1-Python.ipynb

{"cells":[{"cell_type":"markdown","id":"e4f07d77","metadata":{"id":"e4f07d77"},"source":["## Library Step"]},{"cell_type":"code","source":["# @title Import libraries\n","\n","## Autenticación en GCP-BQ\n","from google.colab import auth\n","from google.cloud import bigquery\n","\n","## Manejo de data y estadísticos\n","import pandas as pd\n","import numpy as np\n","\n","## Graficación y carga de datasets populares\n","import seaborn as sns"],"metadata":{"id":"IEVO26zjdqBF","executionInfo":{"status":"ok","timestamp":1696809298981,"user_tz":180,"elapsed":2442,"user":{"displayName":"Mariano Gobea Alcoba","userId":"16590319507306720082"}}},"id":"IEVO26zjdqBF","execution_count":1,"outputs":[]},{"cell_type":"markdown","id":"7db1a914","metadata":{"id":"7db1a914"},"source":["## Extract Step"]},{"cell_type":"code","source":["# @title Set project, location, client y auth\n","project = 'meli-bi-data' # Project ID inserted based on the query results selected to explore\n","location = 'US' # Location inserted based on the query results selected to explore\n","client = bigquery.Client(project=project, location=location)\n","\n","# Nos va a solicitar que autorizemos explicitamente el acceso con nuestras credenciales nominales:\n","auth.authenticate_user()"],"metadata":{"id":"QKfsXz9aomfc"},"id":"QKfsXz9aomfc","execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"id":"b0c92c29","metadata":{"id":"b0c92c29","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1696606896540,"user_tz":180,"elapsed":541,"user":{"displayName":"Mariano Gobea Alcoba","userId":"16590319507306720082"}},"outputId":"d3065455-365a-4e66-a93b-093d371c3184"},"outputs":[{"output_type":"stream","name":"stdout","text":["-- DS sencillo para llevar a capacitación sobre Python\r\n","SELECT \r\n","  SR.SIT_SITE_ID SITE_ID,\r\n","  SR.SHP_LG_FACILITY_ID FACILITY_ID,\r\n","  CAST(DATE_TRUNC(SHP_LG_ROUTE_INIT_DATE, quarter) AS DATE) QUARTER,\r\n","  CAST(DATE_TRUNC(SR.SHP_LG_ROUTE_INIT_DATE , month) AS DATE) MONTH,\r\n","  CASE \r\n","    WHEN SR.SHP_LG_SHIPMENT_SUB_STATUS IN('delivered','delivered_place') THEN 'DELIVERED' -- SHP_LG_SHIPMENT_SUB_STATUS\r\n","    WHEN SR.SHP_LG_SHIPMENT_SUB_STATUS IN ('lost','missing') THEN 'PERDIDOS' \r\n","    ELSE 'NOT DELIVERED' END AS DS,\r\n","  COUNT(SR.SHP_SHIPMENT_ID) QTY_SHIPMENTS\r\n","FROM \r\n","  `meli-bi-data.WHOWNER.BT_SHP_LG_SHIPMENTS_ROUTES` SR\r\n","WHERE \r\n","  1=1\r\n","  AND SR.SHP_LG_ROUTE_INIT_DATE BETWEEN '2023-07-01' AND '2023-09-30' \r\n","  AND SR.SHP_LG_SHIPMENT_SUB_STATUS NOT IN ('transferred') \r\n","GROUP BY \r\n","  1,2,3,4,5\r\n","ORDER BY \r\n","  3,4,1,2 ASC\r\n",";\n"]}],"source":["# @title Print Query\n","job = client.get_job('bquxjob_72037176_18b059f0d37')\n","print(job.query)"]},{"cell_type":"markdown","source":["#### ¿Donde encuentro el job id en BQ?\n","\n","![image.png](data:image/png;base64,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)"],"metadata":{"id":"Kyt2MKc6w7sI"},"id":"Kyt2MKc6w7sI"},{"cell_type":"code","source":["# @title Transformed the BQ Job in Pandas DataFrame\n","df = job.to_dataframe()\n","df.head(10)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"id":"Qd4Mt2eipPgK","executionInfo":{"status":"ok","timestamp":1696607088446,"user_tz":180,"elapsed":2541,"user":{"displayName":"Mariano Gobea Alcoba","userId":"16590319507306720082"}},"outputId":"bf7e9129-7f40-4220-aa47-aca7ec41c4a7"},"id":"Qd4Mt2eipPgK","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["  SITE_ID FACILITY_ID     QUARTER       MONTH             DS  QTY_SHIPMENTS\n","0    None        SAM1  2023-07-01  2023-07-01  NOT DELIVERED             74\n","1    None        SAN1  2023-07-01  2023-07-01  NOT DELIVERED             22\n","2    None        SBA1  2023-07-01  2023-07-01  NOT DELIVERED           1512\n","3    None        SBA3  2023-07-01  2023-07-01  NOT DELIVERED            461\n","4    None        SBO1  2023-07-01  2023-07-01  NOT DELIVERED             41\n","5    None        SCN1  2023-07-01  2023-07-01  NOT DELIVERED             68\n","6    None        SCO1  2023-07-01  2023-07-01  NOT DELIVERED             61\n","7    None       SC_ZS  2023-07-01  2023-07-01  NOT DELIVERED           1238\n","8    None        SDF1  2023-07-01  2023-07-01  NOT DELIVERED             87\n","9    None        SES3  2023-07-01  2023-07-01  NOT DELIVERED             83"],"text/html":["\n","  <div id=\"df-2cf6af41-452c-4a2b-8e46-6edc9745b356\" 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>SITE_ID</th>\n","      <th>FACILITY_ID</th>\n","      <th>QUARTER</th>\n","      <th>MONTH</th>\n","      <th>DS</th>\n","      <th>QTY_SHIPMENTS</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>None</td>\n","      <td>SAM1</td>\n","      <td>2023-07-01</td>\n","      <td>2023-07-01</td>\n","      <td>NOT DELIVERED</td>\n","      <td>74</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>None</td>\n","      <td>SAN1</td>\n","      <td>2023-07-01</td>\n","      <td>2023-07-01</td>\n","      <td>NOT DELIVERED</td>\n","      <td>22</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>None</td>\n","      <td>SBA1</td>\n","      <td>2023-07-01</td>\n","      <td>2023-07-01</td>\n","      <td>NOT DELIVERED</td>\n","      <td>1512</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>None</td>\n","      <td>SBA3</td>\n","      <td>2023-07-01</td>\n","      <td>2023-07-01</td>\n","      <td>NOT DELIVERED</td>\n","      <td>461</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>None</td>\n","      <td>SBO1</td>\n","      <td>2023-07-01</td>\n","      <td>2023-07-01</td>\n","      <td>NOT DELIVERED</td>\n","      <td>41</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>None</td>\n","      <td>SCN1</td>\n","      <td>2023-07-01</td>\n","      <td>2023-07-01</td>\n","      <td>NOT DELIVERED</td>\n","      <td>68</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>None</td>\n","      <td>SCO1</td>\n","      <td>2023-07-01</td>\n","      <td>2023-07-01</td>\n","      <td>NOT DELIVERED</td>\n","      <td>61</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>None</td>\n","      <td>SC_ZS</td>\n","      <td>2023-07-01</td>\n","      <td>2023-07-01</td>\n","      <td>NOT DELIVERED</td>\n","      <td>1238</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>None</td>\n","      <td>SDF1</td>\n","      <td>2023-07-01</td>\n","      <td>2023-07-01</td>\n","      <td>NOT DELIVERED</td>\n","      <td>87</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>None</td>\n","      <td>SES3</td>\n","      <td>2023-07-01</td>\n","      <td>2023-07-01</td>\n","      <td>NOT DELIVERED</td>\n","      <td>83</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-2cf6af41-452c-4a2b-8e46-6edc9745b356')\"\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-2cf6af41-452c-4a2b-8e46-6edc9745b356 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-2cf6af41-452c-4a2b-8e46-6edc9745b356');\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-e7f760d8-6feb-47d3-8e65-04c2d849cb11\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e7f760d8-6feb-47d3-8e65-04c2d849cb11')\"\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-e7f760d8-6feb-47d3-8e65-04c2d849cb11 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":12}]},{"cell_type":"markdown","source":["### Dado que la anterior Query sería comprendida por Shipping, pero dificilmente por las demás BU's vamos a trabajar en esta oportunidad con un dataset muy popular en el mundo de la Ciencia de Datos que es el de los pasajeros del titanic"],"metadata":{"id":"KSIrGRBTqk-Y"},"id":"KSIrGRBTqk-Y"},{"cell_type":"markdown","id":"20a1464a","metadata":{"id":"20a1464a"},"source":["## Inspect