.send_message(msg)\n",
        "print('Mail con archivo adjunto enviado')\n",
        "server.quit();\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F348O0-vwWXh"
      },
      "source": [
        "## Trabajando con PDFs\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oLNg0LFi0N8A",
        "outputId": "69927277-d626-4ea0-95ab-f8ebbe7e3ecc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2022-08-30 14:57:52--  http://www.africau.edu/images/default/sample.pdf\n",
            "Resolving www.africau.edu (www.africau.edu)... 104.196.162.239\n",
            "Connecting to www.africau.edu (www.africau.edu)|104.196.162.239|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3028 (3.0K) [application/pdf]\n",
            "Saving to: ‘ejemplo.pdf’\n",
            "\n",
            "\rejemplo.pdf           0%[                    ]       0  --.-KB/s               \rejemplo.pdf         100%[===================>]   2.96K  --.-KB/s    in 0s      \n",
            "\n",
            "2022-08-30 14:57:52 (498 MB/s) - ‘ejemplo.pdf’ saved [3028/3028]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget -O ejemplo.pdf http://www.africau.edu/images/default/sample.pdf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4DdwI9uMweZt",
        "outputId": "76a8f3a0-64cc-4b5d-d06d-4576c09e80a8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 103 kB 35.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 69 kB 7.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 10.1 MB 75.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 32.8 MB 1.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 5.6 MB 66.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 106 kB 99.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 2.3 MB 60.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 112 kB 60.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 128 kB 59.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 73 kB 3.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 149 kB 56.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 70 kB 8.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 336 kB 63.8 MB/s \n",
            "\u001b[?25h  Building wheel for docx2txt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for compressed-rtf (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for olefile (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for python-pptx (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "ipython 7.9.0 requires jedi>=0.10, which is not installed.\n",
            "google-api-python-client 1.12.11 requires six<2dev,>=1.13.0, but you have six 1.12.0 which is incompatible.\n",
            "google-api-core 1.31.6 requires six>=1.13.0, but you have six 1.12.0 which is incompatible.\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "# instalamos la librería que vamos a utilizar\n",
        "!pip install textract -q"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "id": "AOCYDDpY09oL"
      },
      "outputs": [],
      "source": [
        "import textract"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0-4p9u130wFo",
        "outputId": "e13b7306-24ac-47f4-e460-0932c65873f0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " A Simple PDF File \n",
            "\n",
            " This is a small demonstration .pdf file - \n",
            "\n",
            " just for use in the Virtual Mechanics tutorials. More text. And more \n",
            " text. And more text. And more text. And more text. \n",
            "\n",
            " And more text. And more text. And more text. And more text. And more \n",
            " text. And more text. Boring, zzzzz. And more text. And more text. And \n",
            " more text. And more text. And more text. And more text. And more text. \n",
            " And more text. And more text. \n",
            "\n",
            " And more text. And more text. And more text. And more text. And more \n",
            " text. And more text. And more text. Even more. Continued on page 2 ...\n",
            "\n",
            "\f Simple PDF File 2 \n",
            "\n",
            " ...continued from page 1. Yet more text. And more text. And more text. \n",
            " And more text. And more text. And more text. And more text. And more \n",
            " text. Oh, how boring typing this stuff. But not as boring as watching \n",
            " paint dry. And more text. And more text. And more text. And more text. \n",
            " Boring.  More, a little more text. The end, and just as well. \n",
            "\n",
            "\f\n"
          ]
        }
      ],
      "source": [
        "ARCHIVO = 'ejemplo.pdf'\n",
        "\n",
        "text = textract.process(f'/content/{ARCHIVO}').decode()\n",
        "\n",
        "print(text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EDkNECOS200i"
      },
      "source": [
        "### Un PDF un poco mas complejo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2uTGi_rI24Gc",
        "outputId": "3ff229b8-d3a1-4a01-aabb-0e293fa6cc7f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2022-08-30 14:58:16--  https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf\n",
            "Resolving proceedings.neurips.cc (proceedings.neurips.cc)... 198.202.70.94\n",
            "Connecting to proceedings.neurips.cc (proceedings.neurips.cc)|198.202.70.94|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 569417 (556K) [application/pdf]\n",
            "Saving to: ‘paper.pdf’\n",
