2_2_Python_Manejo_de_DataFrames.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# **Manejo de DataFrames - Introducción a Pandas**\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "xsDgltH5uN5S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Realizó: Gabriela Valle\n",
        "\n",
        "Revisó: Fernanda Saldívar"
      ],
      "metadata": {
        "id": "rT-mnW73v9st"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Contacto: gabrielavallenunez@gmail.com, fer.saldivarg@gmail.com"
      ],
      "metadata": {
        "id": "z6x9wbB9wUrr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Objetivos\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "qSP4lEyvwkSt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   Introducir definiciones y funciones básicas de Pandas.\n",
        "*   Presentar el concepto de DataFrame.\n",
        "*   Importar una base de datos de compuestos con actividad antiviral existente.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "UPmgqKKiwypw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Introducción:"
      ],
      "metadata": {
        "id": "eAywjE75yI8X"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "[Pandas](https://pandas.pydata.org/docs/) es una popular biblioteca de análisis y manipulación de datos de código abierto para el lenguaje de programación Python. Proporciona funciones fáciles de utilizar para el trabajo con datos estructurados.\n",
        "\n"
      ],
      "metadata": {
        "id": "ZhqfG4KryQUh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pandas se complementa con otras bibliotecas populares de Python, tales como: [NumPy](https://numpy.org/doc/) para el manejo de información numérica y [Seaborn](https://seaborn.pydata.org/) basada en Matplotlib para la visualización de datos."
      ],
      "metadata": {
        "id": "0A9bik9u71IN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pandas se destaca en el manejo de datos faltantes, reestructuración de conjuntos de datos y ejecución de operaciones complejas, de tal forma que Pandas es de gran ayuda para la limpieza y manipulación de datos."
      ],
      "metadata": {
        "id": "JQ_M-MYL8aI3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tabla de Contenidos\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "W-P7bQuw2afY"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   Estructuras de Datos\n",
        "*   ¿Cómo importar bibliotecas?\n",
        "*   ¿Cómo cargar el conjunto de datos?\n",
        "*   Análisis de datos exploratorios (EDA)\n",
        "*   Limpieza de Datos\n",
        "*   Edición del DataFrame\n",
        "*   Ejemplo práctico. Cálculo de pIC50\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "AjYGwqv828k7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Estructuras de Datos\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "P5xEsLvs3WqM"
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    },
    {
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        "Pandas presenta dos estructuras de datos clave:\n",
        "\n",
        "\n",
        "*   **Series**: Objeto unidimensional, similar a una matriz, puede contener cualquier tipo de datos.\n",
        "*   **DataFrame**: Tabla bidimensional  con ejes etiquetados en filas y columnas\n",
        "\n",
        "Tales estructuras permiten al usuario manipular, limpiar y analizar conjuntos de datos eficientemente.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Zg0m1LIs49g7"
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      "cell_type": "markdown",
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)"
      ],
      "metadata": {
        "id": "PXmijWxDDy7F"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ¿Cómo importar bibliotecas?\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "eajEAjqREFsp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para ahorrar tiempo al escribir, se suelen importar las bibliotecas con las siguientes abreviaciones:"
      ],
      "metadata": {
        "id": "sDUlWenQERLR"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_dOOuifjenkZ"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Esto nos permite acortar la instrucción, logrando que nuestro código sea más eficiente y legible."
      ],
      "metadata": {
        "id": "MWfNUundFE_F"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ¿Cómo cargar el conjunto de datos?\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "DTSUt0-xFwag"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "**Cargar base de datos en un DataFrame de Pandas**\n",
        "\n"
      ],
      "metadata": {
        "id": "9rtyGpweg4FM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "En este ejemplo, estaremos utilizando una base de datos correspondiente al IC50 de compuestos con actividad antiviral, extraída de [ChEMBL](https://www.ebi.ac.uk/chembl/)."
      ],
      "metadata": {
        "id": "WnDXzHAdGToy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se carga el conjunto de datos en una variable llamada `df`.\n",
        "\n"
      ],
      "metadata": {
        "id": "nzQp-AewK8yz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df= pd.read_csv(\"https://raw.githubusercontent.com/DIFACQUIM/Cursos/main/Datasets/IC50_CHEMBL.csv\")\n"
      ],
      "metadata": {
        "id": "PKmG6meb9zar"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "Z-38APiqD2wa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Análisis de datos exploratorios (EDA)\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "5Ubw0fWVL3Ii"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Algunas funciones de Pandas nos permiten obtener información sobre nuestro conjunto de datos. Estas funciones se llaman con la variable del conjunto de datos más la función deseada: `variable.funcion`\n"
      ],
      "metadata": {
        "id": "DhTwXMcXMN7G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Llamar a las primeras 5 filas del conjunto de datos.\n",
        "#Se puede especificar el número de filas que se deseen mostrar entre paréntesis\n",
        "\n",
        "df.head(5)"
      ],
      "metadata": {
        "id": "1iOeDWKbLhFV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "8bcae0a0-4d90-4259-ea92-3e76467c4c15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  ID  \\\n",
              "0  CHEMBL1020343, CHEMBL1020342, CHEMBL1020339, C...   \n",
              "1  CHEMBL1020342, CHEMBL1020339, CHEMBL1020340, C...   \n",
              "2                                       CHEMBL875215   \n",
              "3                                       CHEMBL875215   \n",
              "4                                       CHEMBL875215   \n",
              "\n",
              "                                              SMILES  \\\n",
              "0  BP(=O)(COC(C)Cn1cnc2c(=N)[nH]cnc21)O[PH](=O)(=...   \n",
              "1  BP(=O)(COCCn1cnc2c(=N)[nH]cnc21)O[PH](=O)(=O)O...   \n",
              "2  BP(=O)(OCC1C=CC(n2cc(C)c(=O)[nH]c2=O)O1)O[PH](...   \n",
              "3  BP(=O)(OCC1C=CC(n2cnc3c(=O)[nH]c(=N)[nH]c32)C1...   \n",
              "4  BP(=O)(OCC1CCC(n2cc(C)c(=O)[nH]c2=O)O1)O[PH](=...   \n",
              "\n",
              "                          standard_units  \\\n",
              "0  nM, nM, nM, nM, nan, nan, nan, nan, %   \n",
              "1  nM, nM, nM, nM, nan, nan, nan, nan, %   \n",
              "2                                     nM   \n",
              "3                                     nM   \n",
              "4                                     nM   \n",
              "\n",
              "                                      standard_value  \\\n",
              "0  35000.0, 33000.0, 14500.0, 18000.0, 0.7, 0.17,...   \n",
              "1  6500.0, 17000.0, 12000.0, 7100.0, 0.3, 0.2, 21...   \n",
              "2                                                 45   \n",
              "3                                                 34   \n",
              "4                                                236   \n",
              "\n",
              "                                   units  \\\n",
              "0  uM, uM, uM, uM, nan, nan, nan, nan, %   \n",
              "1  uM, uM, uM, uM, nan, nan, nan, nan, %   \n",
              "2                                     uM   \n",
              "3                                     uM   \n",
              "4                                     uM   \n",
              "\n",
              "                                               value  \n",
              "0  35.0, 33.0, 14.5, 18.0, 0.7, 0.17, 9.6, 10.0, ...  \n",
              "1    6.5, 17.0, 12.0, 7.1, 0.3, 0.2, 21.7, 8.8, 69.0  \n",
              "2                                              0,045  \n",
              "3                                              0,034  \n",
              "4                                              0,236  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b74acc8c-6948-4cef-ad97-4775c079b29f\" 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",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>SMILES</th>\n",
              "      <th>standard_units</th>\n",
              "      <th>standard_value</th>\n",
              "      <th>units</th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL1020343, CHEMBL1020342, CHEMBL1020339, C...</td>\n",
              "      <td>BP(=O)(COC(C)Cn1cnc2c(=N)[nH]cnc21)O[PH](=O)(=...</td>\n",
              "      <td>nM, nM, nM, nM, nan, nan, nan, nan, %</td>\n",
              "      <td>35000.0, 33000.0, 14500.0, 18000.0, 0.7, 0.17,...</td>\n",
              "      <td>uM, uM, uM, uM, nan, nan, nan, nan, %</td>\n",
              "      <td>35.0, 33.0, 14.5, 18.0, 0.7, 0.17, 9.6, 10.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL1020342, CHEMBL1020339, CHEMBL1020340, C...</td>\n",
              "      <td>BP(=O)(COCCn1cnc2c(=N)[nH]cnc21)O[PH](=O)(=O)O...</td>\n",
              "      <td>nM, nM, nM, nM, nan, nan, nan, nan, %</td>\n",
              "      <td>6500.0, 17000.0, 12000.0, 7100.0, 0.3, 0.2, 21...</td>\n",
              "      <td>uM, uM, uM, uM, nan, nan, nan, nan, %</td>\n",
              "      <td>6.5, 17.0, 12.0, 7.1, 0.3, 0.2, 21.7, 8.8, 69.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL875215</td>\n",
              "      <td>BP(=O)(OCC1C=CC(n2cc(C)c(=O)[nH]c2=O)O1)O[PH](...</td>\n",
              "      <td>nM</td>\n",
              "      <td>45</td>\n",
              "      <td>uM</td>\n",
              "      <td>0,045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL875215</td>\n",
              "      <td>BP(=O)(OCC1C=CC(n2cnc3c(=O)[nH]c(=N)[nH]c32)C1...</td>\n",
              "      <td>nM</td>\n",
              "      <td>34</td>\n",
              "      <td>uM</td>\n",
              "      <td>0,034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL875215</td>\n",
              "      <td>BP(=O)(OCC1CCC(n2cc(C)c(=O)[nH]c2=O)O1)O[PH](=...</td>\n",
              "      <td>nM</td>\n",
              "      <td>236</td>\n",
              "      <td>uM</td>\n",
              "      <td>0,236</td>\n",
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              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    animation:\n",
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              "\n",
              "  @keyframes spin {\n",
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              "    60% {\n",
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              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 37450,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5740,\n        \"samples\": [\n          \"CHEMBL888081, CHEMBL888080\",\n          \"CHEMBL797529, CHEMBL804227, CHEMBL804313, CHEMBL804312, CHEMBL804311, CHEMBL797531, CHEMBL798330, CHEMBL798331\",\n          \"CHEMBL4407783, CHEMBL4407782, CHEMBL4407785\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SMILES\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 37450,\n        \"samples\": [\n          \"CC(=O)NC(C(=O)NC(C(=O)C1CC(Oc2cccnc2)CC1C(=O)CC1(C(=O)O)CC1)C(C)C)C1CCCCC1\",\n          \"CNC(=O)c1c(-c2ccc(F)cc2)oc2cc(N(C)S(C)(=O)=O)c(-c3ccc4nc(C)n(C(CCO)c5ccc(F)cc5)c(=O)c4n3)cc12\",\n          \"O=C(N=c1[nH]c2cc(O)c(O)cc2s1)C(c1ccc(Cl)cc1)c1ccc(Cl)cc1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"standard_units\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 880,\n        \"samples\": [\n          \"nan, nan, nM, nM\",\n          \"nan, nan, nM, %, nM, %\",\n          \"nM, nM, nM, nM, nM, nM, nM, nM, nM, nM, %, nM, degrees C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"standard_value\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16994,\n        \"samples\": [\n          \"208\",\n          \"800.0, 12500.0\",\n          \"44000.0, 25000.0, 47000.0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"units\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1106,\n        \"samples\": [\n          \"%, %, uM, uM, uM, uM, ug ml-1, ug ml-1, ug ml-1, ug ml-1\",\n          \"nan, nan, nM, uM\",\n          \"uM, uM, uM, uM, uM, uM, uM, uM, nan, nan, nan, nan, nan, nan, nan, nan\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"value\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16569,\n        \"samples\": [\n          \"820\",\n          \"25.0, 0.3, 5.7, 5.7, 5.6, 100.0, 24.2, 25.0, 24.2, 1.0, 80.0, 82.0, 5.0, 69.0, 333.0, 54.0, 5.7, 5.7, 25.0, 5.4, nan, 5.4, 3.2, 100.0, 24.2, 0.48, 21.3\",\n          \"19.0, 25.0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Mostrar el nombre de todas las columnas presentes en el DataFrame, así como el tipo de datos que contienen.\n",
        "\n",
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RyjP0plGmRj2",
        "outputId": "285d11fc-2b03-43b5-ff34-bc215150945f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'SMILES', 'standard_units', 'standard_value', 'units', 'value'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para nuestro ejemplo, no necesitamos todas las columnas, podemos seleccionar únicamente las columnas a utilizar y gurdarlas en una nueva variable."