Step"]},{"cell_type":"code","execution_count":2,"id":"e9ac51b4","metadata":{"id":"e9ac51b4","executionInfo":{"status":"ok","timestamp":1696809307639,"user_tz":180,"elapsed":788,"user":{"displayName":"Mariano Gobea Alcoba","userId":"16590319507306720082"}}},"outputs":[],"source":["# @title Cargo el dataframe del titanic:\n","df = sns.load_dataset('titanic')"]},{"cell_type":"code","execution_count":3,"id":"5ec5f691","metadata":{"id":"5ec5f691","outputId":"2b70003a-130e-4ccb-c5f1-60ce27ce4aa5","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1696809310401,"user_tz":180,"elapsed":479,"user":{"displayName":"Mariano Gobea Alcoba","userId":"16590319507306720082"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n","0         0       3    male  22.0      1      0   7.2500        S  Third   \n","1         1       1  female  38.0      1      0  71.2833        C  First   \n","2         1       3  female  26.0      0      0   7.9250        S  Third   \n","3         1       1  female  35.0      1      0  53.1000        S  First   \n","4         0       3    male  35.0      0      0   8.0500        S  Third   \n","\n","     who  adult_male deck  embark_town alive  alone  \n","0    man        True  NaN  Southampton    no  False  \n","1  woman       False    C    Cherbourg   yes  False  \n","2  woman       False  NaN  Southampton   yes   True  \n","3  woman       False    C  Southampton   yes  False  \n","4    man        True  NaN  Southampton    no   True  "],"text/html":["\n","  <div id=\"df-69870ccc-a5ff-4f68-b49f-cfb3f2721ecf\" 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>survived</th>\n","      <th>pclass</th>\n","      <th>sex</th>\n","      <th>age</th>\n","      <th>sibsp</th>\n","      <th>parch</th>\n","      <th>fare</th>\n","      <th>embarked</th>\n","      <th>class</th>\n","      <th>who</th>\n","      <th>adult_male</th>\n","      <th>deck</th>\n","      <th>embark_town</th>\n","      <th>alive</th>\n","      <th>alone</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>male</td>\n","      <td>22.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>7.2500</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>man</td>\n","      <td>True</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>no</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>female</td>\n","      <td>38.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>71.2833</td>\n","      <td>C</td>\n","      <td>First</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>C</td>\n","      <td>Cherbourg</td>\n","      <td>yes</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>female</td>\n","      <td>26.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7.9250</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>yes</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>female</td>\n","      <td>35.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>53.1000</td>\n","      <td>S</td>\n","      <td>First</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>C</td>\n","      <td>Southampton</td>\n","      <td>yes</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>male</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>8.0500</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>man</td>\n","      <td>True</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>no</td>\n","      <td>True</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-69870ccc-a5ff-4f68-b49f-cfb3f2721ecf')\"\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-69870ccc-a5ff-4f68-b49f-cfb3f2721ecf 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-69870ccc-a5ff-4f68-b49f-cfb3f2721ecf');\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-24787699-9172-4cab-a793-89d45bcfa606\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-24787699-9172-4cab-a793-89d45bcfa606')\"\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-24787699-9172-4cab-a793-89d45bcfa606 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":3}],"source":["# @title Veo las primeras 5 rows/filas\n","df.head()"]},{"cell_type":"markdown","source":["#### ¿Que información tiene cada columna del DataFrame?\n","\n","1. survived: outcome of survival (0 = No; 1 = Yes)\n","2. pclass: socio-economic class (1 = Upper class; 2 = Middle class; 3 = Lower class)\n","4. sex: sex of the passenger\n","5. age: age of the passenger (Some entries contain NaN)\n","6. sibsp: number of siblings and spouses of the passenger aboard\n","7. parch: number of parents and children of the passenger aboard\n","9. fare: fare paid by the passenger\n","10. embarked: initial letter of ship port\n","11. class: class in which the passenger traveled\n","12. who: passenger gender\n","13. adult_male: tag that verifies whether it is a male adult\n","14. deck: deck of his flagship\n","15. embark_town: city/town in which the passenger boarded\n","16. alive: boolean indicating whether it survived or not\n","17. alone: boolean indicating whether you boarded alone or not"],"metadata":{"id":"FlniMHGgx3du"},"id":"FlniMHGgx3du"},{"cell_type":"code","execution_count":null,"id":"697aa22d","metadata":{"id":"697aa22d","outputId":"92118f68-d960-4a6c-ff24-7c2060e4c647","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1696607483450,"user_tz":180,"elapsed":6,"user":{"displayName":"Mariano Gobea Alcoba","userId":"16590319507306720082"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 891 entries, 0 to 890\n","Data columns (total 15 columns):\n"," #   Column       Non-Null Count  Dtype   \n","---  ------       --------------  -----   \n"," 0   survived     891 non-null    int64   \n"," 1   pclass       891 non-null    int64   \n"," 2   sex          891 non-null    object  \n"," 3   age          714 non-null    float64 \n"," 4   sibsp        891 non-null    int64   \n"," 5   parch        891 non-null    int64   \n"," 6   fare         891 non-null    float64 \n"," 7   embarked     889 non-null    object  \n"," 8   class        891 non-null    category\n"," 9   who          891 non-null    object  \n"," 10  adult_male   891 non-null    bool    \n"," 11  deck         203 non-null    category\n"," 12  embark_town  889 non-null    object  \n"," 13  alive        891 non-null    object  \n"," 14  alone        891 non-null    bool    \n","dtypes: bool(2), category(2), float64(2), int64(4), object(5)\n","memory usage: 80.7+ KB\n"]}],"source":["# @title Imprimo informacion de tipo de datos del df y sobre la existencia de datos nulos en el dataset\n","df.info()"]},{"cell_type":"code","execution_count":null,"id":"22f6674f","metadata":{"id":"22f6674f","outputId":"db1bc495-2ef8-495a-833b-549e252ff171","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1696607572416,"user_tz":180,"elapsed":3,"user":{"displayName":"Mariano Gobea Alcoba","userId":"16590319507306720082"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["30.0"]},"metadata":{},"execution_count":20}],"source":["# @title Calculo promedio de edad de los pasajeros del Titanic\n","round(df['age'].mean(),0)"]},{"cell_type":"code","execution_count":null,"id":"7c712b2e","metadata":{"id":"7c712b2e","outputId":"3afa28bc-6324-4527-8afb-83486116d876","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1696607687604,"user_tz":180,"elapsed":298,"user":{"displayName":"Mariano Gobea Alcoba","userId":"16590319507306720082"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["28693.9493"]},"metadata":{},"execution_count":23}],"source":["# @title Calculo la suma de todas las tarifas pagadas para embarcar al titanic\n","df['fare'].sum()"]},{"cell_type":"code","execution_count":null,"id":"a67a310c","metadata":{"id":"a67a310c","outputId":"5ad86062-bc3c-468a-8aca-430a807bc688","colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"status":"ok","timestamp":1696607718666,"user_tz":180,"elapsed":6,"user":{"displayName":"Mariano Gobea Alcoba","userId":"16590319507306720082"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["         survived      pclass         age       sibsp       parch        fare\n","count  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n","mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n","std      0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n","min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n","25%      0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n","50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n","75%      1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n","max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"],"text/html":["\n","  <div id=\"df-f22e946e-9329-464c-a35e-3e457adf7c3e\" 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>survived</th>\n","      <th>pclass</th>\n","      <th>age</th>\n","      <th>sibsp</th>\n","      <th>parch</th>\n","      <th>fare</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>891.000000</td>\n","      <td>891.000000</td>\n","      <td>714.000000</td>\n","      <td>891.000000</td>\n","      <td>891.000000</td>\n","      <td>891.