            "\n",
            "paper.pdf           100%[===================>] 556.07K  1.60MB/s    in 0.3s    \n",
            "\n",
            "2022-08-30 14:58:17 (1.60 MB/s) - ‘paper.pdf’ saved [569417/569417]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget -O paper.pdf https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vSnbKTUH2y81",
        "outputId": "e2d1ae91-4604-4df1-fe02-612968da8f15"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Attention Is All You\n"
          ]
        }
      ],
      "source": [
        "ARCHIVO = 'paper.pdf'\n",
        "\n",
        "text = textract.process(f'/content/{ARCHIVO}').decode()\n",
        "\n",
        "print(text[:20])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BaSGwJiu4Cst",
        "outputId": "1226487f-1cd9-4d35-9f75-dd121ff7e6c1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 8.8 MB 10.6 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install pymupdf -q"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "id": "QtXtWo_93Njf"
      },
      "outputs": [],
      "source": [
        "import fitz\n",
        "import PIL.Image\n",
        "import io\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "id": "8I_hZnxM5QI8"
      },
      "outputs": [],
      "source": [
        "# Extraemos imagenes \n",
        "\n",
        "pdf = fitz.open('paper.pdf')\n",
        "cant_imagenes = 0\n",
        "\n",
        "for i in range(len(pdf)):\n",
        "  pagina = pdf[i]\n",
        "  imagenes = pagina.get_images()\n",
        "  for imagen in imagenes:\n",
        "    imagen_base = pdf.extract_image(imagen[0])\n",
        "    datos_imagen = imagen_base['image']\n",
        "    img = PIL.Image.open(io.BytesIO(datos_imagen))\n",
        "    extension = imagen_base['ext']\n",
        "    img.save(open(f'imagen_{cant_imagenes}.{extension}', 'wb'))\n",
        "    cant_imagenes += 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MecRtFHC6n1J"
      },
      "outputs": [],
      "source": [
        "# Extraemos tablas\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6yr9Eyf67AWw",
        "outputId": "4f71e718-be00-47ca-fad2-e8e3aeaf7497"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 12.0 MB 34.0 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install tabula-py -q"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "FTIY7fi27D44",
        "outputId": "dba1f150-0f66-458c-d5f7-c79300db9ce9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tabula.io:Got stderr: Aug 30, 2022 2:59:00 PM org.apache.pdfbox.pdmodel.font.FileSystemFontProvider loadDiskCache\n",
            "WARNING: New fonts found, font cache will be re-built\n",
            "Aug 30, 2022 2:59:00 PM org.apache.pdfbox.pdmodel.font.FileSystemFontProvider <init>\n",
            "WARNING: Building on-disk font cache, this may take a while\n",
            "Aug 30, 2022 2:59:00 PM org.apache.pdfbox.pdmodel.font.FileSystemFontProvider <init>\n",
            "WARNING: Finished building on-disk font cache, found 17 fonts\n",
            "Aug 30, 2022 2:59:00 PM org.apache.pdfbox.pdmodel.font.PDType1Font <init>\n",
            "WARNING: Using fallback font LiberationSans for base font Symbol\n",
            "Aug 30, 2022 2:59:00 PM org.apache.pdfbox.pdmodel.font.PDType1Font <init>\n",
            "WARNING: Using fallback font LiberationSans for base font ZapfDingbats\n",
            "Aug 30, 2022 2:59:06 PM org.apache.pdfbox.pdmodel.font.PDSimpleFont toUnicode\n",
            "WARNING: No Unicode mapping for summationtext (80) in font THPNLT+CMEX9\n",
            "Aug 30, 2022 2:59:07 PM org.apache.pdfbox.pdmodel.font.PDSimpleFont toUnicode\n",
            "WARNING: No Unicode mapping for epsilon1 (15) in font LICAEO+CMMI10\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d378ca2d-1778-46bb-a26b-4f38bc238f23\">\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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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th>Unnamed: 6</th>\n",
              "      <th>Unnamed: 7</th>\n",
              "      <th>Unnamed: 8</th>\n",
              "      <th>train</th>\n",
              "      <th>PPL</th>\n",
              "      <th>BLEU</th>\n",
              "      <th>params</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>dmodel dff</td>\n",
              "      <td>h</td>\n",
              "      <td>dk</td>\n",
              "      <td>dv</td>\n",
              "      <td>Pdrop</td>\n",
              "      <td>εls</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>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>steps</td>\n",
              "      <td>(dev)</td>\n",
              "      <td>(dev)</td>\n",
              "      <td>×106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>base</td>\n",
              "      <td>6</td>\n",
              "      <td>512 2048</td>\n",
              "      <td>8</td>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100K</td>\n",
              "      <td>4.92</td>\n",
              "      <td>25.8</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>512</td>\n",
              "      <td>512</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.29</td>\n",
              "      <td>24.9</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>128</td>\n",
              "      <td>128</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.00</td>\n",