      ],
      "metadata": {
        "id": "aIHwB-o4zndX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "DB = df[['ID',\"standard_units\", \"standard_value\"]]\n",
        "DB"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "7SZtPLNGmzxy",
        "outputId": "6a6bf82a-657d-4700-9abd-bbe91401919d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                      ID  \\\n",
              "0      CHEMBL1020343, CHEMBL1020342, CHEMBL1020339, C...   \n",
              "1      CHEMBL1020342, CHEMBL1020339, CHEMBL1020340, C...   \n",
              "2                                           CHEMBL875215   \n",
              "3                                           CHEMBL875215   \n",
              "4                                           CHEMBL875215   \n",
              "...                                                  ...   \n",
              "37445                                      CHEMBL3772460   \n",
              "37446                                      CHEMBL3772460   \n",
              "37447                                      CHEMBL3772460   \n",
              "37448                                      CHEMBL2329896   \n",
              "37449                                      CHEMBL4610360   \n",
              "\n",
              "                              standard_units  \\\n",
              "0      nM, nM, nM, nM, nan, nan, nan, nan, %   \n",
              "1      nM, nM, nM, nM, nan, nan, nan, nan, %   \n",
              "2                                         nM   \n",
              "3                                         nM   \n",
              "4                                         nM   \n",
              "...                                      ...   \n",
              "37445                                      %   \n",
              "37446                                      %   \n",
              "37447                                      %   \n",
              "37448                                      %   \n",
              "37449                                      %   \n",
              "\n",
              "                                          standard_value  \n",
              "0      35000.0, 33000.0, 14500.0, 18000.0, 0.7, 0.17,...  \n",
              "1      6500.0, 17000.0, 12000.0, 7100.0, 0.3, 0.2, 21...  \n",
              "2                                                     45  \n",
              "3                                                     34  \n",
              "4                                                    236  \n",
              "...                                                  ...  \n",
              "37445                                                 13  \n",
              "37446                                                 16  \n",
              "37447                                                 21  \n",
              "37448                                                 10  \n",
              "37449                                                 35  \n",
              "\n",
              "[37450 rows x 3 columns]"
            ],
            "text/html": [
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              "      <th>ID</th>\n",
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              "      <th>standard_value</th>\n",
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              "      <th>0</th>\n",
              "      <td>CHEMBL1020343, CHEMBL1020342, CHEMBL1020339, C...</td>\n",
              "      <td>nM, nM, nM, nM, nan, nan, nan, nan, %</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL1020342, CHEMBL1020339, CHEMBL1020340, C...</td>\n",
              "      <td>nM, nM, nM, nM, nan, nan, nan, nan, %</td>\n",
              "      <td>6500.0, 17000.0, 12000.0, 7100.0, 0.3, 0.2, 21...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL875215</td>\n",
              "      <td>nM</td>\n",
              "      <td>45</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL875215</td>\n",
              "      <td>nM</td>\n",
              "      <td>34</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL875215</td>\n",
              "      <td>nM</td>\n",
              "      <td>236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>37445</th>\n",
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              "      <td>%</td>\n",
              "      <td>13</td>\n",
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              "    <tr>\n",
              "      <th>37446</th>\n",
              "      <td>CHEMBL3772460</td>\n",
              "      <td>%</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37447</th>\n",
              "      <td>CHEMBL3772460</td>\n",
              "      <td>%</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37448</th>\n",
              "      <td>CHEMBL2329896</td>\n",
              "      <td>%</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37449</th>\n",
              "      <td>CHEMBL4610360</td>\n",
              "      <td>%</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>37450 rows × 3 columns</p>\n",
              "</div>\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
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              "        height: 32px;\n",
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              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        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-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('DB')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "DB",
              "summary": "{\n  \"name\": \"DB\",\n  \"rows\": 37450,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5740,\n        \"samples\": [\n          \"CHEMBL888081, CHEMBL888080\",\n          \"CHEMBL797529, CHEMBL804227, CHEMBL804313, CHEMBL804312, CHEMBL804311, CHEMBL797531, CHEMBL798330, CHEMBL798331\",\n          \"CHEMBL4407783, CHEMBL4407782, CHEMBL4407785\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"standard_units\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 880,\n        \"samples\": [\n          \"nan, nan, nM, nM\",\n          \"nan, nan, nM, %, nM, %\",\n          \"nM, nM, nM, nM, nM, nM, nM, nM, nM, nM, %, nM, degrees C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"standard_value\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16994,\n        \"samples\": [\n          \"208\",\n          \"800.0, 12500.0\",\n          \"44000.0, 25000.0, 47000.0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Confirmamos las columnas guardadas en nuestro nuevo DataFrame\n",
        "DB.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "diwmcIoxnbeG",
        "outputId": "78b9ee1d-e460-4173-a35c-8634b6caf43f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'standard_units', 'standard_value'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para conocer el tamaño de nuestro DataFrame, es decir, conocer cuántas filas y cuántas columnas lo conforman, utilizamos la siguiente función:"
      ],
      "metadata": {
        "id": "4MK2G1YgKLrY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "DB.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BLWM3v2Xnhb1",
        "outputId": "0a1403df-5e4f-4c32-b162-46bbdcdd0090"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(37450, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "También pueden transponerse los ejes de las filas y columnas, esto puede lograrse con la siguiente función:"
      ],
      "metadata": {
        "id": "9FWua9LkqaiD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Transposición de filas y columnas\n",
        "DB.T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "JcqIcZdeqoGg",
        "outputId": "390e23f1-50b2-4ec8-f882-f9c87b938944"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                            0      \\\n",
              "ID              CHEMBL1020343, CHEMBL1020342, CHEMBL1020339, C...   \n",
              "standard_units              nM, nM, nM, nM, nan, nan, nan, nan, %   \n",
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              "\n",
              "                                                            1      \\\n",
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              "standard_value  6500.0, 17000.0, 12000.0, 7100.0, 0.3, 0.2, 21...   \n",
              "\n",
              "                       2             3             4             5      \\\n",
              "ID              CHEMBL875215  CHEMBL875215  CHEMBL875215  CHEMBL875215   \n",
              "standard_units            nM            nM            nM            nM   \n",
              "standard_value            45            34           236            23   \n",
              "\n",
              "                       6             7             8             9      ...  \\\n",
              "ID              CHEMBL875215  CHEMBL875215  CHEMBL875215  CHEMBL875215  ...   \n",
              "standard_units            nM            nM            nM            nM  ...   \n",
              "standard_value           438            47            71            46  ...   \n",
              "\n",
              "                        37440         37441         37442         37443  \\\n",
              "ID              CHEMBL3772460  CHEMBL836164  CHEMBL836164  CHEMBL836164   \n",
              "standard_units              %             %             %             %   \n",
              "standard_value             11            10            11            14   \n",
              "\n",
              "                       37444          37445          37446          37447  \\\n",
              "ID              CHEMBL836164  CHEMBL3772460  CHEMBL3772460  CHEMBL3772460   \n",
              "standard_units             %              %              %              %   \n",
              "standard_value             4             13             16             21   \n",
              "\n",
              "                        37448          37449  \n",
              "ID              CHEMBL2329896  CHEMBL4610360  \n",
              "standard_units              %              %  \n",
              "standard_value             10             35  \n",
              "\n",
              "[3 rows x 37450 columns]"
            ],
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              "      <td>438</td>\n",
              "      <td>47</td>\n",
              "      <td>71</td>\n",
              "      <td>46</td>\n",
              "      <td>...</td>\n",
              "      <td>11</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>14</td>\n",
              "      <td>4</td>\n",
              "      <td>13</td>\n",
              "      <td>16</td>\n",
              "      <td>21</td>\n",
              "      <td>10</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 37450 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-db9b0ce1-71ac-48ea-bec8-d98293874ddc')\"\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",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-db9b0ce1-71ac-48ea-bec8-d98293874ddc 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-db9b0ce1-71ac-48ea-bec8-d98293874ddc');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e9bb9d3b-31ab-42cf-8a15-96f3eb5d9272\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e9bb9d3b-31ab-42cf-8a15-96f3eb5d9272')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e9bb9d3b-31ab-42cf-8a15-96f3eb5d9272 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Limpieza de Datos\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "WYsaPQOLLqPi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "La limpieza de datos implica identificar y rectificar errores, inconsistencias y valores faltantes dentro de nuestro conjunto de datos. Es necesario preparar nuestros datos antes de procesarlos."
      ],
      "metadata": {
        "id": "fBV7hGHcNEHe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Los modelos de aprendizaje automático no se pueden entrenar con conjuntos de datos a los que les faltan valores \"NAN\", ya que pueden alterar el resultado final durante el análisis."