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>0.383838</td>\n","      <td>2.308642</td>\n","      <td>29.699118</td>\n","      <td>0.523008</td>\n","      <td>0.381594</td>\n","      <td>32.204208</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>0.486592</td>\n","      <td>0.836071</td>\n","      <td>14.526497</td>\n","      <td>1.102743</td>\n","      <td>0.806057</td>\n","      <td>49.693429</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>0.420000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>0.000000</td>\n","      <td>2.000000</td>\n","      <td>20.125000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>7.910400</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>0.000000</td>\n","      <td>3.000000</td>\n","      <td>28.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>14.454200</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>1.000000</td>\n","      <td>3.000000</td>\n","      <td>38.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>31.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>1.000000</td>\n","      <td>3.000000</td>\n","      <td>80.000000</td>\n","      <td>8.000000</td>\n","      <td>6.000000</td>\n","      <td>512.329200</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-f22e946e-9329-464c-a35e-3e457adf7c3e')\"\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-f22e946e-9329-464c-a35e-3e457adf7c3e 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-f22e946e-9329-464c-a35e-3e457adf7c3e');\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-33ccbd38-0df5-4c16-a88a-0e4fbbb9003b\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-33ccbd38-0df5-4c16-a88a-0e4fbbb9003b')\"\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-33ccbd38-0df5-4c16-a88a-0e4fbbb9003b button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":24}],"source":["# @title Veo los principales estadísticos para cada una de las columnas numericas\n","df.describe()"]},{"cell_type":"code","source":["# @title Veo información descriptiva de mis variables categóricas:\n","df.describe(include=object)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"QBDukxrTs6PA","executionInfo":{"status":"ok","timestamp":1696607783819,"user_tz":180,"elapsed":290,"user":{"displayName":"Mariano Gobea Alcoba","userId":"16590319507306720082"}},"outputId":"bed7543e-2fd4-4d13-fb3d-55deaa83d86b"},"id":"QBDukxrTs6PA","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         sex embarked  who  embark_town alive\n","count    891      889  891          889   891\n","unique     2        3    3            3     2\n","top     male        S  man  Southampton    no\n","freq     577      644  537          644   549"],"text/html":["\n","  <div id=\"df-bb2147e3-9554-44ed-a138-31e642f92f21\" 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>sex</th>\n","      <th>embarked</th>\n","      <th>who</th>\n","      <th>embark_town</th>\n","      <th>alive</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>891</td>\n","      <td>889</td>\n","      <td>891</td>\n","      <td>889</td>\n","      <td>891</td>\n","    </tr>\n","    <tr>\n","      <th>unique</th>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>top</th>\n","      <td>male</td>\n","      <td>S</td>\n","      <td>man</td>\n","      <td>Southampton</td>\n","      <td>no</td>\n","    </tr>\n","    <tr>\n","      <th>freq</th>\n","      <td>577</td>\n","      <td>644</td>\n","      <td>537</td>\n","      <td>644</td>\n","      <td>549</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-bb2147e3-9554-44ed-a138-31e642f92f21')\"\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-bb2147e3-9554-44ed-a138-31e642f92f21 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-bb2147e3-9554-44ed-a138-31e642f92f21');\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-3b85e7a1-fb8c-4067-a2a2-62752288a5dd\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3b85e7a1-fb8c-4067-a2a2-62752288a5dd')\"\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-3b85e7a1-fb8c-4067-a2a2-62752288a5dd button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":25}]},{"cell_type":"code","execution_count":null,"id":"7c86563b","metadata":{"id":"7c86563b","outputId":"d165b0d5-9fe3-4c1c-d097-47e7c2b9734e","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1696607887242,"user_tz":180,"elapsed":7,"user":{"displayName":"Mariano Gobea Alcoba","userId":"16590319507306720082"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0    Southampton\n","1      Cherbourg\n","2    Southampton\n","3    Southampton\n","4    Southampton\n","Name: embark_town, dtype: object"]},"metadata":{},"execution_count":27}],"source":["# @title Imprimo las primeras lineas de una única columna\n","df['embark_town'].head()"]},{"cell_type":"code","execution_count":null,"id":"0cc83683","metadata":{"id":"0cc83683","outputId":"8b52fb93-d193-4da7-fadd-ec53b31489a4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1696607907180,"user_tz":180,"elapsed":289,"user":{"displayName":"Mariano Gobea Alcoba","userId":"16590319507306720082"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Southampton    644\n","Cherbourg      168\n","Queenstown      77\n","Name: embark_town, dtype: int64"]},"metadata":{},"execution_count":28}],"source":["# @title Resumen de cantidad de datos para cada valor de la columna\n","df['embark_town'].value_counts()"]},{"cell_type":"code","execution_count":null,"id":"9884c079","metadata":{"id":"9884c079","outputId":"c7f5081d-2e84-48af-f00a-1dd7d8bb470d","colab":{"base_uri":"https://localhost:8080/","height":424},"executionInfo":{"status":"ok","timestamp":1696607944535,"user_tz":180,"elapsed":303,"user":{"displayName":"Mariano Gobea Alcoba","userId":"16590319507306720082"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n","7           0       3    male   2.0      3      1  21.0750        S   Third   \n","9           1       2  female  14.0      1      0  30.0708        C  Second   \n","10          1       3  female   4.0      1      1  16.7000        S   Third   \n","14          0       3  female  14.0      0      0   7.8542        S   Third   \n","16          0       3    male   2.0      4      1  29.1250        Q   Third   \n","..