              "      <td>25.5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>(A)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>6</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16</td>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.91</td>\n",
              "      <td>25.8</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.01</td>\n",
              "      <td>25.4</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.16</td>\n",
              "      <td>25.1</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>(B)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>10</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.01</td>\n",
              "      <td>25.4</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.11</td>\n",
              "      <td>23.7</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.19</td>\n",
              "      <td>25.3</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.88</td>\n",
              "      <td>25.5</td>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>(C)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>256</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.75</td>\n",
              "      <td>24.5</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1024</td>\n",
              "      <td>NaN</td>\n",
              "      <td>128</td>\n",
              "      <td>128</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.66</td>\n",
              "      <td>26.0</td>\n",
              "      <td>168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1024</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.12</td>\n",
              "      <td>25.4</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4096</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.75</td>\n",
              "      <td>26.2</td>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.77</td>\n",
              "      <td>24.6</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.95</td>\n",
              "      <td>25.5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>(D)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>21</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.67</td>\n",
              "      <td>25.3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.47</td>\n",
              "      <td>25.7</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>(E)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>positional embedding instead of sinusoids</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.92</td>\n",
              "      <td>25.7</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>big</td>\n",
              "      <td>6</td>\n",
              "      <td>1024 4096</td>\n",
              "      <td>16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>300K</td>\n",
              "      <td>4.33</td>\n",
              "      <td>26.4</td>\n",
              "      <td>213</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d378ca2d-1778-46bb-a26b-4f38bc238f23')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </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",
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              "\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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-d378ca2d-1778-46bb-a26b-4f38bc238f23');\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",
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              "  "
            ],
            "text/plain": [
              "   Unnamed: 0 Unnamed: 1                                 Unnamed: 2  \\\n",
              "0         NaN          N                                 dmodel dff   \n",
              "1         NaN        NaN                                        NaN   \n",
              "2        base          6                                   512 2048   \n",
              "3         NaN        NaN                                        NaN   \n",
              "4         NaN        NaN                                        NaN   \n",
              "5         (A)        NaN                                        NaN   \n",
              "6         NaN        NaN                                        NaN   \n",
              "7         NaN        NaN                                        NaN   \n",
              "8         NaN        NaN                                        NaN   \n",
              "9         (B)        NaN                                        NaN   \n",
              "10        NaN        NaN                                        NaN   \n",
              "11        NaN          2                                        NaN   \n",
              "12        NaN          4                                        NaN   \n",
              "13        NaN          8                                        NaN   \n",
              "14        (C)        NaN                                        256   \n",
              "15        NaN        NaN                                       1024   \n",
              "16        NaN        NaN                                       1024   \n",
              "17        NaN        NaN                                       4096   \n",
              "18        NaN        NaN                                        NaN   \n",
              "19        NaN        NaN                                        NaN   \n",
              "20        (D)        NaN                                        NaN   \n",