      ],
      "metadata": {
        "id": "Pzoqw1pLHwIt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Eliminar las filas donde haya valores faltantes \"NAN\"\n",
        "DB = DB.dropna()\n",
        "\n",
        "#El tamaño del DataFrame ha cambiado\n",
        "DB.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R63XmmMapphL",
        "outputId": "8dc19d38-d66e-4ffd-9b47-b8244c43bdf7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(35225, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Los valores duplicados pueden alterar el análisis de resultados, pueden producirse valores atípicos."
      ],
      "metadata": {
        "id": "ZQYX3vUPHvmJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Eliminar las filas duplicadas\n",
        "DB = DB.drop_duplicates()\n",
        "\n",
        "#El tamaño del DataFrame ha cambiado\n",
        "DB.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JSq-XG6wnxSa",
        "outputId": "393d7c06-9e92-44bc-e287-b7a53b07c1ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(29832, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Edición del DataFrame\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "3rLlx2iGJEdD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "En ocasiones puede ser que necesitemos renombrar las columnas del DataFrame, esto podemos lograrlo aplicando la siguiente función a la variable `DB.rename` seguido de un nuevo diccionario que contenga a las columnas existentes asignadas con su nuevo nombre."
      ],
      "metadata": {
        "id": "1ugmIZq1Jwkt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Renombrar columnas\n",
        "DB.rename(columns = {'standard_value':'IC50', \"standard_units\": \"Units\"}, inplace = True)\n",
        "\n",
        "#Exploramos el DataFrame con las columnas editadas\n",
        "DB.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "uuN3zk1lqiA1",
        "outputId": "e4a6de72-61b6-45ff-808a-88dbf3eedc7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  ID  \\\n",
              "0  CHEMBL1020343, CHEMBL1020342, CHEMBL1020339, C...   \n",
              "1  CHEMBL1020342, CHEMBL1020339, CHEMBL1020340, C...   \n",
              "2                                       CHEMBL875215   \n",
              "3                                       CHEMBL875215   \n",
              "4                                       CHEMBL875215   \n",
              "\n",
              "                                   Units  \\\n",
              "0  nM, nM, nM, nM, nan, nan, nan, nan, %   \n",
              "1  nM, nM, nM, nM, nan, nan, nan, nan, %   \n",
              "2                                     nM   \n",
              "3                                     nM   \n",
              "4                                     nM   \n",
              "\n",
              "                                                IC50  \n",
              "0  35000.0, 33000.0, 14500.0, 18000.0, 0.7, 0.17,...  \n",
              "1  6500.0, 17000.0, 12000.0, 7100.0, 0.3, 0.2, 21...  \n",
              "2                                                 45  \n",
              "3                                                 34  \n",
              "4                                                236  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-40e45f77-e51d-4405-848b-fe7a2061be6c\" 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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
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              "      <th>ID</th>\n",
              "      <th>Units</th>\n",
              "      <th>IC50</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL1020343, CHEMBL1020342, CHEMBL1020339, C...</td>\n",
              "      <td>nM, nM, nM, nM, nan, nan, nan, nan, %</td>\n",
              "      <td>35000.0, 33000.0, 14500.0, 18000.0, 0.7, 0.17,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL1020342, CHEMBL1020339, CHEMBL1020340, C...</td>\n",
              "      <td>nM, nM, nM, nM, nan, nan, nan, nan, %</td>\n",
              "      <td>6500.0, 17000.0, 12000.0, 7100.0, 0.3, 0.2, 21...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL875215</td>\n",
              "      <td>nM</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL875215</td>\n",
              "      <td>nM</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL875215</td>\n",
              "      <td>nM</td>\n",
              "      <td>236</td>\n",
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              "  </tbody>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      width: 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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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-40e45f77-e51d-4405-848b-fe7a2061be6c 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-40e45f77-e51d-4405-848b-fe7a2061be6c');\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",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3057f184-6ec0-43a3-9cf0-faff795797c5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "DB",
              "summary": "{\n  \"name\": \"DB\",\n  \"rows\": 29832,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5405,\n        \"samples\": [\n          \"CHEMBL4831799, CHEMBL4831801, CHEMBL4831802, CHEMBL4831803, CHEMBL5151820\",\n          \"CHEMBL4327419, CHEMBL4327417, CHEMBL4327418\",\n          \"CHEMBL829517, CHEMBL3293759, CHEMBL881407, CHEMBL880241, CHEMBL881942, CHEMBL880629, CHEMBL865324, CHEMBL869770, CHEMBL885920, CHEMBL926887, CHEMBL926892, CHEMBL926891, CHEMBL926890, CHEMBL926889, CHEMBL926888, CHEMBL1015285, CHEMBL1016223, CHEMBL1016229, CHEMBL1016230, CHEMBL1016231, CHEMBL1262283, CHEMBL1262420, CHEMBL1262283, CHEMBL1635106, CHEMBL1675359, CHEMBL1675360, CHEMBL2185500, CHEMBL2185924, CHEMBL3292068\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Units\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 877,\n        \"samples\": [\n          \"nM, nM, /uM/min, /uM/min, %, %\",\n          \"nan, nan, nM, nM, nM, nM, nM, nan, nan\",\n          \"nM, nM, nM, nan, nM, nM, nM, nM, nM, nM, nM, nM, nM, nM, nM, nan\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IC50\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 16944,\n        \"samples\": [\n          \"100000.0, 24000.0\",\n          \"78,6\",\n          \"100.9, 1130.0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Como podemos notar, tenemos filas que tienen asignados varios valores separados por comas en una única celda en las tres columnas, sin embargo, con la función `.split` podemos hacer que tales valores se separen y resulten cada uno asignado a una fila diferente. Veamos el ejemplo para separar los valores de la columna `Units`, generaremos una nueva columna llamada `New_Units` con los valores separados:"
      ],
      "metadata": {
        "id": "v3TJQoW0O92Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Definir una función para separar los valores y crear nuevas filas\n",
        "def separar_valores(row):\n",
        "    valores = row['Units'].split(',')  # Cambia 'columna_con_valores' por el nombre de la columna que contiene los valores separados por comas\n",
        "    return pd.Series(valores)\n",
        "\n",
        "# Aplicar la función a la columna específica y generar nuevas filas\n",
        "nuevo_df = DB.apply(separar_valores, axis=1).stack().reset_index(level=1, drop=True)\n",
        "nuevo_df.name = 'New_Units'  # Cambia 'nueva_columna' por el nombre que quieras darle a la nueva columna\n",
        "\n",
        "# Eliminar la columna original y unir el DataFrame original con el nuevo DataFrame\n",
        "df = DB.drop('Units', axis=1).join(nuevo_df)\n"
      ],
      "metadata": {
        "id": "5tfNh9RL_w79"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Explorar el nuevo DataFrame\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "8_HuS5FIAfIu",
        "outputId": "e6e261b0-081b-49de-acee-ad232df0f440"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  ID  \\\n",
              "0  CHEMBL1020343, CHEMBL1020342, CHEMBL1020339, C...   \n",
              "0  CHEMBL1020343, CHEMBL1020342, CHEMBL1020339, C...   \n",
              "0  CHEMBL1020343, CHEMBL1020342, CHEMBL1020339, C...   \n",
              "0  CHEMBL1020343, CHEMBL1020342, CHEMBL1020339, C...   \n",
              "0  CHEMBL1020343, CHEMBL1020342, CHEMBL1020339, C...   \n",
              "\n",
              "                                                IC50 New_Units  \n",
              "0  35000.0, 33000.0, 14500.0, 18000.0, 0.7, 0.17,...        nM  \n",
              "0  35000.0, 33000.0, 14500.0, 18000.0, 0.7, 0.17,...        nM  \n",
              "0  35000.0, 33000.0, 14500.0, 18000.0, 0.7, 0.17,...        nM  \n",
              "0  35000.0, 33000.0, 14500.0, 18000.0, 0.7, 0.17,...        nM  \n",
              "0  35000.0, 33000.0, 14500.0, 18000.0, 0.7, 0.17,...       nan  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4959231e-e41f-440e-8565-90cbb04f1abc\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
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              "      <td>nM</td>\n",
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              "      <th>0</th>\n",
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              "      <td>nM</td>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL1020343, CHEMBL1020342, CHEMBL1020339, C...</td>\n",
              "      <td>35000.0, 33000.0, 14500.0, 18000.0, 0.7, 0.17,...</td>\n",
              "      <td>nM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL1020343, CHEMBL1020342, CHEMBL1020339, C...</td>\n",
              "      <td>35000.0, 33000.0, 14500.0, 18000.0, 0.7, 0.17,...</td>\n",
              "      <td>nM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL1020343, CHEMBL1020342, CHEMBL1020339, C...</td>\n",
              "      <td>35000.0, 33000.0, 14500.0, 18000.0, 0.7, 0.17,...</td>\n",
              "      <td>nan</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4959231e-e41f-440e-8565-90cbb04f1abc')\"\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",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4959231e-e41f-440e-8565-90cbb04f1abc 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-4959231e-e41f-440e-8565-90cbb04f1abc');\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",
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              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8139ff89-2119-46ed-98b9-4233fbe90b60')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8139ff89-2119-46ed-98b9-4233fbe90b60 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
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              "type": "dataframe",
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            }
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para este ejemplo, únicamente requerimos unidades de nM o de uM, por lo tanto, podemos filtrar los datos mediante la columna `New_Units` de tal forma que las filas que contengan cualquier otra unidad se eliminen."