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n","850         0       3    male   4.0      4      2  31.2750        S   Third   \n","852         0       3  female   9.0      1      1  15.2458        C   Third   \n","853         1       1  female  16.0      0      1  39.4000        S   First   \n","869         1       3    male   4.0      1      1  11.1333        S   Third   \n","875         1       3  female  15.0      0      0   7.2250        C   Third   \n","\n","       who  adult_male deck  embark_town alive  alone  \n","7    child       False  NaN  Southampton    no  False  \n","9    child       False  NaN    Cherbourg   yes  False  \n","10   child       False    G  Southampton   yes  False  \n","14   child       False  NaN  Southampton    no   True  \n","16   child       False  NaN   Queenstown    no  False  \n","..     ...         ...  ...          ...   ...    ...  \n","850  child       False  NaN  Southampton    no  False  \n","852  child       False  NaN    Cherbourg    no  False  \n","853  woman       False    D  Southampton   yes  False  \n","869  child       False  NaN  Southampton   yes  False  \n","875  child       False  NaN    Cherbourg   yes   True  \n","\n","[113 rows x 15 columns]"],"text/html":["\n","  <div id=\"df-a2500487-3a9f-47c4-b089-7e9b8af5f840\" 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>survived</th>\n","      <th>pclass</th>\n","      <th>sex</th>\n","      <th>age</th>\n","      <th>sibsp</th>\n","      <th>parch</th>\n","      <th>fare</th>\n","      <th>embarked</th>\n","      <th>class</th>\n","      <th>who</th>\n","      <th>adult_male</th>\n","      <th>deck</th>\n","      <th>embark_town</th>\n","      <th>alive</th>\n","      <th>alone</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>7</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>male</td>\n","      <td>2.0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>21.0750</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>child</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>no</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>female</td>\n","      <td>14.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>30.0708</td>\n","      <td>C</td>\n","      <td>Second</td>\n","      <td>child</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>Cherbourg</td>\n","      <td>yes</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>female</td>\n","      <td>4.0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>16.7000</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>child</td>\n","      <td>False</td>\n","      <td>G</td>\n","      <td>Southampton</td>\n","      <td>yes</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>female</td>\n","      <td>14.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7.8542</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>child</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>no</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>male</td>\n","      <td>2.0</td>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>29.1250</td>\n","      <td>Q</td>\n","      <td>Third</td>\n","      <td>child</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>Queenstown</td>\n","      <td>no</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>850</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>male</td>\n","      <td>4.0</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>31.2750</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>child</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>no</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>852</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>female</td>\n","      <td>9.0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>15.2458</td>\n","      <td>C</td>\n","      <td>Third</td>\n","      <td>child</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>Cherbourg</td>\n","      <td>no</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>853</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>female</td>\n","      <td>16.0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>39.4000</td>\n","      <td>S</td>\n","      <td>First</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>D</td>\n","      <td>Southampton</td>\n","      <td>yes</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>869</th>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>male</td>\n","      <td>4.0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>11.1333</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>child</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>yes</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>875</th>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>female</td>\n","      <td>15.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7.2250</td>\n","      <td>C</td>\n","      <td>Third</td>\n","      <td>child</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>Cherbourg</td>\n","      <td>yes</td>\n","      <td>True</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>113 rows × 15 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-a2500487-3a9f-47c4-b089-7e9b8af5f840')\"\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-a2500487-3a9f-47c4-b089-7e9b8af5f840 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-a2500487-3a9f-47c4-b089-7e9b8af5f840');\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-0a9ea262-90da-47e8-b98e-8ab30e9e6ec6\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0a9ea262-90da-47e8-b98e-8ab30e9e6ec6')\"\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-0a9ea262-90da-47e8-b98e-8ab30e9e6ec6 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":29}],"source":["# @title Filtro una columna por un valor\n","df[df['age'] < 18] # Me quedo solamente con los pasajeros menores de edad"]},{"cell_type":"code","source":["# @title Puedo filtrar también por 2 variable o más:\n","df[(df['age'] > 18) & (df['sex'] == 'female')] # Me quedo solo con las mujeres mayores."],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"A7wU1O98twap","executionInfo":{"status":"ok","timestamp":1696608082896,"user_tz":180,"elapsed":314,"user":{"displayName":"Mariano Gobea Alcoba","userId":"16590319507306720082"}},"outputId":"7575b40a-a2a0-43f7-e0d4-3e19d384eb6a"},"id":"A7wU1O98twap","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n","1           1       1  female  38.0      1      0  71.2833        C   First   \n","2           1       3  female  26.0      0      0   7.9250        S   Third   \n","3           1       1  female  35.0      1      0  53.1000        S   First   \n","8           1       3  female  27.0      0      2  11.1333        S   Third   \n","11          1       1  female  58.0      0      0  26.5500        S   First   \n","..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n","879         1       1  female  56.0      0      1  83.1583        C   First   \n","880         1       2  female  25.0      0      1  26.0000        S  Second   \n","882         0       3  female  22.0      0      0  10.5167        S   Third   \n","885         0       3  female  39.0      0      5  29.1250        Q   Third   \n","887         1       1  female  19.0      0      0  30.0000        S   First   \n","\n","       who  adult_male deck  embark_town alive  alone  \n","1    woman       False    C    Cherbourg   yes  False  \n","2    woman       False  NaN  Southampton   yes   True  \n","3    woman       False    C  Southampton   yes  False  \n","8    woman       False  NaN  Southampton   yes  False  \n","11   woman       False    C  Southampton   yes   True  \n","..     ...         ...  ...          ...   ...    ...  \n","879  woman       False    C    Cherbourg   yes  False  \n","880  woman       False  NaN  Southampton   yes  False  \n","882  woman       False  NaN  Southampton    no   True  \n","885  woman       False  NaN   Queenstown    no  False  \n","887  woman       False    B  Southampton   yes   True  \n","\n","[193 rows x 15 columns]"],"text/html":["\n","  <div id=\"df-fcf1b22b-80f3-4b01-b5fb-5f4dc302a829\" 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>survived</th>\n","      <th>pclass</th>\n","      <th>sex</th>\n","      <th>age</th>\n","      <th>sibsp</th>\n","      <th>parch</th>\n","      <th>fare</th>\n","      <th>embarked</th>\n","      <th>class</th>\n","      <th>who</th>\n","      <th>adult_male</th>\n","      <th>deck</th>\n","      <th>embark_town</th>\n","      <th>alive</th>\n","      <th>alone</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>female</td>\n","      <td>38.