              "21        NaN        NaN                                        NaN   \n",
              "22        NaN        NaN                                        NaN   \n",
              "23        (E)        NaN  positional embedding instead of sinusoids   \n",
              "24        big          6                                  1024 4096   \n",
              "\n",
              "   Unnamed: 3 Unnamed: 4 Unnamed: 5 Unnamed: 6 Unnamed: 7  Unnamed: 8  train  \\\n",
              "0           h         dk         dv      Pdrop        εls         NaN    NaN   \n",
              "1         NaN        NaN        NaN        NaN        NaN         NaN  steps   \n",
              "2           8         64         64        0.1        0.1         NaN   100K   \n",
              "3           1        512        512        NaN        NaN         NaN    NaN   \n",
              "4           4        128        128        NaN        NaN         NaN    NaN   \n",
              "5         NaN        NaN        NaN        NaN        NaN         NaN    NaN   \n",
              "6          16         32         32        NaN        NaN         NaN    NaN   \n",
              "7          32         16         16        NaN        NaN         NaN    NaN   \n",
              "8         NaN         16        NaN        NaN        NaN         NaN    NaN   \n",
              "9         NaN        NaN        NaN        NaN        NaN         NaN    NaN   \n",
              "10        NaN         32        NaN        NaN        NaN         NaN    NaN   \n",
              "11        NaN        NaN        NaN        NaN        NaN         NaN    NaN   \n",
              "12        NaN        NaN        NaN        NaN        NaN         NaN    NaN   \n",
              "13        NaN        NaN        NaN        NaN        NaN         NaN    NaN   \n",
              "14        NaN         32         32        NaN        NaN         NaN    NaN   \n",
              "15        NaN        128        128        NaN        NaN         NaN    NaN   \n",
              "16        NaN        NaN        NaN        NaN        NaN         NaN    NaN   \n",
              "17        NaN        NaN        NaN        NaN        NaN         NaN    NaN   \n",
              "18        NaN        NaN        NaN        0.0        NaN         NaN    NaN   \n",
              "19        NaN        NaN        NaN        0.2        NaN         NaN    NaN   \n",
              "20        NaN        NaN        NaN        NaN        NaN         NaN    NaN   \n",
              "21        NaN        NaN        NaN        NaN        0.0         NaN    NaN   \n",
              "22        NaN        NaN        NaN        NaN        0.2         NaN    NaN   \n",
              "23        NaN        NaN        NaN        NaN        NaN         NaN    NaN   \n",
              "24         16        NaN        NaN        0.3        NaN         NaN   300K   \n",
              "\n",
              "      PPL   BLEU params  \n",
              "0     NaN    NaN    NaN  \n",
              "1   (dev)  (dev)   ×106  \n",
              "2    4.92   25.8     65  \n",
              "3    5.29   24.9    NaN  \n",
              "4    5.00   25.5    NaN  \n",
              "5     NaN    NaN    NaN  \n",
              "6    4.91   25.8    NaN  \n",
              "7    5.01   25.4    NaN  \n",
              "8    5.16   25.1     58  \n",
              "9     NaN    NaN    NaN  \n",
              "10   5.01   25.4     60  \n",
              "11   6.11   23.7     36  \n",
              "12   5.19   25.3     50  \n",
              "13   4.88   25.5     80  \n",
              "14   5.75   24.5     28  \n",
              "15   4.66   26.0    168  \n",
              "16   5.12   25.4     53  \n",
              "17   4.75   26.2     90  \n",
              "18   5.77   24.6    NaN  \n",
              "19   4.95   25.5    NaN  \n",
              "20    NaN    NaN    NaN  \n",
              "21   4.67   25.3    NaN  \n",
              "22   5.47   25.7    NaN  \n",
              "23   4.92   25.7    NaN  \n",
              "24   4.33   26.4    213  "
            ]
          },
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from tabula.io import read_pdf\n",
        "\n",
        "tablas = read_pdf('paper.pdf',pages='all')\n",
        "tablas[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FnqGhoxe72uG"
      },
      "outputs": [],
      "source": [
        "tablas[1]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CviXLbQL85Jl"
      },
      "source": [
        "### Y para crear un PDF?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8B1FHZgb89Qn",
        "outputId": "0164a73f-dc0e-4abf-cb40-90078fb64b93"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 2.8 MB 34.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 3.1 MB 60.4 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!python3 -m pip install reportlab -q"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "id": "2R2GPlO48_H7"
      },
      "outputs": [],
      "source": [
        "from reportlab.pdfgen import canvas\n",
        "from datetime import date\n",
        "\n",
        "FECHA_ACTUAL = str(date.today().strftime('%d/%m/%Y'))\n",
        "\n",
        "# Se crea un objeto \"canvas\" que funciona con una hoja a la que podemos acceder mediante ejes\n",
        "# (0, 0) es la esquina inferior izquierda, \"x\" va para arriba e \"y\" hacia la derecha\n",
        "\n",
        "ARCHIVO = \"probando_reportlab.pdf\"\n",