      ],
      "metadata": {
        "id": "t7-3C3owSJEF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Filtrar filas con valores diferentes a \"nM\" o \"uM\" en la columna \"New_Units\"\n",
        "df = df[df['New_Units'].isin(['nM', 'uM'])]\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "U7BGMWvkCc5B",
        "outputId": "1c2eb22d-9528-46f3-8727-e572b1827c04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                                  ID  \\\n",
              "0  CHEMBL1020343, CHEMBL1020342, CHEMBL1020339, C...   \n",
              "1  CHEMBL1020342, CHEMBL1020339, CHEMBL1020340, C...   \n",
              "2                                       CHEMBL875215   \n",
              "3                                       CHEMBL875215   \n",
              "4                                       CHEMBL875215   \n",
              "\n",
              "                                                IC50 New_Units  \n",
              "0  35000.0, 33000.0, 14500.0, 18000.0, 0.7, 0.17,...        nM  \n",
              "1  6500.0, 17000.0, 12000.0, 7100.0, 0.3, 0.2, 21...        nM  \n",
              "2                                                 45        nM  \n",
              "3                                                 34        nM  \n",
              "4                                                236        nM  "
            ],
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              "    async function quickchart(key) {\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 25346,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4306,\n        \"samples\": [\n          \"CHEMBL4223564\",\n          \"CHEMBL886265, CHEMBL915787\",\n          \"CHEMBL4386709, CHEMBL4386710, CHEMBL4386711, CHEMBL4386720, CHEMBL4386721, CHEMBL4386779, CHEMBL4386780\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IC50\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 13972,\n        \"samples\": [\n          \"109000.0, 111000.0\",\n          \"13319\",\n          \"65\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_Units\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"uM\",\n          \"nM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#El tamaño del DataFrame ha cambiado\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2azf0IUUEhe0",
        "outputId": "aa88f8f3-025f-41cc-84e6-42b741b693e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25346, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ahora que ya conocemos cómo separar los datos separados por comas en una misma celda, es necesario aplicar la misma función para la columna `IC50`, guardaremos los datos en una nueva columna llamada `New_IC50`:"
      ],
      "metadata": {
        "id": "ONSCI00-T8oi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Definir una función para separar los valores y crear nuevas filas\n",
        "def separar_valores1(row):\n",
        "    valores1 = row['IC50'].split(',')  # Cambia 'columna_con_valores' por el nombre de la columna que contiene los valores separados por comas\n",
        "    return pd.Series(valores1)\n",
        "\n",
        "# Aplicar la función a la columna específica y generar nuevas filas\n",
        "nuevo_df1 = df.apply(separar_valores1, axis=1).stack().reset_index(level=1, drop=True)\n",
        "nuevo_df1.name = 'New_IC50'  # Cambia 'nueva_columna' por el nombre que quieras darle a la nueva columna\n",
        "\n",
        "# Eliminar la columna original y unir el DataFrame original con el nuevo DataFrame\n",
        "df1 = df.drop('IC50', axis=1).join(nuevo_df1)\n",
        "df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VM9HS6DTExf2",
        "outputId": "73fe1d96-4384-4af6-c3a7-b5fe4db6834b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                                  ID New_Units  New_IC50\n",
              "0  CHEMBL1020343, CHEMBL1020342, CHEMBL1020339, C...        nM   35000.0\n",
              "0  CHEMBL1020343, CHEMBL1020342, CHEMBL1020339, C...        nM   33000.0\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b5e6a353-cc16-42b0-935f-c6828d0b47f1 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-b5e6a353-cc16-42b0-935f-c6828d0b47f1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e7110604-1c09-4a1b-9429-be614d130d07\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e7110604-1c09-4a1b-9429-be614d130d07')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e7110604-1c09-4a1b-9429-be614d130d07 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 54266,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4306,\n        \"samples\": [\n          \"CHEMBL4223564\",\n          \"CHEMBL886265, CHEMBL915787\",\n          \"CHEMBL4386709, CHEMBL4386710, CHEMBL4386711, CHEMBL4386720, CHEMBL4386721, CHEMBL4386779, CHEMBL4386780\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_Units\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"uM\",\n          \"nM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_IC50\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11243,\n        \"samples\": [\n          \"0.2483\",\n          \"42657951880\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para efectos del ejemplo conservaremos únicamente el primer valor por cada blanco, por lo tanto, eliminamos los blancos duplicados:"
      ],
      "metadata": {
        "id": "5tC0sm2UVJDT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df1.drop_duplicates(subset='ID', keep='first')\n",
        "df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2cj9Swf8tYfS",
        "outputId": "c4358e8f-14e1-4149-e7e5-a5881351c7aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   ID New_Units   New_IC50\n",
              "0   CHEMBL1020343, CHEMBL1020342, CHEMBL1020339, C...        nM    35000.0\n",
              "1   CHEMBL1020342, CHEMBL1020339, CHEMBL1020340, C...        nM     6500.0\n",
              "2                                        CHEMBL875215        nM         45\n",
              "13                                      CHEMBL3887699        nM       1300\n",
              "14        CHEMBL2215079, CHEMBL2215078, CHEMBL2215079        nM  199526.23"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-62394879-ed90-4ff2-b138-55d0e1d205ca\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>New_Units</th>\n",
              "      <th>New_IC50</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL1020343, CHEMBL1020342, CHEMBL1020339, C...</td>\n",
              "      <td>nM</td>\n",
              "      <td>35000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL1020342, CHEMBL1020339, CHEMBL1020340, C...</td>\n",
              "      <td>nM</td>\n",
              "      <td>6500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL875215</td>\n",
              "      <td>nM</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>CHEMBL3887699</td>\n",
              "      <td>nM</td>\n",
              "      <td>1300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>CHEMBL2215079, CHEMBL2215078, CHEMBL2215079</td>\n",
              "      <td>nM</td>\n",
              "      <td>199526.23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-62394879-ed90-4ff2-b138-55d0e1d205ca')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-62394879-ed90-4ff2-b138-55d0e1d205ca 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-62394879-ed90-4ff2-b138-55d0e1d205ca');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-68116250-753a-4718-8d3d-9d1fedd117b3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-68116250-753a-4718-8d3d-9d1fedd117b3')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-68116250-753a-4718-8d3d-9d1fedd117b3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 4306,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4306,\n        \"samples\": [\n          \"CHEMBL4223564\",\n          \"CHEMBL886265, CHEMBL915787\",\n          \"CHEMBL4386709, CHEMBL4386710, CHEMBL4386711, CHEMBL4386720, CHEMBL4386721, CHEMBL4386779, CHEMBL4386780\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_Units\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"uM\",\n          \"nM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_IC50\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1704,\n        \"samples\": [\n          \"11200.0\",\n          \"6680\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podemos realizar una limpieza final."
      ],
      "metadata": {
        "id": "vxjmGvYM2iap"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Eliminar las filas donde haya valores faltantes \"NAN\"\n",
        "df1 = df1.dropna()\n",
        "#df1 = df1.dropna(subset=['New_IC50'], inplace = True)\n",
        "#El tamaño del DataFrame ha cambiado\n",
        "df1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bOl4hngq2kYM",
        "outputId": "816d19b9-c666-4649-ebac-7f61d3e36068"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4306, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 183
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podemos guardar nuestro DataFrame como un archivo `.csv` aplicando la siguiente función:"
      ],
      "metadata": {
        "id": "D4aBcRzTVM3x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Guardar el DataFrame resultante en un nuevo archivo CSV\n",
        "df1.to_csv('CHEMBL_IC50_CURED.csv', index=False)"
      ],
      "metadata": {
        "id": "C7fYMpKFAOmC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ejemplo práctico. Cálculo de pIC50\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "m9J90Xp3KNcM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "El IC50 es una medida de la eficacia de un compuesto para inhibir una actividad biológica específica.\n",
        "\n",
        "El pIC50 se define como el negativo del logaritmo base 10 de la concentración inhibitoria 50 (IC50), calcularlo es de gran relevancia en el campo del diseño de fármacos ya que:\n",
        "\n",
        "\n",
        "\n",
        "1.   Permite comparar la actividad inhibitoria de diferentes compuestos. Esto es fundamental para seleccionar los compuestos más prometedores para estudios adicionales o desarrollo clínico.\n",
        "\n",
        "2.    Puede utilizarse en estudios de estructura-actividad para optimizar la estructura química de un fármaco y mejorar su potencia inhibitoria. puede utilizarse en estudios de estructura-actividad para optimizar la estructura química de un fármaco y mejorar su potencia inhibitoria.\n",
        "\n"
      ],
      "metadata": {
        "id": "h1Z4-TT4W-pM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Convertir IC50 a pIC50**"
      ],
      "metadata": {
        "id": "NJKd1TLsGj-R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Primero importamos las librerías necesarias:"
      ],
      "metadata": {
        "id": "paQ_Fu2OscSe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "tic =time.time()\n",
        "from IPython.utils import io\n",
        "import tqdm.notebook\n",
        "import os, sys, random\n",
        "total = 100\n",
        "with tqdm.notebook.tqdm(total=total) as pbar:\n",
        "    with io.capture_output() as captured:\n",
        "\n",
        "        #from platform import python_version\n",
        "        #if python_version() < \"3.7\":\n",
        "        #!pip install https://github.com/biotite-dev/biotite/archive/master.tar.gz\n",
        "\n",
        "        pbar.update(20)\n",
        "        #Graphic libraries\n",
        "        !pip install matplotlib\n",
        "        import matplotlib.pyplot as plt\n",
        "        %matplotlib inline\n",
        "        import matplotlib.font_manager as font_manager\n",
        "        !pip install seaborn\n",
        "        import seaborn as sns\n",
        "        pbar.update(40)\n",
        "\n",
        "        #System libraries and primary tools\n",
        "        import os.path\n",
        "        import os, subprocess\n",
        "        os.getcwd()\n",
        "        !pip install cufflinks\n",
        "        import cufflinks as cf\n",
        "        import warnings\n",
        "        warnings.filterwarnings(\"ignore\")\n",
        "        import math\n",
        "        from math import pi\n",
        "        from pathlib import Path\n",
        "        from tempfile import TemporaryDirectory\n",
        "        !pip install numpy\n",
        "        import numpy as np\n",
        "        !pip install simplejson\n",
        "        import simplejson as json\n",
        "        %config Completer.use_jedi = False\n",
        "        pbar.update(30)\n",
        "        pbar.update(10)\n",
        "toc =time.time()\n",
        "print(\"Execution time: \"+ str(1000*(toc-tic))+\" ms\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "c239b6ec7ec24ac58f8e0539e53ec28e",
            "bf54c1fe697c49eea32258650f3ce467",
            "b46aa0fdc7c145e2ae2064f073aa047c",
            "dfbbb5155f80406db656924ebef943d5",
            "4c63fc3346364caf98f85855f01e523a",
            "0a745d2eaa6549e383573f5d07bd0041",
            "b868ee1487c54ad5a20278ca19be0fe2",
            "809d4237f9764689bc8a2577c0e042da",
            "3c4b85695e4b40afa0f8508fc024d95c",
            "a73a693e33fb48e2819e95cbff8e45df",
            "6fe25115039c442d9fdd2451c113b94c"
          ]
        },
        "id": "E2me7XAoyGfS",
        "outputId": "0272e263-945e-4e9d-82cc-d8775a0d48be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/100 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c239b6ec7ec24ac58f8e0539e53ec28e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Execution time: 35108.05702209473 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cargamos nuestro archivo .csv"
      ],
      "metadata": {
        "id": "glQERY8qsCAo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "La7IdxygxMoU",
        "outputId": "95ffc476-2686-40eb-df86-659d4e2bc8b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   ID New_Units   New_IC50\n",
              "0   CHEMBL1020343, CHEMBL1020342, CHEMBL1020339, C...        nM    35000.0\n",
              "1   CHEMBL1020342, CHEMBL1020339, CHEMBL1020340, C...        nM     6500.0\n",
              "2                                        CHEMBL875215        nM         45\n",
              "13                                      CHEMBL3887699        nM       1300\n",
              "14        CHEMBL2215079, CHEMBL2215078, CHEMBL2215079        nM  199526.23"
            ],
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              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <td>CHEMBL875215</td>\n",
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              "      <td>45</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>CHEMBL3887699</td>\n",
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              "      <td>1300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>CHEMBL2215079, CHEMBL2215078, CHEMBL2215079</td>\n",
              "      <td>nM</td>\n",
              "      <td>199526.23</td>\n",
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              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a7bdbfa6-c71b-41d8-b10c-b1ae64377685')\"\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-a7bdbfa6-c71b-41d8-b10c-b1ae64377685 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-a7bdbfa6-c71b-41d8-b10c-b1ae64377685');\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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              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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            ],
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              "type": "dataframe",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 4306,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4306,\n        \"samples\": [\n          \"CHEMBL4223564\",\n          \"CHEMBL886265, CHEMBL915787\",\n          \"CHEMBL4386709, CHEMBL4386710, CHEMBL4386711, CHEMBL4386720, CHEMBL4386721, CHEMBL4386779, CHEMBL4386780\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_Units\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"uM\",\n          \"nM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_IC50\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1704,\n        \"samples\": [\n          \"11200.0\",\n          \"6680\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para poder realizar el cálculo correctamente, debemos eliminar aquellos valores que sean igual a 0."