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>71.2833</td>\n","      <td>C</td>\n","      <td>First</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>C</td>\n","      <td>Cherbourg</td>\n","      <td>yes</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>female</td>\n","      <td>26.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7.9250</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>yes</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>female</td>\n","      <td>35.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>53.1000</td>\n","      <td>S</td>\n","      <td>First</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>C</td>\n","      <td>Southampton</td>\n","      <td>yes</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>female</td>\n","      <td>27.0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>11.1333</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>yes</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>female</td>\n","      <td>58.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>26.5500</td>\n","      <td>S</td>\n","      <td>First</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>C</td>\n","      <td>Southampton</td>\n","      <td>yes</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>879</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>female</td>\n","      <td>56.0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>83.1583</td>\n","      <td>C</td>\n","      <td>First</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>C</td>\n","      <td>Cherbourg</td>\n","      <td>yes</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>880</th>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>female</td>\n","      <td>25.0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>26.0000</td>\n","      <td>S</td>\n","      <td>Second</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>yes</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>882</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>female</td>\n","      <td>22.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>10.5167</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>no</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>885</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>female</td>\n","      <td>39.0</td>\n","      <td>0</td>\n","      <td>5</td>\n","      <td>29.1250</td>\n","      <td>Q</td>\n","      <td>Third</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>Queenstown</td>\n","      <td>no</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>887</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>female</td>\n","      <td>19.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>30.0000</td>\n","      <td>S</td>\n","      <td>First</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>B</td>\n","      <td>Southampton</td>\n","      <td>yes</td>\n","      <td>True</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>193 rows × 15 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-fcf1b22b-80f3-4b01-b5fb-5f4dc302a829')\"\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-fcf1b22b-80f3-4b01-b5fb-5f4dc302a829 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-fcf1b22b-80f3-4b01-b5fb-5f4dc302a829');\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-ef7b5c11-1fce-4499-9fcf-32111bb2dda2\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ef7b5c11-1fce-4499-9fcf-32111bb2dda2')\"\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-ef7b5c11-1fce-4499-9fcf-32111bb2dda2 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":32}]},{"cell_type":"markdown","id":"6dac8923","metadata":{"id":"6dac8923"},"source":["## Transform Step\n","Para mas informacion entrar a:   \n","https://sites.google.com/mercadolibre.com/wiki-biops/inicio/fda/transform"]},{"cell_type":"code","execution_count":null,"id":"5d5d0eed","metadata":{"id":"5d5d0eed","outputId":"1e662ec7-a189-49da-f15e-0994bc1ece23","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1696608167819,"user_tz":180,"elapsed":318,"user":{"displayName":"Mariano Gobea Alcoba","userId":"16590319507306720082"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["    survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n","7          0       3    male   2.0      3      1  21.0750        S   Third   \n","9          1       2  female  14.0      1      0  30.0708        C  Second   \n","10         1       3  female   4.0      1      1  16.7000        S   Third   \n","14         0       3  female  14.0      0      0   7.8542        S   Third   \n","16         0       3    male   2.0      4      1  29.1250        Q   Third   \n","\n","      who  adult_male deck  embark_town alive  alone  \n","7   child       False  NaN  Southampton    no  False  \n","9   child       False  NaN    Cherbourg   yes  False  \n","10  child       False    G  Southampton   yes  False  \n","14  child       False  NaN  Southampton    no   True  \n","16  child       False  NaN   Queenstown    no  False  "],"text/html":["\n","  <div id=\"df-c748847c-168e-4b1b-9776-01d4298e169c\" 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>survived</th>\n","      <th>pclass</th>\n","      <th>sex</th>\n","      <th>age</th>\n","      <th>sibsp</th>\n","      <th>parch</th>\n","      <th>fare</th>\n","      <th>embarked</th>\n","      <th>class</th>\n","      <th>who</th>\n","      <th>adult_male</th>\n","      <th>deck</th>\n","      <th>embark_town</th>\n","      <th>alive</th>\n","      <th>alone</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>7</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>male</td>\n","      <td>2.0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>21.0750</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>child</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>no</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>female</td>\n","      <td>14.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>30.0708</td>\n","      <td>C</td>\n","      <td>Second</td>\n","      <td>child</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>Cherbourg</td>\n","      <td>yes</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>female</td>\n","      <td>4.0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>16.7000</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>child</td>\n","      <td>False</td>\n","      <td>G</td>\n","      <td>Southampton</td>\n","      <td>yes</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>female</td>\n","      <td>14.