        "my_canvas = canvas.Canvas(ARCHIVO)\n",
        "my_canvas.drawString(100, 750, \"Hola mundo!\")\n",
        "my_canvas.save()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "id": "6fahZaEP_o0J"
      },
      "outputs": [],
      "source": [
        "# Complejizando un poco\n",
        "# Guia de uso: https://docs.reportlab.com/reportlab/userguide/ch1_intro/\n",
        "\n",
        "my_canvas = canvas.Canvas(ARCHIVO)\n",
        "my_canvas.setLineWidth(.3)\n",
        "my_canvas.setFont('Helvetica', 12)\n",
        "my_canvas.drawString(30, 750, 'COMUNICADO OFICIAL')\n",
        "my_canvas.drawString(500, 750, f\"{FECHA_ACTUAL}\")\n",
        "my_canvas.line(480, 747, 580, 747)\n",
        "my_canvas.drawString(275, 725, 'CANTIDAD:')\n",
        "my_canvas.drawString(500, 725, \"$1,000.00\")\n",
        "my_canvas.line(378, 723, 580, 723)\n",
        "my_canvas.drawString(30, 703, 'RECIBIDO POR:')\n",
        "my_canvas.line(120, 700, 580, 700)\n",
        "my_canvas.drawString(120, 703, \"COSME FULANITO\")\n",
        "\n",
        "\n",
        "my_canvas.drawImage('humai_logo.png', 30, 500, width=100, height=100, mask='auto')\n",
        "\n",
        "my_canvas.save()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0NA7MIRZHZn8"
      },
      "source": [
        "## Scheduling con CRON\n",
        "\n",
        "En el sistema operativo Unix, cron es un administrador regular de procesos en segundo plano (demonio) que ejecuta procesos o guiones a intervalos regulares (por ejemplo, cada minuto, día, semana o mes). Los procesos que deben ejecutarse y la hora a la que deben hacerlo se especifican en el archivo crontab. \n",
        "\n",
        "<img src=\"https://i.ibb.co/ZWCbc2m/crontab.png\" alt=\"crontab\" border=\"0\">\n",
        "\n",
        "\n",
        "Cron se puede definir como el equivalente a Tareas Programadas de Windows.\n",
        "\n",
        "<img src='https://www.solvetic.com/uploads/monthly_01_2017/tutorials-9832-0-90051600-1484655732.png'> <br>\n",
        "Fuente: https://www.solvetic.com/tutoriales/article/3441-como-abrir-y-configurar-programador-tareas-windows-10/\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E-20gWgcHxZ5"
      },
      "source": [
        "### Comandos básicos de Cron\n",
        "\n",
        "En la terminal: <br>\n",
        "**`crontab -l`** -> Permite ver la lista de las tareas programadas <br>\n",
        "**`crontab -e`** -> Permite ver editar las tareas programadas\n",
        "\n",
        "<font color='red'><h3>ATENCION! </h3></font>\n",
        "\n",
        "**`crontab -r`** -> Permite borrar las tareas programadas  <br>\n",
        "Es importante que lo uses cuando quieras que tu tarea deje de ser ejecutada, sino va a quedar funcionando indefinidamente "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9sIZzo3ZIIxY"
      },
      "outputs": [],
      "source": [
        "# ┌───────────── Minutos (0 - 59)\n",
        "# │ ┌───────────── Hora (0 - 23)\n",
        "# │ │ ┌───────────── Dia del mes (1 - 31)\n",
        "# │ │ │ ┌───────────── Mes (1 - 12) o jan,feb,mar,apr,may,jun,jul... (meses en inglés)\n",
        "# │ │ │ │ ┌─────────────  día de la semana (0-6) (domingo=0 o 7) o sun,mon,tue,wed,thu,fri,sat (días en inglés) \n",
        "# │ │ │ │ │                                       \n",
        "# │ │ │ │ │\n",
        "# │ │ │ │ │\n",
        "# * * * * *  comando_a_ejecutar"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GFTtqGxxxxgC"
      },
      "source": [
        "Algunos ejemplos:\n",
        "\n",
        "Todos los dias a las 12 y media del mediodia corre esto\n",
        "\n",
        "`30 12 * * * python /ruta/a/mi/archivo/script.py`\n",
        "\n",
        "El 10 de cada mes corre esto a las 3 de la tarde\n",
        "\n",
        "` * 3 10 * * python /ruta/a/mi/archivo/script.py`\n",
        "\n",
        "Consideraciones\n",
        "* Dependiendo del intérprete de Python que tengan instalado pueden tener que poner algo distinto a la palabra python. Ejemplo:\n",
        "` * * * * * python3 /ruta/a/mi/archivo/script.py`\n",
        "\n",
        "* Otra forma es que cron se posicione en la ruta del archivo y luego solo lo corra. Ejemplo:\n",
        "`* * * * * cd /ruta/a/mi/archivo && python script.py`\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VLbKxKvOH3Ru"
      },
      "outputs": [],
      "source": [
        "def escribir_archivo():\n",
        "\twith open('prueba.txt', 'a+') as f:\n",
        "\t\tf.write('Esribiendo archivo desde Cron\\n')\n",
        "\t\t\n",
        "if __name__ == '__main__':\n",
        "\tescribir_archivo()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O7mhNV3-TAXy"
      },
      "source": [
        "### Recursos\n",
        "\n",
        "* cron: \n",
        "  * [Video de Corey Schafer](https://www.youtube.com/watch?v=QZJ1drMQz1A&t=12s&ab_channel=CoreySchafer) , aunque esta en ingles se los super recomiendo\n",
        "  * https://crontab.guru/ Ayuda a escribir los comandos de cron\n",
        "\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Taller_automatizacion_con_python.ipynb",
      "provenance": []
    },
    "interpreter": {
      "hash": "9458a9a65781ffd0b5785bc24d0483247d085e6235fe3761510e837e1e2f897e"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
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  "nbformat": 4,
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