      ],
      "metadata": {
        "id": "2xApogDmtD2v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Eliminar filas donde el valor en la columna \"New_IC50\" sea 0\n",
        "df = df1[df1['New_IC50'] != 0]\n",
        "\n",
        "# Si deseas eliminar las filas directamente del DataFrame original, puedes hacer:\n",
        "df.drop(df[df['New_IC50'] == 0].index, inplace=True)\n",
        "\n",
        "# Si deseas guardar el DataFrame modificado en un nuevo archivo CSV, puedes hacer:\n",
        "# df.to_csv('nuevo_dataframe.csv', index=False)\n",
        "\n",
        "# Si deseas ver el DataFrame resultante, puedes hacer:\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4hWy2sSN1i7j",
        "outputId": "ff31ce09-6d74-4b0b-8dbb-f5c48d205202"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                      ID New_Units   New_IC50\n",
            "0      CHEMBL1020343, CHEMBL1020342, CHEMBL1020339, C...        nM    35000.0\n",
            "1      CHEMBL1020342, CHEMBL1020339, CHEMBL1020340, C...        nM     6500.0\n",
            "2                                           CHEMBL875215        nM         45\n",
            "13                                         CHEMBL3887699        nM       1300\n",
            "14           CHEMBL2215079, CHEMBL2215078, CHEMBL2215079        nM  199526.23\n",
            "...                                                  ...       ...        ...\n",
            "37317                                      CHEMBL4355998        nM       5200\n",
            "37332  CHEMBL3386731, CHEMBL3386732, CHEMBL1018093, C...        nM    25300.0\n",
            "37400        CHEMBL3610409, CHEMBL3610410, CHEMBL3610411        nM    36000.0\n",
            "37415         CHEMBL798996, CHEMBL1909126, CHEMBL1909126        nM    14600.0\n",
            "37437        CHEMBL3090996, CHEMBL3090995, CHEMBL4270383        nM   100000.0\n",
            "\n",
            "[4306 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dado que el cálculo del pIC50 es diferente según las unidades de medida que correspondan al valor, separaremos los datos cuyas unidades sean nM en un DataFrame 1 (df_1) y los datos cuyas unidades sean uM en otro DataFrame 2 (df_2)\n"
      ],
      "metadata": {
        "id": "XsljUEHTtpSC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Separar DataFrames\n",
        "df_1 = df[df[\"New_Units\"] == \"nM\"]\n",
        "df_2 = df[df[\"New_Units\"] == \"uM\"]"
      ],
      "metadata": {
        "id": "4J0UHEVg-A3O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Exploramos el DataFrame 1\n",
        "df_1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "OsqMBM3fzkK8",
        "outputId": "65683de4-f9c1-4a44-eace-d5ec9afd813c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   ID New_Units   New_IC50\n",
              "0   CHEMBL1020343, CHEMBL1020342, CHEMBL1020339, C...        nM    35000.0\n",
              "1   CHEMBL1020342, CHEMBL1020339, CHEMBL1020340, C...        nM     6500.0\n",
              "2                                        CHEMBL875215        nM         45\n",
              "13                                      CHEMBL3887699        nM       1300\n",
              "14        CHEMBL2215079, CHEMBL2215078, CHEMBL2215079        nM  199526.23"
            ],
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      }\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_1",
              "summary": "{\n  \"name\": \"df_1\",\n  \"rows\": 4214,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4214,\n        \"samples\": [\n          \"CHEMBL1057308, CHEMBL1057307\",\n          \"CHEMBL3882102, CHEMBL2065354, CHEMBL2065355, CHEMBL2065356, CHEMBL2065357\",\n          \"CHEMBL763923, CHEMBL763923, CHEMBL858358\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_Units\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"nM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_IC50\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1675,\n        \"samples\": [\n          \"20200.0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_1 = df_1.copy()                                    # Creaar una copia del DataFrame\n",
        "df_1['New_IC50'] = df_1['New_IC50'].astype(float)\n",
        "df_1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "HOMR5Ea-c469",
        "outputId": "97a871ad-d543-4ffc-d80a-fbd247ead8fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                      ID New_Units   New_IC50\n",
              "0      CHEMBL1020343, CHEMBL1020342, CHEMBL1020339, C...        nM   35000.00\n",
              "1      CHEMBL1020342, CHEMBL1020339, CHEMBL1020340, C...        nM    6500.00\n",
              "2                                           CHEMBL875215        nM      45.00\n",
              "13                                         CHEMBL3887699        nM    1300.00\n",
              "14           CHEMBL2215079, CHEMBL2215078, CHEMBL2215079        nM  199526.23\n",
              "...                                                  ...       ...        ...\n",
              "37317                                      CHEMBL4355998        nM    5200.00\n",
              "37332  CHEMBL3386731, CHEMBL3386732, CHEMBL1018093, C...        nM   25300.00\n",
              "37400        CHEMBL3610409, CHEMBL3610410, CHEMBL3610411        nM   36000.00\n",
              "37415         CHEMBL798996, CHEMBL1909126, CHEMBL1909126        nM   14600.00\n",
              "37437        CHEMBL3090996, CHEMBL3090995, CHEMBL4270383        nM  100000.00\n",
              "\n",
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              "    <tr>\n",
              "      <th>37317</th>\n",
              "      <td>CHEMBL4355998</td>\n",
              "      <td>nM</td>\n",
              "      <td>5200.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37332</th>\n",
              "      <td>CHEMBL3386731, CHEMBL3386732, CHEMBL1018093, C...</td>\n",
              "      <td>nM</td>\n",
              "      <td>25300.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37400</th>\n",
              "      <td>CHEMBL3610409, CHEMBL3610410, CHEMBL3610411</td>\n",
              "      <td>nM</td>\n",
              "      <td>36000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37415</th>\n",
              "      <td>CHEMBL798996, CHEMBL1909126, CHEMBL1909126</td>\n",
              "      <td>nM</td>\n",
              "      <td>14600.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37437</th>\n",
              "      <td>CHEMBL3090996, CHEMBL3090995, CHEMBL4270383</td>\n",
              "      <td>nM</td>\n",
              "      <td>100000.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4214 rows × 3 columns</p>\n",
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              "\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_1')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_1",
              "summary": "{\n  \"name\": \"df_1\",\n  \"rows\": 4214,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4214,\n        \"samples\": [\n          \"CHEMBL1057308, CHEMBL1057307\",\n          \"CHEMBL3882102, CHEMBL2065354, CHEMBL2065355, CHEMBL2065356, CHEMBL2065357\",\n          \"CHEMBL763923, CHEMBL763923, CHEMBL858358\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_Units\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"nM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_IC50\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62926280110.047134,\n        \"min\": 0.0,\n        \"max\": 4073802778041.12,\n        \"num_unique_values\": 1342,\n        \"samples\": [\n          157.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 193
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para calcular el pIC50 en unidades de nM, el orden de magnitud es de -9 en la fórmula, quedando:"
      ],
      "metadata": {
        "id": "vwr1ijMYuZqE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def convert_ic50_to_pic50(IC50_value):\n",
        "    if IC50_value <= 0:\n",
        "        return 0\n",
        "    else:\n",
        "        pIC50_value = 9 - math.log10(IC50_value)\n",
        "        return pIC50_value"
      ],
      "metadata": {
        "id": "99zhUyoBRbr8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_1[\"pIC50\"] = df_1.apply(lambda x: convert_ic50_to_pic50(x.New_IC50), axis=1)\n",
        "# We will sort the dataframe considering pIC50\n",
        "df_1.sort_values(by=\"pIC50\", ascending=False, inplace=True)\n",
        "# Reset index\n",
        "df_1.reset_index(drop=True, inplace=True)\n",
        "df_1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ziNZhZ5j0A27",
        "outputId": "7945fbd0-0c9e-4196-d10e-dfb708716034"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  ID New_Units  New_IC50  \\\n",
              "0  CHEMBL869622, CHEMBL926963, CHEMBL926968, CHEM...        nM  0.000015   \n",
              "1  CHEMBL5128345, CHEMBL5128346, CHEMBL5128347, C...        nM  0.000700   \n",
              "2  CHEMBL3888971, CHEMBL3888971, CHEMBL4672423, C...        nM  0.000790   \n",
              "3  CHEMBL909835, CHEMBL909836, CHEMBL909837, CHEM...        nM  0.000800   \n",
              "4                                      CHEMBL1959806        nM  0.001800   \n",
              "\n",
              "       pIC50  \n",
              "0  10.823909  \n",
              "1   9.154902  \n",
              "2   9.102373  \n",
              "3   9.096910  \n",
              "4   8.744727  "
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "        vertical-align: top;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>New_Units</th>\n",
              "      <th>New_IC50</th>\n",
              "      <th>pIC50</th>\n",
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              "      <th>0</th>\n",
              "      <td>CHEMBL869622, CHEMBL926963, CHEMBL926968, CHEM...</td>\n",
              "      <td>nM</td>\n",
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              "      <td>10.823909</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>CHEMBL5128345, CHEMBL5128346, CHEMBL5128347, C...</td>\n",
              "      <td>nM</td>\n",
              "      <td>0.000700</td>\n",
              "      <td>9.154902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL3888971, CHEMBL3888971, CHEMBL4672423, C...</td>\n",
              "      <td>nM</td>\n",
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              "      <th>3</th>\n",
              "      <td>CHEMBL909835, CHEMBL909836, CHEMBL909837, CHEM...</td>\n",
              "      <td>nM</td>\n",
              "      <td>0.000800</td>\n",
              "      <td>9.096910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL1959806</td>\n",
              "      <td>nM</td>\n",
              "      <td>0.001800</td>\n",
              "      <td>8.744727</td>\n",
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              "    }\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-a838c85d-c7ea-40d5-87aa-a5df4b08e669 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-a838c85d-c7ea-40d5-87aa-a5df4b08e669');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-dea69a59-6b35-43e8-a051-b0e4028dcfe1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dea69a59-6b35-43e8-a051-b0e4028dcfe1')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-dea69a59-6b35-43e8-a051-b0e4028dcfe1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_1",
              "summary": "{\n  \"name\": \"df_1\",\n  \"rows\": 4214,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4214,\n        \"samples\": [\n          \"CHEMBL763373\",\n          \"CHEMBL4705571, CHEMBL4705573\",\n          \"CHEMBL873425, CHEMBL771133\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_Units\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"nM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_IC50\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62926280110.047134,\n        \"min\": 0.0,\n        \"max\": 4073802778041.12,\n        \"num_unique_values\": 1342,\n        \"samples\": [\n          5158.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pIC50\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8723992412648869,\n        \"min\": -6.609999999999999,\n        \"max\": 10.823908740944319,\n        \"num_unique_values\": 1341,\n        \"samples\": [\n          2.287518662198081\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ahora aplicamos los mismos pasos para el DataFrame 2"