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7.8542</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>child</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>no</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>male</td>\n","      <td>2.0</td>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>29.1250</td>\n","      <td>Q</td>\n","      <td>Third</td>\n","      <td>child</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>Queenstown</td>\n","      <td>no</td>\n","      <td>False</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-c748847c-168e-4b1b-9776-01d4298e169c')\"\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-c748847c-168e-4b1b-9776-01d4298e169c 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-c748847c-168e-4b1b-9776-01d4298e169c');\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-b895e7cc-8841-4824-9b63-5d053ca74145\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b895e7cc-8841-4824-9b63-5d053ca74145')\"\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-b895e7cc-8841-4824-9b63-5d053ca74145 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":34}],"source":["# @title Filtro el df por una variable y la guardo como un nuevo DataFrame\n","df_filtrado = df[df['age'] < 18]\n","df_filtrado.head()"]},{"cell_type":"code","execution_count":null,"id":"5e3d252f","metadata":{"id":"5e3d252f","outputId":"f68ec162-4e77-4793-baff-4828457cc1b0","colab":{"base_uri":"https://localhost:8080/","height":269},"executionInfo":{"status":"ok","timestamp":1696608680945,"user_tz":180,"elapsed":6,"user":{"displayName":"Mariano Gobea Alcoba","userId":"16590319507306720082"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n","580         1       2  female  25.0      1      1  30.0000        S  Second   \n","9           1       2  female  14.0      1      0  30.0708        C  Second   \n","569         1       3    male  32.0      0      0   7.8542        S   Third   \n","449         1       1    male  52.0      0      0  30.5000        S   First   \n","283         1       3    male  19.0      0      0   8.0500        S   Third   \n","619         0       2    male  26.0      0      0  10.5000        S  Second   \n","749         0       3    male  31.0      0      0   7.7500        Q   Third   \n","\n","       who  adult_male deck  embark_town alive  alone  tag_legal_age  \n","580  woman       False  NaN  Southampton   yes  False           True  \n","9    child       False  NaN    Cherbourg   yes  False          False  \n","569    man        True  NaN  Southampton   yes   True           True  \n","449    man        True    C  Southampton   yes   True           True  \n","283    man        True  NaN  Southampton   yes   True           True  \n","619    man        True  NaN  Southampton    no   True           True  \n","749    man        True  NaN   Queenstown    no   True           True  "],"text/html":["\n","  <div id=\"df-7e829b97-5b5b-4510-8236-43803ce231f1\" 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>survived</th>\n","      <th>pclass</th>\n","      <th>sex</th>\n","      <th>age</th>\n","      <th>sibsp</th>\n","      <th>parch</th>\n","      <th>fare</th>\n","      <th>embarked</th>\n","      <th>class</th>\n","      <th>who</th>\n","      <th>adult_male</th>\n","      <th>deck</th>\n","      <th>embark_town</th>\n","      <th>alive</th>\n","      <th>alone</th>\n","      <th>tag_legal_age</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>580</th>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>female</td>\n","      <td>25.0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>30.0000</td>\n","      <td>S</td>\n","      <td>Second</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>yes</td>\n","      <td>False</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>female</td>\n","      <td>14.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>30.0708</td>\n","      <td>C</td>\n","      <td>Second</td>\n","      <td>child</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>Cherbourg</td>\n","      <td>yes</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>569</th>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>male</td>\n","      <td>32.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7.8542</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>man</td>\n","      <td>True</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>yes</td>\n","      <td>True</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>449</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>male</td>\n","      <td>52.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>30.5000</td>\n","      <td>S</td>\n","      <td>First</td>\n","      <td>man</td>\n","      <td>True</td>\n","      <td>C</td>\n","      <td>Southampton</td>\n","      <td>yes</td>\n","      <td>True</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>283</th>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>male</td>\n","      <td>19.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>8.0500</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>man</td>\n","      <td>True</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>yes</td>\n","      <td>True</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>619</th>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>male</td>\n","      <td>26.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>10.5000</td>\n","      <td>S</td>\n","      <td>Second</td>\n","      <td>man</td>\n","      <td>True</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>no</td>\n","      <td>True</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>749</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>male</td>\n","      <td>31.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7.7500</td>\n","      <td>Q</td>\n","      <td>Third</td>\n","      <td>man</td>\n","      <td>True</td>\n","      <td>NaN</td>\n","      <td>Queenstown</td>\n","      <td>no</td>\n","      <td>True</td>\n","      <td>True</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-7e829b97-5b5b-4510-8236-43803ce231f1')\"\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-7e829b97-5b5b-4510-8236-43803ce231f1 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-7e829b97-5b5b-4510-8236-43803ce231f1');\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-2dd336d1-71e3-45ad-9222-02e4c379a6da\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2dd336d1-71e3-45ad-9222-02e4c379a6da')\"\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-2dd336d1-71e3-45ad-9222-02e4c379a6da button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":36}],"source":["# @title Creo una columna nueva\n","df['tag_legal_age'] = df['age'] > 18\n","df.sample(7) # Otra forma de ver solo algunas columnas pero de manera aleatoria"]},{"cell_type":"code","execution_count":null,"id":"30b3a45a","metadata":{"id":"30b3a45a","outputId":"9ab82b78-6679-4330-91d1-9ee78fda4c0b","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1696609651562,"user_tz":180,"elapsed":320,"user":{"displayName":"Mariano Gobea Alcoba","userId":"16590319507306720082"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n","0         0       3    male  22.0      1      0   7.2500        S  Third   \n","1         1       1  female  38.0      1      0  71.2833        C  First   \n","2         1       3  female  26.0      0      0   7.9250        S  Third   \n","3         1       1  female  35.0      1      0  53.1000        S  First   \n","4         0       3    male  35.0      0      0   8.0500        S  Third   \n","\n","     who  adult_male deck  embark_town alive  alone  tag_legal_age  \\\n","0    man        True  NaN  Southampton    no  False           True   \n","1  woman       False    C    Cherbourg   yes  False           True   \n","2  woman       False  NaN  Southampton   yes   True           True   \n","3  woman       False    C  Southampton   yes  False           True   \n","4    man        True  NaN  Southampton    no   True           True   \n","\n","   family_on_board  \n","0                1  \n","1                1  \n","2                0  \n","3                1  \n","4                0  "],"text/html":["\n","  <div id=\"df-e2e27873-cf91-4993-912e-a889948f1d96\" 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>survived</th>\n","      <th>pclass</th>\n","      <th>sex</th>\n","      <th>age</th>\n","      <th>sibsp</th>\n","      <th>parch</th>\n","      <th>fare</th>\n","      <th>embarked</th>\n","      <th>class</th>\n","      <th>who</th>\n","      <th>adult_male</th>\n","      <th>deck</th>\n","      <th>embark_town</th>\n","      <th>alive</th>\n","      <th>alone</th>\n","      <th>tag_legal_age</th>\n","      <th>family_on_board</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>male</td>\n","      <td>22.