      ],
      "metadata": {
        "id": "v6Q0fz_tu3IR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Exploramos el DataFrame 2\n",
        "df_2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "6C-eNloWzmz8",
        "outputId": "da9ddb68-a18c-4bb7-9608-30b82e1c196d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                     ID New_Units New_IC50\n",
              "18                         CHEMBL1826381, CHEMBL1826380        uM    12.24\n",
              "32                                         CHEMBL803376        uM        1\n",
              "94             CHEMBL803376, CHEMBL804016, CHEMBL797513        uM   0.4376\n",
              "1636  CHEMBL803376, CHEMBL802583, CHEMBL798029, CHEM...        uM     15.3\n",
              "1950                                       CHEMBL761816        uM       23"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-91c21a4a-f577-42d5-aa54-bca61e3fbffc\" 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",
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              "        vertical-align: top;\n",
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              "\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>ID</th>\n",
              "      <th>New_Units</th>\n",
              "      <th>New_IC50</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>CHEMBL1826381, CHEMBL1826380</td>\n",
              "      <td>uM</td>\n",
              "      <td>12.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>CHEMBL803376</td>\n",
              "      <td>uM</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>CHEMBL803376, CHEMBL804016, CHEMBL797513</td>\n",
              "      <td>uM</td>\n",
              "      <td>0.4376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1636</th>\n",
              "      <td>CHEMBL803376, CHEMBL802583, CHEMBL798029, CHEM...</td>\n",
              "      <td>uM</td>\n",
              "      <td>15.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1950</th>\n",
              "      <td>CHEMBL761816</td>\n",
              "      <td>uM</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-91c21a4a-f577-42d5-aa54-bca61e3fbffc')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-91c21a4a-f577-42d5-aa54-bca61e3fbffc 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-91c21a4a-f577-42d5-aa54-bca61e3fbffc');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c545c45b-7dc2-4c9d-ab45-40a93ad0f111')\"\n",
              "            title=\"Suggest charts\"\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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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c545c45b-7dc2-4c9d-ab45-40a93ad0f111 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_2",
              "summary": "{\n  \"name\": \"df_2\",\n  \"rows\": 92,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 92,\n        \"samples\": [\n          \"CHEMBL689257, CHEMBL689258\",\n          \"CHEMBL803376, CHEMBL797524\",\n          \"CHEMBL988671, CHEMBL988672, CHEMBL988673, CHEMBL988680, CHEMBL988681, CHEMBL988682, CHEMBL988683, CHEMBL988684, CHEMBL988685, CHEMBL988671, CHEMBL988672, CHEMBL988673, CHEMBL988679, CHEMBL988679, CHEMBL988680, CHEMBL988681, CHEMBL988682, CHEMBL988683, CHEMBL988684, CHEMBL988685, CHEMBL988671, CHEMBL988672, CHEMBL988673, CHEMBL988671, CHEMBL988672, CHEMBL988673, CHEMBL988671, CHEMBL988672, CHEMBL988673, CHEMBL3791516\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_Units\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"uM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_IC50\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 80,\n        \"samples\": [\n          \"0.029\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 195
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_2 = df_2.copy()                                    # Creaar una copia del DataFrame\n",
        "df_2['New_IC50'] = df_2['New_IC50'].astype(float)\n",
        "df_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "rw-PRa31hYL7",
        "outputId": "91ed6005-d915-4e59-c924-b659005b2a76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                      ID New_Units  New_IC50\n",
              "18                          CHEMBL1826381, CHEMBL1826380        uM   12.2400\n",
              "32                                          CHEMBL803376        uM    1.0000\n",
              "94              CHEMBL803376, CHEMBL804016, CHEMBL797513        uM    0.4376\n",
              "1636   CHEMBL803376, CHEMBL802583, CHEMBL798029, CHEM...        uM   15.3000\n",
              "1950                                        CHEMBL761816        uM   23.0000\n",
              "...                                                  ...       ...       ...\n",
              "37035                       CHEMBL3054358, CHEMBL3066005        uM    0.2300\n",
              "37216  CHEMBL803376, CHEMBL797524, CHEMBL798329, CHEM...        uM    1.3000\n",
              "37238  CHEMBL803376, CHEMBL797524, CHEMBL797514, CHEM...        uM    0.0500\n",
              "37249          CHEMBL803376, CHEMBL797524, CHEMBL1679574        uM    0.2000\n",
              "37299  CHEMBL984668, CHEMBL984669, CHEMBL984671, CHEM...        uM   10.0000\n",
              "\n",
              "[92 rows x 3 columns]"
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              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>New_Units</th>\n",
              "      <th>New_IC50</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>CHEMBL1826381, CHEMBL1826380</td>\n",
              "      <td>uM</td>\n",
              "      <td>12.2400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>CHEMBL803376</td>\n",
              "      <td>uM</td>\n",
              "      <td>1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>CHEMBL803376, CHEMBL804016, CHEMBL797513</td>\n",
              "      <td>uM</td>\n",
              "      <td>0.4376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1636</th>\n",
              "      <td>CHEMBL803376, CHEMBL802583, CHEMBL798029, CHEM...</td>\n",
              "      <td>uM</td>\n",
              "      <td>15.3000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1950</th>\n",
              "      <td>CHEMBL761816</td>\n",
              "      <td>uM</td>\n",
              "      <td>23.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>37035</th>\n",
              "      <td>CHEMBL3054358, CHEMBL3066005</td>\n",
              "      <td>uM</td>\n",
              "      <td>0.2300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37216</th>\n",
              "      <td>CHEMBL803376, CHEMBL797524, CHEMBL798329, CHEM...</td>\n",
              "      <td>uM</td>\n",
              "      <td>1.3000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37238</th>\n",
              "      <td>CHEMBL803376, CHEMBL797524, CHEMBL797514, CHEM...</td>\n",
              "      <td>uM</td>\n",
              "      <td>0.0500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37249</th>\n",
              "      <td>CHEMBL803376, CHEMBL797524, CHEMBL1679574</td>\n",
              "      <td>uM</td>\n",
              "      <td>0.2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37299</th>\n",
              "      <td>CHEMBL984668, CHEMBL984669, CHEMBL984671, CHEM...</td>\n",
              "      <td>uM</td>\n",
              "      <td>10.0000</td>\n",
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              "  </tbody>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_2",
              "summary": "{\n  \"name\": \"df_2\",\n  \"rows\": 92,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 92,\n        \"samples\": [\n          \"CHEMBL689257, CHEMBL689258\",\n          \"CHEMBL803376, CHEMBL797524\",\n          \"CHEMBL988671, CHEMBL988672, CHEMBL988673, CHEMBL988680, CHEMBL988681, CHEMBL988682, CHEMBL988683, CHEMBL988684, CHEMBL988685, CHEMBL988671, CHEMBL988672, CHEMBL988673, CHEMBL988679, CHEMBL988679, CHEMBL988680, CHEMBL988681, CHEMBL988682, CHEMBL988683, CHEMBL988684, CHEMBL988685, CHEMBL988671, CHEMBL988672, CHEMBL988673, CHEMBL988671, CHEMBL988672, CHEMBL988673, CHEMBL988671, CHEMBL988672, CHEMBL988673, CHEMBL3791516\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_Units\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"uM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_IC50\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 88.74937499711783,\n        \"min\": 0.0,\n        \"max\": 746.0,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          0.029\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 196
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para calcular el pIC50 en unidades de uM, el orden de magnitud es de -6 en la fórmula, quedando:"
      ],
      "metadata": {
        "id": "6Vhn3M5mvLoe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def convert_ic50_to_pic50(IC50_value):\n",
        "    if IC50_value <= 0:\n",
        "        return 0\n",
        "    else:\n",
        "        pIC50_value = 6 - math.log10(IC50_value)\n",
        "        return pIC50_value"
      ],
      "metadata": {
        "id": "ZKyBVJD0z5HL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_2[\"pIC50\"] = df_2.apply(lambda x: convert_ic50_to_pic50(x.New_IC50), axis=1)\n",
        "# We will sort the dataframe considering pIC50\n",
        "df_2.sort_values(by=\"pIC50\", ascending=False, inplace=True)\n",
        "# Reset index\n",
        "df_2.reset_index(drop=True, inplace=True)\n",
        "df_2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "t9DQB4oU0C2j",
        "outputId": "605eca77-7e1a-4842-f5af-aab93544f917"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  ID New_Units  New_IC50  \\\n",
              "0  CHEMBL3292851, CHEMBL3292852, CHEMBL3292853, C...        uM    0.0003   \n",
              "1  CHEMBL3292851, CHEMBL3292852, CHEMBL3292853, C...        uM    0.0020   \n",
              "2           CHEMBL803376, CHEMBL804264, CHEMBL804270        uM    0.0027   \n",
              "3          CHEMBL803376, CHEMBL804016, CHEMBL3061802        uM    0.0030   \n",
              "4  CHEMBL862632, CHEMBL864453, CHEMBL864454, CHEM...        uM    0.0040   \n",
              "\n",
              "      pIC50  \n",
              "0  9.522879  \n",
              "1  8.698970  \n",
              "2  8.568636  \n",
              "3  8.522879  \n",
              "4  8.397940  "
            ],
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              "      <th>ID</th>\n",
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              "      <th>New_IC50</th>\n",
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              "      <th>0</th>\n",
              "      <td>CHEMBL3292851, CHEMBL3292852, CHEMBL3292853, C...</td>\n",
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              "      <td>0.0003</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL3292851, CHEMBL3292852, CHEMBL3292853, C...</td>\n",
              "      <td>uM</td>\n",
              "      <td>0.0020</td>\n",
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              "      <th>2</th>\n",
              "      <td>CHEMBL803376, CHEMBL804264, CHEMBL804270</td>\n",
              "      <td>uM</td>\n",
              "      <td>0.0027</td>\n",
              "      <td>8.568636</td>\n",
              "    </tr>\n",
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              "      <th>3</th>\n",
              "      <td>CHEMBL803376, CHEMBL804016, CHEMBL3061802</td>\n",
              "      <td>uM</td>\n",
              "      <td>0.0030</td>\n",
              "      <td>8.522879</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL862632, CHEMBL864453, CHEMBL864454, CHEM...</td>\n",
              "      <td>uM</td>\n",
              "      <td>0.0040</td>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_2",
              "summary": "{\n  \"name\": \"df_2\",\n  \"rows\": 92,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 92,\n        \"samples\": [\n          \"CHEMBL903134, CHEMBL903136, CHEMBL988679, CHEMBL988679, CHEMBL988671, CHEMBL988672, CHEMBL988673\",\n          \"CHEMBL1826381, CHEMBL1826380, CHEMBL1826385\",\n          \"CHEMBL803376, CHEMBL797524\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_Units\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"uM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_IC50\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 88.74937499711783,\n        \"min\": 0.0,\n        \"max\": 746.0,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          0.17\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pIC50\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5982017057533036,\n        \"min\": 0.0,\n        \"max\": 9.522878745280337,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          6.769551078621726\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 198
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Concatenamos ambos DataFrames 1 y 2 para obtener un único DataFrame con todos los valores calculados de pIC50:"
      ],
      "metadata": {
        "id": "O4kvceMBvXJO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Unir DataFrames\n",
        "df_all = pd.concat([df_1, df_2])\n",
        "df_all.head()"
      ],
      "metadata": {
        "id": "RlYwp2JuYlK0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "ae8c880a-2cb6-4ec8-a1b4-53d17e218086"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                                                  ID New_Units  New_IC50  \\\n",
              "0  CHEMBL869622, CHEMBL926963, CHEMBL926968, CHEM...        nM  0.000015   \n",
              "1  CHEMBL5128345, CHEMBL5128346, CHEMBL5128347, C...        nM  0.000700   \n",
              "2  CHEMBL3888971, CHEMBL3888971, CHEMBL4672423, C...        nM  0.000790   \n",
              "3  CHEMBL909835, CHEMBL909836, CHEMBL909837, CHEM...        nM  0.000800   \n",
              "4                                      CHEMBL1959806        nM  0.001800   \n",
              "\n",
              "       pIC50  \n",
              "0  10.823909  \n",
              "1   9.154902  \n",
              "2   9.102373  \n",
              "3   9.096910  \n",
              "4   8.744727  "
            ],
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>New_Units</th>\n",
              "      <th>New_IC50</th>\n",
              "      <th>pIC50</th>\n",
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              "      <th>0</th>\n",
              "      <td>CHEMBL869622, CHEMBL926963, CHEMBL926968, CHEM...</td>\n",
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              "      <td>0.000015</td>\n",
              "      <td>10.823909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL5128345, CHEMBL5128346, CHEMBL5128347, C...</td>\n",
              "      <td>nM</td>\n",
              "      <td>0.000700</td>\n",
              "      <td>9.154902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL3888971, CHEMBL3888971, CHEMBL4672423, C...</td>\n",
              "      <td>nM</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL909835, CHEMBL909836, CHEMBL909837, CHEM...</td>\n",
              "      <td>nM</td>\n",
              "      <td>0.000800</td>\n",
              "      <td>9.096910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL1959806</td>\n",
              "      <td>nM</td>\n",
              "      <td>0.001800</td>\n",
              "      <td>8.744727</td>\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0042ad8c-57eb-4207-bb6a-a874d87173aa');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-db6ce525-19ee-40c5-98f0-9a8c76fed32a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-db6ce525-19ee-40c5-98f0-9a8c76fed32a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-db6ce525-19ee-40c5-98f0-9a8c76fed32a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_all",
              "summary": "{\n  \"name\": \"df_all\",\n  \"rows\": 4306,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4306,\n        \"samples\": [\n          \"CHEMBL4618925, CHEMBL4618931\",\n          \"CHEMBL803376, CHEMBL800747, CHEMBL804264, CHEMBL799138, CHEMBL804270\",\n          \"CHEMBL1821372\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_Units\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"uM\",\n          \"nM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_IC50\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62247745150.919945,\n        \"min\": 0.0,\n        \"max\": 4073802778041.12,\n        \"num_unique_values\": 1371,\n        \"samples\": [\n          153.0,\n          760.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pIC50\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9175389782600696,\n        \"min\": -6.609999999999999,\n        \"max\": 10.823908740944319,\n        \"num_unique_values\": 1370,\n        \"samples\": [\n          3.826813731587726,\n          1.3344096290729333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Exploramos nuestro DataFrame final\n",
        "print(df_all)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xvIPZmFt4Blr",
        "outputId": "e45f484a-2c7e-480c-c761-cf45c5e02048"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                   ID New_Units    New_IC50  \\\n",
            "0   CHEMBL869622, CHEMBL926963, CHEMBL926968, CHEM...        nM    0.000015   \n",
            "1   CHEMBL5128345, CHEMBL5128346, CHEMBL5128347, C...        nM    0.000700   \n",
            "2   CHEMBL3888971, CHEMBL3888971, CHEMBL4672423, C...        nM    0.000790   \n",
            "3   CHEMBL909835, CHEMBL909836, CHEMBL909837, CHEM...        nM    0.000800   \n",
            "4                                       CHEMBL1959806        nM    0.001800   \n",
            "..                                                ...       ...         ...   \n",
            "87                         CHEMBL701795, CHEMBL701796        uM  200.000000   \n",
            "88                                      CHEMBL4398309        uM  213.000000   \n",
            "89                       CHEMBL4398309, CHEMBL5098516        uM  257.400000   \n",
            "90  CHEMBL926892, CHEMBL926891, CHEMBL926890, CHEM...        uM  746.000000   \n",
            "91                         CHEMBL803376, CHEMBL804016        uM    0.000000   \n",
            "\n",
            "        pIC50  \n",
            "0   10.823909  \n",
            "1    9.154902  \n",
            "2    9.102373  \n",
            "3    9.096910  \n",
            "4    8.744727  \n",
            "..        ...  \n",
            "87   3.698970  \n",
            "88   3.671620  \n",
            "89   3.589391  \n",
            "90   3.127261  \n",
            "91   0.000000  \n",
            "\n",
            "[4306 rows x 4 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Guardar archivo en formato csv\n",
        "df_all.to_csv('pIC50_CHEMBL.csv', index=False)"
      ],
      "metadata": {
        "id": "Bqpu7yUy4F3b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Análisis de resultados**"
      ],
      "metadata": {
        "id": "3-gQwsUs5kKH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Una vez calculados los valores de pIC50, podemos hacer un análisis rápido de los resultados."
      ],
      "metadata": {
        "id": "7a1pRRbZwA1U"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Otra de las ventajas de Pandas es que nos permite realizar cálculos de estadística básica. Con la siguiente función obtenemos la estadística de nuestros datos:"
      ],
      "metadata": {
        "id": "xa8T285bw5Ai"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_all.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "8DbyvdAy-XG3",
        "outputId": "ce9e541c-a709-4bf4-f8db-2ac6b16a10ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           New_IC50        pIC50\n",
              "count  4.289000e+03  4289.000000\n",
              "mean   9.938528e+08     3.224717\n",
              "std    6.224775e+10     1.917539\n",
              "min    0.000000e+00    -6.610000\n",
              "25%    1.700000e+01     1.787812\n",
              "50%    5.400000e+02     3.086504\n",
              "75%    1.030000e+04     4.602060\n",
              "max    4.073803e+12    10.823909"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e5e18f34-a33c-44b1-99cc-82f878ab16bc\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>New_IC50</th>\n",
              "      <th>pIC50</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4.289000e+03</td>\n",
              "      <td>4289.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>9.938528e+08</td>\n",
              "      <td>3.224717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6.224775e+10</td>\n",
              "      <td>1.917539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>-6.610000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.700000e+01</td>\n",
              "      <td>1.787812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.400000e+02</td>\n",
              "      <td>3.086504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.030000e+04</td>\n",
              "      <td>4.602060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.073803e+12</td>\n",
              "      <td>10.823909</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e5e18f34-a33c-44b1-99cc-82f878ab16bc')\"\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",
              "      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",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e5e18f34-a33c-44b1-99cc-82f878ab16bc 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-e5e18f34-a33c-44b1-99cc-82f878ab16bc');\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",
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              "        element.appendChild(docLink);\n",
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              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    height: 32px;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fed8280e-9235-4336-b00a-139ddddfb6ae button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_all\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"New_IC50\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1437276830233.1204,\n        \"min\": 0.0,\n        \"max\": 4073802778041.12,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          993852838.4642606,\n          540.0,\n          4289.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pIC50\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1515.4467599546379,\n        \"min\": -6.609999999999999,\n        \"max\": 4289.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.224717442074289,\n          3.0865040403828763,\n          4289.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 201
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "También podemos obtener información acerca del tipo de datos con el que estamos trabajando en nuestro DataFrame:"
      ],
      "metadata": {
        "id": "uc6W-mcexOzk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_all.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N67qmZnI-ZzV",
        "outputId": "2b74c7aa-8607-4db5-fb74-d094157dfc3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 4306 entries, 0 to 91\n",
            "Data columns (total 4 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   ID         4306 non-null   object \n",
            " 1   New_Units  4306 non-null   object \n",
            " 2   New_IC50   4289 non-null   float64\n",
            " 3   pIC50      4289 non-null   float64\n",
            "dtypes: float64(2), object(2)\n",
            "memory usage: 168.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Análisis del pIC50**"
      ],
      "metadata": {
        "id": "lqDKoksd-ghN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para poder determinar si un compuesto es \"Activo\" o \"Inactivo\" podemos agregar una nueva columna al DataFrame que nos indique respecto al pIC50 si el compuesto lo es o no, bajo la condición de que: \"Un compuesto será activo si su pIC50 es mayor o igual a 5\". El resto de los compuestos que no cumplan tal condición se etiquetan como inactivos."