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>7.2500</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>man</td>\n","      <td>True</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>no</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>female</td>\n","      <td>38.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>71.2833</td>\n","      <td>C</td>\n","      <td>First</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>C</td>\n","      <td>Cherbourg</td>\n","      <td>yes</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>female</td>\n","      <td>26.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7.9250</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>yes</td>\n","      <td>True</td>\n","      <td>True</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>female</td>\n","      <td>35.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>53.1000</td>\n","      <td>S</td>\n","      <td>First</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>C</td>\n","      <td>Southampton</td>\n","      <td>yes</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>male</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>8.0500</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>man</td>\n","      <td>True</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>no</td>\n","      <td>True</td>\n","      <td>True</td>\n","      <td>0</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-e2e27873-cf91-4993-912e-a889948f1d96')\"\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-e2e27873-cf91-4993-912e-a889948f1d96 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-e2e27873-cf91-4993-912e-a889948f1d96');\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-64c0a81e-3ba6-4f06-b2ec-b585f9d0a1de\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-64c0a81e-3ba6-4f06-b2ec-b585f9d0a1de')\"\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-64c0a81e-3ba6-4f06-b2ec-b585f9d0a1de button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":37}],"source":["# @title Sumo dos columnas y genero una columna nueva\n","df['family_on_board'] = df['sibsp']+df['parch']\n","df.head()"]},{"cell_type":"code","source":["# @title Group By\n","## Puedo agrupar por una variable categórica y calcular sobre otra numérica\n","\n","df_group_by_sex = df.groupby(\"sex\").mean()\n","df_group_by_sex"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":198},"id":"L-1q2FKWt6k0","executionInfo":{"status":"ok","timestamp":1696809436159,"user_tz":180,"elapsed":8,"user":{"displayName":"Mariano Gobea Alcoba","userId":"16590319507306720082"}},"outputId":"8fac533f-3740-40e7-847c-703114614dd6"},"id":"L-1q2FKWt6k0","execution_count":5,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-5-d47887685b2b>:4: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n","  df_group_by_sex = df.groupby(\"sex\").mean()\n"]},{"output_type":"execute_result","data":{"text/plain":["        survived    pclass        age     sibsp     parch       fare  \\\n","sex                                                                    \n","female  0.742038  2.159236  27.915709  0.694268  0.649682  44.479818   \n","male    0.188908  2.389948  30.726645  0.429809  0.235702  25.523893   \n","\n","        adult_male     alone  \n","sex                           \n","female    0.000000  0.401274  \n","male      0.930676  0.712305  "],"text/html":["\n","  <div id=\"df-22bb6b5a-7d78-46c0-939f-a5057303f8fa\" 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>survived</th>\n","      <th>pclass</th>\n","      <th>age</th>\n","      <th>sibsp</th>\n","      <th>parch</th>\n","      <th>fare</th>\n","      <th>adult_male</th>\n","      <th>alone</th>\n","    </tr>\n","    <tr>\n","      <th>sex</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>female</th>\n","      <td>0.742038</td>\n","      <td>2.159236</td>\n","      <td>27.915709</td>\n","      <td>0.694268</td>\n","      <td>0.649682</td>\n","      <td>44.479818</td>\n","      <td>0.000000</td>\n","      <td>0.401274</td>\n","    </tr>\n","    <tr>\n","      <th>male</th>\n","      <td>0.188908</td>\n","      <td>2.389948</td>\n","      <td>30.726645</td>\n","      <td>0.429809</td>\n","      <td>0.235702</td>\n","      <td>25.523893</td>\n","      <td>0.930676</td>\n","      <td>0.712305</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-22bb6b5a-7d78-46c0-939f-a5057303f8fa')\"\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-22bb6b5a-7d78-46c0-939f-a5057303f8fa 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-22bb6b5a-7d78-46c0-939f-a5057303f8fa');\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-0c38d5a2-96a8-452a-9f04-3ec2a74616c6\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0c38d5a2-96a8-452a-9f04-3ec2a74616c6')\"\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-0c38d5a2-96a8-452a-9f04-3ec2a74616c6 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":5}]},{"cell_type":"markdown","source":["**Por default vendrán todas las columnas numéricas con su promedio calculado. Pero puedo seleccionar solo la/s que me interesen**"],"metadata":{"id":"F-EWo6I7uZN1"},"id":"F-EWo6I7uZN1"},{"cell_type":"code","source":["# @title Group By Column Selected\n","df_group_by_sex = df.groupby(\"sex\")[\"survived\",\"age\"].mean()\n","df_group_by_sex"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":178},"id":"VDgrPn-Uug6I","executionInfo":{"status":"ok","timestamp":1696809706898,"user_tz":180,"elapsed":304,"user":{"displayName":"Mariano Gobea Alcoba","userId":"16590319507306720082"}},"outputId":"34c5c39a-3c22-4da0-da90-19237a177f59"},"id":"VDgrPn-Uug6I","execution_count":9,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-9-b52eb3630da3>:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n","  df_group_by_sex = df.groupby(\"sex\")[\"survived\",\"age\"].mean()\n"]},{"output_type":"execute_result","data":{"text/plain":["        survived        age\n","sex                        \n","female  0.742038  27.915709\n","male    0.188908  30.726645"],"text/html":["\n","  <div id=\"df-d6efca10-5648-46ee-b35c-244d171551ec\" 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>survived</th>\n","      <th>age</th>\n","    </tr>\n","    <tr>\n","      <th>sex</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>female</th>\n","      <td>0.742038</td>\n","      <td>27.915709</td>\n","    </tr>\n","    <tr>\n","      <th>male</th>\n","      <td>0.188908</td>\n","      <td>30.726645</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-d6efca10-5648-46ee-b35c-244d171551ec')\"\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-d6efca10-5648-46ee-b35c-244d171551ec 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-d6efca10-5648-46ee-b35c-244d171551ec');\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-00254901-0ed4-4391-a0c6-84798896a046\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-00254901-0ed4-4391-a0c6-84798896a046')\"\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-00254901-0ed4-4391-a0c6-84798896a046 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","source":["# @title Group By with distinct metrics\n","\n","# Realiza un groupby por la columna \"sex\" y aplica las funciones mean() y max()\n","result = df.groupby(\"sex\").agg({\"survived\": \"mean\",\n","                                \"age\": \"mean\",\n","                                \"fare\": \"max\"})\n","\n","# Renombra las columnas resultantes\n","result = result.rename(columns={\"survived\": \"survived_mean\",\n","                                \"age\": \"age_mean\",\n","                                \"fare\": \"fare_max\"})\n","\n","result"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"NlT_QPCgvXFW","executionInfo":{"status":"ok","timestamp":1696809781581,"user_tz":180,"elapsed":298,"user":{"displayName":"Mariano Gobea Alcoba","userId":"16590319507306720082"}},"outputId":"7fffa7b9-8c25-48dc-e7d0-d86c465f9a4a"},"id":"NlT_QPCgvXFW","execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        survived_mean   age_mean  fare_max\n","sex                                       \n","female       0.742038  27.915709  512.3292\n","male         0.188908  30.726645  512.3292"],"text/html":["\n","  <div id=\"df-869b928c-152b-40ce-aec8-0c5c19bbadbe\" 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>survived_mean</th>\n","      <th>age_mean</th>\n","      <th>fare_max</th>\n","    </tr>\n","    <tr>\n","      <th>sex</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>female</th>\n","      <td>0.742038</td>\n","      <td>27.915709</td>\n","      <td>512.3292</td>\n","    </tr>\n","    <tr>\n","      <th>male</th>\n","      <td>0.188908</td>\n","      <td>30.726645</td>\n","      <td>512.3292</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-869b928c-152b-40ce-aec8-0c5c19bbadbe')\"\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-869b928c-152b-40ce-aec8-0c5c19bbadbe 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-869b928c-152b-40ce-aec8-0c5c19bbadbe');\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-ae666047-469b-4210-bb5e-fdf441668e00\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ae666047-469b-4210-bb5e-fdf441668e00')\"\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-ae666047-469b-4210-bb5e-fdf441668e00 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["# @title Pivot Table\n","\n","## Si necesitamos hacer calculos sobre el entrecruze de dos variables entonces usamos pivot_table:\n","df_pivot = df.pivot_table(index=[\"sex\"],\n","                          columns=[\"class\", \"alone\"],\n","                          values=[\"survived\",\"age\",\"fare\"],\n","                          aggfunc={\"survived\":\"mean\",\n","                                   \"age\":\"median\",\n","                                   \"fare\":\"min\"})\n","\n","df_pivot\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"_F1c4nYov3Gd","executionInfo":{"status":"ok","timestamp":1696810175700,"user_tz":180,"elapsed":323,"user":{"displayName":"Mariano Gobea Alcoba","userId":"16590319507306720082"}},"outputId":"0e47a76f-1a54-4955-e7ed-55c6426c580f"},"id":"_F1c4nYov3Gd","execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         age                                     fare                        \\\n","class  First       Second        Third          First          Second         \n","alone  False True   False  True  False True     False    True   False True    \n","sex                                                                           \n","female  35.0  32.5   25.5  32.25  19.5  22.0  26.2833  25.9292   13.0  10.5   \n","male    37.0  45.0   29.0  30.00  17.5  27.0  29.7000   0.0000   11.5   0.0   \n","\n","                      survived                                          \\\n","class    Third           First              Second               Third   \n","alone    False True      False     True      False     True      False   \n","sex                                                                      \n","female  7.7500  6.75  0.966667  0.970588  0.931818  0.906250  0.416667   \n","male    6.4958  0.00  0.425532  0.333333  0.277778  0.097222  0.180723   \n","\n","                  \n","class             \n","alone      True   \n","sex               \n","female  0.616667  \n","male    0.121212  "],"text/html":["\n","  <div id=\"df-a1646a7c-f788-400d-98ee-027f635e08eb\" 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 tr th {\n","        text-align: left;\n","    }\n","\n","    .dataframe thead tr:last-of-type th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr>\n","      <th></th>\n","      <th colspan=\"6\" halign=\"left\">age</th>\n","      <th colspan=\"6\" halign=\"left\">fare</th>\n","      <th colspan=\"6\" halign=\"left\">survived</th>\n","    </tr>\n","    <tr>\n","      <th>class</th>\n","      <th colspan=\"2\" halign=\"left\">First</th>\n","      <th colspan=\"2\" halign=\"left\">Second</th>\n","      <th colspan=\"2\" halign=\"left\">Third</th>\n","      <th colspan=\"2\" halign=\"left\">First</th>\n","      <th colspan=\"2\" halign=\"left\">Second</th>\n","      <th colspan=\"2\" halign=\"left\">Third</th>\n","      <th colspan=\"2\" halign=\"left\">First</th>\n","      <th colspan=\"2\" halign=\"left\">Second</th>\n","      <th colspan=\"2\" halign=\"left\">Third</th>\n","    </tr>\n","    <tr>\n","      <th>alone</th>\n","      <th>False</th>\n","      <th>True</th>\n","      <th>False</th>\n","      <th>True</th>\n","      <th>False</th>\n","      <th>True</th>\n","      <th>False</th>\n","      <th>True</th>\n","      <th>False</th>\n","      <th>True</th>\n","      <th>False</th>\n","      <th>True</th>\n","      <th>False</th>\n","      <th>True</th>\n","      <th>False</th>\n","      <th>True</th>\n","      <th>False</th>\n","      <th>True</th>\n","    </tr>\n","    <tr>\n","      <th>sex</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>female</th>\n","      <td>35.0</td>\n","      <td>32.5</td>\n","      <td>25.5</td>\n","      <td>32.25</td>\n","      <td>19.5</td>\n","      <td>22.0</td>\n","      <td>26.2833</td>\n","      <td>25.9292</td>\n","      <td>13.0</td>\n","      <td>10.5</td>\n","      <td>7.7500</td>\n","      <td>6.75</td>\n","      <td>0.966667</td>\n","      <td>0.970588</td>\n","      <td>0.931818</td>\n","      <td>0.906250</td>\n","      <td>0.416667</td>\n","      <td>0.616667</td>\n","    </tr>\n","    <tr>\n","      <th>male</th>\n","      <td>37.0</td>\n","      <td>45.0</td>\n","      <td>29.0</td>\n","      <td>30.00</td>\n","      <td>17.5</td>\n","      <td>27.0</td>\n","      <td>29.7000</td>\n","      <td>0.0000</td>\n","      <td>11.5</td>\n","      <td>0.0</td>\n","      <td>6.4958</td>\n","      <td>0.00</td>\n","      <td>0.425532</td>\n","      <td>0.333333</td>\n","      <td>0.277778</td>\n","      <td>0.097222</td>\n","      <td>0.180723</td>\n","      <td>0.121212</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-a1646a7c-f788-400d-98ee-027f635e08eb')\"\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-a1646a7c-f788-400d-98ee-027f635e08eb 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-a1646a7c-f788-400d-98ee-027f635e08eb');\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-41ca2f97-54f0-47b9-b0a0-843fd8f80a02\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-41ca2f97-54f0-47b9-b0a0-843fd8f80a02')\"\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-41ca2f97-54f0-47b9-b0a0-843fd8f80a02 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":12}]},{"cell_type":"markdown","source":["## Challenges:\n","\n","1. Crea una nueva columna en el dataset quue valide si se trata de una pasajera mujer y mayor de edad y llamala \"adult_female\"\n","2. Calcula la tarifa promedio que se pagó para embarcarse en el titanica\n","3. Calcula la tarifa mediana que se pagó para embarcarse(vas a tener que recurrir a la [documentación de numpy](https://https://numpy.org/doc/stable/reference/generated/numpy.median.html), a [ChatGPT](https://https://chat.openai.com) o a tu imaginación si te animas! Es muy facil!)"],"metadata":{"id":"5MpvLhYF0S_d"},"id":"5MpvLhYF0S_d"}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.4"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5}