      ],
      "metadata": {
        "id": "-vjPMvnPxnz3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Label compounds with pIC50 >= 5 as \"active\":\n",
        "\n"
      ],
      "metadata": {
        "id": "2pTK_uVO-79L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Nombrar los compuestos con pIC50 mayor o igual a 5 como \"Active\"\n",
        "df_all[\"Activity_Label\"] = np.where(df_all['pIC50']>=5, 'Active', 'Inactive')\n",
        "df_all"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "egMVko1X-eU_",
        "outputId": "8be2b16b-44d5-4ceb-b568-0200df492356"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   ID New_Units    New_IC50  \\\n",
              "0   CHEMBL869622, CHEMBL926963, CHEMBL926968, CHEM...        nM    0.000015   \n",
              "1   CHEMBL5128345, CHEMBL5128346, CHEMBL5128347, C...        nM    0.000700   \n",
              "2   CHEMBL3888971, CHEMBL3888971, CHEMBL4672423, C...        nM    0.000790   \n",
              "3   CHEMBL909835, CHEMBL909836, CHEMBL909837, CHEM...        nM    0.000800   \n",
              "4                                       CHEMBL1959806        nM    0.001800   \n",
              "..                                                ...       ...         ...   \n",
              "87                         CHEMBL701795, CHEMBL701796        uM  200.000000   \n",
              "88                                      CHEMBL4398309        uM  213.000000   \n",
              "89                       CHEMBL4398309, CHEMBL5098516        uM  257.400000   \n",
              "90  CHEMBL926892, CHEMBL926891, CHEMBL926890, CHEM...        uM  746.000000   \n",
              "91                         CHEMBL803376, CHEMBL804016        uM    0.000000   \n",
              "\n",
              "        pIC50 Activity_Label  \n",
              "0   10.823909         Active  \n",
              "1    9.154902         Active  \n",
              "2    9.102373         Active  \n",
              "3    9.096910         Active  \n",
              "4    8.744727         Active  \n",
              "..        ...            ...  \n",
              "87   3.698970       Inactive  \n",
              "88   3.671620       Inactive  \n",
              "89   3.589391       Inactive  \n",
              "90   3.127261       Inactive  \n",
              "91   0.000000       Inactive  \n",
              "\n",
              "[4306 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d47e316f-4a84-4b5f-a271-cb4044fa8eb7\" 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",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>New_Units</th>\n",
              "      <th>New_IC50</th>\n",
              "      <th>pIC50</th>\n",
              "      <th>Activity_Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL869622, CHEMBL926963, CHEMBL926968, CHEM...</td>\n",
              "      <td>nM</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>10.823909</td>\n",
              "      <td>Active</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL5128345, CHEMBL5128346, CHEMBL5128347, C...</td>\n",
              "      <td>nM</td>\n",
              "      <td>0.000700</td>\n",
              "      <td>9.154902</td>\n",
              "      <td>Active</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL3888971, CHEMBL3888971, CHEMBL4672423, C...</td>\n",
              "      <td>nM</td>\n",
              "      <td>0.000790</td>\n",
              "      <td>9.102373</td>\n",
              "      <td>Active</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL909835, CHEMBL909836, CHEMBL909837, CHEM...</td>\n",
              "      <td>nM</td>\n",
              "      <td>0.000800</td>\n",
              "      <td>9.096910</td>\n",
              "      <td>Active</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL1959806</td>\n",
              "      <td>nM</td>\n",
              "      <td>0.001800</td>\n",
              "      <td>8.744727</td>\n",
              "      <td>Active</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>CHEMBL701795, CHEMBL701796</td>\n",
              "      <td>uM</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>3.698970</td>\n",
              "      <td>Inactive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>CHEMBL4398309</td>\n",
              "      <td>uM</td>\n",
              "      <td>213.000000</td>\n",
              "      <td>3.671620</td>\n",
              "      <td>Inactive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>CHEMBL4398309, CHEMBL5098516</td>\n",
              "      <td>uM</td>\n",
              "      <td>257.400000</td>\n",
              "      <td>3.589391</td>\n",
              "      <td>Inactive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>CHEMBL926892, CHEMBL926891, CHEMBL926890, CHEM...</td>\n",
              "      <td>uM</td>\n",
              "      <td>746.000000</td>\n",
              "      <td>3.127261</td>\n",
              "      <td>Inactive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>CHEMBL803376, CHEMBL804016</td>\n",
              "      <td>uM</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Inactive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4306 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d47e316f-4a84-4b5f-a271-cb4044fa8eb7')\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-d47e316f-4a84-4b5f-a271-cb4044fa8eb7 button.colab-df-convert');\n",
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              "        const element = document.querySelector('#df-d47e316f-4a84-4b5f-a271-cb4044fa8eb7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
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              "    80% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "\n",
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              "type": "dataframe",
              "variable_name": "df_all",
              "summary": "{\n  \"name\": \"df_all\",\n  \"rows\": 4306,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4306,\n        \"samples\": [\n          \"CHEMBL4618925, CHEMBL4618931\",\n          \"CHEMBL803376, CHEMBL800747, CHEMBL804264, CHEMBL799138, CHEMBL804270\",\n          \"CHEMBL1821372\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_Units\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"uM\",\n          \"nM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_IC50\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62247745150.919945,\n        \"min\": 0.0,\n        \"max\": 4073802778041.12,\n        \"num_unique_values\": 1371,\n        \"samples\": [\n          153.0,\n          760.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pIC50\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9175389782600696,\n        \"min\": -6.609999999999999,\n        \"max\": 10.823908740944319,\n        \"num_unique_values\": 1370,\n        \"samples\": [\n          3.826813731587726,\n          1.3344096290729333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Activity_Label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Inactive\",\n          \"Active\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 203
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podemos realizar ahora la estadística por datos agrupados en la clasificación \"Activo/Inactivo\". Para agrupar datos se utiliza la función `.groupby` y aplicamos posteriormente la función `.describe`:"
      ],
      "metadata": {
        "id": "fxL1QuWiyqVQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Group data by activity label:"
      ],
      "metadata": {
        "id": "KAHRGPyM-3my"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Agrupar datos según la etiqueta de actividad \"Activity_Label\" que implica si el compuesto es \"Activo/Inactivo\"\n",
        "group= df_all.groupby('Activity_Label').describe()['pIC50']\n",
        "group"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "YSg3NSF9-ozD",
        "outputId": "dd479cce-6b6e-4074-c23b-21f5846b3fc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 count      mean       std   min      25%       50%       75%  \\\n",
              "Activity_Label                                                                  \n",
              "Active           827.0  6.033136  0.947035  5.00  5.30103  5.698970  6.560739   \n",
              "Inactive        3462.0  2.553844  1.416571 -6.61  1.49519  2.582535  3.698970   \n",
              "\n",
              "                      max  \n",
              "Activity_Label             \n",
              "Active          10.823909  \n",
              "Inactive         4.986741  "
            ],
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              "type": "dataframe",
              "variable_name": "group",
              "summary": "{\n  \"name\": \"group\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Activity_Label\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Inactive\",\n          \"Active\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1863.2263684265527,\n        \"min\": 827.0,\n        \"max\": 3462.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3462.0,\n          827.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4602307365639664,\n        \"min\": 2.5538444612916913,\n        \"max\": 6.033136135507601,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2.5538444612916913,\n          6.033136135507601\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3320122917910239,\n        \"min\": 0.947034668135461,\n        \"max\": 1.4165709540609004,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.4165709540609004,\n          0.947034668135461\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.209509729575815,\n        \"min\": -6.609999999999999,\n        \"max\": 5.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          -6.609999999999999,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.691135378295967,\n        \"min\": 1.495189845495775,\n        \"max\": 5.301029995663981,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.495189845495775,\n          5.301029995663981\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.203652005243699,\n        \"min\": 2.5825354517697123,\n        \"max\": 5.698970004336019,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2.5825354517697123,\n          5.698970004336019\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0235763353246585,\n        \"min\": 3.6989700043360187,\n        \"max\": 6.560739102249396,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3.6989700043360187,\n          6.560739102249396\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.127500655864793,\n        \"min\": 4.986741334716483,\n        \"max\": 10.823908740944319,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4.986741334716483,\n          10.823908740944319\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 204
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Otra de las ventajas de Pandas es que viene integrado con Matplotlib, una librería muy conocida para hacer gráficas. Por lo que se puede realizar de forma muy cómoda y sencilla cualquier gráfico directamente a partir de un DataFrame o Series. De esta forma podemos tener una visualización de los datos más efectiva. Se pueden realizar distintos tipos de gráficos, circulares, de caja y bigote, etc. Sin embargo para el ejemplo usaremos un gráfico de distribución, en donde el eje X corresponde a los valores de pIC50 y el eje Y corresponde al número de compuestos existentes en la categoría \"Active\" y en la categoría \"Inactive\" según su distribución."
      ],
      "metadata": {
        "id": "8tZfH_LlYWeq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Distribution graphs:"
      ],
      "metadata": {
        "id": "Vf0jEQij_CeL"
      }
    },
    {
      "source": [
        "# @title pIC50\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "df_all['pIC50'].plot(kind='hist', bins=20, title='pIC50')\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ],
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "rdRkjTnhjcQC",
        "outputId": "cdf4a264-e817-44e0-aa93-d0a94092f4e3"
      }
    },
    {
      "source": [
        "# @title Activity_Label\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "df_all.groupby('Activity_Label').size().plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ],
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "4rBVYtiovOiY",
        "outputId": "02d4e032-0ab7-4685-c6b1-fb6785c959a2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conclusión\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "uKw1yDhgLxhi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "La preparación y el tratamiento inicial de los datos son pasos esenciales para cualquier proyecto de análisis de datos o de aprendizaje automático. Pandas nos permite mediante su amplia gama de funciones simplificar estas tareas eficientemente.\n",
        "\n",
        "Es primordial garantizar que nuestro conjunto de datos esté adecuadamente acondicionado para el análisis y la modelización, lo cual, se traducirá en resultados más precisos y confiables."
      ],
      "metadata": {
        "id": "8O14FEs_L2R1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "# Para saber más:\n",
        "\n",
        "* How to Use Pandas for Data Cleaning and Preprocessing. https://www.freecodecamp.org/news/data-cleaning-and-preprocessing-with-pandasbdvhj/ Recuperado: Junio 2024.\n",
        "* Zdrazil B, Felix E, Hunter F, Manners EJ, Blackshaw J, Corbett S, de Veij M, Ioannidis H, Mendez-Lopez D, Mosquera JF, Magarinos MP, Bosc N, Arcila R, Kizilören T, Gaulton A, Bento AP, Adasme MF, Monecke P, Landrum GA, Leach AR (2023) The ChEMBL [Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods.](https://academic.oup.com/nar/article/52/D1/D1180/7337608?login=true) Nucleic Acids Research. doi: 10.1093/nar/gkad1004\n"
      ],
      "metadata": {
        "id": "9jhIOl6U8HUr"
      }
    }
  ]
}